Salesforce is in the process of acquiring US-based defense-tech startup Rune Technologies to add offline capabilities to Missionforce, its Agentforce avatar for national security, defense, aerospace, and government agencies.

The startup’s TyrOS platform will be used to extend Missionforce into frontline military logistics, enabling deployed units to forecast supply requirements, optimize resource allocation, and make operational decisions even when network connectivity is unavailable, the CRM software provider said in a statement.

That is possible due to TyrOS’s offline-first architecture that caches last-known information about inventory, vehicles, and transportation routes using NFC-based hardware, allowing deployed units to access operational data without a continuous connection to centralized systems, according to the company’s website.

The platform can also exchange prioritized information between distributed nodes through a mesh network when connectivity becomes available, the website showed.

Currently, Missionforce acts as a system of record for personnel, equipment, and supplies across defense organizations, while providing tools for strategic planning.

For CIOs of defense organizations, the acquisition could mean improved operational continuity and better utilization of available resources, said Premal Shah, principal analyst at Avasant.

This would be helpful because frontline units could now continue to access critical logistics information and coordinate resource requirements during connectivity disruptions, rather than relying on manual records or waiting for centralized systems to become available, Shah added.

Once connectivity is restored, information can be synchronized across distributed nodes, helping headquarters regain visibility into frontline requirements and make more informed decisions about resource allocation and resupply, he further added.

However, implementing technologies such as TyrOS comes with its own set of risks, according to Pareekh Jain, principal analyst at Pareekh Consulting.

CIOs need to ensure that information updated by different military units while offline does not result in conflicting records when their systems reconnect, while also maintaining data security across distributed locations, Jain said.

TyrOS, though, is not an untested technology in military environments. Its platform is already deployed with the US Army and US Marine Corps, giving Rune an established presence in the defense sector, Salesforce said.

It plans to use the acquisition to build the foundation for a recent 10-year contract it won with the US Army, which has a $5.6 billion ceiling and focuses on modernizing military operations. The transaction is expected to close by April 2027.

Oracle is adding a new orchestration layer to its Fusion Cloud Applications suite to help enterprises automate business workflows, coordinate AI agents, and execute tasks across applications with less manual intervention.

The new layer, named Fusion Companion, will allow enterprise users to initiate business tasks through external AI assistants such as ChatGPT, rather than navigating individual enterprise applications to find the right workflows or tools, the company said in a statement.

It interprets users’ objectives and coordinates the transactions, workflows, approvals, and AI agents needed to complete tasks, including taking authorized actions across other enterprise systems, the company added.

Companion aims to reduce friction in enterprise AI adoption

That ability to coordinate and complete tasks without manual intervention will result in improved productivity for business users in an enterprise, said Stephanie Walter, practice leader of AI stack at HyperFrame Research.

“Companion’s automation means that an employee does not have to initiate multiple steps separately to complete a particular task. That could replace a sequence of clicks and handoffs with a request and any necessary approvals. It reduces the need to navigate applications, while keeping their business processes in place,” Walter added.

Fusion Companion’s ability to let employees initiate tasks through external AI assistants while keeping execution within enterprise systems could help CIOs address the tension between employees’ growing use of these tools and the need to preserve established business processes, security policies, and approval controls, said Ashish Chaturvedi, executive research leader at HFS Research.

“Access through ChatGPT carries real weight because it meets employees where they already spend their day. Many knowledge workers now open an AI assistant before they open any enterprise application. If you have ever watched a colleague draft an email in ChatGPT and then switch to an ERP screen to copy figures across, you have seen the friction Oracle is trying to remove,” Chaturvedi said.

Walter, meanwhile, sees a potential reduction in integration complexity for CIOs, as Fusion Companion could provide a common orchestration layer for employees to access enterprise workflows through different AI assistants, reducing the need to build and maintain separate integrations for each interface.

Challenges around implementation

However, the implementation of Fusion Companion is not without caveats.

“One of the caveats concerns data movement. When a user works through ChatGPT, some business context has to travel to that external assistant for the conversation to make sense. CIOs, therefore, should want precise answers on what data leaves the Oracle environment, how it is retained, and how this fits with their contracts with OpenAI,” Chaturvedi said.

Another concern is the reliability of intent interpretation, Chaturvedi added: “A well-phrased request will usually map to the correct process, but ambiguous requests could trigger the wrong transaction, and the consequences grow as agents gain authority to act.”

There are also concerns around dependency and erosion of process knowledge among enterprise users.

“When Companion becomes the layer that decides how work flows, including across non-Oracle systems, Oracle gains considerable influence over the enterprise’s process architecture. While that is convenient for Fusion-centric organizations, it could be a concern for those that want to keep orchestration neutral,” Chaturvedi said.

Over time, he added, delegating more work to AI agents could also erode employees’ understanding of underlying business processes, making exceptions and failures harder to diagnose when automated workflows break down.

Alongside Fusion Companion, Oracle is adding Fusion Agentic Home, a native interface within Fusion Applications that serves as an alternative for enterprise users who prefer not to rely on external AI assistants to delegate tasks and manage AI-driven workflows, while using Companion as the underlying orchestration layer.

Both Fusion Companion and Agentic Home are being made available as part of the Oracle Fusion Cloud Applications Release 26D, the company said.

The article originally appeared on InfoWorld.

AI fluency has long mattered to Sambit Dutta. When he joined global chemical manufacturer Hexion this year as SVP of AI transformation, his strategy included bringing “AI to the masses, where the people can not only just consume AI, but also become what we call builders.”

Dutta has made citizen AI a cornerstone of Hexion’s IT strategy because he believes that when the creativity and domain expertise of employees is centralized and funneled through a business analyst or developer, “there are always things lost in translation.” Further, “there’s a queuing process where that request ultimately gets queued, and then based on dollars and other priorities, never gets executed,” he adds.

Dutta also believes that innovation unleashed at the grassroots level is how enterprises achieve true transformation. And it facilitates change management because people become more receptive to absorbing technology at that level, he says.

“We firmly believe that we are at a stage where citizen development has finally taken off” at Hexion, Dutton says.

Citizen AI — a development strategy that has CIOs making AI tools available to employees outside IT to create their own solutions — is taking hold inside enterprises. It democratizes AI by enabling business users to solve problems and create efficiencies in their roles without the overhead of full IT-led development.

“Historically, turning a good idea into a technology solution required specialist skills, funding, and space on a development roadmap,” says Ankur Anand, group CIO at technology recruiter Harvey Nash. “AI lowers that barrier. People can explore an idea, create an agent or prototype, and establish whether it genuinely solves a business problem before the organization commits significant resources.”

But the objective isn’t to create hundreds of unofficial production systems, Anand adds. “It’s to create hundreds of experiments and make it much easier for good ideas to emerge from anywhere in the organization.”

Freedom first, governance to follow

Travel tech company eSky Group is one such company moving toward an AI-first model, “where using it is as natural as using email or a browser,” says Piotr Mynarski, technology director there.

“Employees creating their own agents is not a fringe behavior,” he says. “It is one of the clearest trends we see inside innovative companies today, and the most visible change in how our engineering works.”

At the end of 2025, eSky made AI a company objective, and by spring 2026, the company had agreements with several AI providers whose tools were opened up to everyone, not just to engineering staff.

eSky expects about 60% of its more than 650 employees worldwide outside the contact center to become fluent with AI. Regular use of AI is climbing toward 80%, Mynarski says, who notes that the company’s approach was a bit unusual: Company officials “barely capped budgets, let people use the tools for personal purposes, too, and gave everyone room to experiment rather than mandating a narrow toolset,” he says.

That approach deliberately gave employees freedom first, with governance to follow once leaders saw what they actually built.

“That order was a conscious bet and so far, the right one,” Mynarski says. “Today, we have over 30 agents across the company, almost every engineering team works with at least one, and several teams have more, with some already running in production.”

The agents were developed from a shared codebase built by eSky engineers, and they specialize in different needs: development of new products and features, maintenance, analytics, and monitoring business metrics.

“The rules are catching up rather than leading,” Mynarski notes. “Every agent has its own identity and credentials, guardrails and observability are standard, and responsibility for an agent sits with the team that runs it, not with a central AI department.”

AI reduces the barrier to entry — but still requires guardrails

Before joining Hexion, Dutta worked at Nestlé, where he implemented “a pretty robust program around digital fluency.” Parts of the program worked, but some didn’t, particularly where sophistication was required “in thinking, and a little bit of a technical depth.”

Some citizen development can be performed with drag-and-drop tools, but often written instructions are required, he notes. “A lot of people get really scared when you introduce those kinds of concepts to them.” That creates a barrier when employees want to leverage citizen development in areas such as data science, real-time automation, and Microsoft Power Apps, Dutta points out.

“Those are one type of automation that requires a little bit more sophistication,” he says. But that has changed. “The biggest unlock is now with prompt. You can avoid going into the developer tool altogether. … You can simply go in the prompt and describe what you want,and accordingly, the backend generates the application for it.”

As a result, Power Apps, which was more challenging for users to master and scale, no longer requires business users “to know the nuances about how to create a Power app.” Dutta says, unlocking a new level of productivity to a broader range of users.

At Hexion, employees use a centralized IT-owned platform to create agents. Business users have several tool options, “but those options are restricted to what we believe are enterprise-grade tools, and we have to secure it,” Dutta says.

For example, whereas everyone in the company can use Power App Vibe, only some employees have access to AI building platform Replit for coding.

Replit is “a little bit of a clamped-down application because we want to control what goes into production and how it connects with enterprise databases,” Dutta explains. “Replit can be dangerous because it if it is connected to SAP and other critical systems in a way that the users are doing something unusual with their app, it may bring down the system altogether.”

Users can, however, immediately connect the Replit applications they create to a data lake or other repositories, he adds. “It’s only when a transactional system gets impacted that we impose some condition.”

Unlocking business-user creativity

Hexion’s citizen AI strategy is unleashing creativity across the company, Dutta says. For example, the leader for environmental health and safety built an app she calls Wingman that can be used in the company’s factories to run health and safety processes. “We did some light editing on the core to make it a little bit enterprise standard,” and then it was deployed, Dutta says.

Given the divide between vibe-coded apps and production systems, governance remains an essential ingredient of any citizen AI development strategy. One of the AI governance tools Hexion uses is Microsoft Agent 365, which Dutta says lets IT see which agent is running and the various statuses of agents that employees deploy.

“We can start and stop anything we want across the enterprise,” he says. “It’s one single-pane governance layer.”

At eSky, employees are automating their daily work and building small applications for reporting and for automating specific business cases, Mynarski says. They also lean heavily on specialized agents that can take over parts of a job for them, he says.

In one instance, a performance marketing specialist built an agent that reads campaign data across eSky’s markets and is starting to generate ad content. The operations team runs a platform of 16 scheduled automations and small apps for controlling, reconciliation, and refunds, built by the people who used to do that work by hand, he says.

However, “this often becomes a problem the moment a personal tool needs to become a production system,” Mynarski says. “We have that covered with practices and guardrails that these teams can plug into, so the initiative stays with them and the hardening comes from us.”

At Harvey Nash, a content manager has created an agent that helps the company’s recruitment consultants find approved posts and assets, personalize it to their role, and post them to their social channels.

“This supports us with brand amplification and helping position them as market experts without requiring them to create new content,” Anand says. “We can measure the number of posts created, adding a gamification element to it to encourage usage.”

Coding isn’t the most important capability needed to become an AI citizen, he notes. “It’s understanding a problem well enough to describe it, knowing the context and data behind it, and having the judgment to recognize whether the result is useful.”

Overall, citizen AI is about dramatically increasing the number of experiments an organization can undertake, Anand says. “Give people the freedom and guardrails to turn ideas into proof points,” he says. “When an idea demonstrates real value, that’s when technology steps in to make it enterprise-ready and scale it. You get grassroots innovation without creating a technology Wild West.”

For CIOs considering launching a citizen AI strategy, Mynarski advises keeping costs in mind. “You have to ask, more than ever before, whether a given use brings value to the organization, because it is very easy to spin up AI that works beautifully and adds nothing,” he says.

Still, Mynarski believes the issue CIOs face is not employees doing too much with AI without permission. “The challenge is teams doing too little,” he says. “My advice to other CIOs: Talk about security in the same breath as innovation. If you let that gap grow, the freedom you gave people will be taken away from them later, and that is the expensive scenario.”

See also:

• How IT leaders can make vibe coding work at enterprise scale
• Your employees are building AI agents. Do you know what they’re doing?
• How to make sense of the agentic AI tools ecosystem
• What world-class IT looks like in the gen AI era
• How AI agents are shaping the future of work
• AI governance is fast becoming an unmanageable chore

SAP says it is ushering in a new era of agentic AI, one where agents don’t just learn a business, but help run it.

This week at its TechEd conference, the company is announcing new tools in its SAP Business AI Platform that will extend AI agents’ capabilities, give them richer, more enterprise-specific data and context, and beef up governance controls so they can be rolled out safely across enterprise workflows.

“There used to be a time where AI assistants or interfaces could do a couple queries, and you had to get the query more or less right,” SAP chief AI officer Jonathan von Rüden told CIO. “And then if you asked three, four questions, eventually it would say, ‘I can’t go that far.’ Now that constraint isn’t really there anymore.”

New orchestration layer

SAP has introduced new capabilities in its AI-driven workspace Joule Work and its AI no-code/pro-code agent building environment, Joule Studio.

Joule Studio will now be natively integrated with Joule Work, giving users a single build layer. They will be able to describe their intent, and the system will orchestrate the right agents, apps, and data. Joule Work is built on the SAP Business AI Platform, which provides context, data, and governance.

“Effectively, business users get a coding agent into their hands, but they don’t know it is one, so they come up with all kinds of very, very interesting use cases,” von Rüden said.

SAP has put a good deal of R&D effort into graduating from conversations to a generative UI to “step-by-step” replace classic UIs, he noted. “You don’t need those anymore, you can have them tailor made per person.”

A new orchestration layer in Joule Work will support more complex, multi-step workflows. Beyond working across SAP environments, agents will be able to plug into external systems, data, and tools via Modern Context Protocol (MCP) and Agent2Agent (A2A) Protocol.

Further, Joule Studio will be more tightly integrated with SAP Business Data Cloud so that builders can access various data products across SAP, and users across different teams will be able to collaborate on agents while customizing them to their different roles. These shared environments will have role-based access, and developers will be able to design and run n8n workflows, test pre-deployment in built-in sandboxes, and perform offline evaluations.

Governance, debugging tools

The company also announced that the SAP Knowledge Graph will now have the ability to pull data from both SAP and non-SAP sources, while interoperability tools will allow third-party agents to securely invoke Joule Agents through a governed gateway. This will enable safe collaboration between agents from different providers, SAP said.

New AI capabilities in the SAP Integration Suite will provide tools that analyze bugs, flag anomalies, simplify schema setups, manage GitHub integrations, perform core Git operations, standardize communications, and automate processes across platforms like Gmail, Microsoft Excel, and Salesforce Service Cloud.

From a governance perspective, a new SAP Enterprise AI Gateway will consolidate authentication, policy enforcement, routing, monitoring, usage tracking, and auditing, giving users the ability to view and govern all traffic and interactions. Joule Studio also has new observability and administrative controls, and a new Governance Agent will help companies comply with the EU AI Act.

The Enterprise AI Gateway will bring “enterprise-grade governance to AI interactions” in one control plane, SAP said.

SAP is rolling out Joule Work and the various new Joule Studio features in phases, and general availability for SAP Enterprise AI Gateway and the new SAP Knowledge Graph capabilities are planned for Q1 2027.

Accessing hundreds of assistants for enterprise workflows

With Joule Work and Joule Studio, users can immediately customize a skill load, von Rüden said, and can easily build a reusable dashboard. “Now they have these standard backend connections to SAP.”

For instance, he explained, say a user wants to plan deliveries for the next six weeks. They can pull in data from Jira and GitHub, but also overlay that with data from project planning. “I crunch all of this up in completely natural language, and with out of the box UIs, I can see whether I can actually deliver on what I’ve planned over the next six weeks,” he said.

If a user likes something they co-designed with AI, they can tell it to persist that workflow as a skill. “I can share it back with other colleagues, and that becomes a very powerful workflow,” von Rüden said. “I’m not queuing for eight months in the IT backlog.”

With Joule Work, users have access to 40 specific assistants that can orchestrate hundreds of agents beneath. These include, for example, billing, campaign, enterprise architecture, case management, or HR operations assistants.

“Customers can map these assistants to actual personas,” von Rüden said. Then, assistants can be connected to other relevant assistants (like accounts payable and accounts receivable). “We have one central place where they can exchange trust, and we can secure everything, do policy enforcement.”

Essentially, the right integrations can support near-infinite iteration in AI systems, von Rüden asserted.

New data tools, Tabular foundation model

Also at TechEd this week, SAP is introducing new ontology tools, lakehouse capabilities, and governance and admin controls to take AI from “intent to trusted execution.” The company is building on its recent acquisition of Dremio and an expanded partnership with Databricks to unify data, simplify its management, and support faster contextual insights.

The company is also infusing AI into RISE with SAP, its subscription-based service that helps enterprises transition from on-premises enterprise resource planning (ERP) systems to the cloud. Customers will get AI-assisted implementation guidance, best practices, roadmaps, onboarding, and assessment, alignment, and business mapping tools.

Finally, SAP has announced its latest tabular AI model, SAP-RPT-2.0, which will go beyond classification and regression to anomaly and outlier detection and AI-powered analysis. These capabilities will reduce manual engineering and help customers predict values across business metrics for forecasting purposes. Customers will be able to use SAP-RPT-2.0 through the SAP Integrated Business Planning platform.

von Rüden said that the model is being used for “so many wild use cases,” ranging from running small business loans and performing financial forecasting to the classification of astronaut health by NASA.

Agentic AI is the main topic at most industry events this year, with vendors adding agents to their products with gleeful abandon. NetSuite is no different; at its SuiteWorld conference this week, it is unveiling agents and AI workspaces designed to do everything from autonomous financial closing to AI-based supply chain management.

SuiteExpress, announced today, however, is something different. Rather than automating processes within the ERP suite, it is tied into Customer Success services, using AI to help customers implement the product.

“We’re leveraging the opportunity to bring AI to bear on the implementation, and accelerating the delivery of NetSuite’s implementations for our customers, as well as for helping them understand and take advantage of new capabilities as their business evolves and their business needs change,” explained Craig Sullivan, group VP of product management at Oracle NetSuite. “It’s the fast track onto NetSuite, delivers faster implementations at a lower cost, taking into account all of the key knowledge that the customer has about the way that they run their business today, as well as all of the knowledge that NetSuite has about how other customers in similar industries have been able to be successful on NetSuite.”

The company said this will help customers implement more of what they need at the beginning, to realize greater value more quickly. It claims that, using SuiteExpress, NetSuite implementation time is reduced “to weeks, or even days.”

SuiteExpress is powered by two connected knowledge bases that combine customer-specific context with NetSuite’s proprietary expertise, the company said in a news release. “The Customer Knowledge Base ingests meeting notes, documents, spreadsheets, recordings, and data from other systems to capture requirements, business processes, configuration decisions, and validation history, giving customers and implementation consultants a shared understanding of how the business works,” it said, while the NetSuite Knowledge Base adds proprietary product, implementation, and industry expertise from NetSuite’s experience with its customers. The two knowledge bases provide the context and expertise needed by the AI to recommend next steps, and to perform implementation tasks such as preparing data for migration, with oversight from consultants. It will also help customers continuously improve how they use NetSuite after go-live.

NetSuite SuiteExpress is available today to customers in the software industry in North America as part of a phased rollout. The company plans to broaden availability to other industries and geographies in 2027.

Oracle has patched 1295 security vulnerabilities with its October Critical Patch Update Advisory; six of them scored the highest vulnerability rating of 10.0 on the CVSS version 3.1 scale.

The company’s flagship Oracle E-Business Suite was home to one of the vulnerabiltiies, tracked as CVE-2026-101809. This enables any unauthenticated attacker with network access via HTTP to compromise the workflow notification mailer in the suite’s Workflow component, taking over Workflow and potentially affecting other products too. The flaw affects Workflow versions 12.2.3 through 12.2.15.

Oracle SOA Suite, part of Oracle Fusion Middleware, was hit by three of the bugs rated 10.0.

One, tracked as CVE-2026-91231, is in the Adapters component, and enables unauthenticated attackers to compromise Oracle SOA Suite resulting in its takeover, with potential impact on connected products.

Two others were in the Fabric Layer component of Oracle SOA Suite: CVE-2026-91216 and CVE-2026-91221.

Both are easily exploitable, Oracle said, and enable an unauthenticated attacker with network access via the T3 or IIOP protocols to compromise Oracle SOA Suite and perhaps affect other products too. The bugs appear in supported versions 12.2.1.4.0 and 14.1.2.0.0.

There’s a 10.0-rated vulnerability in another part of Oracle Fusion Middleware too. The flaw in Oracle Access Manager (tracked as CVE-2026-87337 leaves it open to compromise by any unauthenticated attacker with network access via HTTP. Successful attacks of this vulnerability can result in unauthorized creation, deletion or modification of critical data on Oracle Access Manager versions 12.2.1.4.0 and 14.1.2.1.0.

The last “perfect 10” vulnerability in Oracle’s latest report is in the JD Edwards EnterpriseOne Orchestrator product of Oracle JD Edwards, tracked as CVE-2026-91299. This enables an unauthenticated attacker with network access via HTTP to compromise and take over versions 9.2.0.0 through 9.2.26.5 of the orchestrator.

Salesforce is set to acquire US-headquartered customer engagement software provider, Braze, for $5.66 billion in an effort to help enterprises personalize customer interactions in real time.

Founded in 2011 as Appboy and renamed Braze in 2017, the company’s offerings include the Braze Customer Engagement Platform that enables businesses to track customer behavior across digital channels and coordinate interactions through mobile apps, websites, email, push notifications, SMS, and WhatsApp.

Braze differentiates itself from other customer engagement platforms through its event-streaming architecture, which processes customer interactions in real time and enables businesses to trigger personalized communications across channels as behavior changes, the companies said in a joint statement.

That real-time processing capability will bolster Salesforce’s existing data and AI platforms, including Data 360, Customer 360, Agentforce, and SIForce, by enabling enterprises to act on customer signals as they emerge and use AI to deliver more relevant interactions across marketing, sales, commerce, and service use cases, they added.

Braze’s acquisition could reduce engineering work

The ability to act on real-time customer signals directly within Salesforce products will reduce the need for enterprises to build and maintain complex engineering pipelines to connect customer data, AI-driven decision-making, and engagement systems, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

“Most enterprises today still move customer data from the CRM into a warehouse and then batch it into an engagement tool through reverse-ETL jobs. Those jobs add hours and break often,” Chopra said.

“With Data 360 holding the unified profile and Braze acting on events directly, a customer’s action can trigger a response within minutes,” Chopra added.

That, in turn, could improve customer experience and retention by enabling enterprises to identify signs of customer dissatisfaction sooner and respond with targeted offers, timely support, or personalized recommendations before customers disengage, echoed Pareekh Jain, principal analyst at Pareekh Consulting.

The reduction in engineering work is also likely to reduce integration costs while helping CIOs consolidate technology vendors, Jain added.

Product overlap and vendor lock-in could complicate integration

However, the extent of those benefits will depend on how smoothly Salesforce integrates Braze’s capabilities into its existing products and whether enterprises can avoid greater dependence on a single vendor.

“Overlap is the first risk. Data 360 and Braze both store customer profiles, and Journey Builder and Braze Canvas both orchestrate journeys. Without one clear owner for each, profiles drift, and customers get conflicting messages,” Chopra said.

Also, Salesforce already offers Marketing Cloud, which could create further overlap with Braze’s engagement capabilities and complicate product integration, said Manoj Chandra Jha, principal analyst at Nord-IQ Research.

Therefore, CIOs will need to watch how the acquisition affects pricing, APIs, product roadmaps, and support for third-party platforms, he added.

Moreover, the acquisition’s promise of reducing the number of vendors an enterprise relies on for customer data management, marketing automation, and engagement also comes with risks around vendor dependence.

While tighter integration could simplify operations, it could also make it harder for enterprises to switch vendors or retain flexibility in how they assemble their technology stacks, Jain said.

That dependence could deepen even without Salesforce explicitly discontinuing a product or changing a contract.

“Many Braze customers use the platform alongside data warehouses such as Snowflake and Databricks, or customer data platforms such as Segment. If Salesforce prioritizes native integrations over those third-party connections, enterprises could face higher switching costs as their workflows become more tightly coupled to Salesforce,” Chopra said.

“Bundled pricing could also make it more expensive to move away from the platform when contracts come up for renewal,” Chopra added.

To preserve flexibility, CIOs should retain control over their data architecture rather than making Salesforce the only route through which customer data can be accessed or activated, Chopra further added.

The acquisition is expected to close by January 2028. Until then, Salesforce and Braze will continue to operate as separate, independent companies, and integration of their products will not begin until the deal closes, Braze wrote in an SEC filing.

CIOs experiencing sticker shock from recent software bills should be questioning how they got there.

That’s because IT leaders at large enterprises are paying a huge so-called software tax for vendor-driven upgrades, migrations, and licensing changes they would not have chosen for their organizations.

About 28% of software budgets at large organizations — an average of nearly $12.9 million per year — are being eaten up by vendor-pushed initiatives, according to a survey commissioned by independent software maintenance partner Origina.

Ninety-five percent of IT leaders surveyed said their organizations have paid for a vendor-driven upgrade in the past three years that they believed would actually reduce performance, according to the survey, which covered US and UK organizations with annual revenues higher than $1 billion or with more than 10,000 employees.

The sheer size of this “software tax” surprised Tomás O’Leary, Origina’s CEO, even though the company expected to see substantial costs for vendor-driven upgrades.

“Software was supposed to make businesses better, and it does,” he says. “But it’s also imposing a hidden cost on companies, quite a significant one. I’m quite taken aback because we know this exists, but we never quantified it before.”

When vendors control your IT spend

Big vendors have several major advantages over software customers, with the cost of switching or refusing to upgrade topping the list, O’Leary notes. In many cases, CIOs are faced with a loss of support if they turn down vendor-driven upgrades, leaving them with little room to refuse, he says. CIOs dependent on VMware are familiar with this squeeze play, as are those facing SAP deadlines.

“CIOs are left in a situation where the alternative may be worse, so upgrading is the lesser of two evils,” O’Leary adds. “When people buy technology, they don’t realize how long this stuff is going to hang around. Nobody realized this thing was going to be embedded, and the dependency was going to be greater and greater with time.”

In addition, software vendors have an experience advantage when negotiating contracts, O’Leary says. CIOs and procurement officers may try to negotiate favorable terms for upgrades, but big software vendors are expert at writing contracts that give them an edge, he notes, adding that the survey results suggest IT leaders’ current approach to negotiating better contracts is a failed tactic.

“If you keep trying the same thing over and over again, and it doesn’t work, you’ve got to try something different,” O’Leary says. “There’s a relief on the buyer side when the negotiations are over, and on the seller side for sure. But the seller side has to get back up again and do it again the next morning with somebody else, and the buyers don’t.”

Omar Amassine, founder and technical director at TripleW Digital, says between 20% and 30% of the IT services firm’s software and maintenance budget goes to vendor-driven migrations he didn’t request.

The biggest culprits? CRM, marketing automation suites, and cloud infrastructure, he says.

“Vendors frequently bundle mandatory AI features or restructure seat-based pricing into usage tiers, driving effective 20% to 35% price jumps with zero incremental utility for the core workflow,” he says.

IT leaders can fight back, however, by using modular, open standards and headless architecture, he adds. “The less proprietary logic locked inside a vendor’s walled garden, the higher your leverage when they mandate expensive upgrades,” Amassine says.

TripleW Digital also runs migration feasibility studies six months before software contract renewals. Having a migration roadmap can bring the vendor back to the negotiating table offering multi-year price freezes, he notes.

Emphasize partnership — and a credible threat of defection

Zachary Lewis, CIO of University of Health Sciences and Pharmacy in St. Louis, hasn’t seen the same software spending mandates as the organizations reflected in the survey.

The university has negotiated many of its software contracts to ensure upgrades and security patches are handled by the vendor, either with Lewis’ approval or during an agreed-on maintenance window, he says.

Even without costly upgrades, there’s still a time tax, he adds.

“With the sheer volume of new vulnerabilities being discovered, we’re spending more time following up with vendors to make sure they’re applying patches in a timely manner,” Lewis says. “My team simply isn’t large enough to manually manage patching across every application and software component we use.”

Here, establishing a vendor management strategy that emphasizes partnership over provisioning can help. And if vendors still drive costly upgrades, IT leaders can always switch providers.

But Lewis cautions that, as tempting as that is, it’s often a complicated process.

“Switching vendors is always an option, but it’s rarely simple,” he says. “Depending on the application, you may have years of integrations, processes, and institutional knowledge built around it. Sometimes the cost of moving away from a vendor is significantly higher than the cost you’re trying to avoid.”

Customers of large ERP solutions in particular are facing a significant reckoning on decades of customizations.

Collective bargaining power comes with communication

To regain their purchasing power, Origina’s O’Leary advises CIOs and procurement officers to negotiate shorter contracts, which would enable them to renegotiate terms more often and exit more quickly if their vendor jacks up prices.

“You have to get to a point where you can have a more frequent engagement with the vendor,” he says. “Negotiate your exit, and negotiate more frequently.”

While short-term contracts may impose more up-front costs on customers, they can pay off with the additional flexibility they provide, he says.

CIOs can also turn to third-party software management firms or consider internal support of their software packages if they have the resources, O’Leary advises.

CIOs should also share their software purchasing success stories and nightmares with each other more than they do now, he says.

“We all benefit from having a little bit more collective bargaining power,” he adds. “CIOs just need to take that leap of faith that others have the same problems, and maybe we can have some sort of collective pushback on this.”

The legal disputes between Germany’s largest software company, SAP, and the Munich-based process mining specialist Celonis took another turn last week. After the German Federal Cartel Office most recently dismissed a complaint filed by Celonis in July, the Munich I Regional Court ruled entirely in favor of Celonis in a case regarding the infringement of a European patent held by Celonis.

Case No. 7 O 12849/25 concerned patent EP 3 139 274 B1 describing a method for determining a net lead time between process steps using process data stored in a structured manner in main memory (in-memory).

Celonis had filed a lawsuit alleging that the patent was infringed by a feature of the SAP Signavio Process Intelligence software solution — SAP acquired Celonis’s rival Signavio in March 2021.

SAP must cease use and provide information

The court ruling prohibits SAP from offering the patent-protected method in Germany or using it itself. SAP must also provide Celonis with information regarding the extent to which it has infringed the patent since April 22, 2020, including details on:

• where the patent-infringing processes originated and through which channels they were offered and distributed;
• which customers purchased them, including names and addresses;
• how many access licenses were granted and how much users paid for them;
• for how many users the methods were used per calendar year;
• which other individuals were involved in offering and using the methods.

In addition, SAP must provide a transparent account of the extent of the patent infringements based on its financial figures. The company is obligated to pay appropriate compensation or damages.

“The ruling by the Munich Regional Court confirms our legal position: SAP Signavio’s technology infringes on Celonis’s patent rights. We consistently protect our intellectual property and remain committed to fair competition,” explained a Celonis spokesperson.

SAP says no consequences for its customers

SAP, however, points out that the court ruling pertains to a single, specific feature within SAP Signavio Process Intelligence, not to the product as a whole. SAP Signavio Process Intelligence remains available to customers, according to a company spokesperson.

The spokesperson also dismissed concerns that the ruling could hinder companies’ migration to SAP S/4HANA Cloud. Migrations to SAP S/4HANA Cloud are not affected by this, he said. SAP is working to ensure that there are no disruptions for its customers.

The concern is not entirely unfounded: SAP Signavio is part of Rise with SAP, a SaaS solution from the Walldorf-based company designed to support migration to the S/4HANA Cloud. SAP Signavio’s process mining tools are designed to provide insight into actual process flows and identify inefficiencies and bottlenecks.

SAP will appeal

The Regional Court’s ruling does not mean the matter is by any means settled: As the spokesperson for the Walldorf-based software giant explained, SAP does not share the Munich court’s legal opinion and will file an appeal against the ruling. In addition, SAP has challenged the validity of the patent in question before the Federal Patent Court.

“SAP’s top priority is to ensure that customers are not affected by this. All other issues will be resolved through the appropriate legal channels,” said the spokesperson for the Walldorf-based company.

This article first appeared on Computerwoche on Oct. 13, 2026.

For the past half century or more, the teams running enterprise software stacks have nurtured a vast ecosystem of tools to keep software humming along. They’ve built CI/CD pipelines, elaborate test suites, code repositories, and tools galore, all with the goal of keeping the systems up and running.

Now AI is throwing a wrench into the whole endeavor. Software is now more complex and sometimes mysteriously unpredictable. Expectations are sky high, but no one is certain what to expect in good conscience.

Luckily, dozens of companies are rushing to build new tools designed to keep agentic stacks on the straight and narrow, delivering value to everyone. The field is rapidly evolving, probably having changed since you started reading this article.

Some of the tools involved in the emerging agent stack aren’t that different from what enterprise teams already have in store. In the end, LLMs are just piles of bits that need to be loaded into a processing chip, just like old-fashioned software. Many of the old tools that watch for overloaded nodes or overpacked disk drives are just as valuable as ever, perhaps more so because LLMs chew through storage even faster. And some of the companies tackling these problems are the same steadfast suppliers who have earned the trust of your DevOps teams; they’ve just extended their own offerings to handle LLMs and AI.

Other challenges, however, are quite different. For example, LLMs are terrifyingly non-deterministic, a real challenge for belts-and-suspenders engineers who traditionally tend the software. Tool vendors are building entirely new systems to handle these somewhat random deviations so that traditional DevOps teams — and their C-suites — don’t have heart attacks.

The field is also rapidly expanding in wildly divergent ways. Some vendors are building tools for developers who are creating new LLMs. Others are concentrating on the challenges that come from worrying about an LLM, day in and day out, in large scale production.

We’ve compiled lists introducing the most intriguing companies developing tools for this rapidly altering space, including tools aimed at AI orchestration; AI observability, aka AgentOps; AI evaluation and benchmarking; and AI trust, guardrail, and governance. We’ve even thrown in some vibe-coding tools for citizen development and AI workflow tools for business processes.

While it was relatively easy to split the field into distinct lists, that process will only grow more challenging because the tools — and categories — are starting to overlap. As vendors evolve their strategies, this overlap will grow further, with a variety of entries tackling a wider range of challenges, vying for a greater percentage of CIOs’ AI middleware and operational budgets.

To help you make sense of this complex tool landscape, here are the most common categories vendors are using to identify what their products do, each of which maps to what are becoming the key components IT needs to keep their AI and agentic stacks productive and humming.

Observability

Vendors often use the term “observability” to refer to the most basic of data collection work — that is, testing to make sure the various machines are running, the RAM isn’t too full, latency is acceptable, and the logs aren’t filled with error codes.

Watching these metrics isn’t much different from caring for an old-school collection of microservices, and often the tools aren’t much different, either. Many of the most popular tools are the same dominant market leaders that have been extended to collect data from the various GPUs, too.

Leading vendors: DataDog, Dynatrace, New Relic, Splunk
Upstarts: Ariz, LangSmith, Braintrust, Galileo
Open-source alternatives: OpenTelemetry, Phoenix, Langfuse

Cost engineering

Many AI pipelines are often more complex than regular microservice architectures, and that means they need someone to carefully watch the bill. Plus, as attention to AI ROI rises, capturing spend is essential — especially given how quickly AI tokens can add up.

Cost engineering tools can integrate information from various portals like OpenRouter and compare it against internal hardware. Sometimes breaking down these comparisons is difficult because the right way to allocate costs to each user isn’t as obvious, even though they’re often just one number. These accounting operations tools will become even more important as LLM bills grow.

Leading vendors: CloudZero, Finout
Upstarts:  Amnic, Bifrost, Portkey
Open-source alternatives: OpenCost, Langfuse

AgentOps

When metrics and telemetry begin to focus on the LLM, the word “AgentOps” starts to appear. It’s just like DevOps but it’s concerned with watching token counts, tracking the performance of RAG databases, and collecting traces for debugging purposes.

When the work is truly agentic, AgentOps must also juggle the state that agents generate while looping through their various activities. Agents aren’t just a one-and-done call to an LLM. They’re software packages that need to store information as they work toward a final answer.

This job of an AgentsOps team gets trickier because these areas are where the models often behave non-deterministically. Is some model hallucinating? Are an agent’s prompt chains running to completion? The tools in this space track all these metrics, often in a dashboard with nice graphs that make problems stand out.

Leading vendors: Databricks, Datadog, Dynatrace
Upstarts: Arize, Braintrust, Comet
Open-source alternatives: Langfus, LlamaIndex

Evaluation

The buzzword “evaluation” starts to appear alongside tools that are used in the earlier stages of development, when various models and prompt combinations need to be tested again and again.

In many cases, these tools use another model to evaluate the quality of the answer from the first model, as well as to keep an eye out for potential hallucinations or other failures. In some cases, the tools query humans who sit in the evaluation loop to give their opinions of how well the model is performing. Good tools can watch the agents continuously and reroute any sketchy behavior to the human-in-the-loop.

Many of the vendors use the word “evaluation” to describe measuring just how well a model is performing for a particular business application. That is, how well it’s working for what your boss wants it to do.

Leading vendors: Databricks, SuperAnnotate, Scale AI
Upstarts: Braintrust, Galileo AI, MaximAI
Open-source alternatives: Langfuse, Prompfoo, Tagas

Benchmarking

The word “benchmarking” is similar to “evaluation” in AI vendor literature, but it is usually found more in the tools used by the creators of the models. There are a number of public benchmarks filled with standard queries, and the model developers rank themselves on how well they do solving these problems. This often generates a raw score that is then compared to another’s raw number. Some teams create their own benchmarking problem sets and use them during evaluation to assess how well models might meet their needs.

Leading vendors: Hugging Face, Databricks
Upstarts: Reka AI, Patronus AI
Open-source alternatives: Promptfoo, DeepEval

Trust

The tools that wear the “trust” benchmark are often meant to help evaluate how well a model is protecting the needs of any business that is deploying it. That is, can they trust it to deliver accurate and useful answers without hallucinations, data leakage, inappropriate behavior, or worse? Prompts with malicious instructions are especially tricky to detect. Some enterprises want a trust score that measures the consistency of the answers. Overall, tools that target the “trust” metric are often utilized more during development and early deployment.

Leading vendors: Azure AI Content Safety, Bedrock Guardrails, Vertex AI Search
Upstarts: Patronus AI, Arthur AI
Open-source alternatives: NeMo Guardrails, Llama Guard

Guardrails

The word “guardrails” is usually applied to tools that are part of the deployment stack. That is, they sit as watchdogs and make sure the LLM doesn’t spin out of control. They also ensure the model is trustworthy, but they’re more tuned to operating in real-time than those aimed at trust, as outlined above.

These tools are often filled with fixed rules, such as scanning for social security numbers or watching for noxious words. The challenges get trickier because agents are executing software, and that means the guardrails must do more than analyze outputs. They also need to manage the execution environment, often running in a sandbox, and watch for exploits and other malicious behavior. They need to validate and double-check not just code, but the OS-level commands for manipulating files and processes.

Leading vendors: Azure AI Content Safety, Bedrock Guardrails, Vertex AI Search
Upstarts: Patronus AI, Arthur AI
Open-source alternatives: NeMo Guardrails, Llama Guard

Context

Many systems rely on a vector database to store embedded data to chunks of trustworthy documents. These RAG databases need much of the same care as regular databases, and the tools for their care are often similar. PostgreSQL, for example, has a vector addition that needs the same kind of scrutiny from a database administrator as a regular PostgreSQL database.

Leading vendors: MongoDB, Elasticsearch
Upstarts: Pinecone, Weaviate
Open-source alternatives: Milvus, PostgreSQL

Gateway routing

Increasingly, AI systems employ models in constellations, and a gateway router is what examines a prompt to determine which model or agent is best for answering the query. Moreover, as agents undertake their work, the router can distribute the agent’s various subcalls to the fastest, cheapest, or most accurate LLM or subagent.

Watching over this stage in the pipeline requires a different set of intelligence because the questions can often involve both applicability and cost. Sometimes this layer must balance the extra cost versus the need for a better answer.

Leading vendors: Cloudflare, AWS, Google Cloud, Azure
Upstarts: OpenRouter, ModelsLab, Respan
Open-source alternatives: LangChain, Bifrost, LiteLLM

Access control

Who gets to use the model? Who is paying? Many access control tools are the same ones that were part of standard APIs or web services. Is there an API key? Is the account below its budget? The tools watch for overspending because both the customer and the boss want to make sure the money keeps flowing and no tokens are wasted.

Leading vendors: Auth0, Apigee
Upstarts: Amnic, Portfoundry
Open-source alternatives: LiteLLM, Bifrost

Compliance

Some industries have explicit compliance requirements dictated by law. Some have their own internal controls. Tools devoted to compliance are often a subset of the ones devoted to guardrails. But they’re often treated separately from guardrail tools because they’re more likely to be of interest to lawyers and that makes the stakes higher.

Leading vendors: ServiceNow, IBM
Upstarts: VerifyWise, Norm AI
Open-source alternatives: Aether, Giskard

Fresh off its $1.1 billion acquisition of AI startup Sana, Workday is making another bet on enterprise AI, this time targeting the governance challenge. The company said it will acquire agentic identity platform C1.ai to help customers manage and secure AI agents operating across enterprise applications.

The acquisition will build on Workday’s Agent System of Record, which gives enterprises a centralized view of their AI agents, by adding controls over what those agents can access across Workday and other enterprise systems, the company said.

That would allow enterprises to grant agents the permissions they need to perform specific tasks and revoke them when those tasks are completed, rather than risk agents retaining access to data and applications they no longer need, it added.

The combined capabilities could also help streamline access management for employees as they join an organization, change roles, or leave, reducing the manual work involved in managing permissions while limiting potential security gaps, it further said.

These governance and identity management capabilities become increasingly important as Workday, alongside the acquisition, also continues to expand the role of AI agents across its suite.

During the week, the company announced several updates to Sana, including making it the new home interface for its applications.

The interface brings tasks, decisions, meetings, and agent activity into one place, allowing employees to use natural-language prompts to get Workday agents to complete tasks rather than simply retrieve information, the company said.

The company also extended Sana into third-party tools, including Microsoft Teams, Slack, and Microsoft Copilot, allowing employees to invoke Workday capabilities from the applications they already use.

That expanded ability to invoke Workday agents across applications underscores the need for enterprises to govern what these agents can access and do across systems. C1.ai’s acquisition is expected to close by the end of January.

When Workday acquired Sana just about a year ago, it said that the combination of Sana’s AI with Workday’s platform would create a new “front door” to work. Today at its Workday Rising conference, it unveiled that front door, turning Sana into Workday’s default home screen that, it said, “brings tasks, decisions, meetings, and agent activity into one place, helping employees move directly from what needs attention to getting it done.”

The new interface goes beyond merely answering questions to orchestrating Workday’s agents to execute tasks. Each employee’s experience is tailored to their role; when they open the application, they see the tasks and decisions that need their attention and can take action directly from the UI. And, using TypeSafe’s Jev AI model, Sana adapts its UI to each task, the company said, generating forms, calendars, approval cards, visualizations, and other interactive experiences in real time and completing tasks once the employee approves the action. It also supports work that spans multiple days, displaying relevant information at every stage of functions such as financial closing or preparation of workforce plans by HR.

Sana also works within other AI tools and platforms including Anthropic Claude, Google Cloud’s Gemini Enterprise, and Microsoft Copilot, as well as within Microsoft Teams and Slack. But, Workday said in its announcement, “third-party AI tools and platforms never get raw access to Workday data — they can now access this data through Sana, and Sana does the reasoning, applies each company’s own permissions, approvals, and policies, and carries out the work so actions are lawful by design and recorded, regardless of the tool it started in.”

Along with the Sana home page, known as Workday Home, the company introduced Sana Concierge, which brings together the self-service capabilities and Sana for IT Service Management released earlier this year with Workday Help and Case Agent to create AI-native employee services spanning HR and IT. Workday said it can resolve requests such as password resets, access requests, and questions about time off and payroll, as well as performing onboarding and offboarding. The employee just submits a natural language request, and Concierge looks up relevant policies and, based on the employee’s Workday information, guides them through the steps to complete their request.

If a human decision is needed, Concierge routes the request to the right person in the background, but remains the employee’s point of contact.

The Sana Workday Home experience is available today, with more capabilities and support “coming soon,” the company said. Sana Concierge is available now to early adopter customers, with general availability expected in the first quarter of 2027.

From copilots to autopilots

The company also introduced four new autonomous AI agents that act as autopilots across hiring, payroll, accounting, and compliance.

“From co-pilots to autopilots, that’s a big transition that you will see across our agents today,” said Joel Hellermark, former CEO of Sana, who is now Workday’s chief AI officer. “These are now agents that anticipate what needs attention, they apply the company’s policies, they advise on the next step, and then act once the appropriate person approves that. And these autopilots run completely on Workday’s rails, so they’re self-governed and compliant.”

And, he said, while a typical copilot-driven system “sits there” until the user logs in, autopilot agents run on a heartbeat, operating whether or not the user is online.

The Talent Acquisition Agent, for example, monitors the open job queue from the time a position is posted, finding qualified candidates and giving the recruiter information about them. It can generate outreach to potential hires and present it to the recruiter for approval, explain why candidates on a short list are a good fit, and schedule interviews with those the recruiter flags. It then automatically joins the interview and takes notes. Based on recruiter feedback, the agent learns how to improve its recommendations.

The agent works across multiple recruiting tools, including Workday Recruiting, HiredScore, Paradox, native Workday skills, and connected sourcing providers. The autopilot capabilities are available to early adopter customers today, with general availability planned for early 2027.

Three more autopilots will arrive for early adopters in early 2027, for Payroll Agent, Accounting Agent, and Deployment Agent.

This could be the start of a change in the coordination of work, said Melody Brue, analyst in residence at Moor Insights & Strategy. “Workday is trying to connect more of the work around a decision, from weighing options to carrying out the approved plan,” she said. “Sana could become the starting point for that process, giving Workday a larger role in coordinating work beyond its own applications. For current customers, the meaningful change would be less manual coordination between a request, an approval, and a completed transaction. The value depends on whether that reduces the work employees have to manage or shifts it into resolving exceptions when a workflow crosses systems.”

Extending Decision Intelligence

Workday Decision Intelligence, first seen in Adaptive from Workday, is spreading into more areas of the business, said Yasmeen Ahmad, SVP and GM, Workday Data Cloud.

Decision Intelligence is not an agent performing a task, she said. “It’s a planning and reasoning experience built for decisions that are too complex, too high stakes, or too cross-functional for any single task to capture.”

Ahmad described a scenario in which HR revised a hiring plan using Decision Intelligence, passing it to Finance to use as an input for reviewing its quarterly projections with the same tool.

“Normally this would have taken six weeks, five spreadsheets, and analysts trying to stitch it together. Today, it was all just a prompt,” she said.

Brue said that what she finds interesting is whether Workday can take more of the manual modeling out of a staffing discussion. “Workday already supports connected workforce and financial planning,” she said. “If teams can work through several options with fewer model adjustments and less reliance on specialists for routine ones, planning could be more responsive when business needs change.”

Work Intelligence

Work Intelligence, available to early access Workday Human Capital Management customers, breaks jobs into their component tasks, describing the skills employees need to perform them. It draws on Workday Skills Cloud, giving leaders a look at how jobs are changing, and then, using Decision Intelligence, companies can get a picture of their internal readiness for evolving work.

“The task-level analysis is worth watching,” Brue said. “A role is rarely entirely automatable or entirely human-led. Looking inside the role could give customers a more useful basis for redesigning work. But saving time across a dozen tasks doesn’t automatically free someone up for a different assignment. Companies may need to regroup the work, letting agents handle routine steps together and giving employees clear responsibility for what comes next.”

Off with its head

Workday also announced that its Trusted Agent Platform, on which organizations build and run AI agents governed by their business rules, is now headless, allowing customers to use the development tools and models of their choice. It brings together the existing Workday Data Cloud, Agent-Ready Tools, Developer Agent, custom agents, and Agent Harness.

However, Workday CTO Gabe Monroy emphasized that, at the same time, “headless can’t mean lawless. The rules for HR and finance already live in Workday, so customers don’t have to rebuild them for every agent they create. This is what lets companies move fast with agents without losing control.”

And, to enhance the controls, in early 2027 the company will add Agent Context Service and Grounded Reasoning to Agent Harness to provide Workday context that agents need and check requests against company policies, business processes, and tenant data before the agent is allowed to act.

“Governance gets more consequential as agents move from recommendations to actions,” Brue pointed out. “An agent can be authorized and still make the wrong call. Customers need to distinguish permission to recommend from permission to execute. And know which actions require human approval, especially in hiring and finance. The governance question extends to any workflow that crosses platforms. Customers need clear ownership of approvals, exceptions, and the audit trail when a request starts in one system, and an agent acts in another.”

For companies already using Workday and ServiceNow together, she said, there’s an additional consideration: Workday’s expansion into employee-facing AI and agent governance could create more overlap between platforms that have traditionally played complementary roles.

“Joint customers don’t suddenly need to choose one, but they may need to clarify which platform coordinates the work and how controls operate across both,” she said. “A unified experience won’t necessarily mean unified control.”

SAP is continuing its acquisition spree to help its enterprise customers address what it sees as one of the key challenges around scaling AI: giving Joule agents the appropriate context they need to reason across enterprise data and systems.

The enterprise software giant, late on Tuesday, said that it was signing an agreement to acquire Ghent-based AI startup TechWolf to augment its SuccessFactors HCM platform by giving its AI agents a more detailed understanding of how employees work and what skills they have.

The startup’s central offering is a data model called context graph for work, which it builds by using an AI model after analyzing data from HR and business systems. The graph creates a continuously updated view of the work employees perform and the skills they use, and connects that information with jobs, workforce requirements, and external labor-market data.

For SAP, that graph will serve as a grounding layer and provide additional context for Joule AI agents, allowing them to reason across skills, tasks, and workforce requirements when handling enterprise use cases such as skills-based hiring, workforce planning, and role redesign, the company said in a statement.

For enterprises, the additional context is likely to result in more accurate workforce-related agent responses, potentially reducing token consumption and, in turn, the cost of deploying workforce agents, the company added.

Earlier this year in March, SAP acquired Reltio, a master data management platform, to help enterprises unify and harmonize data across SAP and non-SAP systems, in turn making it easy for its AI agents with the context they need to reason across information spread across different systems.

Later in May, the company announced plans to acquire Dremio, an agentic lakehouse provider, to further help enterprises bring together data from different sources and make it available for AI workloads, giving its agents access to the data and context needed to reason across enterprise information.

While Reltio and Dremio address the context problem for SAP from the data side, TechWolf extends that approach to HCM.

However, there is a difference in how these acquisitions are being integrated into SAP’s portfolio. While Reltio and Dremio have been folded into SAP’s broader data platform, TechWolf, at least for now, will continue to operate independently, allowing it to serve both SAP and non-SAP customers while its technology is integrated with SuccessFactors and Joule.

TechWolf’s acquisition is expected to close by December this year, SAP said.

SAP isn’t alone in making acquisitions to address the context problem around enterprise AI.

Its CRM-focused rival Salesforce acquired Informatica in November last year to strengthen its Agentforce platform with data management, governance, metadata, and lineage capabilities, giving AI agents access to trusted enterprise data and the business context needed to reason and act across different systems.

Similarly, ServiceNow completed its acquisition of Moveworks in December last year, adding enterprise search, an AI assistant, and a reasoning engine to its platform to help AI agents access information spread across enterprise applications.

Workday, an HCM-focused rival, too, acquired Sana in November last year to combine its AI-powered search, agents and learning capabilities with Workday’s data and context around people and money, creating what the company described as a horizontal intelligence layer across the enterprise.

It later agreed to acquire Pipedream in November to connect Workday’s people and finance data to more than 3,000 business applications, allowing AI agents to retrieve data and execute tasks across the systems where work happens.

SAP has launched new AI agents and assistants across multiple domains at its SAP Connect event, saying that its October update is fulfilling the promises it made around its vision of the Autonomous Enterprise at Sapphire in May.

The SAP Autonomous Enterprise comprises capabilities in Joule Work and SAP Autonomous Suite, built on the SAP Business AI Platform. The assistants and agents, as well as Joule Studio and Joule Work, will be offered as SAP-managed products via the SAP for Me portal; their general availability begins this month.

Like many other vendors, SAP is turning its agents into a front end for its products.

While Joule used to be just a natural language-based chat client, its scope has expanded, said Manoj Swaminathan, president and chief product officer, SAP Autonomous Suite. “Joule Work now becomes one harmonized experience for anything and everything that the users can interact with, with agents backing it as part of the autonomous suite, fueled and powered by the platform,” he said.

Users can still use the traditional SAP interface if they wish to.

“Systems-of-record-based applications still will be there, but they are now provided with these agentic experiences as well as AI on apps,” he said, “so that if a customer chooses to, they can now take advantage of them either in a semi-autonomous way or a fully autonomous way.”

SAP said that Joule Work is already live with 110,000 employees within the company, and claims 20% productivity gains across finance, HR, and procurement.

At the conference, SAP showcased SAP Autonomous Suite capabilities in finance, where it can automate revenue recognition, disclosures, and trade compliance; in supply chain, where planners “can turn multi-hour disruptions into one-click resolutions across planning, manufacturing and logistics;” as well as in HR, procurement, and customer experience.

Cloud ERP updates

The latest release of the SAP-managed public cloud ERP offers deeper integration across SAP Autonomous Suite. SAP S/4HANA Cloud Public Edition now integrates with SAP Ariba and SAP Sales Cloud solutions, while links to SAP Taulia, SAP Subscription Billing, and SAP SuccessFactors will be made available before year end.

Even SAP’s on-premises offering, Cloud ERP Private, is getting the managed AI services treatment, with new integrations with Joule all delivered and maintained by SAP. It is currently available through the SAP Early Adopter Care program, with general availability planned by year-end.

Analysts displayed varying levels of enthusiasm about the announcements.

Thomas Randall, research lead at Info-Tech Research Group, was unimpressed. “SAP Connect is not giving us any breakthrough moments,” he said. “Microsoft, Salesforce, ServiceNow, Oracle, and the hyperscalers have already established comparable assistants, agent studios, multi-agent orchestration, model choice, and governance capabilities. SAP’s announcements are best understood as fast-following AI technology for business-process suites.”

However, he does see potential. “SAP’s opportunity to pull ahead lies in its positioning AI as a controlled execution layer for back-office operations,” he noted. “Few vendors can match its combination of process depth, transactional authority, industry-specific configuration, and installed footprint. This gives SAP a distinct advantage: its systems are already trusted to move money, adjust plans, release orders, and record the resulting business consequences. I look forward to seeing this advantage in action.”

Jason Andersen, principal analyst, apps, agents and automation at Moor Insights & Strategy, has a more positive view.

First, he said, SAP is showing that it can deliver as planned on the promises made at Sapphire earlier this year. “Notable are a series of task or role driven agents that will augment common roles in many enterprises such as procurement and supply chain roles,” he said.

And, he added, “There is also the backing of the SAP Business AI Platform, which will help enterprises customize these agents to the specific processes and standards for each company. This is a great way for providers like SAP to embed decades of customer understanding within agents but also enable customers to tune them to suit their needs. While the more specific agents get a lot of attention, I also think that SAP’s advances with Joule Studio and Desktop can help workers deliver SAP capabilities in the context of common business remain notable.”

Risk management

However, independent technology analyst Carmi Levy cautioned that there is a wide gap between plans and reality. “As agentic tools draw ever closer to core organizational workflows, enterprise decision makers face amplified concerns around data integrity, privacy, and accuracy,” he said. “Elevated agentic risks should be a primary concern for SAP shops, while SAP’s third-party integration should raise alarm bells among leaders worried about guardrails being violated across the organization.”

While SAP’s announcement goes into significant detail explaining how tightened protocols around authentication, governance, and identity management ensure agents won’t exceed defined limits, he said, it fails to address agents’ vulnerability to new forms of attack such as model injection that organizations must now deal with.

“Agentic AI is already rewriting the cybersecurity playbook for all enterprises, and vendors owe it to customers to explicitly explain how they intend to mitigate the risk,” he said.

“Automated AI agents will ultimately rewrite how work gets done in the same way earlier technologies, like PCs and smartphones, similarly rewrote the rules of modern work,” he said. “But AI agents introduce enough additional baggage into the equation that handing entire processes over to automation at this fraught moment in their development is hardly the recipe for C-Suite harmony.”

The theft of FBI employee data by hacking group ShinyHunters, and the subsequent shutdown of the FBI’s Peoplesoft-based jobs portal, is causing concern for enterprise users of the Oracle product, with analysts recommending extreme measures in response.

Law enforcement has made some progress in its pursuit of the cyber criminals involved, but there has still been no official word from either the FBI or Oracle about Peoplesoft’s alleged involvement.

On Saturday, Reuters reported that a suspected member of ShinyHunters had been arrested in Jordan and has been cooperating with law enforcement to help identify other member of the group. The arrest comes a week after the arrest of another suspected ShinyHunters member by the Dutch National Police. In a video about that arrest, an FBI official warned gang members that arrests have a way of changing who is willing to talk, concluding, “You know how to find us, and we know how to find you.” Before the arrests, the gang told The Register it had discovered a new PeopleSoft zero-day hole and leveraged it to break into an FBI system, stealing information on all FBI employees. The FBI has since confirmed the breach without providing details.

This wouldn’t be the first flaw ShinyHunters had found in PeopleSoft. It discovered a PeopleSoft zero-day hole in June that resulted in a flurry of extortion attempts, long after Oracle said it had patched the bugs.

Oracle did not respond to a request for comment on the alleged new vulnerability.

Significant risk exposure for enterprises

Analysts and consultants played up the risk exposure from the potentially new vulnerability even as they sharply played down the implications of the arrest.

Frank Dickson, principal analyst at Dickson Research, recommended that PeopleSoft users take immediate action.

“If it is a new flaw, the patch is necessary but not sufficient. PeopleSoft customers should pull the Environment Management Hub and the Integration Broker off the public internet,” he said. “Doing so does not break normal user sessions.”

He also encouraged users to quickly implement many of the recommendations from Google unit Mandiant’s report late last month on PeopleSoft security issues, especially “hunting for the web shells and outbound traffic Mandiant describes.”

“For PeopleSoft shops, the to-do list is short,” Dickson said. “Apply Oracle’s patch. Disable or remove the Environment Management Hub if you do not use it. Search your logs for encoded variants of the PSEMHUB path, not just the literal string. If you find a web shell, treat the server as compromised and rotate every credential it could read.”

This is a dangerous situation, he said: “An unpatched PeopleSoft flaw is a Sword of Damocles over every PeopleSoft shop

But despite the extreme risk, even after the patch was released, rather than applying it, some organizations have opted for other mitigations. “Many organizations chose to block the vulnerable endpoint with web application firewall rules rather than patch,” Dickson said. “The attackers walked past those rules by changing a single character in the web address, writing ‘%50’ in place of the letter ‘P.’”

Could signal a bigger problem

However, IDC Research Director Philip Harris encouraged CISOs to view the FBI’s confirmation of its breach with caution, given the attack vector was not revealed.

“ShinyHunters says it used a second, previously undocumented PeopleSoft preauth RCE zero-day, distinct from the CVE-2026-35273 flaw exploited in the earlier campaign,” he said. Yet, he pointed out, “no CVE has been assigned to it, it has not appeared on CISA’s Known Exploited Vulnerabilities catalog and Oracle has not commented on it. Every outlet covering this is explicit that the zero-day claim comes only from the threat actor, not from independent forensic confirmation or from Oracle. That distinction matters.”

But if the hole is as the group has suggested, Harris said it signals a much bigger problem.

“Assuming it is real, this would mean PeopleSoft has a second critical, unpatched preauth RCE in the same window as CVE-2026-35273, a pattern of recurring critical exposure rather than one isolated bug,” he said. “ShinyHunters’ own claim that they are already using it against other, unnamed Fortune 500 targets would mean every PeopleSoft customer is currently exposed to an unpatchable, undisclosed flaw with no vendor guidance to act on, a meaningfully worse position than the WAF-bypass story, since there is no mitigation to attempt while waiting on Oracle to confirm something it has not acknowledged.”

Jeff Valdes, a director at consulting firm Acceligence, added that Oracle’s silence is unwarranted.

“I think customers reasonably want more communication from the vendor,” he said. “They want to understand whether Oracle’s original guidance remains sufficient, whether there are additional mitigations customers should implement, whether Oracle is seeing anything through its own support organization that changes the risk picture, and whether there are configurations or architectural choices that materially increase exposure. Those are operational security questions that can be addressed without discussing attribution, arrests, investigative techniques or anything else that might interfere with an FBI investigation.”

This article originally appeared on CSOonline.

Oracle is adding a new execution layer to its Fusion Cloud Applications Suite that it says will help enterprises lower the costs of automating complex and long-running business processes with AI agents.

The execution runtime, Fusion Claw, uses a combination of a frontier model, deterministic policies, and governed execution capabilities to plan and carry out a given task or process, instead of relying for each step on AI inference, which can result in repeated model calls as the task progresses. That can help enterprises keep inference and compute costs under control, the company said.

The deterministic policies can include approved algorithms, business rules, calculations, optimization services, reconciliation logic, and transaction-processing capabilities. Enterprises can define or configure them via Fusion’s AI Agent Studio while building the agent or modifying existing agentic applications, Oracle said.

Claw then uses two built-in features, the Enterprise Operating Envelope and the Enterprise Trust Harness, to enforce those policies as controls during task execution, ensuring that the agent operates within its delegated authority and the required governance boundaries, while also generating an auditable record of agent actions and decisions, it further said.

Fusion Claw could help improve an agent’s unit economics

For Manoj Chandra Jha, principal analyst at Nord-IQ research, Fusion Claw’s approach to agentic execution is likely to help enterprises with the unit economics of deploying AI agents to automate business processes.

“By keeping the expensive model for planning and running high-volume work on cheap deterministic computation, Fusion Claw can make agentic costs both lower and more predictable for CIOs,” Jha said.

Beyond cost, Fusion Claw’s approach to separating AI reasoning from deterministic execution can also make agentic automation more reliable and repeatable, which could be “critically important” for CIOs trying to move agentic applications from prototype to production, according to Scott Bickley, advisory fellow at Info-Tech Research Group.

The deterministic controls combined with delegated authority, approvals, escalation rules and an auditable record of decisions and transactions can give enterprises a measure of “safe autonomy,” he said.

There are advantages for developers, too, especially in terms of productivity.

As the new execution layer packages up most of the underlying plumbing stack required to operationalize an agentic application, developers can focus on process design, offloading the mundane work of integrating and orchestrating the underlying components needed to put an agentic application into production, Bickley said.

That, in turn, is likely to result in business processes being automated faster, he added.

Lower AI consumption does not necessarily mean lower costs

However, that reduction in development and orchestration work does not eliminate the work required to govern agentic applications, but rather adds to the governance work required, Jha cautioned: “Development teams must encode policies and approval rules, choose autonomy levels, and verify outcomes.”

That outcome verification will also be important for CIOs in determining whether the promised cost savings and cheaper unit economics actually translate into lower costs, rather than simply lower AI consumption, according to Bickley.

While Oracle’s architecture can reduce the amount of AI inference needed for a task, the actual cost of an agentic application can vary based on factors including the actions it performs, token consumption, model used, testing, human review, exception handling and ongoing operational support, Bickley said.

As a result, CIOs should measure the cost of successfully completing a business outcome, such as resolving an accounting exception or filling a staffing gap, rather than looking only at the cost of AI units or tokens, Bickley added.

The analyst also cautioned CIOs about Oracle’s AI unit policy, which he said could introduce additional considerations into the cost of running Fusion Claw-based applications: “Unused pooled units expire without reimbursement, while Oracle can modify its action and model tables with at least 30 days’ notice.”

CIOs should demand per-outcome pricing in writing and let a bounded pilot build the business case, said Jha.

Adoption will depend on process and governance maturity

Those considerations, combined with the added governance requirements, are also likely to shape which enterprises adopt Fusion Claw first, even among existing Oracle customers.

“Existing Oracle Fusion customers with standardized processes, reliable master data, mature controls and high-volume work with measurable outcomes are likely to be the initial adopters. Finance reconciliation, supply-chain planning, staffing and other constraint-heavy processes are logical candidates because they combine analysis with calculations and repeatable execution,” Bickley said.

On the flip side, adoption will be “slower” in enterprises with heavily customized Fusion estates, fragmented data, multiple ERP platforms, weak segregation of duties, or limited AI governance, he added.

For enterprises willing to try out Fusion Claw, the execution runtime is now available as part of the Suite, Oracle said.

The company is also making 25 pre-built Claw-powered agentic applications available, including Ledger, Workforce Staffing, Shipping Consolidation, and Sales Territory Planning, aimed at helping divisional leaders, such as CFOs, CSOs, supply chain heads, and CHROs, improve productivity and reduce costs.

The company will make the other 21 apps available in October, it said.

The market for enterprise resource planning (ERP) systems is entering what some industry experts are calling an upgrade “supercycle,” fueled by a convergence of key factors.

Grandview Research predicts global ERP software sales will grow 9.5% annually from 2026 to 2033. Fortune Business Insights puts the growth rate from 2026 to 2034 at 13%. And Morgan Stanley analyst Chris Quintero is even more bullish, saying the ERP market will grow by 17% annually.

This projected growth spurt is significant because, as Forrester analyst Faram Medhora points out, “The ERP solutions market is mature, saturated in large enterprise adoption, and driven primarily by modernization rather than net-new ERP buying.”

Quintero adds that 50% of CIOs in a recent Morgan Stanley survey indicated they planned to upgrade and modernize their ERP systems over the next few years.

For many organizations, inertia is a powerful force. If the finance, supply chain, manufacturing and HR software are fully paid for and doing the job, then there’s little incentive to embark on a costly, risky upgrade. But the days of running mission-critical business processes on decades-old software are quickly coming to an end. Here’s why.

1. AI is having a transformative impact

Medhora, who uses the term “supercycle” to describe the rapid pace of ERP modernization, says, “AI-driven automation is the main innovation trend, moving ERP toward active orchestration across a federated application estate connected by APIs.”

Gartner analyst Johan Jartelius adds that AI “is transforming ERP systems from transactional to intelligent platforms that provide insights while reducing the need for human intervention in routine tasks.” Gartner predicts that by 2030, over 50% of foundational ERP tasks will be autonomously executed by AI, reducing human involvement in finance, supply chain, and HR.

But So Chan, US enterprise performance leader at Deloitte, cautions that AI’s rise is by no means marginalizing the centrality of ERP.

“AI isn’t the death of ERP; ERP is actually what enables the value of AI. That’s what we’re seeing,” Chan says. “The way we’ve been articulating it with our clients when we think about how their ecosystem works is this idea of a core, the systems of record, and that’s ERP. Then we have this outer layer that we are defining as systems of action. These are the agentic AI-enabled processes that wrap around the core system of record.”

To the question of where AI should be deployed, either into ERP systems or sitting on top, “what we’re finding is that being embedded in the transactional process is where you’re going to get that really important notion of context that is required to unlock AI value,” Chan says. “With legacy ERP systems you need to modernize the core before you layer on AI capabilities. Modernizing ERP isn’t a cost; it’s actually a catalyst to unlocking the value that you’re looking to achieve.”

Organizations that have integrated AI into their ERP systems have reported significant operational improvements, including a 25% reduction in delivery times and a 15% decrease in operational costs, according to Mordor Intelligence.

2. End-of-life deadlines loom large

ERP vendors that have been trying for years to nudge customers off legacy, on-premises systems and onto cloud platforms have found a new, highly effective tool: announcing end-of-support deadlines.

SAP announced it will end support for SAP Business Suite 7 on Dec. 31, 2027. And Microsoft said it will end support for Dynamics GP on Sept. 30, 2029. Running mission-critical software without a support contract means no new security patches, which constitutes an unacceptable risk for most enterprises.

“Legacy on-premises systems are approaching end-of-life, creating a ‘forcing function’ for cloud migration,” says Morgan Stanley equity strategist Simon Clark, adding that an estimated 60% to -70% of SAP’s support base has yet to move to the cloud.

These deadlines leave CIOs with the choice of migrating to the cloud on the incumbent vendor’s platform or switching to an entirely new vendor. Because it can take 18 to 36 months, not to mention millions of dollars, and considerable effort in areas such as data integrity and change management, to complete a cloud migration, CIOs need to start making those decisions now.

Chan adds that from her experience advising enterprise clients, the end-of-life deadlines are more of an impetus to start the upgrade conversation than an actual threat. From the vendor side, it’s more about “how do you envision unlocking the value that you are trying to achieve as an organization and how best can we get you there,” she says.

But the deadlines are having the intended effect of getting those conversations started.

3. Data sovereignty, geopolitics reshape cloud deployment decisions

Regulatory pressures, particularly in Europe and Asia/Pacific, are forcing CIOs of multinational enterprises to rethink their ERP strategies, particularly when it comes to the sensitive data contained in finance and HR applications.

“Geopolitical pressures are shaping cloud ERP choices: Increasing geopolitical tensions and regulations are driving organizations to use more local and regional cloud providers, especially in Europe, Asia/Pacific, and Canada, which is having a flow-on effect for ERP selection,” says Gartner analyst Neha Ralhan.

She points out that cloud sovereignty issues are also being driven by specific business and application requirements, which can vary by country, industry, and workload. Gartner recommends organizations “establish a sovereignty strategy by assessing legal, industry, and workload-specific requirements, and evaluate cloud vendors based on their ability to meet these needs in a sustainable manner.”

4. Cloud migration accelerates

Those three factors — AI, end-of-life deadlines, and data sovereignty requirements — are driving CIOs to rethink their ERP strategy, which in most cases means migrating from on-prem to the cloud.

The cloud ERP market is projected to expand from $56.53 billion in 2026 to $138.56 billion by 2031, a 20% growth rate, according to Mordor Intelligence. “The market continues to transition from traditional on-premises environments toward multitenant, consumption-based architectures that deliver faster time-to-value and lower total cost of ownership,” Mordor’s report says.

Morgan Stanley’s Clark adds, “As organizations seek to leverage generative AI, cloud infrastructure becomes essential. This need is prompting enterprises to fast-track their cloud strategies. For example, SAP SE has seen increased adoption of its S/4HANA cloud offering, driven by demand for AI capabilities.”

Deloitte’s Chan says, “Sitting and watching in a market that is moving so fast with the options and the technological advancement available is not really a sound business plan or strategy.” The discussion she has with clients is not whether to move to the cloud, but the pace of adoption and how to make that move while infusing AI into the mix.

At the same time, private cloud isn’t dead yet. Mordor Intelligence reports that private cloud ERP is growing at 22% a year, reflecting regulatory requirements, particularly in banking and healthcare.

“Cloud is now the architectural standard, but hybrid is the default in many regulated environments that require on-premises ledgers,” Forrester’s Medhora notes.

5. Monolithic apps are getting unbundled

When organizations take a step back and analyze cloud migration options, one approach is to break up their monolithic ERP platform into smaller, composable pieces.

“The obvious assumption is that the key beneficiaries of this ERP upgrade cycle will largely be the same companies with their cloud-equivalent offering,” says Justin Overdorff, a partner at Lightspeed Ventures.

But that’s not necessarily the case. “As organizations modernize, many are moving away from expensive on-premises ERP vendors like SAP and Oracle and towards modern cloud-based systems that support digital transformation and AI-readiness,” Overdorff says. “But, instead of adopting next-gen versions of these monolithic, do-everything systems, enterprises are opting for an unbundled, best-of-breed stack across finance, HR, procurement, supply chain, CRM, and more — tools that snap together like Lego pieces.”

6. The door opens to ERP startups

Lifghtspeed’s Overdorff argues that the acceleration of cloud ERP deployments combined with the breakup of monolithic ERP systems presents a unique opportunity for startups to capture market share in specific functional domains.

“This is more than just a tech refresh; we believe it’s an opportunity to turn the $150 billion ERP market on its head and reset the cost structure and performance of core business operations for the next decade,” he says.

He adds that the “all-in-one approach created high barriers to entry for new competitors (few could build such a broad system), and it locked customers into a single vendor’s ecosystem. However, we are now seeing a shift toward disintermediation of the enterprise OS, as companies opt for more modular point solutions. Rather than trying to build an entire ERP suite, many new entrants are targeting subsegments — individual functional areas traditionally served by a module of a big ERP — and doing them better.”

For example, the growing list of challengers offering cloud-native, AI-driven ERP specifically for accounting and finance includes Rillet, Pennylane, Ramp, Light, DualEntry, Campfire, Everest, and Digits.

Overdorff adds that there is also an opportunity for startups at the orchestration layer that sits above individual modules. “With agentic AI, such a platform can ingest semi-structured/unstructured data, reason across multiple workflows, and route tasks to the right tool with minimal human intervention, delivering maximal output and efficiency,” he says.

At a Fortune 500 insurer, I watched an AI agent inside the month-end close do exactly what we asked. It posted the recurring accruals. It worked the intercompany exception queue, the reconciliation that normally falls to a staff accountant. And when items would not match, it cleared them to a suspense account within the configured tolerance rather than escalating them to a human. Clearing the queue was the objective. Nobody had defined escalation as success.

Every journal entry carried the controller’s user ID. No one set out to obscure anything. The job step had been mapped to her credentials at implementation, which is how it had been done since the system went live.

Segregation of duties had become a fiction. The control requires a named preparer and a named approver. The agent did both, under one identity: it prepared the entries and it disposed of the exceptions. The record named the controller for each. She had done neither. When internal audit walked those entries the following quarter, they asked her to explain postings she had never seen.

None of this was a model failure. The agent was accurate. It was authorized. Every entry looked clean, which is exactly why nobody caught it.

Vipin Jain

Here is the number that should frame this year. Gartner expects 40 percent of enterprise applications to carry task-specific AI agents by the end of 2026, up from under 5 percent in 2025. Okta finds only 10 percent of organizations have a strategy for managing the agents they are already running. And Deloitte, surveying more than 3,000 leaders across 24 countries, puts mature agentic governance at 21 percent against 74 percent who expect to be using agents within two years.

Adoption is racing. The control model is not. Every one of those agents inherits a permission model built for a person clicking a button, which means the question that matters is no longer whether an agent can do the work. It is whether, when it does, anyone can say who authorized it. McKinsey frames the same shift from the governance side: organizations can no longer concern themselves only with systems that say the wrong thing. They now have to contend with systems that do the wrong thing.

What actually broke?

ERP, CRM and core administration platforms rest on four assumptions so foundational that nobody writes them down: identity, permission, action, receipt. Each one places a named human at the center. An agent breaks all four at once, and none of them repairs itself.

Vipin Jain

Intent. A person submits a bounded request, and the request is the transaction boundary. An agent receives a goal and decomposes it into steps at runtime. On a state eligibility modernization program, we gave an agent one goal: clear the verification backlog. It re-ran electronic verification, and where a federal source returned no match, it advanced the case toward closure and generated the notice. Nobody listed that step. Nobody had to. It is a defensible reading of clear the backlog, and it is precisely what the agent was rewarded for. But a no-match is not a finding of ineligibility. It triggers a reasonable opportunity period, and closure carries notice and appeal rights that attach to a determination, not to a data lookup. The rule that breaks here is least privilege. You cannot scope permissions to an action set nobody enumerated in advance.

Authority. Applications grant entitlement to a person. It is standing and role-based, and that person’s judgment is the unwritten limiter on it. At a specialty retailer, I watched a quoting agent go into Salesforce to compress turnaround at quarter close. The approval matrix was intact: manager above fifteen percent, director above twenty-five. None of it fired. A submission action in the user interface triggers a Salesforce approval process, and the agent wrote to the record through the API. It never submitted. An integration user provisioned with more rights than anyone intended bypassed the validation rules and record locks that would ordinarily catch it.

Salesforce’s own architects are blunt about the mechanism. The client credentials flow, they warn, creates a service-account pattern in which every call runs as a single identity, and both per-user permissions and the audit trail are lost along the way. Their guidance on identity propagation goes further: when an agent calls a downstream service using client credentials, the request carries the agent’s service identity rather than the end user’s, which makes per-user authorization and audit trails impossible.

That approval process was never a security control. It was a workflow convention that humans had no reason to route around.

Logic. Business rules used to be a configuration artifact: versioned, testable, under change control. At a health insurer, I traced one authorization threshold for advanced imaging to three separate homes. Configured in the core administration platform. Routed in the orchestration layer. And written in plain English inside the agent’s instruction, as a sentence somebody added during a sprint. Medical policy tightened the first. Nobody touched the third, because the third is not a configuration item. No change control that clinical policy or compliance has ever seen covers it.

In every payer program I have worked, that drift runs one direction only. An agent cannot lawfully deny since adverse determinations require review by a licensed clinician. So the exposure is silent over-authorization against a threshold the organization believes it already tightened, surfacing months later through a delegated vendor audit or a utilization variance that finance notices before medical management does. Then someone asks the question with no good answer: how long, and how many.

Evidence. The log names whichever credential was presented. That is the insurer’s close, and it is the most measurable of the four failures. The Cloud Security Alliance found that 68 percent of organizations cannot clearly distinguish between human and AI agent activity, even as 73 percent expect agents to become vital to operations within a year.

An agent without a delegation chain is not automation. It is an unsigned transaction.

Table 1. The same failure, four industries

Why didn’t the controls catch it?

Because in every one of these cases, the controls worked as designed. That is the uncomfortable part, and in the programs I advise it is the finding that changes the conversation. This is not a problem the CIO can hand to the security team and consider closed.

Consider the insurer’s segregation-of-duties conflict. It never appeared in the access risk analysis, and the tooling did not fail. Most implementations exclude technical and service accounts from risk-analysis scope entirely, on the reasonable historical assumption that a service account does not exercise judgment. Nobody ever analyzed the account. The conflict was not missed. It was never in view.

Most writing on agent risk assumes an adversary. The OWASP Top 10 for Agentic Applications, built with input from more than 100 practitioners, catalogs goal hijack and memory poisoning and places Identity and Privilege Abuse third on the list. What I see most often is duller. Nobody attacked the payer. A threshold governed in one of three places simply drifted, and it took a vendor audit to notice.

The pattern holds across all four, and each one breaks a control an auditor already asks about. Intent breaks least privilege. Authority breaks separation of duties. Logic breaks configuration management. Evidence breaks non-repudiation. Those four are the spine of any controls walkthrough, which is precisely why this arrives on the CIO’s desk rather than staying in the security function.

None of these produced a headline. They produced audit findings and rework: a control deficiency to disclose, authorizations to re-adjudicate, cases to reprocess, notices to reissue. That cost lands on the CIO’s budget and the CFO’s certification, not on the security team’s incident log.

The repair is the same in all four cases. Stop letting an agent borrow a person’s credential, and start recording who stands behind it.

Vipin Jain

What has the market already conceded?

What I find most striking this year is not that a vendor shipped something. It is that vendors, standards bodies, regulators and advisers all converged on the same requirement within roughly six months of each other, without coordinating.

Vipin Jain

Microsoft made the human sponsor mandatory. Every agent identity carries a named business owner accountable for it, and if that person leaves, sponsorship transfers to their manager so accountability never quietly disappears. SAP now governs an agent lifecycle that runs from proposed to evaluated to approved to active to retired, with a verification badge controlling which agents are cleared for use. And Okta has moved access decisions out of individual applications and into the identity layer, with AWS, Google Cloud and Salesforce behind the effort.

Google reached general availability this month with the bluntest statement any vendor has made. Its agent identity documentation argues against the service account directly: agent identities are not shared, cannot be impersonated, and permit no long-lived keys. Every action resolves to a cryptographic ID rather than to whoever the credential once belonged to. That is the insurer’s close, described by a hyperscaler as a design flaw to engineer out.

The pattern held into this month. At Black Hat on 4 August, Rubrik unveiled a service that grants an agent access one tool call at a time, minting a short-lived token for each call and eliminating standing permissions outright. Its general manager for AI put the diagnosis in one line: the access models we built for humans were never designed for autonomous actors.

The standards work is further behind, and honest about it. NIST’s National Cybersecurity Center of Excellence opened a concept paper on software and AI agent identity and authorization in February and closed comment in April, explicitly seeking input on agent auditing, non-repudiation and prompt-injection controls. The gap is officially acknowledged. The standard is not written.

Regulators moved in a way most leaders misread. Brussels deferred the EU AI Act’s high-risk obligations in June, pushing them to 2027 and 2028, and the headline everyone absorbed was that the AI Act had been delayed. Half true, and imprecise in a way that matters: the Article 50 transparency duties, including telling people when they are interacting with an AI system, were untouched. They took effect on 2 August and are live now.

The advisory community has landed in the same place. BCG, writing on scaling agents in regulated industries, tells CIOs to define clear approaches to agent identity, authority and accountability: what agents can access, what actions they can take, and how those actions remain traceable. Booz Allen, surveying 105 federal IT and cybersecurity decision makers this spring, found 22 percent who have not determined who bears responsibility when an agent causes an incident.

Read those together and the picture is unambiguous. The identity layer has been rebuilt around delegated, sponsored, per-action authority. The business applications those agents act inside have not moved at all.

The plumbing now encodes accountability. The applications still do not ask for it.

Where should CIOs start?

None of this requires a reorganization. The leaders I see getting ahead are not doing more; they are doing six things in order, on the bounded use cases where they can afford to learn.

1. Inventory the agents. Enumerate every agent acting inside a system of record, the identity it runs under, and its standing entitlements. Only 23 percent of leaders report full visibility into the agents already running in their environment, and most organizations I work with cannot produce that list today, and producing it is usually the moment this becomes real to the executive team.
2. Bind identity to a named human. Retire shared service accounts and borrowed logins. Every agent gets its own identity with a named sponsor who answers for it. This is the change that fixes the insurer’s close because the document header stops naming a bystander.
3. Scope authority per action. Short-lived, time-bound, just-in-time. And move approval enforcement out of the workflow layer into the authorization layer, where an API call cannot walk around it. This is the step I almost never see taken, and it is the one that fixes the retailer.
4. Consolidate the logic. The instruction becomes a configuration item: versioned, evaluated, approved and reviewed alongside the rules engine. If a threshold changes in medical policy, it changes in all three places or it changes in none.
5. Instrument the evidence. The transaction record carries the delegation chain, meaning agent, sponsor, instruction version and model version. Without those four, every incident investigation starts from zero.
6. Graduate autonomy deliberately. Sanctioned, then supervised, then autonomous, with sunset as a real state rather than a euphemism for neglect.

Vipin Jain

The gates between rungs are what make the ladder operational rather than decorative. Sanctioned to supervised requires a named sponsor and a declared scope. Supervised to autonomous requires an evidence record showing the agent stayed inside it. And sunset is not a failure state. It is what happens when an agent stops earning the authority it holds, which most organizations have no process to detect.

Do these in order and autonomy compounds. Skip them and you join the more than 40 percent of agentic projects Gartner expects to be canceled by the end of 2027, undone not by weak models but by escalating cost, unclear value and inadequate risk controls.

The takeaway

The most common strategic mistake I see this year does not look like a mistake. Leaders are granting agents authority inside systems whose architects assumed a human held it, and then treating the resulting mess as a technology problem. It is not. It is the same failure I have watched for thirty years, from trading floors to Medicaid programs: business strategy, IT and organizational culture are not architected to move together, and the technology simply industrializes the gap.

I recommend a reframe most leaders resist at first. Guardrails here are not friction. You can give an agent that proves who authorized it more autonomy, sooner, and with far less anxiety than one that cannot. The delegation chain is what makes acceleration survivable.

So put three questions to your team this week. None of them requires a program to answer, and all three will tell you more than a maturity assessment.

Table 2. Three questions, and what a good answer sounds like

The agent is the new user. Your applications have not been told.

This article was made possible by our partnership with the IASA Chief Architect Forum. The CAF’s purpose is to test, challenge and support the art and science of Business Technology Architecture and its evolution over time as well as grow the influence and leadership of chief architects both inside and outside the profession. The CAF is a leadership community of the IASA, the leading non-profit professional association for business technology architects. 

A decade ago, UiPath built its business on watching people work, recording keystrokes and clicks to automate tasks employees already did by hand. This week, amid an industry consumed by autonomous agents, the company’s newest product suggests that same instinct still has a place.

UiPath used its Fusion conference in Las Vegas to launch Cartographer, a tool for building what the company calls a Map of Work: a living, governed record of the informal, undocumented knowledge that keeps enterprise processes running, the exceptions, workarounds, and judgment calls that never make it into an official workflow document.

Existing automation tools capture the click-by-click steps of a task. Cartographer works at the process level instead, documenting how work actually gets done versus how it’s designed to run. From the main stage, CEO Daniel Dines described this information as a missing manual enterprises need before trusting agents with real work. “A process map tells you how work is designed to run,” he said. “UiPath Cartographer is the key to uncovering the operational knowledge of how that work actually runs.”

Even with Cartographer, the first drafts of a Map of Work “will be incomplete for sure,” Dines admitted on stage.

That missing knowledge has kept enterprises’ most complex, exception-heavy processes out of automation’s reach, said Mark Geene, UiPath’s group senior vice president of agentic solutions. He characterized Cartographer as the next stage in a five-year push to extend the automation platform into messier enterprise areas. Capturing that knowledge, if it works, opens the door to automating processes once too undocumented and judgment-dependent to hand over to machines at all.

How well it will work is a different question. Analysts and customers on the ground described a tool that’s promising on paper, but whose real test comes later, in whether enterprises do the disciplined work of capturing and maintaining what Cartographer helps them uncover.

What’s different

That capture process has a real limit, said Amy Loomis, a research director at IDC, one that depends on whether people actually share what they know. “That step will only be as strong as the tribal knowledge of teams and the ability to capture it accurately,” she said.

Pairing deterministic guardrails with the inherently unpredictable behavior of large language models (LLMs) differentiates UiPath’s approach, Loomis said. She pointed to the spectrum of options UiPath will offer: prebuilt maps ready out of the box at one end, a curated Process Atlas in the middle, and Cartographer itself handling processes requiring full customization. A human “cartographer” role, an existing business analyst repositioned to own a Map of Work, is the other new piece, she said.

Getting a full picture of a process has been a persistent problem, one that often doesn’t surface until well after a bot is live, said Andrea Simpson, IT manager of automation at USI Insurance Services. “So much knowledge is in a person’s head, and it can be hard to get it out of our subject matter experts,” she said.

Knowledge management — a problem enterprises have been dealing with since the cold war — is the pain point UiPath aims to ease. But there are limits. Some knowledge will likely stay out of reach, Simpson said.

USI is an early adopter of Maestro, UiPath’s orchestration tool, to automate its marketing processes, tying together producer meetings, carrier submissions, and client communication. But the biggest hole is what happens off the record: a phone call, a conversation at someone’s desk. “We wouldn’t want that automated,” she said. “We want to keep that human.” That knowledge never touches a company system until a producer chooses to report it.

On-the-job training for agents

Perfection won’t be required to get started. “The goal isn’t to understand all the rules and processes from all the years you’ve been doing a process,” said UiPath’s Geene. “The goal is to capture enough information to get agents executing the process. The key is to get live early.”

Typically, once an LLM is built and deployed, “their brains freeze,” said Doug Marcey, CTO of Coronis Health, a healthcare revenue-cycle-management company that has used UiPath for about a year and a half. Cartographer’s promise, he said, is closer to on-the-job training for agents, gaining institutional knowledge the way a new hire gradually would, with each new exception routed through a “Decision Ledger” for a human owner to approve before feeding back into the map. That trail matters as much as the automation itself in a regulated industry. “The difficulty in healthcare is that compliance is all about vibes,” Marcey said. “The Decision Ledger would be very helpful for us.”

Coronis’s denial-management work shows what captured judgment can do. Agents draw on claims data, orchestration coordinates the workflow, and generative AI drafts appeal letters, a combination that’s made it financially viable to contest denials on smaller claims that previously would have been written off, Marcey said. Another UiPath healthcare customer was losing money on tens of millions of denied claims because generating an appeal could take 45 to 75 minutes, so it often wouldn’t happen despite a 50%-to-60% overturn rate on filed appeals, Geene said. With agents built on a Cartographer-style Map of Work, that process now takes four to five minutes.

The human factor

UiPath’s human cartographer role suggests the tool is meant to reshape jobs, not eliminate them. “This isn’t about replacing people,” said IDC’s Loomis.

At USI, much of a business analyst’s time today goes into repetitive work, translating what a stakeholder describes into documentation a developer can build from, work Simpson is eager to hand off. “It’s going to save so much time,” she said. “It’ll grow my team’s knowledge and improve the outcome of our processes.”

How it all shakes out will come down to people, as with any tech-driven transformation. “The hard problem is the change management at the organizational level,” said Marcey of Coronis Health.

And the accountability will remain with humans as well. “We can’t go to clients and say, ‘Hey, the bots screwed up,’” Marcey said.

Picture two retail chains. Akkar & Co Group is the giant — hundreds of stores, the biggest revenue in the sector, the name every shopper recognizes. Tripoli Boutique is the smaller player — a regional chain with a loyal but modest footprint, competing against Akkar & Co’s sheer size. Both start the same way: manual inventory counts, paper-based reordering, spreadsheets stitched together by exhausted store managers — no real digital backbone anywhere.

Then both decide it’s time to grow up, picking the same cloud ERP platform and hiring the same systems integrator (SI) to implement it. Same software, same partner, same rough timeline — on paper, Akkar & Co and Tripoli Boutique are starting the race from the exact same line.

But one choice splits their paths. Akkar & Co asks for the system to be heavily customized around its own way of doing things — its own merchandising logic, its own supplier terms, its own store-level workflows. It has a simple conviction behind the request: our processes are the reason we’re winning, and no off-the-shelf system should be allowed to flatten them. Tripoli Boutique, without that same conviction — or the budget to test it — accepts the standard, out-of-the-box configuration the SI recommends.

Three years later, where do these two retailers actually stand — and what does that tell us about the risk of believing your own processes are worth defending?

The fork in the road

Because the software and the implementer are identical, we can rule those out as reasons for whatever happens next. The one real variable is a decision: bend the software to fit the business, or bend the business to fit the software.

Akkar & Co takes the first path, with confidence rather than stubbornness. Leadership had spent years refining exactly how the company merchandises, prices, and replenishes stores — habits and judgment calls that made it the biggest name in the category. “The ERP should adapt to us, not the other way round,” the CIO tells the board, “Because what we do isn’t generic, and we’re not going to let a piece of software pretend otherwise.” So Akkar & Co asks for custom pricing engines, custom store-replenishment logic, custom loyalty integrations — everything tailored to encode what actually makes it good at retail.

Tripoli Boutique takes the second path, and it’s worth being honest about why. It isn’t just budget. Tripoli Boutique has never been entirely sure its own processes are anything special — it has grown steadily, but never by doing anything a competitor couldn’t copy. Standard configuration isn’t a disciplined strategic choice so much as the safer, cheaper default for a company that has never had the confidence, or the resources, to bet that its own way of working is worth preserving in code.

That difference — conviction versus caution — is the whole story.

Two go-lives, one clear fit

Akkar & Co’s go-live is hard. Customization means a longer project, a bigger bill, and more testing when real store data — SKUs, promotions, seasonal pricing — hits a system built to order. Change management is heavy too, because store staff are being asked to fully commit to a tool built around exactly how they already work. But once it stabilizes, the system fits Akkar & Co like a tailored suit, because it was cut from Akkar & Co’s own pattern. Store managers don’t just tolerate it — they recognize their own judgment reflected back at them in how the system behaves.

This isn’t a small footnote. A 2025 report on ERP customization costs found most ERP implementations cost three to four times the original budget, and that modifications made “to improve usability” — the polite name for customization — drive the overspend in roughly two out of three projects. Akkar & Co walks into that statistic with open eyes. For a company confident in what it’s protecting, the extra spend isn’t a risk. It’s the price of keeping an edge intact.

Tripoli Boutique’s go-live, by contrast, is smooth and cheap — and unremarkable. Standard configuration means fewer surprises and a shorter timeline. Staff adjust to the system’s way of doing things because there was never really a “Tripoli Boutique way” strong enough to insist on keeping. It works fine. It just doesn’t do anything for the company that a hundred other retailers on the same platform aren’t already doing.

The AI divide

About a year or two after go-live, the ERP vendor rolls out its next big wave of features — built-in AI: demand forecasting, anomaly detection, automated markdown suggestions, a copilot for store performance questions. This is not a separate project either retailer asked for. It’s simply the next version of the software both of them already own. But what each company gets out of it depends entirely on what it built underneath.

For Tripoli Boutique, the AI is a wash. Because its configuration is standard, the AI slots in cleanly and works well — genuinely useful for demand forecasting and reordering. But it’s the same AI every other retailer running the same standard setup gets. It doesn’t help Tripoli Boutique stand out. It helps Tripoli Boutique keep up, and nothing more.

For Akkar & Co, the AI becomes something else entirely. Because the vendor’s AI learns from the data and workflows underneath it, and Akkar & Co’s data and workflows encode three years of genuinely differentiated retail judgment — pricing logic built around real customer behavior, replenishment rules shaped by real store-level patterns — Akkar & Co’s AI comes out sharper. It doesn’t just forecast demand; it starts predicting the shape of a customer’s experience: what a shopper is likely to want next, when they’re likely to want it, what would make them spend more or come back sooner. The same AI feature, running on top of a genuinely superior foundation, produces a genuinely superior result. This is the payoff the customization was always betting on.

A 2026 industry briefing on ERP and generative AI makes a related point: the retailers getting the most out of AI aren’t necessarily the ones with the newest technology, but the ones whose underlying data and processes were distinctive enough to give the AI something worth learning from in the first place. Generic input, generic output. Distinctive input, distinctive output. Akkar & Co bet on distinctive input three years earlier, without knowing exactly what it would pay off as.

3 years later: the scoreboard

So, three years on — who’s ahead?

Akkar & Co is not just still leading; the lead has grown. That lines up with the broader market too: the biggest ERP vendors and their biggest customers tend to stay on top for a long stretch, and current global ERP market data shows the same concentration pattern, with the largest players controlling a disproportionate share of a market still growing at double digits every year. Size, once earned, tends to keep earning — but this time, faster.

A few things are true at once:

• Momentum was never really at risk. Market leadership built over years — brand trust, customer relationships, scale, supplier power — was always going to be hard for Tripoli Boutique to close in three years. But the ERP decision didn’t just fail to shrink that gap. It widened it.
• The customization was the whole point, not a side bet. Akkar & Co’s custom workflows encoded genuine competitive advantage — pricing logic, replenishment rules, merchandising judgment that actually differentiate the business. When AI arrived, that advantage didn’t just survive the upgrade. It compounded through it.
• Tripoli Boutique’s caution had a ceiling. Lower cost and a smoother go-live were real, honest advantages in year one. But without a genuine edge underneath its systems, Tripoli Boutique had nothing for the AI wave to amplify. Standard configuration kept it safe. It never once made it distinctive.
• The gap that might have narrowed didn’t. In a story where Tripoli Boutique’s agility slowly closes the distance, this isn’t that story. Akkar & Co’s AI-driven differentiation opened a second front Tripoli Boutique had no way to compete on, on top of the first one it was already losing.

Akkar & Co is ahead because of the customization, not in spite of the cost and friction it created going in. The bet on “our processes are superior” turned out to be correct, and correct bets compound.

Image is everything

Once Akkar & Co’s AI started genuinely shaping the customer experience — not just the back office — the company didn’t stay quiet about it. It built a one-year, aggressive marketing campaign around it. Customer-facing stories about AI that seemed to know what a shopper wanted before they did. Public demonstrations of personalized recommendations, faster and more relevant than anything a shopper had seen from a competitor. Messaging that made a simple, confident claim: this is what happens when a retailer’s technology is built around genuinely better judgment.

This wasn’t a defensive move. Akkar & Co wasn’t reacting to a closing gap — there wasn’t one to close. It was amplifying a real advantage it already had. And it worked, for a very ordinary reason: shoppers don’t audit a retailer’s back-end systems, but they do notice when a store seems to understand them better than the last one did, and they talk about it. A 2026 review of branding statistics found that the large majority of marketing executives consider branding essential to business growth, and that companies with consistent, visible branding see measurably higher revenue growth than those without it — branding ROI shows up directly in customer lifetime value and market share, not just recognition. None of this proves marketing alone drove Akkar & Co’s growth — but paired with a genuine capability underneath it, the campaign didn’t need to invent a story. It only had to tell a true one, loudly.

Technology gave Akkar & Co something real to say. Marketing made sure everyone heard it.

The long game

Here’s the part nobody outside Akkar & Co’s boardroom saw coming. Buried in Akkar & Co’s five-year strategic plan, quietly sitting behind the public narrative of “AI leadership,” was a single line item: acquire Tripoli Boutique.

It wasn’t about market share — Tripoli Boutique’s share was modest enough that absorbing it barely moved Akkar & Co’s numbers. What Akkar & Co wanted was something its own mass-market base could never give it: Tripoli Boutique’s customers. Despite its unremarkable technology, Tripoli Boutique had spent 20 years quietly building a small but genuinely valuable clientele — customers with serious money, who bought high-end jewelry and premium cosmetics, and who trusted Tripoli Boutique’s staff enough to keep coming back for exactly that kind of purchase. It was a relationship built the old way, person to person, long before either company touched an ERP system.

Akkar & Co’s leadership recognized that value the moment its own AI proved what it could do with distinctive data. Feed 20 years of high-value jewelry and cosmetics purchase history into an AI engine already sharp enough to predict ordinary shoppers’ behavior, and something bigger becomes possible: a premium tier Akkar & Co could never have built organically, and a much clearer picture of exactly which VIP customers were worth a personal call, a private preview, or a nudge toward the next expensive purchase.

Tripoli Boutique’s board wasn’t blind to the timing. They’d turned down Akkar & Co’s interest twice before, confident their customer relationships were worth more standing alone. This time, watching a competitor’s AI-driven personalization pull further ahead every quarter with no real answer of their own, the board concluded that their most valuable asset — the trust of their VIP customers — would do more good inside a company that could finally act on it at scale than it would sitting inside a smaller one that couldn’t. It wasn’t a surrender. It was a recognition that the asset and the capability to use it well finally belonged in the same building.

The deal wouldn’t be frictionless in real life — a regulator would want a look at combined market share in overlapping regions, and any acquisition of this size rarely closes without scrutiny — but in this telling, the story is about strategy, not scrutiny.

Akkar & Co didn’t just out-execute Tripoli Boutique. It built a genuine edge, told the truth about it loudly, and then bought the one thing its own strength couldn’t manufacture: a customer base built entirely on old-fashioned, human trust.

Closing thought

Akkar & Co’s bet was the right one. Superior processes, encoded deliberately rather than smoothed away by a generic system, turned out to be worth defending — and when AI arrived, that decision didn’t just hold up, it paid out. Tripoli Boutique’s caution wasn’t foolish, exactly. It was simply insufficient: a safe choice that kept the lights on but never gave the company anything to amplify when the moment came.

And here’s the detail that makes the whole plan feel like design, not luck: both companies had been running the same ERP platform all along, just configured differently, so the integration itself was almost frictionless by construction. After the deal closed, Akkar & Co brought the vendor back to the table, not to ask whether the two systems could talk to each other, but to decide how deliberately Tripoli Boutique’s data should be folded in. They chose to merge it fully into Akkar & Co’s customized core, rather than bolt it on as a separate system, precisely so that Tripoli Boutique’s 20 years of purchase history could run directly through Akkar & Co’s optimized processes and AI models — not sit beside them as an afterthought but become part of the same intelligence from day one.

But sit with the ending for a second longer. What Akkar & Co actually acquired wasn’t a smaller retailer’s revenue — it was 20 years of trust that wealthy customers placed in people who knew them by name, and it’s about to run that trust through a machine built to predict what those same customers will want to buy next. The strategy worked exactly as designed. That’s not the uncomfortable part. The uncomfortable part is this: once a relationship built on being known becomes a dataset built on being predicted, is it still the same relationship — and does it matter, to the business or to the customer, if nobody involved can quite say when the answer changed?