The next generation of founders aren’t pinned to one mindset, one technology, and for that matter, one coast. TechCrunch’s Founder Summit event in Boston’s SoWa Power Station on November 4 gives the East Coast its due and is focused on equipping the next generation of disruptive ideas with the tools and connections they need to succeed. 

For investors, this is an incredible opportunity to get in early with the minds who will power the next era of innovation. 

For founders, you’ll be learning directly from peers who have been in your position in close-knit, hands-on workshops that will give your peers envy. 

And for IT leaders, you’ll glean insights into the operations and innovations of potential startup partners and learn how to capitalize on the market for your team’s own innovations as the call for revenue-generating CIOs gets louder. 

That all comes along with a 50% discount if you follow this link, or you use the code CIO50 with any of your tickets. 

And now there are more ways to save. Current ticket offers include buy one, get one free, up to $100 off individual tickets, 50% off a second ticket, and up to 30% off group orders. Plus, CIO readers can use code CIO50 for an additional 50% discount on eligible purchases, subject to the applicable offer terms.

What’s on the Founder Summit agenda?  

Founder Summit is a mix of sessions (NOT lectures), small breakout panels, networking opportunities, and industry events happening across Boston throughout the week. 

For just a glimpse of the interactive sessions packed into the one-day agenda:

• The Boston Founder Playbook: HqO co-founder and CEO Chase Garbarino on building a category-defining company outside Silicon Valley, having raised $200 million and scaled to 30+ countries.    
• The New Rules of Raising Capital: Brian Devaney, partner at Underscore, breaking down what investors look for now and where founders lose leverage without realizing it.  
• Built for AI From Day One: Hear what it takes to build an AI-native organization — from team structure to operations — from 7AI CEO and co-founder Lior Div.  
• Your Company Is Who You Hire: Learn how leading startups identify and sign the right talent for growth when every hire in a tight market matters from NEA partner Melissa Taunton.
• Building 10x Teams: Cogent Security co-founder Vineet Edupuganti sheds light on how to build a high-performing team with the ambition to build something of their own. 
• Plus, sessions on determining product market fit, what constitutes a market-creating technology solution, and GraphRAG in action — as well as the opportunity to meet founders looking to partner with enterprise customers before raising Series A capital.

The full agenda is available here. And you can explore all the side events during Founder Summit week here, including interactive product demos from Boston’s leading physical AI startup scene.  If you want all of that, and much, much more as detailed in this lengthy guide from TechCrunch, you need to act swiftly. Your best bet to save early, find emerging talent before competitors do, empower your own founding journey, and show what the Northeast is capable of, is right here with code CIO50 to save 50%. 

Artificial intelligence is forcing CIOs to reconsider the economics of enterprise technology. While application development costs are falling, autonomous systems are complicating governance as traditional productivity measures struggle to capture returns on transformation investments for C-suite executives and boards of directors who are interested in documenting tangible business gains from agentic deployments.

These were among the issues that dominated CIO 100 Leadership Live Chicago on Oct. 13, where enterprise technology executives, industry specialists and investors examined the practical implications of moving AI from experimentation into everyday business operations.

Speakers repeatedly observed that technological innovation is outpacing the ability of traditional enterprise processes to absorb it. The theme surfaced first in the opening panel, “Championing Tech-Enabled Business Transformation,” moderated by Dan Roberts, host of the Tech Whisperers podcast.

Laura Walsh, CIO of Culligan, described the conflicting expectations technology leaders face. They are asked to accelerate AI adoption, demonstrate immediate financial returns, maintain security and quality, and fund new initiatives within existing budgets.

Walsh stressed the importance of established management disciplines, including data governance, project execution and technology business management. She outlined a hub-and-spoke approach that establishes common AI controls while allowing individual business units to experiment.

Jamie Head, founder and CEO of Stack360, contended that CIOs must work more closely with CEOs and CFOs to identify a limited number of strategic priorities and decide which initiatives to defer. Business leaders, he added, increasingly look to automation to improve margins and productivity, creating an imperative for CIOs to play a key role in separating worthwhile investments from technological distractions.

AI economics move beyond productivity

The financial implications of AI adoption surfaced throughout the day, particularly the gap between gains in employee productivity and measurable financial returns.

In a discussion of the intelligent enterprise moderated by Jon Glick, principal and advisory CMO at PwC, PwC principal Scott Behrens said AI transformation requires redesigning business processes from the ground up. Organizations that layer disconnected AI applications onto existing workflows risk increasing technology consumption without corresponding business benefits, he warned.

Meghna Shah, principal for engineering and AI at PwC, pointed to the widening gap between the speed of AI-assisted software development and the engineering discipline required to deploy reliable enterprise applications. AI can dramatically accelerate prototyping, she noted, but organizations must still address architecture, integration, security and quality assurance before those applications can support critical business operations.

Andy Xydakis, global CIO of Aon, said the company has expanded its cloud footprint from about 35% to 85% of its application portfolio while introducing AI capabilities to roughly 60,000 employees. AI-assisted proposal development has cut work that once required 40 to 50 hours to about four or five hours, according to Xydakis. Aon has also strengthened its financial operations capabilities to monitor technology consumption and manage costs as adoption expands.

A subsequent session examined how to secure returns on transformation investments in emerging technologies such as AI. Chris Campbell, CIO of DeVry University, said that his team measures investments against broad institutional objectives, including student enrollment, persistence, retention and graduation rates, in addition to savings from individual technology projects.

Campbell also maintained that CIOs must understand the business capabilities their organizations need before choosing applications or technologies. AI is making software development more accessible, he noted, so technology leaders must work with business stakeholders to set common priorities and guardrails while managing expectations about what emerging technologies can realistically accomplish.

Prashant Shanti Kumar, global head of data domains at Marsh, recommended setting clear business objectives and breaking complex transformation initiatives into manageable phases. Drawing on his experience integrating organizations and business processes, including at a major national insurance carrier, Kumar advocated 90-day implementation plans with measurable outcomes that let teams adjust priorities as conditions change.

Gone, both intimated, are the days of multi-year strategic technology and deployment plans.

Distributed development raises governance challenges

As the economics of enterprise technology grow more complicated as the result of increasing numbers of autonomous agents and applications, Michael Blank, chief operating officer of Retool, said AI is dramatically reducing the cost of generating software while leaving enterprises responsible for the infrastructure, maintenance, security and governance required to run it.

Blank warned that business employees can now build applications without IT’s knowledge, creating operational dependencies and exposing information to poorly governed systems. He recommended allowing development throughout the organization under consistent enterprise controls over identities, permissions, data access and application deployment.

The growing autonomy of AI systems is also forcing cybersecurity leaders to rethink traditional protection strategies, the focus of a security and risk panel moderated by CIO contributor Joan Goodchild.

Heather Ceylan, chief information security officer at Box, said organizations must monitor and restrict what an AI agent actually does, in addition to controlling which resources it can access. Agents may operate with broad permissions, process large volumes of sensitive information and execute actions far faster than human employees, she explained. Ceylan said security controls therefore need to operate during execution, when potentially harmful behavior can be identified and interrupted.

Ceylan described using security agents to review code, perform threat modeling and help enforce engineering requirements as software development accelerates.

Greg Sylvester, vice president of enterprise AI and infrastructure at Cisco, discussed the importance of monitoring agent activity, detecting malicious instructions and building responsible AI requirements directly into enterprise platforms.

Michael Chahino, CIO of Elgin Community College, outlined additional challenges facing higher education, where decentralized decision-making and widespread use of personal AI subscriptions complicate governance. His institution is emphasizing education and cross-functional oversight while developing policies for responsible AI use.

The discussion pointed to a broader need for greater visibility into the behavior of autonomous systems and clearer accountability for their decisions as those systems gain the ability to communicate and act independently.

Legacy systems and data remain critical

Several speakers stressed that AI transformation depends on the quality of existing enterprise systems and information. The issue received detailed attention in the “Modernizing the Enterprise Application Estate” session, moderated by Woody Evans, vice president of global solutions engineering at Perforce Delphix.

Ron Guerrier, chief technology officer of Save the Children, warned that AI-generated applications can create substantial operational risks when business teams expect prototypes to move directly into production. He advocated established engineering practices and continuous improvement principles, along with investment in employees who understand the organization’s technology environment and business processes.

Deepak Gangwaar, senior vice president at Northern Trust, highlighted the importance of reliable, accessible test data and appropriate compliance controls. Faster code generation offers limited benefits if developers must still wait for suitable data to validate applications, he noted.

Panelists also explored AI’s potential to help organizations analyze legacy code, understand embedded business logic and identify modernization opportunities.

Guerrier cautioned that enterprises must weigh the full economics of modernization, including ongoing infrastructure expenses, AI consumption costs and long-term vendor commitments.

CIOs reconsider how they invest

Uncertainty in AI technology markets prompted a discussion of whether CIOs should adopt some of the investment practices venture capital firms use.

Mike Asem, founding partner at M25, and Gale Wilkinson, founder and managing partner of Vitalize, said organizations can benefit from testing promising technologies through structured pilot programs with defined objectives and acceptable levels of risk.

Asem pointed to the opportunity cost of avoiding emerging technologies and the value of validating assumptions before committing substantial resources.

Wilkinson described growing investor interest in specialized AI applications that address specific business processes. She suggested that enterprises can gain by working with early-stage technology companies as design partners, helping shape solutions around operational requirements.

Their remarks highlighted the need to balance governance and risk management against the possibility that excessive caution could leave organizations behind competitors.

Leadership and workforce development take center stage

The conference closed with a discussion of how CIO responsibilities may evolve over the next 12 to 24 months.

Samir Shah, senior vice president and global CIO of Brunswick Corp., described an AI operating model in which business executives identify priority processes, commit funding and take responsibility for financial outcomes.

Shah said Brunswick has generated about $8 million to $10 million in tangible savings over the past 18 months. The company is pursuing a portfolio of 18 to 20 AI use cases across areas including supply chain operations, customer service, pricing and software development, with more than $50 million in targeted value.

Darlene Reina, director of IT and AI enablement at Babylist, said her organization is giving business employees the tools and guardrails to build applications themselves, freeing IT specialists to concentrate on higher-value initiatives and production requirements.

Reina also addressed the workforce implications of AI adoption. As routine technology tasks become automated, she said, organizations must prepare existing employees for new responsibilities and rethink how they recruit and develop entry-level talent.

She advocated retraining and redeploying employees whose responsibilities are changing, noting that their knowledge of business operations remains valuable as technology capabilities evolve.

Speakers throughout the day identified financial discipline, governance, sound technical foundations and workforce development as the requirements for sustaining AI-driven transformation as the technology continues to evolve.

The next generation of founders aren’t pinned to one mindset, one technology, and for that matter, one coast. TechCrunch’s Founder Summit event in Boston’s SoWa Power Station on November 4 gives the East Coast its due and is focused on equipping the next generation of disruptive ideas with the tools and connections they need to succeed. 

For investors, this is an incredible opportunity to get in early with the minds who will power the next era of innovation. 

For founders, you’ll be learning directly from peers who have been in your position in close-knit, hands-on workshops that will give your peers envy. 

And for IT leaders, you’ll glean insights into the operations and innovations of potential startup partners and learn how to capitalize on the market for your team’s own innovations as the call for revenue-generating CIOs gets louder. 

That all comes along with a 25% discount if you follow this link, or you use the code CIO25 with any of your tickets. 

And now there are more ways to save. Current ticket offers include buy one, get one free, up to $100 off individual tickets, 50% off a second ticket, and up to 30% off group orders. Plus, CIO readers can use code CIO25 for an additional 25% discount on eligible purchases, subject to the applicable offer terms.

What’s on the Founder Summit agenda?  

Founder Summit is a mix of sessions (NOT lectures), small breakout panels, networking opportunities, and industry events happening across Boston throughout the week. 

For just a glimpse of the interactive sessions packed into the one-day agenda:

• The Boston Founder Playbook: HqO co-founder and CEO Chase Garbarino on building a category-defining company outside Silicon Valley, having raised $200 million and scaled to 30+ countries.    
• The New Rules of Raising Capital: Brian Devaney, partner at Underscore, breaking down what investors look for now and where founders lose leverage without realizing it.  
• Built for AI From Day One: Hear what it takes to build an AI-native organization — from team structure to operations — from 7AI CEO and co-founder Lior Div.  
• Your Company Is Who You Hire: Learn how leading startups identify and sign the right talent for growth when every hire in a tight market matters from NEA partner Melissa Taunton.
• Building 10x Teams: Cogent Security co-founder Vineet Edupuganti sheds light on how to build a high-performing team with the ambition to build something of their own. 
• Plus, sessions on determining product market fit, what constitutes a market-creating technology solution, and GraphRAG in action — as well as the opportunity to meet founders looking to partner with enterprise customers before raising Series A capital.

The full agenda is available here. And you can explore all the side events during Founder Summit week here, including interactive product demos from Boston’s leading physical AI startup scene.  If you want all of that, and much, much more as detailed in this lengthy guide from TechCrunch, you need to act swiftly. Your best bet to save early, find emerging talent before competitors do, empower your own founding journey, and show what the Northeast is capable of, is right here with code CIO25 to save 25%. 

BOSTON — Business leaders are looking beyond a single-minded focus on artificial intelligence and the returns generated by individual agents as they confront the broader challenge of continuously reinventing their organizations around what emerging technologies make possible.

This shifting mindset emerged during CIO 100 Leadership Live Boston, where discussions began with the human dimensions of transformation and quickly moved into the dollars and cents of technology investments. The day ultimately revealed a deeper conversation about how enterprises must continually rethink processes, operating models, technology architectures and investment priorities as capabilities evolve.

Panelists, and an extremely active group of audience participants, presented perspectives increasingly focused on what businesses should become.

It represents a major evolution from the broad calls for “tokenmaxxing” that dominated the AI narrative as we closed out 2025. The idea of setting ambitious transformation goals followed by a disciplined assessment of emerging technological capabilities has fundamentally changed the context of AI agents, automation and emerging autonomous systems. In the process, agentic initiatives, large language models (LLMs), and enterprise and small language models (SLMs) are now seen as the means to a more sustainable end by becoming components of broader enterprise reinvention strategies.

Discussions on return on investment similarly moved beyond measuring the productivity generated by individual tools toward determining whether technology investments systematically and comprehensively improve revenue, customer experience, operating efficiency, resilience or other strategic business outcomes.

Transformation starts with people

The strategic shape of day’s discussions began taking form during the opening session, “Real Leaders, Real Challenges: Championing Tech-Enabled Business Transformation,” featuring Mona Bates, senior vice president and chief information and digital officer at BAE Systems; Sal Companieh, chief information and digital officer at Cushman & Wakefield; and Paul Hlivko, CIO at Optum Financial. Dan Roberts, host of the Tech Whisperers Podcast, moderated the session.

Hlivko described AI as the early stage of a potentially long technology cycle and suggested that leaders should focus on increasing the speed at which their organizations learn rather than simply counting the number of applications moved into production. He compared emerging AI investments with venture capital portfolios, where leaders make multiple bets knowing some will fail while others could generate disproportionate returns.

The discussion nevertheless focused heavily on people. Bates described the CIO as occupying an increasingly important position at the intersection of business and technology. Companieh emphasized frequent communication and creating environments in which employees feel comfortable acknowledging uncertainty. In this context, Hlivko summarized today’s leadership imperative around providing their teams with clarity, culture and conviction in a climate of uncertainty.

Bates added adaptability to the leadership equation, arguing for what she described as AQ alongside traditional measures of IQ and emotional intelligence.

Effective transformation, the panel agreed, will depend first on creating organizations capable of absorbing change.

Complexity becomes a business problem

In a fireside chat titled “The Foundation for AI: Building an Enterprise Ready to Scale,” Jim Chilton, executive vice president of enterprise ventures at Southern New Hampshire University and author of “Enterprise Coherence in the Age of AI,” explored how accumulated technology and legacy process complexity can inhibit the accomplishment of important business transformation objectives.

Chilton argued that complexity rarely results from a single bad decision. It accumulates as organizations add applications, vendors and processes over time to address legitimate requirements as they emerged. AI, he said, requires leaders to expose those layers because deploying agents across fragmented systems requires organizations to understand how information and work actually move through the enterprise.

This, in turn, requires an intimate understanding of all the processes that underpin critical operations. Chilton distinguished between standardized activities that can be automated and differentiating capabilities that may be better augmented by AI. The decision centers on where automation or augmentation contributes to the way an organization creates value.

In “Where AI Delivers: Strategy, Architecture and the Path to the Intelligent Enterprise,” PwC’s Stephen Coyle and Robbie Voigtmann joined Boston Scientific’s Shoubhik Sinha in extending the question of “core value” into enterprise architecture.

Their discussion focused on moving beyond disconnected AI tools and proofs of concept toward reusable capabilities that support production deployments across the enterprise. That requires business, technology and risk leaders to identify use cases according to value, feasibility, risk and their potential to be reused elsewhere.

Companieh illustrated how that reuse plays out in practice when she returned for the afternoon session on “Build, Buy, or Partner: A CIO’s Framework for Scaling AI Across the Enterprise.” She walked through the framework Cushman & Wakefield uses to decide whether to buy a point tool, build in-house or partner for a given AI investment.

She described how a single use case in lease extraction became the seed of an enterprise-wide data foundation. Companieh credited clear roles and shared ownership between Cushman & Wakefield and its technology partner, Unframe, with converting a fast-moving product into measurable business impact.

The push toward reuse, she explained, also exposes how executives can look at increasingly sophisticated AI demonstrations and conclude that their organizations are ready to scale, while technology teams see fragmented data, redundant applications, governance gaps and architectural dependencies that must be addressed.

Transformation confronts the dollars and cents

Those architectural questions led into the session on “Return on Transformation: Time, Talent and Tradeoffs,” where the conversation explicitly addressed the new economics of strategic planning and execution. The discussion featured Lesley Dickson of VantagePoint Strategic Partners, MGX Beverage Group CIO Scott Gardner and Afshean Talasaz, former senior vice president of strategic projects and innovation and chief technology and data officer at Colonial Pipeline.

The panel challenged the idea that organizations should evaluate transformation primarily by measuring individual AI initiatives. The more consequential question, they all agreed, is whether the enterprise itself is becoming more competitive, productive, responsive and/or resilient.

As a result, true transformation begins with defining strategic business outcomes before committing resources to technology, and then assessing how existing tools, available resources and emerging innovations can optimize the chances of success. This last part, they emphasized, is critical, given that the vast majority of large scale transformation initiatives (including mergers and acquisitions) fail to meet their stated objectives.

The panelists warned against automating legacy processes simply because AI makes automation possible. Poor data, unnecessary work and outdated processes do not necessarily improve when machines execute them faster. Organizations, they suggested, should simplify and redesign operations before deciding what to automate.

The same discipline applies to investment. Leaders, they advised, should establish ambitious enterprise goals while making smaller, testable bets underneath them. Successful initiatives can receive additional resources, while those failing to produce results can be terminated.

That approach also requires deciding what not to transform. Some processes warrant fundamental reinvention. Others may require incremental improvement. Many others will likely derive little strategic value from the application of AI in any form.

The result is a broader definition of return. Productivity remains important, but ROI can also include contribution to revenue growth, risk reduction, operating leverage and improved customer experience. In short, CEOs and boards should establish realistic expectations about the economic path from experimentation to enterprise maturity.

Failure becomes something to contain

The afternoon session “Running Enterprise Technology When Failure Isn’t an Option” brought to the stage Paul Beswick, senior vice president and chief information and operations officer at Marsh McLennan, and Sri Sriraman, chief technology officer at Mass General Brigham. The session was moderated by Joan Goodchild, a contributor to CIO and CSO.

The panel kicked off by noting the inherent flaw in the session title. With breaches and incidents occurring every day across a threat landscape that continues to worsen, the leadership team would be well advised to focus on preventing individual failures from becoming catastrophic.

As autonomous systems enter production, it is a task easier said than done. Uncoordinated pilots and vendor agents can create fragmented workflows and dependencies that are difficult to observe or control. Enterprise deployment therefore requires governance, observability and mechanisms that allow organizations to intervene when autonomous systems behave unexpectedly.

Resilience and trusted data become equally important, according to the panelists. High-stakes systems require reliable infrastructure and information foundations calibrated to the risk and value of the functions, assets and technologies they support. The amount of autonomy granted to AI should reflect the consequences should things go wrong at different points on that spectrum.

CIOs begin thinking like investors

The session, “Think Like a VC: Investment Shifts Towards Focused AI Applications,” returned to an idea introduced during the opening session.

Rudina Seseri, founder and managing partner of Glasswing Ventures, and Chris Gardner, general partner at Underscore VC, explored how venture investment practices could apply to enterprise technology decisions.

Seseri described an investment environment focused on identifying sustainable opportunities in AI-native companies serving enterprise, science and cybersecurity markets.

Both advised CIOs to replace static notions of future-proofing by embracing the concept of “continuous portfolio management.” Gardner observed that technology leaders increasingly need to identify market signals amid AI hype, evaluate the adaptability of vendor management teams and distinguish defensible enterprise capabilities from impressive demonstrations. Investments can then be governed through milestones that determine whether organizations scale, maintain or exit them.

In the process, enterprises will need to become more comfortable making calculated technology bets while becoming less tolerant of uncontrolled risk inside production environments.

Reinvention becomes continuous

The closing session, “What’s Next for the CIO: Preparing for the Next 12-24 Months,” extended that argument into the future of work.

Boston Dynamics CIO Chad Wright said organizations should begin preparing for three categories of coworkers: humans, virtual agents and physical robots. Boston Dynamics already has its Spot (quadriped) robots working across hundreds of organizations, while humanoid (bi-ped) robots introduce additional questions involving infrastructure, physical safety, accountability and workforce acceptance.

Terry Carpenter, CIO of MIT Lincoln Laboratory, stated that, as agents assume tasks traditionally performed by entry-level employees, organizations will need to seriously consider how younger professionals are trained to develop the judgment, delegation skills and institutional knowledge to move into middle management roles, and ultimately, senior leadership positions.

Jacqui Nevils, Global CIO, Fresenius Medical Care, and a veteran Air Force officer, emphasized that there is a growing case for new and significant investments to be made in onboarding talent early and training them right away for major responsibilities. If the next generation of employees are to supervise multiple agents (along with human subordinates) while remaining accountable for their output, organizations will need to teach skills that were previously acquired through years of progressively more responsible work.

The discussions ultimately brought the program back to where it started.

While the agentic economy is introducing another generation of enterprise technology, it is also accelerating the rate at which organizations must reconsider how work gets done, where capital gets allocated, who does what, and which risks are acceptable as new capabilities are deployed to provide competitive differentiation.

Enterprise technology leaders are entering a more demanding phase of artificial intelligence adoption, with attention shifting from experimentation toward the organizational, financial, and architectural changes required to deploy AI at scale.

That shift will be the focus at the CIO 100 Leadership Live Boston event on Sept. 24. Technology executives, investors, and industry leaders will examine how AI is changing transformation priorities, operating models, and the expectations placed on CIOs. Sign up to attend here.

While AI remains central to the technology leadership conversation, the management challenge now extends beyond selecting models or identifying use cases to harder questions surrounding the ability to achieve sustained value across the enterprise. Sessions throughout this one-day event will test whether organizations possess the data infrastructure, governance mechanisms, talent, processes, and financial discipline required to turn fast-moving technologies into sustainable business capabilities.

To this end, panels will examine how those issues affect enterprise architecture, transformation economics, AI in regulated environments, venture investment, and the priorities likely to shape CIO agendas over the next 12 to 24 months.

AI scale puts enterprise foundations under scrutiny

The morning program opens with the leadership demands surrounding technology-enabled business transformation.

Mona Bates, SVP and CIDO at BAE Systems; Sal Companieh, CIDO at Cushman & Wakefield; and Paul Hlivko, CIO of Optum Financial, will discuss how technology leaders navigate transformation while managing culture, talent, architecture, and changing operating models. The panel will draw on lessons from initiatives that succeeded and from initiatives that forced executives to reconsider their approach.

Jim Chilton, a five-time CIO who is now EVP of Enterprise Ventures at Southern New Hampshire University, is the author of the recently release book Enterprise Coherence in the Age of AI. He will explain why scaling AI requires coordination across business processes, data, governance, and people.

Those requirements have become increasingly critical as enterprises move from contained AI experiments toward technologies that operate across workflows, applications, and organizational boundaries. He will discuss how AI deployments are exposing weaknesses that organizations have tolerated for years, including fragmented data, redundant systems, inconsistent governance, and disconnected business processes.

Transformation economics move to the foreground

Technology modernization faces greater financial scrutiny this year. The “Return on Transformation: Time, Talent, and Tradeoffs” session will examine the costs that accumulate around major initiatives, including budget overruns, organizational resistance, and investments that fail to produce expected results.

Scott Gardner, CIO of MGX Beverage Group; Angelic Gibson, chief product and technology officer at ISG; and Afshean Talasaz, former SVP of strategic projects and innovation and CTDO at Colonial Pipeline, will examine how organizations respond when transformation programs run into those pressures.

Panelists will discuss whether organizations may need to modernize aging infrastructure, fund AI initiatives, strengthen cybersecurity, improve data environments, and maintain existing operations at the same time. Smaller discussion groups later in the morning will extend the conversation into AI-ready data infrastructure, workflow automation, and AI return on investment.

A Next CIO luncheon will turn to talent, focusing on the leadership and workforce requirements facing emerging technology executives. The program will examine skills gaps, technology hiring, leadership pipelines, and succession as organizations try to assemble teams capable of operating in a rapidly changing technology environment.

Governance, investment, and the next CIO agenda

The afternoon shifts toward the boundaries organizations are encountering as AI moves deeper into business operations.

Agam Upadhyay, SVP and CDIO at Vertex Pharmaceuticals, will examine AI adoption in healthcare and life sciences, where organizations face pressure to innovate while operating within significant regulatory, governance, security and risk constraints. He will address how enterprises can balance those requirements without letting either speed or control dominate technology strategy.

Rudina Seseri, founder and managing partner of Glasswing Ventures, will bring a different view of the AI market from the investment community. She will examine the movement of investment toward focused AI applications and the signals CIOs can use to distinguish potentially durable technologies from market hype.

Enterprise technology portfolios are expanding, which makes that perspective increasingly relevant. This session will explore how modern CIOs should evaluate a growing field of AI platforms, applications, and infrastructure providers.

Terry Carpenter, CIO of MIT Lincoln Laboratory; Jacqui Nevils, global CIO of Fresenius Medical Care; and Chad Wright, CIO of Boston Dynamics, will examine emerging priorities ranging from AI economics to organizational redesign.

Their discussion returns to the changing responsibilities of the CIO because the next stage of enterprise AI will require technology executives to make decisions across several dimensions at once. In the process, the panelists will describe the kinds of frameworks that can help determine which emerging technologies warrant investment, which legacy environments require modernization, how governance should evolve, where organizational structures need to change and how technology investments contribute to measurable business performance.

Register to attend here. Attendance is complimentary for qualified IT professionals.

At CIO100 in Frisco, Texas, roughly 100 rising technology leaders sat down for our “Next CIO” session. The group was asked to reflect on a single question: Are you ready to take on the role of CIO? Using the CIO Readiness Framework that we have developed and refined over years of advisory work, we asked each person in the room to score themselves across the five dimensions of the framework. The results point to a gap that should worry any organization building its next generation of technology leaders.

The CIO Readiness Framework

The CIO Readiness Framework organizes the CIO job into five dimensions. We asked each rising leader to score themselves on the same 1-to-5 scale, from “Emerging” to “CIO-Ready.” The five dimensions of the framework are:

• Enterprise leadership: the ability to lead beyond your own function, anticipate where the business is headed and mobilize people through change.
• Business value and financial acumen: understanding how the enterprise makes money well enough to connect technology decisions to growth, margin and risk.
• Influence, narrative and enterprise selling: building belief and support before a decision is ever formally proposed, not just presenting sound logic once it is. 
• Relationships, talent and operating leverage: building trusted executive relationships, developing successors and creating an organization that delivers beyond your own personal reach.
• Technology stewardship and digital judgment: the technical fluency and architectural judgment needed to make durable enterprise technology decisions.

Where the room stands

Across the five dimensions, the average self-assessment landed at 3.4 out of 5, squarely in ‘Proficient’ territory. Consider who was in the room: people already selected by their own organizations as ready to be developed for the next level. Even so, not one of the five dimensions averaged ‘Advanced’ or higher across the entire group. Technology Stewardship and Digital Judgment (the ability to make sound decisions on platforms, architecture and risk) came in as the most mature dimension in the room. At the bottom sat two dimensions in a near tie: Influence, Narrative and Enterprise Selling; and Relationships, Talent and Operating Leverage.

Much more interesting, however, is the spread between the highest- and lowest-rated dimensions. On these bottom two dimensions, ~65% of attendees rated themselves Proficient or below. Compare that to Technology Stewardship, where the number was only 36%. Put plainly, the people in that room are confident in their technical judgment. They are far less confident in the parts of the job that have nothing to do with technology at all.

Why the human dimensions lag, and what to do about it

This tracks with what we hear constantly in our advisory work. Most people who reach the doorstep of the CIO role got there by being excellent at the technical and operational core of IT. Few of them spent their first fifteen years being evaluated on stakeholder mapping, coalition-building, or developing a successor. Those muscles simply were not required until now.

The good news is that these are learnable skills. We recommend a simple approach to close these capability gaps: for the dimensions where you rated yourself lowest, identify a goal that targets your weaknesses, then attach a tactic (a concrete action or behavior) that moves you toward achieving your goal. Lastly, give the whole thing a timeframe. Six months is often a good starting point, as it is long enough to make real progress and short enough that you’ll actually check.

In this activity, the goal represents the destination – for example, to develop a brand of “enterprise leader,” rather than just “strong IT operator.” The tactic is how you get there, something specific enough that you’ll know in six months whether you did it or not. “Get better at influence” is a goal with no tactic attached, which is exactly why it rarely changes anything. “Hold pre-alignment conversations with three sponsors before my next major proposal” is a tactic, and it’s either done or it isn’t.

Here’s what that pairing looks like applied to the two lowest-scoring dimensions from the CIO100 room:

• For Influence, narrative and enterprise selling, a reasonable goal is building support for ideas before they ever reach a formal decision point. Tactics in service of that goal include identifying the informal decision-makers behind a priority and earning their support early, or taking on an external opportunity (e.g., industry panels, published point of views) to build credibility beyond the building.
• For Relationships, talent and operating leverage, a reasonable goal is creating executive capacity instead of personally absorbing more of the work. Tactics in service of that goal may include adding standing one-on-ones with two peers on the executive team, and delegating two recurring items off your own plate with clear decision rights attached.

The takeaway for CIOs building their bench

If you’re a sitting CIO developing your own successors, this data serves as a useful gut check. The people you’re grooming may already operate at an advanced level technically while carrying real gaps in the skills that determine whether they succeed once they have the title. Executive presence, coalition-building and delegation take years to build, so the earlier you start, the better.

The future leaders we worked with at CIO100 had no shortage of ability. What most of them lacked were the specific, practiced habits that turn a strong technology leader into an enterprise one, and the self-assessment data shows they already know it. Acknowledging that gap is the first step toward closing it.

When Sastry Durvasula joined TIAA in early 2022, he saw an organization fighting against outdated legacy technologies and in need of a major IT refresh.

Since then, the financial services organization has completed two phases of a comprehensive transformation initiative called Technology Ecosystem Transformation, or TETRIS, leading to a huge reduction in tech debt and a major expansion of functionality for customers.

The ongoing project, anchored in cloud and AI technologies, started in 2023 with phase one that modernized the core technology stack with 10 new enterprise platforms. Phase two, launched in late 2024, went further by enabling 87 use cases across all major lines of the business.

The project, for example, allowed TIAA to launch its MyChoice Multi-Year Guaranteed Annuity product, and helped create the TIAA Gateway portal, an API-based suite that integrates with partners in retirement and wealth planning using industry standards.

TIAA Gateway took home a CIO 100 Award in 2025, and phase two received a CIO 100 Award in 2026.

Durvasula, TIAA’s chief operating officer, pitched the multimillion-dollar TETRIS project to the board as a three-pronged strategy, with empowering business growth, fueling innovation, and transforming the IT core as its key goals.

Not only did TETRIS need to modernize the company’s IT systems, decommission legacy processes, and automate other processes, but Durvasula pitched it as the way to expand the reach of TIAA’s products and move the company into the future.

“As you expect in a company of our size, we have problems of yesterday, today, and tomorrow being solved at the same time,” he says.

Focus on business use cases

As TETRIS moved into phase two, project leaders shifted their goals from pure technology modernization to business outcome-driven prioritization. So once phase one delivered needed IT platforms like a data cloud and design studio, TIAA pivoted toward enabling business use cases.

This business-first approach ensured continuing executive support and clear ROI at every key milestone, TIAA says.

In 2022, just before the project launched, more than 80% of TIAA’s IT workloads resided in fragmented, end-of-life platforms, which created operational risk, compromised security and resilience, and constrained its ability to innovate. Through TETRIS phase two, however, the organization has cut that tech debt nearly in half.

And consolidating 17 design systems also led to digital products looking and behaving differently, depending on the team that designed them, and accelerated product launches by 35%, enabled multi-lingual capabilities, and increased accessibility to more than 185,000 customers who don’t speak English.

In addition, TETRIS allowed TIAA to combine multiple middleware systems and data lakes, Durvasula says, and the organization moved mainframe applications and data center infrastructure to the cloud.

A giant leap forward

TETRIS has been a huge project, with the company saying it empowered TIAA to have one of the largest leapfrog moments in company history in its submission for the 2026 CIO 100 Award.

Despite the reported failure rates of large transformation projects — some estimates suggest up to 95% fail to meet their goals — TETRIS was essential to keep TIAA competitive and move it forward in the market, Durvasula says.

A big part of the project has been workflow modernization, he says, because TIAA were using some technologies and workflows that were decades old.

“There’s your classical platform and application rationalization, and then there’s your end-of-support, end-of-life stuff that should’ve been remediated long ago,” he says. “Some of the processes we have, because we’re such a large, old company, were designed when the internet just came along.”

Stick to the metrics

Two keys to pulling off such a large project are establishing metrics for success and transparency with leadership, Durvasula says. Project leaders set milestones to indicate when things went well, and they planned for bumps in the road so the TIAA board knew when setbacks happened.

“Not everything is as pretty as it sounds in an awards application, but the success measures we established with our board were based on both phases,” he says. “For the first one, we said we’d deliver enterprise-grade platforms and accomplish migration objectives, but not tied to any specific business objectives.”

Phase two metrics focused more on business objectives, and the project team kept the TIAA board updated as TETRIS moved forward. Setting realistic goals was important, he says, with the team determined not to overpromise results.

“Large programs have a range of objectives, and if you publish the outcomes you’re looking for, people start looking for them, especially stakeholders, the C-suite, and board,” he says. “You have to be honest about which metrics or KPIs you can deliver in the first and second year, and when you’ll start seeing real business scale and impact, which definitely won’t be that soon in a large program like this.”

Goals also need to be flexible, Durvasula says, so transparency with leadership sometimes means telling them the project needs to reset. “If something doesn’t go well, what’s the level of fungibility you have?” he says. “We pick this tool, but what if it doesn’t work? You need to have a plan B.”

So TIAA’s IT team is heavily focused on flexible systems, and what was contemporary three years ago is probably legacy now, especially thanks to AI.

The power of change management

Another big lesson from a project of this size is the need to focus on change management. Retiring old IT systems requires the organization to bring employees along on the journey and convince them the changes are for the better.

TIAA established a multi-disciplinary team to implement a change management program focusing on breaking down silos and setting common adoption goals across the organization and its lines of business. Stakeholder forms and a huge focus on continuous collaboration helped employees understand the need for the changes.

“It’s a big organizational change,” Durvasula says. “If you’re working on a legacy system, and you think at some point it’s going to be modernized, then you become a legacy talent, and won’t have a job.” But the right change management program can convince these employees they can upskill and bring value to the new systems.

“You can bring your functional knowledge of the business and learn new technical skills,” he says. “It’s a massive culture- and people-change initiative as much as tech initiative.”

TIAA’s change management efforts were also made easier because TETRIS happened at the same time as the recent AI boom and involved AI elements. So it wasn’t hard to convince employees they needed to improve their AI skills.

“Because of AI, everybody woke up to this new reality,” he says. “We rode that wave when transformation drove from a cultural and organizational change management point.”

With vision comes perspective, and over a distinguished career, IT and digital transformation leader Niraj Bhatt has held may titles, and earned three consecutive CIO 100 awards since 2023.

As a storied advisor for startups and Fortune 500 companies, helping them navigate the unpredictability and fluidity of AI, Bhatt knows how emerging tech is rapidly reshaping the way organizations build products and deliver value, and how challenges shift as companies move from experimentation to real-world deployment.

AI, of course means a lot of different things to different people, and also for frictionless startups and large enterprises. For the former, speed is a huge asset, allowing them to punch above their weight. But it also means they need lightning fast reactions when landscapes shift. “The same speed can also hurt them when larger AI companies release new offerings that disrupt what startups are building,” he says, referencing recent moves by Anthropic and Google.

On the enterprise side, the conversation is more about scale and risk. Many large organizations have moved past the POC stage and now wrestle with the realities of putting AI into production.

Cost for both is naturally a recurring theme as organizations scale up AI efforts, and true expenses become clear only after the initial excitement fades. “Every input and output token, and the model you’re selecting, add up,” he says. Some customers like Open AI, he adds, get throttled because their usage, volumes, and costs are growing so fast, making planning, observability, and monitoring critical for any team moving beyond experimentation.

So understanding the full software development lifecycle is also vital. Therefore, before committing to production, he helps clients see the big picture, and make sure they understand technical requirements as well as operational and financial implications. “The cost picture isn’t just about usage, but scale and the model choices teams make,” he says.

Bhatt also discusses effective approaches to AI and enterprise IT, technology leadership, and the evolving role of today’s CIOs. Watch the full video below for more insights, and be sure to subscribe to the monthly Center Stage newsletter by clicking here.

On AI hype: If you can’t explain something to someone who’s eight or 80, you don’t really understand it. It’s gone from LLMs, to RAG, to agentic AI, and now the essence is all about tokens. It’s predicting that next token and understanding that is key. So when LLMs came out, they were good at doing that on the data on which they were trained. When the enterprises looked at it, they wanted to make those LLMs work for their data. And the question became how to provide our data and context. It’s about building the right context for the LLM. Agentic AI is similar and that’s where the RAG evolution came in, in that I’ve got my data because every LLM has limitations in terms of how much context it can carry.

There are ranges of LLMs, where Google has the highest in regard to the context window size and what they support. Agentic AI is more action oriented, though. LLMs rely on the metadata you provide for the tools. Then they’re doing token prediction in that whatever I’m looking for, I should use a specific tool. Then it’s the infrastructure underlying which LLM it relies on to invoke the agent. So if you try to explain the microservices to a person, you’re going to struggle. But it’s very important to understand the evolution and that’s where you can cut through the hype. Understanding in this context is key.

On navigating challenges around talent: What I’m seeing on the IT side is there’s so much cognitive load, so how do we empower people to build solutions with the right mix of products and platforms? I think it’s about democratizing AI for the entire organization. Your talent strategy is everyone, all inclusive, starting from interns, the business and tech sides, CEO, everybody.Like your customer success or revenue officers, you need a talent strategy because in the end, IT alone isn’t going to be in a position to deliver for everyone in the organization.

AI has the potential to make everyone in the organization more productive. You have to plan that and facilitate broad innovation across the organization.That’s where the talent strategy, and working with HR and the people officer becomes very important providing those tools. One part of it is training, but how do I build an agent for a receptionist receiving calls, for instance?I’m not going to rely on vibe coding or things of that nature. But what are the tools? Where do I go, where do I host this? I think through that entire ecosystem beyond copilots. That’s where innovation can kick in, and that broader talent strategy is something I’m working with my customers on.

On collaboration: I heard a panel discussion recently, and a question was asked about what’s the number-one trait CIO needs to be successful at in the world of AI, and the answer was collaboration. You need to bring everybody together, move forward together, and make sure everybody’s on board. And in my mind, simplifying that is more like systems thinking when you operate, just bringing everybody along and ensuring they’re meeting outcomes.

But maybe what’s more important is managing expectations. Because if you’re a CIO, there’s a tremendous amount of pressure to deliver and have a rock solid AI strategy. So what I’m doing with my customers is get the board, CEO, and CFO into a room and help them understand what I’m talking about, the evolution, and what’s the art of possible. You don’t want to be a CIO who thinks I have a hammer and everything is a nail. Having buy in from the senior leaders is essential to know you’re headed in the right direction. You’re not reacting to pressure from top leadership, but driving and becoming the change agent for good for the company.

On navigating AI: It’s interesting times. I’m covering a spectrum of startups, non-technical and technical founders, and advising Fortune 500 companies. What I’m seeing is they love the velocity and momentum because that’s what they’ve always wanted, and AI is providing that. They’re able to bring their products to markets very quickly, so something that would’ve taken three years a couple of years ago is probably now taking them three months. There’s a lot of excitement there. But on the flip side, the same velocity is also hurting them. There are so many frontier AI companies getting disrupted. OpenAI, for instance, has offerings in sales and marketing, and Google has an interactive video model. So a lot of startups working in the marketing space are getting stuck. A lot of what I’m focused on is working with founders, helping them pivot in the gen AI space, ensuring their systems and products are built and structured in the right manner.

And on the enterprise space, what I’m seeing is the POC wave, and people have seen the value. There’s some excitement but now the struggle is getting them to production. That’s where you run into cost, latency, legal compliance, privacy issues, and customer concerns that if we get tickets to production, how’s it going to look and how are we going to scale. So engineering and product teams have to be ably supported by the enterprise architecture and R&D teams. I then help them get up to speed and build that internal platform product for the production workloads. It’s exciting times on both sides.

One of the biggest challenges companies like Aetna face every year is an annual HEDIS review of its records to identify gaps in care. For large national payors, the scale of the challenge is immense. So Aetna has deployed a gen AI-driven document intelligence platform that has reduced the need for manual review by 65%.

“We have a large group of amazing trained medical coders who do this every day,” says Nathan Frank, chief digital and technology officer at Aetna. “This is about making it easier for them by speeding up the process. Something that might have taken weeks or months we can now do in days.”

The Healthcare Effectiveness Data and Information Set (HEDIS) is a range of performance measures for the managed care industry. Developed and maintained by the nonprofit National Committee for Quality Assurance (NCQA), the first version of HEDIS was released in 1991.

Under the HEDIS measures, large managed care providers like Aetna review more than 10 million medical records annually to identify gaps in care. These gaps are missed or overdue preventative care or chronic disease management tests including missed cancer screenings, blood sugar tests for diabetics, eye exams, and immunizations. Closing these gaps improves patient outcomes, and health plans are measured in how well they perform. But processing medical records is no easy task.

“We’re talking about medical charts that have white space filled with handwritten notes,” Frank explains. It’s not just structured data, it’s lots of physical clinical documentation.”

Adding up the numbers

Frank says industry benchmarks for large providers indicate an annual review process that requires about 50,000 work weeks, equivalent to nearly 1,000 dedicated full-time employees. It would take a team of 50 reviewers more than 20 years to complete a single annual review using fully manual processes.

Enter AI Medical Chart Review, a platform developed by Aetna that leverages cloud services and gen AI to automatically extract clinically relevant data from records, and prioritize records based on the likelihood of measure closure and evidence strength.

“Large language models and gen AI give us the ability to train a model to decipher the charts, identify the high value codes, and build correlations,” Frank says.

In the space of about six months, Frank’s team ideated the platform, and designed and trained a PoC that was able to process millions of records in just two weeks. As a result, AI Medical Chart Review has earned Aetna a CIO 100 Award in IT innovation.

“Now we’ve gone through 14 million documents,” Franks says. “We’re seeing a reduction of manual effort, which is now being transitioned into other areas like quality control and making sure the automated chart review is working as expected.”

Behind the curtain

Using gen AI, the platform automatically ingests and analyzes unstructured medical records and clinical documents. And as part of that process, it identifies and extracts clinically relevant information for specific HEDIS measures like diagnosis codes, medication records, lab results, and visit documentation. With this data, the platform generates a prioritized set of records based on the likelihood of measure closure and strength of clinical evidence, which is then passed to human employees for review and validation.

Frank says the platform has increased gap closure rates (leading to improved Star Ratings and higher reimbursement), streamlined workflows, and enabled teams once dedicated to manual record review to shift focus to higher-value activities.

Frank says much of the speed and success in building the platform comes down to a shift in the way it approached the design and build process. Rather than exhaustively writing specifications and requirements, Aetna created a team that included engineers and subject matter experts who worked together to build out capabilities iteratively.

“It allowed us to move much faster, and having a business subject matter expert sitting in the same virtual or physical room with us got us a much better outcome,” Frank says. “The product model, our cloud compute model, and our AI governance model allow for quick reviews to make sure we’re using AI responsibly with the right guardrails. It’s increased the speed to get from product launch to go-live.”

He adds that small teams that don’t have to deal with a lot of bureaucracy are key to moving quickly.

“You need to design with security, compliance, and a responsible use of AI as core principles from day one,” he says. “Everything we do from a new build standpoint starts with thinking about how we make it cloud native, how we build with the right elasticity and speed, and how we optimize the cost.”

The most important element of all, he says, is a good relationship with your subject matter experts.

“You can have a great product manager and engineer, but you really need that business subject matter expert who’s excited about it, and who has a passion for transforming the process,” Frank says. “Once you put those three together, you’ll see amazing things like this happen all the time.”

Medical technology companies have always been in the business of trust, and Abbott has been building it with AI for over 10 years. Long before gen AI entered the enterprise conversation, Abbott was using algorithmic AI to help diabetics manage their glucose, and imaging AI to guide surgeons in real time. Here, Sabina Ewing, Abbott’s CIO, explains how a principled approach to AI governance, deep cross-functional partnerships, and a commitment to demonstrating results from within IT have kept them ahead of the curve, and its mission intact.

How is Abbott using AI to achieve its mission and growth strategy?

As a medical technology company, Abbott’s mission is to help people live life to the fullest. For over a decade, we’ve been using AI to deliver on that mission, but whether it’s AI or any other technology, we’re intentional about how it ties to our mission.

Trust is earned in drops and lost in buckets. To ensure we maintain trust with our customers and employees, we’re guided by principles of fairness, safety, quality, and transparency. With these and our mission as our guide, we’re in command of the table we set for ourselves.

How have you been in the AI business for so long?

For decades, we’ve provided FreeStyle Libre, a glucose monitoring sensor built on algorithmic AI, that delivers continuous glucose readings to diabetics, and in some instances, connects to insulin pump applications.

In late 2025, we developed Libre Assist, which leverages generative AI to let FreeStyle Libre users take pictures of their food and receive guidance on the impact of that meal on their glucose levels, including when to eat what, because sequence affects how the body processes glucose.

In our medical devices business, Ultreon, launched in 2021, uses imaging AI to guide optimal stent placement during cardiovascular procedures, supporting the physician’s decision-making in real time.

So whether it’s algorithmic, generative, or agentic, we’re intentional about matching the capability to the specific therapeutic problem.

When technologies evolve, your mission doesn’t change. But how has the CIO evolved during this AI boom?

Today’s CIO must have the strengths of conviction, credibility, and communication. You need technical expertise and to surface data to have the right discussions. You also need to be brilliant on the fundamentals and clear about the strategy, and then execute against it. If I tell the business it can use AI to drive outcomes, then I need to demonstrate it in IT. This is why I’ve committed two commas of results in IT from new AI operational capabilities.

How can CIOs influence their company’s investment in AI?

Working with senior leaders in HR and finance ensures we’re educating the organization and securing necessary investment, and then maintaining financial discipline where investments occur. We hold to that discipline and we’re deliberate about how we deploy the resources of the organization to measurably have impact. We look for high-impact opportunities where new technology delivers results even as it evolves.

We established an executive steering committee on generative AI, and senior leaders are engaged in how we deploy capital. We’re not going out with a thousand flowers blooming.

We also have traditional financial measures we apply to AI investments. And we know you need to be able to identify quantifiable outcomes and then measure them. Those conversations happen in partnership with all senior leaders, especially those business leaders requesting specific capabilities.

As the CIO, you need to have strong relationships with all other parts of the company. I don’t need to be in the spotlight, but you need those relationships in order to lead and effect change. I led a program that helped educate our top leaders on AI foundations, and we’re all working together on the talent side, too. We’ve embedded AI into our talent processes, and we have a continuous cycle of enterprise education through the ranks, both in person and virtual, to ensure our people are ready to use the latest tools and technology. If you want to do something sustainable, you can’t do it by yourself.

What’s your message to your technology team?

What I tell our team is no one is better positioned to lead the organization through this transformative era than its own technical experts. That means we lean into our expertise and AI-first mindset.

Years ago, we crystallized our vision for Abbott IT by unleashing the power of technology and our people in service of Abbott’s purpose. That’s the constant reminder. Our role isn’t to deploy tech but to unlock what technology and people can do together, in service of the mission.

I have asked the team to be bold, bring their best, and pursue excellence. Our strategic pillars are modernization, and protecting Abbott in both enterprise and product cybersecurity, digitization, and advanced analytics. That’s always been part of our mandate.

But what does an AI-first mindset look like? I asked our executive assistants how they, as a community, think about using AI to radically expand what they can do with it as a companion to their work, but not a replacement for it. The models that exist today can’t be a great executive assistant. Models don’t have the judgment, institutional knowledge, or nuanced reasoning required to prioritize work and navigate unspoken rules. That expertise is irreplaceable. Our question is how to augment it.