Results

The "We Must Act Now" AI Job Displacement Letter Is Mostly Alarm-Ringing

The "We Must Act Now" AI Job Displacement Letter Is Mostly Alarm-Ringing

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Read more

This week's open letter from economists and other signatories urges policymakers to prepare for AI-driven job displacement. Larry Dignan isn't convinced there's much there.

He breaks down why the letter leans on hedge words like "could" and "may," why some of the layoffs already being blamed on AI look more like post-pandemic over-hiring corrections, and why it's too early to build incentives and guardrails around a transformation that hasn't proven out yet.

Watch the full episode of ConstellationTV for more enterprise AI analysis.

Future of Work Tech Optimization Data to Decisions AI Agentic AI HR Chief Human Resources Officer Chief People Officer
On Insights

5 Actions From Esteban Kolsky's July Enterprise AI Board Report

5 Actions From Esteban Kolsky's July Enterprise AI Board Report

Esteban Kolsky
Chief Distiller, Board Advisor
Constellation Research

Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies. He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…...

Read more

This month's July Board report shows the conversation has shifted from whether to do AI to how to control its execution. Chief Distiller and Board Advisory Esteban Kolsky breaks it down into 5 key points:

  1. Tech spending is separating from economic caution. Invest in data readiness, security, governance, and infrastructure.
  2. Public frontier models alone no longer differentiate. Context, privileged data, and homegrown models win.
  3. At 74% adoption, agentic AI is now an authority question. Agents need permissions, cost controls, monitoring, and a way to undo mistakes.
  4. Enterprise AI needs balance between "brains" (CPUs) and "brawn" (GPUs), plus storage, edge compute, and observability.
  5. Talent is the hardest constraint. Experienced people who can navigate ambiguity and apply judgment matter most.

Access the full report here: https://www.constellationr.com/research/enterprise-technology-intelligence-monthly-update-july-2026

The Board Board Strategy Chief Executive Officer
Off

H2 2026 Reckoning For Governance, FinOps, and Everyone's AI Budget | CRTV Episode 134

H2 2026 Reckoning For Governance, FinOps, and Everyone's AI Budget | CRTV Episode 134

Esteban Kolsky
Chief Distiller, Board Advisor
Constellation Research

Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies. He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…...

Read more
Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

Read more

Every two weeks, Constellation TV brings together our analysts to break down what's actually happening in enterprise technology. Episode 134 covers what the second half of 2026 has in store, how SAP is building out its agent platform, the five moves enterprises need to make, according to Esteban Kolsky's monthly board report, and why Larry isn't impressed by the latest AI job-displacement open letter.

The H2 2026 debate: screw-ups, FinOps, and the rise of the AI project manager

Liz, Larry, and Martin opened the show with their predictions for the rest of the year, and the group didn't fully agree — which made for a better debate.

  • Larry's take: expect a wave of agentic AI screw-ups serious enough to push laggard enterprises onto the governance bandwagon by year-end. He expects a mix of headline-grabbing failures and the quieter, more familiar kind — botched implementations where the vendor, the consultant, and the customer all point fingers at each other.
  • Martin pushed back with a different angle: instead of governance framed around data and security, the real forcing function will be financial. He argued enterprises are running AI the way someone might run a business off a generator instead of the grid — wildly inefficient — and that a "FinOps for AI" discipline will emerge to rationalize the sprawl of tools, agents, and corporate mandates that are currently canceling each other out. That means new models and new metrics, not just faster versions of old financial ledger processes.
  • Liz brought a project-management lens to the conversation, arguing that enterprises already have the function built to manage this kind of complexity — the project manager — and that AI ops will increasingly fall to that role, pulling in the CIO and CDO to create the cross-functional control layer AI needs.

Asked to name the most "ridiculous" conversation likely to dominate H2, the group landed on outcome-based pricing models. The consensus: AI deployment maturity isn't yet mature enough to draw a straight line from spend to outcome, and anyone selling on outcomes right now is taking on real risk. Larry added his own wildcard prediction — a hyperscaler quietly throttling back AI infrastructure spend, followed by a media narrative about an AI infrastructure "bubble," even as enterprises that get governance right start seeing real ROI. He also expects a NIMBY-style backlash against data centers to build toward a fever pitch ahead of the U.S. midterm elections in November.

Holger Mueller kicks off a 3-part series on Enterprise Application Platforms

Next, Holger Mueller introduced the first in a three-part series on Enterprise Application Platforms (EAPs), using his work with SAP's Joule Studio as the case study. He frames EAPs around three generic use cases — extend, integrate, and build — and argues they've become table stakes for enterprise software since 2022, because no vendor's out-of-the-box product covers everything and enterprises need a platform to build the rest themselves.

The AI angle touches all three use cases: extending an application can now happen through natural-language requests, integration is increasingly AI-assisted, and building, including standing up agents, is moving toward intent-based development, where a prompt generates the code. Part one of the accompanying benchmark report compares Joule Studio against SAP's BTP across three AI agent use cases: creating an agent, adding a skill, and building a full-scale backend application. Parts two and three of the video series will go deeper into the report's findings.

Esteban Kolsky's five actions from this month's board report

From there, Esteban Kolsky's two-minute board report distilled the current state of enterprise AI into five actions:

  1. Technology spending is separating from general economic caution, with enterprises prioritizing investment in data readiness, security, governance, private platforms, and infrastructure.
  2. Public frontier models alone can no longer create differentiated value — context, privileged data, and homegrown models are what separate the organizations doing AI well.
  3. With agentic AI adoption now at 74%, the conversation has shifted from adoption to authority: agents need permissions, cost models, monitoring, and a clear reversal path, with cybersecurity increasingly the foundation for execution governance.
  4. Enterprise AI also requires a balance between "brains" (CPUs) and "brawn" (GPUs), alongside storage, edge computing, and observability.
  5. And finally, talent is becoming the hardest constraint — experienced people who can navigate governance ambiguity and apply judgment are what make AI's cost efficiencies actually pay off.

The question for next month: will enterprises manage to connect AI spending to real impact, or will governance and talent gaps keep widening the space between adoption and value?

Larry vs. the "We Must Act Now" open letter

Closing out the episode, Larry took on this week's open letter from economists and other signatories urging policymakers to prepare for AI-driven job displacement. His read: the letter is long on academic hedging — heavy use of words like "could" and "may" — and short on evidence that displacement is actually happening yet. He points out that where AI has been blamed for layoffs, a lot of those companies were already over-hired coming out of the pandemic and may be using AI as convenient cover.

His bottom line: the topic is worth studying, but it's too early to build incentives, guardrails, and institutions around a transformation that hasn't yet proven out — especially since, as he notes, economists tend to be backward-looking by nature.

Data to Decisions Board Strategy Innovation & Product-led Growth Future of Work Tech Optimization Marketing Transformation Agentic AI AI finance HR Chief AI Officer Chief Executive Officer
On ConstellationTV

AI, Outcomes, and Leadership: What Tomorrow’s Disruptive Companies Are Doing Differently | DisrupTV Ep 445

AI, Outcomes, and Leadership: What Tomorrow’s Disruptive Companies Are Doing Differently | DisrupTV Ep 445

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

Read more
Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Read more

AI, Outcomes, and Leadership: What Tomorrow’s Disruptive Companies Are Doing Differently | DisrupTV Ep 445

The billable hour is dying. Homogeneity is innovation’s enemy. And the leadership trap most likely to cap your organization’s potential is one you built yourself.

Key Takeaways

  • The billable hour is dying. Outcomes are the new arbitrage. Gain-share models — where vendors win only when clients make or save money — are replacing time-and-materials contracts. If you can’t tie your work to measurable results, you’re exposed.
  • AI is the middle market’s great equalizer. Mid-size companies ($50M–$500M in revenue) can now achieve the leverage that once required billions in investment and massive headcount. The ability to pilot forever is gone — but so is the need to.
  • The real AI efficiency gain is eliminating the coordination tax. Middle management layers, compliance handoffs, and low-value transactional overhead are where AI is actually moving the needle today — not in headline-grabbing automation.
  • Governance and culture come before the model. The hard work of AI leadership isn’t choosing the right LLM — it’s setting clear purpose, establishing guardrails, creating safe sandboxes, and getting people to take more risk than they’re naturally comfortable with.
  • Operational AI is just the beginning. Innovation AI is the prize. Companies with vertically specific data sets and a defensible value proposition can use AI not just to run better, but to build entirely new moats around distribution and insight.
  • The services firms that win will be smaller, flatter, and outcome-obsessed. A 1,000-person AI-native services firm could disrupt a global SI. The winners will be built on results, not headcount arbitrage.
  • The “why can’t they be like me” trap kills innovation. Leaders who unconsciously surround themselves with clones of their own thinking, background, and style will cap what their organizations can achieve. Diversity of thought is not a soft value — it is the engine of breakthrough.
  • Your greatest strength, taken to an extreme, becomes your greatest liability. Executive coaching’s most important job is helping leaders see where overused strengths have quietly become blind spots.
  • True executive coaching is still a human relationship. AI can support mid-level leaders with prompts and accountability, but senior executives need a human coach capable of real-time pattern recognition, nuance, and trust built over time.
  • Schedule deliberate random collisions. Once a week or month, have a genuine conversation with someone outside your usual circle, in a part of the business you know nothing about. It’s one of the simplest ways to escape the echo chamber of your own success.

Part 1: AI, Outcomes, and the Disruption of the Services Model

Alejandro Laplana opens with a thesis that is simple and radical in equal measure: the billable hour is dying, and outcomes are the new arbitrage. At Shokworks, the AI-first digital transformation consultancy he leads — whose clients include HBO, FC Barcelona, and the United Nations — he has been building toward gain-share models since before they were fashionable.

In a gain-share arrangement, the vendor wins only when the client makes money or saves money. There are no comfortable invoices for time spent regardless of results. That alignment of incentives fundamentally changes the relationship: it rewards lean, risk-tolerant firms willing to take accountability for outcomes, and it threatens traditional time-and-materials consultancies that have coasted on complexity and hours billed.

“The notion that you would have a very comfortable and thriving business just by invoicing man-hours as opposed to tying yourself to concrete results and business outcomes — the traditional standard time-and-materials model isn’t as relevant as it formerly was.”

In an AI-first world, value is no longer in hours worked. It is in outcomes delivered. Vendors who can quantify and share in that value will displace those who cannot.

Why the Middle Market May Be AI’s Biggest Sleeper Advantage

Unlike much of the AI conversation, which focuses on Global 2000 enterprises, Laplana’s bias is explicit: he prefers the middle market. Companies in roughly the $50 million to $500 million revenue range cannot afford endless pilots and proofs of concept. They must tie AI initiatives directly to outcomes from day one, which forces a clarity of purpose that larger enterprises rarely achieve.

That constraint turns out to be an advantage. With the right strategy and data, mid-market companies can now achieve the leverage that once required billions in investment and massive headcount — competing on efficiency and intelligence rather than scale alone.

“We’re not big enough to actually achieve scale under conventional models, but AI can actually help us achieve that shift where now we have the leverage that you formerly needed billions invested to achieve.”

AI is a genuine force multiplier for companies in this range. The structural disadvantages of size — less capital, smaller teams, fewer resources for experimentation — are rapidly becoming less decisive.

Killing the Coordination Tax

Where is AI actually moving the needle today, beyond the hype? Laplana points directly at middle management layers and transactional overhead — what might be called the coordination tax that most organizations pay without ever examining.

He offers a concrete example from compliance workflows: processes that once required a handful of consultants to manually review and rubber-stamp documentation can be compressed dramatically with AI and automation. Cycle times shrink, third-party involvement drops, and costs fall — all without eliminating the work, just the friction around it.

This is where Ray and Constellation Research’s own analysis converges: BPO is on a long-term structural decline, and outcome-centric, AI-powered services are replacing it. The coordination tax that sustained entire industry segments is being automated away.

Governance, Security, and Culture: The Real Work of AI Leadership

Technology, Laplana is clear, is not the hard part. Change is. When CEOs and boards come to Shokworks asking how to manage AI risk and opportunity, he doesn’t start with models. He starts with governance and acculturation.

Step one is the governance and security layer: establishing exactly how the model should be used, ensuring the model is not training on proprietary client data, and putting the right boundaries in place before anyone touches a prompt. Step two is acculturation: building a sandbox where employees can experiment, fail safely, and — paradoxically — learn to take more risk than they would in their default, risk-averse mode.

Leadership’s role in this process is to set clear purpose and end states for AI initiatives, model the right mindset even if they are not the most technical person in the room, and sponsor and legitimize new ways of working. Not every legacy leader will become an agentic AI power user. But every leader must become an active advocate for the transformation.

From Operational AI to Innovation AI: Where the Real Long-Term Value Lives

Much of the early enterprise AI story has been about operational efficiency: do things faster, cheaper, with fewer people. Laplana argues that the real long-term prize is innovation and data monetization.

Companies with vertically specific data sets and a genuinely differentiated value proposition can use AI not just to run better, but to build entirely new competitive moats. Operational AI improves today’s business. Innovation AI creates tomorrow’s.

Ray connects this to a historical pattern visible in pharma and biotech: the players who own distribution and data repeatedly acquire the innovators and extract the value. The open question for AI-powered services is who will own that distribution layer — hyperscalers, legacy vendors, or new entrants willing to embed gain-sharing into their fundamental business model.

Laplana’s answer is pragmatic: the firms that win will be those that own outcomes, embed risk-sharing, and build defensible relationships and data over time. The usual suspects may not be the ones leading that charge.

The Future of Services: Smaller, Smarter, Outcome-Obsessed

Ray offers a prediction that is hard to dismiss: a 100-person AI-native software company could displace a traditional software vendor, and a 1,000-person AI-native services firm could disrupt a global systems integrator. Laplana essentially agrees.

The services firms that win in the AI era will be smaller and flatter than today’s giants, built on outcomes rather than headcount arbitrage, deeply data- and distribution-aware, and comfortable taking risk in exchange for upside. Those willing to embed their profits into outcome-based delivery will be the challengers that earn a seat at the new table.

Part 2: The Leadership Trap That Caps Every Organization’s Potential

Claire Díaz-Ortiz joins the conversation to discuss her new book co-authored with Marshall Goldsmith: Why Can’t They Be Like Me? A Parable of Shifting Perspectives. The title captures one of the most pervasive and quietly destructive traps in leadership: the belief that because a particular way of behaving, working, and thinking made you successful, everyone around you should simply do the same.

She illustrates the cost of this trap with a vivid analogy: imagine being stuck in an escape room where every other person shares your exact background, demographic, and frame of reference. Your odds of getting out are not good. Innovation, problem-solving, and breakthrough thinking all require diversity of perspective — and leaders who unconsciously build teams of clones are slowly walling themselves off from the ideas they most need.

“If the only other people I am with are people who grew up in the exact same context I did, I would not have high levels of hope. You need diversity of thought to generate innovation.”

This problem is especially pronounced in environments where speed to trust and pattern recognition drive decisions — like Silicon Valley, where investors and leaders often gravitate toward people who look and think like them. The result is execution efficiency in the short term, and innovation poverty in the long run.

When Strengths Become Liabilities

One of the more subtle and important insights Díaz-Ortiz surfaces is the way that the qualities driving individual success can become the very liabilities that limit leadership effectiveness. Incredible focus becomes myopic leadership. High standards become an inability to delegate. Speed becomes impatience with the people and processes needed to scale.

“Anything taken to an extreme can be a challenge in leadership.”

Executive coaching, in this framing, is partly about helping leaders see where their overused strengths have quietly become blind spots — and creating the self-awareness to decide whether they genuinely want to change. That last point matters: you can shift a leader’s trajectory even in crisis, but only if they truly want to change. Coaching cannot manufacture that desire.

What AI Can and Cannot Do in Executive Coaching

Díaz-Ortiz draws a careful line around AI’s role in leadership development. For mid-level leaders and managers, AI tools are increasingly capable of providing useful prompts, supporting accountability, and acting as an always-on nudge coach — accessible, patient, and consistent in ways a human coach cannot always be.

But for senior executives, she draws a hard line: true executive coaching still requires a human relationship built on deep trust. The value in high-end coaching is not generic best practices or behavioral frameworks. It is the real-time pattern recognition, lived experience, and ability to read nuance and non-verbal signals in the moment that no algorithm can yet fully replicate.

The distinction matters because conflating the two — treating AI-assisted coaching and human executive coaching as equivalent — risks leaving senior leaders without the kind of honest, trusted relationship that actually shifts behavior at the highest levels.

Practical Advice: Escaping the “Like Me” Trap

Díaz-Ortiz ends with advice that is simple enough to dismiss and powerful enough to change an organization’s trajectory: once a week, or at minimum once a month, have a genuine conversation with someone who is not in your boardroom, not in your department, and not in your area of expertise. Listen to how another part of the business works, what it is trying to solve, and where it sees the world differently.

These deliberate random collisions — scheduled, intentional, and genuinely curious — are one of the most effective ways to challenge the assumptions that success quietly accumulates around a leader. They expose you to the signals your echo chamber filters out, and they build the diversity of perspective that every escape room — and every organization — actually needs.

Final Thoughts

DisrupTV Episode 445 holds together two conversations that might seem separate but are ultimately about the same thing: the gap between what made organizations successful yesterday and what will make them competitive tomorrow.

Alejandro Laplana’s argument is that the business model itself is being disrupted. Charging for time is being replaced by sharing in outcomes. Headcount arbitrage is being replaced by AI-powered leverage. Endless pilots are being replaced by the discipline to define purpose, set clear end states, and tie every initiative to a measurable result. The firms that internalize this shift early will define the next generation of services. The ones that don’t will find themselves displaced by smaller, faster, and far more accountable competitors.

Claire Díaz-Ortiz’s argument is that the leader themselves is often the constraint. The very patterns of thinking, hiring, and deciding that built a successful career can cap an organization’s potential if they go unexamined. Building diverse teams, seeking out deliberate random collisions, and working with a trusted human coach to surface blind spots are not soft leadership practices — they are the structural requirements for sustained innovation in a world that is changing faster than any single person’s experience can track.

“The organizations that win in the AI era will be outcome-obsessed and perspective-rich. The leaders who get there will be humble enough to know what they don’t know — and intentional enough to go find it.”

Related Episodes

If you found Episode 445 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work New C-Suite AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

On DisrupTV <iframe width="560" height="315" src="https://www.youtube.com/embed/J5CupyHoVng?si=GHB_W8FIfV0UhJBW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

The Right AI for the Right Job: Pega's Tara DeZao on Marketing's AI Maturity Curve

The Right AI for the Right Job: Pega's Tara DeZao on Marketing's AI Maturity Curve

Liz Miller
Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

Read more
Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

Read more

Every year at PegaWorld, Constellation Analyst Liz Miller sits down with a Pegasystems leader, Tara DeZao, for what's become an annual tradition: a candid, marketer-to-marketer conversation about where AI hype ends, and real value begins. This year's conversation cut through a lot of noise and surfaced a few hard truths every enterprise marketing leader should hear.

Outputs Are Not Outcomes

The line that anchored the entire conversation: just because an AI model answers correctly doesn't mean the business has moved forward. Too many organizations are conflating AI outputs - a generated email, a summarized report, a chatbot response - with actual business outcomes like customer lifetime value and sustainable growth.

DeZao's advice: lead with the outcome you want to achieve, then work backward to the right AI for the job. Not every problem needs a model. Some need a new messaging strategy, a partnership, or simply better alignment across teams.

The Martech Stack Is Finally Shrinking

For years, Constellation's analysts have predicted consolidation in marketing technology. This year, DeZao confirmed it's actually happening: enterprises are pursuing vendor takeout, fewer point solutions, and fewer data silos. It's a validating moment for a thesis the industry has held for a long time but rarely seen executed at scale.

It Was Never a Data Problem

Perhaps the most important observation from the conversation: what looks like a data problem or a systems problem is, more often than not, a process problem. CRM promised to solve this in the early 2000s. The Customer Data Platform promised it again a decade later. AI is now closing that workflow gap, not by replacing the systems that came before, but by finally making them work together the way marketers always intended.

Compliance as an Enabler, Not a Blocker

One of the more counterintuitive points: centralizing compliance and governance doesn't slow marketers down; it frees them up. When guardrails are built into the system rather than bolted on after a mistake, marketers in regulated industries can test and learn at a level of granularity that used to be impossible. Decision Hub's approach to centralized compliance lets teams experiment with confidence instead of asking forgiveness.

The Secret Sauce AI Still Can't Replicate

AI can absorb the technical expertise no single marketer can hold in their head, the latest trends, backend configurations, channel mechanics. What it can't replace is the experience, taste, and relationships built over a career. Marketing remains fundamentally relational, and that won't change no matter how sophisticated the tooling becomes.

Stop Talking About Speed

Perhaps the most pointed takeaway from the conversation: enterprises need to stop bragging about how fast they built an agent or launched a workflow. Velocity is not the outcome. In fact, over-indexing on speed risks giving marketing teams a false sense of failure, as if they're behind when, in reality, most organizations are still early on a long maturity curve.

The Bottom Line

AI's promise in marketing isn't about doing more, faster. It's about finally solving the process and workflow problems that CRM and CDP investments never fully closed, while keeping the judgment, taste, and guardrails that protect the brand firmly in human hands. That's the realistic, pragmatic view of AI that Constellation Research continues to advocate for, and it's exactly the conversation our analysts are having with enterprise leaders across every industry.

Marketing Transformation Data to Decisions Future of Work AI Agentic AI Marketing Chief Marketing Officer
On CR Conversations

Context Washing, AI Agents & Qualcomm's Big Bet | CRTV Episode 133

Context Washing, AI Agents & Qualcomm's Big Bet | CRTV Episode 133

Michael Ni
Vice President and Principal Analyst
Constellation Research

Michael Ni is Vice President and Principal Analyst at Constellation Research, covering the evolving Data-to-Decisions landscape—where CDOs, CIOs, and CPOs must modernize data infrastructure, integrate AI into decision-making, and scale automation to improve business outcomes. Ni’s research examines how enterprises operationalize AI, automate decision-making, and integrate data management and analytics into core business processes. He focuses on the challenges of scaling AI-driven decision systems, aligning data strategy with business goals, and the growing role of data and decisioning “products” in enterprise ecosystems. With 25+ years as a product and GTM executive across enterprise software, AI platforms, and analytics-driven technologies, Ni brings a practitioner’s perspective to…...

Read more
Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

Read more

Episode 133 of ConstellationTV brought together analysts and industry leaders to tackle some of the most pressing questions in enterprise AI, from whether "context" is becoming meaningless marketing language to how Qualcomm is making a serious play for the data center.

The Context Washing Debate

Every major AI vendor now has a "context strategy." Snowflake, Databricks, SAP, Microsoft — they all claim it. But ask ten vendors what context actually means, and you'll get ten different answers: metadata, semantic layers, knowledge graphs, vector databases, memory, catalogs.

Constellation analysts Mike Ni, Holger Mueller, and Esteban Kolsky went head-to-head on whether context is a genuine architectural shift or the next AI buzzword. Mueller argued that while context is logically necessary, it's technically unfeasible at the scale vendors are promising; real-time context querying across complex enterprise environments requires infrastructure most organizations simply don't have yet. Kolsky agreed with the architecture challenge but argued that the solution lies in scoping the context to specific, bounded use cases rather than trying to solve it universally. Ni framed the takeaway: context is both marketing and necessary, but what the market currently has is not sufficient. Enterprises are context washing today — but this is where the next major AI control point will emerge.

OpenText: AI Agents as Network Managers

John Radko, SVP of Product Development at OpenText, joined to discuss how business networks are evolving. The core insight: AI isn't simply making transactions faster — it's beginning to actively manage the complex ecosystems that connect thousands of companies.

Radko described the near-term deployment of AI agents that help clients onboard partners faster, detect issues, and resolve them more quickly. Looking further ahead, OpenText envisions agents operating as the first line of defense — managing trading communities autonomously, as demonstrated by a recent deployment with a major automaker where an agent detected a file failure and automatically re-sent it once the issue was resolved.

His advice for enterprises: treat your business network as a platform and consolidate as much as possible into a single infrastructure. As agentic AI rolls out, it will leverage APIs and interfaces across your stack, but fragmented infrastructure will slow you down.

AI in Marketing: Less Stack, More Process

Liz Miller sat down with Tara DeZao of Pegasystems to discuss what AI is actually doing for marketing leaders right now. The headline: martech stacks are finally shrinking. After years of point-solution proliferation, enterprises are consolidating, and Pega's Customer Decision Hub is benefiting.

DeZao pointed to two standout capabilities: gap detection, where an AI agent proactively identifies underserved audiences and surfaces new opportunities mid-campaign; and centralized compliance, which lets marketers in regulated industries experiment more freely because guardrails are built in rather than bolted on after the fact.

The bigger takeaway: most AI failures in marketing aren't data problems or model problems. They're process problems. AI is most valuable when it's solving workflow challenges, not strategy questions.

Qualcomm's Data Center Bet

Holger Mueller closed the episode with his takeaways from Qualcomm's Investors Day. Qualcomm is making a significant move: from silicon and IP licensing toward platform-as-a-service, with data center infrastructure as the new revenue frontier.

The three-stage plan starts with connectivity offerings available now, moves to the Xi Accelerator for inference workloads, and extends to a CPU based on Orion infrastructure arriving in 2028. The headline acquisition: Modula, purchased for nearly $4 billion. Modula's compiler technology claims to run NVIDIA workloads more efficiently than NVIDIA itself does, which, if true, would have major implications for cloud vendors and enterprise AI infrastructure costs.

Physical AI rounds out the picture: Qualcomm has nearly swept automotive design wins and is expanding into industrial IoT and robotics. The ambition is clear: become the platform layer that powers AI wherever it runs.

Episode 133 makes one thing clear: enterprise AI is moving fast, but the real work isn't in picking the flashiest model or the loudest vendor; it's in getting the foundations right. Context, trust, governance, consolidation. The enterprises that nail those unsexy building blocks are the ones that will actually scale.

Data to Decisions Tech Optimization Marketing Transformation New C-Suite Agentic AI AI Generative AI Data to Decisions Chief Executive Officer Chief Marketing Officer Chief Digital Officer
On ConstellationTV

Resilience, AI, and What It Means to Be Human Next | DisrupTV Ep 444

Resilience, AI, and What It Means to Be Human Next | DisrupTV Ep 444

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

Read more
Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Read more

Resilience, AI, and What It Means to Be Human Next | DisrupTV Ep 444

AI is no longer about tools and dashboards. It’s about outcomes, resilience, and the future of human capability itself — and we still have a window to shape how that future unfolds.

Key Takeaways

  • AI is the Industrial Revolution on fast-forward. The projected impact is 10x the Industrial Revolution at 1/10 the time — a compression that makes this feel like a tsunami, not a wave.
  • Busy is not the same as productive. Many organizations are burning tokens without creating outcomes. AI adoption must be measured in business results, not activity or pilot volume.
  • The software model is shifting from tools to outcomes. Customers now expect vendors to own a share of the result, not just the tool. Post-sale is where 90% of the real work begins.
  • Your job is to feed agents work, not compete with them. AI-native hiring means training people to design work for agents — not to manually execute every step themselves.
  • The real moat is the 10% that is vertically differentiated. When everyone has access to the same horizontal AI capabilities, vertical depth, customer intimacy, and outcome-focused execution are the only defensible advantages.
  • Resilience in the AI era must start with institutions, not individuals. AI is embedded in how we see, are ranked, and are judged. Individuals alone cannot counterbalance those forces — we need reinvented and new institutions to do it.
  • AI literacy is table stakes. Existential literacy is what’s next. People need to learn how to remain agents on their own behalf in a world dense with AI mediation — and how to find purpose when traditional work roles shift radically.
  • AI may eat our solitude. We are approaching a world where it is always possible to be entertained, accompanied, or coached by a digital companion — and solitude, essential to creativity and reflection, becomes harder to preserve.
  • Friction is our friend. Deliberate pause points in AI-augmented workflows — to check intuitions, reflect on values, and question what we don’t know — may be essential to staying human.
  • We are the last generation that will know what human capability felt like before AI. That is both a warning and an invitation to shape this transition while we still can.

Part 1: AI at the Enterprise Front Line — Outcomes, Agents, and the 90/10 Rule

TVN Reddy opens with a framing that sets the tone for the episode: AI will do for the digital world what the Industrial Revolution did for the physical one. Unlike that earlier transformation, which unfolded across decades, the projected impact of AI is described as 10x the Industrial Revolution at 1/10 the time. That compression of impact is what makes this moment feel like a tsunami rather than a wave.

Reddy sees this dynamic firsthand across Aptean’s more than 10,000 customers, split roughly between North America and Europe. Their AI adoption falls along a spectrum. Some are all-in, pushing hard to use AI to its fullest within months. Others are just starting, experimenting with a couple of agents and learning before they scale. But almost everyone is feeling the same pressure: board-level expectations, competitive fear, and the nagging question of whether they are actually getting business value from any of it.

The Action Trap: Burning Tokens Without Outcomes

Reddy describes a pattern he sees repeatedly across customers — what he calls the action trap. Leadership says we need to adopt AI. Teams rush in and start using ChatGPT or other large language models. Six months later, someone asks whether the organization is using AI. The answer is yes. Then comes the harder question: what business outcomes have we actually achieved? Often, there isn’t a clear answer.

“Organizations are busy experimenting, but busy is not the same as productive. They’re burning tokens, not necessarily creating value.”

Reddy’s point is direct: AI adoption must be measured in outcomes, not activity. The number of pilots running, the volume of prompts sent, the tools deployed — none of these are the metric that matters.

From Selling Tools to Selling Outcomes

This leads to one of the biggest structural shifts discussed in the episode: the move from selling software as a tool to selling software and AI as an outcomes partnership.

Traditionally, enterprise software followed a familiar pattern. The vendor sells a tool, the customer implements their own processes inside it, the software collects data and produces dashboards and reports, and the customer is left to interpret everything and realize the value on their own. In that world, as Vala put it, the marriage experience was not necessarily better than the courtship. After the sale, the vendor was often 80 to 90 percent done.

With AI and digital labor, that model breaks. Customers now expect vendors to own a share of the outcome, not just the tool. Aptean is moving toward revenue-share and outcome-based models in some cases, where the software, AI, and services are bundled together — if the customer’s business grows, Aptean grows; if not, they don’t.

Concrete outcome targets Aptean focuses on for verticals like food and beverage and logistics include increasing inventory turns, reducing waste especially for perishables, minimizing fuel costs in routing, improving demand and supply prediction, and expanding margins without simply adding headcount or capital expenditure.

That demands an entirely different go-to-market motion. Sales is no longer about throwing a tool over the wall. Post-sale is where 90% of the real work begins, and teams must evolve from what Vala described as mercenaries to missionaries — standing with the customer, not across from them.

Hiring for an AI-Native Future: Feed Work to Agents

Ray asked how this changes hiring, especially in engineering. Reddy’s answer flips the conventional mindset entirely.

“Your job is to feed agents work, not to do the work.”

Aptean is hiring AI-native engineers straight from college with a very specific orientation: they are trained to design work for agents, not to manually execute every step themselves. Interns are given problems to solve, not recipes to follow. The company pairs deep domain experts — in food and beverage, distribution, manufacturing — with younger AI-native engineers who have been building and playing with agents since before graduation. The result is a vertical AI-native company capable of capturing both industry nuance and technical acceleration at the same time.

Data: Important — But Don’t Wait Forever

On data, Reddy acknowledges the classic concern: garbage in, garbage out. But he pushes back against the idea that organizations must freeze their AI efforts until every dataset is perfect.

For decision intelligence, clean and trustworthy data is genuinely critical. But for workflow automation and orchestration, you can often get started while you consolidate and modernize systems and use AI itself to help clean and validate data over time.

He emphasizes the value of ecosystems of agents rather than single agents working in isolation. One agent might produce an initial output that is 60 percent correct. Other agents and human reviewers act as critics and validators, iterating until quality reaches 95 to 99 percent. These agents can detect anomalies, flag unusual spikes, and help curate better data continuously. AI is not just a generator, in Reddy’s framing — it is also the best critic you can deploy against your own logic, assumptions, and data quality.

The 90/10 Rule: Where the Real Moat Lives

Reddy offers a useful mental model for differentiation in an AI-saturated world: the 90/10 rule. Ninety percent of the stack is horizontal and rapidly commoditizing — ledgers, invoices, cash collection, and access to the same underlying large language models. Ten percent is vertically differentiated: deep domain-specific functionality, embedded industry expertise, specialized data sets and workflows, and long-standing customer relationships built on trust. That 10 percent creates 90 percent of the value.

When every company in a sector has access to the same horizontal AI capabilities, the only real moat becomes vertical depth, customer intimacy, and outcome-focused execution.

Reddy carries this same lens into M&A. Aptean often acquires smaller vertical leaders, and the non-negotiable criterion is that they must be number one or number two in their specific niche. That positioning signals pricing power, strong retention, clear customer value, and a defensible micro-moat. Everything else — product gaps, scaling challenges — is more tractable if that core positioning is sound.

Part 2: Societal Resilience — Institutions, Existential Literacy, and the Loss of Solitude

The second half of the episode shifts from enterprise software to societal resilience with Lee Rainie, who spent nearly a quarter century at Pew Research leading internet and technology studies and producing more than 850 reports across the internet and broadband revolution, the mobile revolution, the social media revolution, and now AI.

At Elon University’s Imagining the Digital Future Center, his latest work centers on a deceptively simple question: how will humans cope with the disruptions that AI brings? Initially, Rainie and his team focused on individual resilience — how people personally handle trauma, setbacks, and change. But as they canvassed experts, the emphasis shifted in a direction that surprised them.

The consensus among experts was that resilience in the AI era must start with institutions, not individuals. AI systems are now deeply embedded in daily life — they mediate what we see, what we are offered, how we are ranked and judged. Individuals alone cannot counterbalance or shape those impacts. We will need reinvented institutions across education, governance, work, and media, and likely entirely new institutions focused on AI oversight and accountability, human dignity and agency, and guardrails that protect society while enabling innovation.

This is particularly challenging because trust in institutions is at historic lows, especially in the United States — precisely when we need them most.

Beyond AI Literacy: Toward Existential Literacy

AI literacy — understanding how tools work, how to prompt, how to interpret outputs — is now table stakes. Several experts in Rainie’s research argued we need to go further, into what they called existential literacy.

That means teaching people how to remain agents on their own behalf in a world dense with AI mediation, how to find purpose when traditional work roles and incentives shift radically, and how to build and sustain communities in an environment where AI agents are acting as social proxies, negotiating with other agents, and interacting in ways that may be opaque to us.

This intersects directly with the human capabilities Rainie’s research highlights as most durable: social and emotional intelligence, creativity and curiosity, critical thinking and pattern recognition, and metacognition — the ability to think about our own thinking, biases, and knowledge gaps. The question for education systems is how to deliberately cultivate these traits rather than simply assuming they will emerge on their own.

Agents, Social Complexity, and the Loss of Solitude

Rainie points out that humans are historically wired to manage roughly 150 close relationships — the classic Dunbar’s number. The internet pushed that closer to 600. With agents and social AI, the scale of interactions multiplies again: our agents will interact with others’ agents, new points of entry to influence or manipulate us will appear, and social complexity will explode in ways we have no prior framework to navigate.

One prediction from his research stood out: AI may not just consume our attention — it may consume our solitude. We are approaching a world where it is always possible to be entertained, accompanied, coached, or guided by some form of digital companion. Solitude — unstructured, offline time to be alone with our own thoughts, to reflect, to be bored, to engage directly with our physical environment and a small set of human beings — becomes harder to preserve.

Given how central solitude is to creativity, reflection, and mental health, this is not a trivial externality. It is a fundamental shift in the texture of human experience.

Friction Is Our Friend: Rediscovering Practical Wisdom

Another key insight from Rainie’s research connects to Aristotle’s concept of phronesis — practical wisdom gained through lived experience. Many experts argued that in the AI era, friction is our friend.

We will need deliberate pauses in AI-augmented workflows: moments to check our intuitions, reflect on values, and question what we don’t know. We will need AI systems that build these pause points in — prompting us to reconsider assumptions, encouraging metacognition, and highlighting uncertainty and blind spots rather than concealing them.

Vala echoed this from his own practice, describing how he uses AI not just to accelerate answers but to pressure test his thinking — and how learning to receive that feedback with humility has become a discipline in itself. In a world chasing speed and automation at every turn, intentional friction may be essential to staying human.

Relationship Design, Not Just Process Design

Vala brings the conversation back to the enterprise with a framing that recontextualizes everything discussed in the first half. At Salesforce, after 12 years of AI work and two years focused specifically on agentic and digital labor, the pattern is clear: when you redesign a process to be agentic, you free up human labor, must reskill those people, and then redeploy them into new roles. That triggers budget, organizational, and power shifts — who owns the people now, who owns the dollars?

None of that is primarily technical. It is relational: manager to manager, team to team, human to agent, and human to human in entirely new configurations. His warning is direct: if most of your job is transactional, deterministic, repetitive, and low-impact, you are highly exposed to automation. The durable work is relational, creative, strategic, judgment-heavy, and non-deterministic.

That suggests leaders must practice what Vala calls relationship design: deliberately thinking through how humans and machines co-create value, how humans retain agency and meaning in their work, and how we avoid wasting decades of human potential on work that could have been automated much sooner.

Final Thoughts

DisrupTV Episode 444 closes with a line from researcher Mel Sellick at Arizona State University that Rainie’s team used as the tagline of their report:

“We are the last generation that will know what human capability felt like before it became inseparable from AI.”

It is both a warning and an invitation. A warning that the coming shift is irreversible and profound. An invitation that we still have a window — right now — to shape how this integration happens, through smarter institutions, better guardrails, deeper human skills, outcome-focused use of AI, and a renewed focus on what it means to live a good human life in an agentic world.

TVN Reddy and Lee Rainie approach this from very different vantage points — one from the front lines of enterprise software, one from two and a half decades of research into how technology reshapes society. But they arrive at the same conclusion. The organizations and societies that will navigate this era well are not those with the most tools or the fastest pilots. They are those that stay relentlessly focused on outcomes, build institutions strong enough to protect human agency, and never lose sight of the human being at the center of every process they automate.

The question is not whether AI will transform everything. It will. The question is whether we will be intentional enough, humble enough, and structurally prepared enough to shape that transformation toward human flourishing rather than away from it.

Related Episodes

If you found Episode 444 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work Data to Decisions AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

On DisrupTV <iframe width="560" height="315" src="https://www.youtube.com/embed/J5CupyHoVng?si=GHB_W8FIfV0UhJBW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Pegasystem's Ken Stillwell on Tokenomics and Outcome-Based AI

Pegasystem's Ken Stillwell on Tokenomics and Outcome-Based AI

Liz Miller
Vice President & Principal Analyst
Constellation Research

About Liz Miller: Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement, and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing experience, Miller offers strategic guidance on the leadership, business transformation, and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform everything from…...

Read more
Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

Read more

Somewhere along the way, the AI industry started keeping score by the wrong number. Token leaderboards, token maxing, ever-larger consumption as a badge of sophistication. In a recent conversation with Constellation Research, Pega's COO and CFO, Ken Stillwell, argued that this scoreboard was rigged from the start and that the smarter path forward is to stop measuring activity and start measuring value.

Speaking with Liz Miller, VP and Principal Analyst at Constellation Research, Stillwell laid out a refreshingly blunt view of AI economics from the seat of the person who has to answer for both cost and results.

Nobody should be surprised that AI is expensive

Stillwell's starting point is almost disarmingly simple: you have to pay for the capacity you use, and that capacity is not cheap. The industry is spending trillions on infrastructure that will need to be refreshed every year or two. Many assumed the cost would fall quickly. Instead, it has climbed.

But expensive is not the same as wasteful. Things that cost money can still deliver enormous value. The problem is not the price tag. The problem is paying for value you never actually receive.

Who built the leaderboards, and why

Here, Stillwell makes his most pointed observation. Token leaderboards were created by the very companies that profit when you consume more tokens. It should surprise no one that AI model providers want you to use a lot of their product. And it should surprise no one that the companies with nothing to gain from token maxing were never the ones promoting it.

Those leaderboards faded fast once a few voices in the market pointed out how backward the whole exercise was. The lasting question is the more useful one: how do you connect the value of AI to its cost?

AI is a utility, so treat it like one

Stillwell's preferred analogy is electricity. AI is a powerful utility, and like any utility, you use it when you need it. You don't leave the air conditioning running with the windows open. You don't cool an empty house to 65 degrees for two weeks when no one is home. These are intuitions people already carry through their daily lives, and Stillwell believes organizations will develop the same instincts for tokens and AI.

That mindset leads to a more deliberate approach. In design work, for example, token use is heavy but concentrated and short-term, because you are not designing forever. There, it can make sense to lean in hard, because faster iteration and faster decisions are worth the spend. The value of the acceleration justifies the cost.

Right-size the model to the task

Not every job needs a frontier model. Using the most advanced, most expensive model for a simple probabilistic task is its own form of waste. Part of a disciplined AI strategy is knowing when a more static, less costly model will do the job perfectly well.

Just as important is knowing where AI does not belong at all. Stillwell draws a clear line: anywhere a human would otherwise make a judgment, do analysis, or reason is in scope for AI. But in a deterministic workflow built specifically to remove human judgment, introducing that judgment back in does not help. The entire reason the system exists is to avoid it.

The confusion at the heart of the market: outputs vs. outcomes

The conversation's sharpest point is a distinction that Stillwell and Miller both see being blurred across the industry: outcomes versus outputs. Plenty of vendors promise outcome-based pricing, then define the outcome as something like the model returning an answer without hallucinating. Stillwell argues that it is not an outcome at all. It is an output, and frankly, one you should expect to be correct every single time simply by virtue of doing business with them.

A real business outcome is something you have to step back and define. For Pega, that means a completed case: a loan origination, a client onboarding, a dispute, a fraud investigation. The kernel of work that actually needs to get done, ideally automated end-to-end. The value is in completing that work, not in racking up activity along the way.

Stillwell frames it as the difference between activity-based and outcome-based measures. Activity-based metering, counting microtransactions at various skew levels, makes sense when you are selling raw cloud compute. But value-added work is different. He points out how telecom and internet pricing evolved away from counting minutes and toward simply providing access. The lesson for AI is the same. Tokens measure how much processing is happening, not the value being created. And that disconnect is exactly the trap.

The takeaway

Stillwell's message lands as a useful corrective for any leader feeling pressure to prove AI maturity through sheer consumption. Pay for value, not activity. Match the tool to the job. Know where AI helps and where it doesn't. And above all, get clear on the difference between an output you should take for granted and an outcome worth paying for.

Marketing Transformation Matrix Commerce Future of Work Tech Optimization AI Agentic AI
On CR Conversations

The Invisible Infrastructure of Commerce: OpenText's John Radko on Business Networks and the Rise of AI Agents

The Invisible Infrastructure of Commerce: OpenText's John Radko on Business Networks and the Rise of AI Agents

Chirag Mehta
Vice President and Principal Analyst
Constellation Research

Chirag Mehta is Vice President and Principal Analyst focusing on cybersecurity, next-gen application development, and product-led growth. With over 25 years of experience, he has built, shipped, marketed, and sold successful enterprise SaaS products and solutions across startups, mid-size, and large companies. As a product leader overseeing engineering, product management, and design, he has consistently driven revenue growth and product innovation. He also held key leadership roles in product marketing, corporate strategy, ecosystem partnerships, and business development, leveraging his expertise to make a significant impact on various aspects of product success. His holistic research approach on cybersecurity is grounded in the reality that as sophisticated AI-led attacks become…...

Read more
Hannah Mason
Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

Read more

Every day, companies exchange an enormous flow of information with their partners. Purchase orders, sales orders, shipping notices, invoices, and countless other documents move between organizations in a constant stream. It is, as OpenText's John Radko puts it, the lifeblood of commerce. And most of the time, it happens invisibly.

In a recent conversation with Constellation analyst Chirag Mehta, John Radko, Senior Vice President of Product Development at OpenText, explained what makes that flow possible, why it is so much harder than most people assume, and how AI is beginning to change the picture.

The problem business networks solve

The core challenge is simple to state and difficult to solve: companies need to talk to each other, but no two companies are alike. They run different systems, different generations of technology, and different data formats. Even when two organizations use the same document standard, they often apply it differently, including omitting values or relying on entirely different versions.

The result is that a single process, something as routine as placing an order, can look ten different ways across ten different partners. A business network exists to mediate all of that complexity, connecting organizations and keeping information moving reliably between them.

And the complexity does not hold still. As Radko notes, the moment a company changes its ERP system or adjusts how it handles receipt of goods, that change can cascade across its entire partner ecosystem. The network has to absorb that constant motion.

Onboarding and the build-versus-buy question

Getting partners connected is one of the central capabilities OpenText offers. Smaller companies without communication gateways might connect via a web interface, while large enterprises connect directly to SAP or Oracle. Increasingly, mid-market companies are connecting via cloud ERP platforms such as NetSuite and Microsoft Dynamics.

So why not just build all of this in-house? Radko is refreshingly candid here. There is very little, he says, that clients could not do for themselves, just as they could run their own logistics or cater their own events. The real question is whether it makes business sense. For most organizations, B2B integration is not anyone's full-time job. For OpenText's business network team, it is the only job. That focus, combined with purpose-built technology like the Trading Grid platform and years of refined processes, is where the economies of scale come from.

Managing at scale

When customers are processing hundreds of thousands of data exchanges a day, things inevitably go wrong. A partner goes offline, then comes back and tries to retry thousands of pending transactions at once. Handling that without overwhelming the system requires more than connectivity and data transformation.

That is why a large share of OpenText's software investment goes into what Radko calls community management: software that monitors transaction flow, lets customers manage their partners, and surfaces problems quickly. Their TG Insights product, for example, lets customers search through millions of documents to find a specific transaction, while automatically alerting them to failures across their system.

From acceleration to transformation with AI

On AI, Radko frames the journey in two stages: it begins with acceleration and ends with transformation. Today, AI is speeding up nearly everything OpenText does, from onboarding partners and translating data to resolving issues and detecting changes across the network.

The bigger shift is still ahead. OpenText is deploying AI agents that help clients manage their communities, onboard partners faster, and resolve issues more quickly. Looking forward, Radko sees agents acting as managers of the community and a genuine first line of defense. He points to a recent capability rolled out with a major automaker, where the system detects a file failure and automatically resends the file once the underlying issue is resolved. Simple on the surface, but it requires real knowledge of what is happening across the network to know when the retry will actually succeed.

Radko believes the majority of e-commerce and B2B activity will eventually be performed by agents, a view increasingly shared across the software industry as the conversation shifts from end users to agents.

One piece of advice

Asked what he would tell organizations to do right now, Radko's answer is direct: treat partner connectivity as a platform. Consolidate as much as possible onto a single infrastructure, because every partner you connect to the network unlocks all the capabilities of that network. As agentic AI rolls out, it will work through the interfaces and APIs available across your services, so the more partners and processes you bring into one place, the more scale and momentum you can build.

In other words, the groundwork organizations lay today will determine how ready they are for an agent-driven future. The business networks that do the invisible work of commerce are about to get much more intelligent.

Matrix Commerce Tech Optimization Data to Decisions Agentic AI AI B2B ERP Enterprise Software Cloud Chief Information Officer Chief Digital Officer
On CR Conversations

Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

R "Ray" Wang
Principal Analyst and Founder
Constellation Research

R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...

Read more
Elle DeRosa
Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Read more

Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

Winning with AI isn't a technology problem. It's a leadership, culture, and strategy problem rooted in how organizations actually use data to drive change.

Key Takeaways

  • AI is a leadership test, not a tech rollout. Organizations that hand AI to IT and ask them to “find something to do with it” almost always stall at pilots. AI should serve business strategy, not sit beside it.
  • Kill your pilots. Endless departmental pilots create the illusion of progress. Winners pick a small number of high-value, cross-functional initiatives and treat them as enterprise bets.
  • Speed is the new moat. Everyone has access to similar cloud infrastructure and foundation models. The real differentiator is how fast your people, processes, and operating model can adapt.
  • AI is value reclamation, not just cost cutting. The better question isn’t “how many people can I cut” — it’s “what new capabilities and services become possible” when you 10x your team’s capacity.
  • AI fluency has four building blocks. Mindset, skill set, tool set, and decision set. Most organizations provide tools and skip the time and psychological safety needed for real fluency to take hold.
  • When everyone owns AI, no one does. Organizations need a clear AI value owner ensuring a coherent roadmap and helping every employee answer where the company is headed and what their role is in getting there.
  • The purpose of data is change — not dashboards. If data isn’t changing decisions, behaviors, or how the business runs, the expensive tech stack behind it isn’t earning its keep.
  • Three myths undermine data-driven decisions. Data is not objective, it does not speak for itself, and it rarely provides clean, definitive answers. Pretending otherwise sets organizations up for disappointment.
  • Culture determines whether data can change minds. Psychological safety, rewarding constructive dissent, and visible leadership humility matter more than any dashboard.

Part 1: AI Is Not a Tech Program. It's a Strategy and Leadership Test.

Charlene Li opens with a blunt assessment: most organizations underperform with AI because they treat it as a technology rollout, not a strategic, leadership-driven transformation.

Many organizations toss AI over to IT and ask them to go find something to do with it, instead of leading with business strategy and rethinking how decisions and workflows will actually change. Her core argument is that AI is a leadership and strategy challenge, not a tooling challenge. Leaders who abdicate AI to IT without owning the vision and value agenda almost always stall at pilots — because the real work is organizational adaptation, not just tech adoption.

One of her favorite examples of a sound approach is a strategy document that listed six traditional strategic objectives, then added a seventh: we will use AI to achieve the first six. That framing is the shift — AI should serve business strategy, not be a separate AI strategy off to the side.

Kill Your Pilots: Why “Pilot Purgatory” Destroys Momentum

Li doesn’t mince words on the way most enterprises do AI: endless pilots across departments, no real scaling, no enterprise-level value, and leaders declaring progress simply because they’re running lots of pilots.

“Kill all your pilots.”

Her prescription is to stop boasting about pilot volume — that’s just messing around — and instead pick a small number of high-value, cross-functional initiatives tied directly to strategic objectives. Treat these as enterprise bets, not departmental experiments.

She names a widespread AI hesitancy gap: on one side, excitement, FOMO, and pressure from boards, customers, and employees; on the other, fear of risk, speed of change, and uncertainty — what she jokingly labels FOGY, fear of getting in. Leaders caught in this gap become spectators rather than participants. The organizations that win are those whose leaders step into the mess, champion AI personally, and move decisively from pilots to production value.

Speed Is the New Moat: Why First Movers Win (Again)

Citing investor and AI thinker Vikram Mahidhar, Li argues that speed is the new moat in AI. Everyone can access similar cloud infrastructure and foundation models, and many enterprises are already rich in internal data. The differentiator is how fast you can adapt the organization — people, processes, governance, and operating model.

Li connects this to her long-standing work on disruption: disruptive growth is never incremental. Any outsized growth path will be messy, non-linear, and uncomfortable. AI amplifies this dynamic — it forces relational transformation, redefining roles, workflows, KPIs, and skills all at once.

Vala Afshar underscores this from his own vantage point inside a large AI-driven company, noting it is going through the messiest period in its 27-year history — not just because of the technology itself, but because of the sheer scale of reskilling, process redesign, and organizational change required.

In this environment, traditional distinctions between strategy and operations collapse. As R "Ray" Wang puts it, they are now effectively the same thing because the feedback loops are so fast.

Beyond Cost Cutting: AI as Value Reclamation and Capability Expansion

Much of the classic AI narrative focuses on efficiency and headcount reduction: do more with less, expand margins, cut jobs. Both Afshar and Li push back hard on that framing.

Li describes how many leaders initially think: if I can 10x my people, I can fire the other nine. She argues this is entirely the wrong plane of thinking. The better question is: if we can 10x ten people, how do we get 100x value? What new capabilities and services become possible?

She highlights Connect Up, a call-center company, as a case study. Instead of using AI to cut agents, they reduced error rates by 85 to 90 percent with co-pilots, shortened time-to-proficiency for new hires by 30 to 40 percent, freed agents from routine tasks to focus on higher-value engagement, and built entirely new services for clients — like analyzing non-performing loans for law firms in minutes instead of months. Importantly, they committed to increasing headcount by 5% over two years, not shrinking it.

Afshar reinforces this with his own language of value reclamation: AI can reclaim value that used to be ignored for economic or capacity reasons. By offloading low-impact, deterministic work to machines, leaders can finally invest human talent in activities they previously had no bandwidth to pursue. The mindset shift is from subtraction — efficiency only — to addition: new revenue, new experiences, new capabilities.

Building an AI-Fluent Workforce

Li outlines four building blocks for AI fluency. Mindset — curiosity, openness to change, willingness to experiment, and comfort with never being on top of everything as the space evolves. Skill set — knowing what AI can and cannot do, and the ability to frame problems in ways AI can help solve. Tool set — access to AI capabilities embedded in day-to-day workflows, with practical training on the tools employees actually use. And decision set, or governance — clear rules and processes for responsible, ethical, and sustainable usage, with guardrails that are understood and actionable, not just policy documents.

She compares AI fluency to learning chopsticks: at first it feels awkward and unnatural, but over time you stop noticing the tool and simply flow. The inflection point arrives when you start using AI to ask how you should use AI to help you do this task better.

Most organizations, she notes, provide some tools, minimal training, and almost no time to practice. Without deliberate time and psychological safety to experiment, true fluency never emerges.

The Case for an AI Value Owner

Afshar raises a structural question: should companies appoint a Chief AI Officer, or embed AI value owners within each business line?

Li’s concept of an AI value owner is less about title and more about function. Everyone in the organization owns a piece of the AI puzzle — HR owns the future workforce, GTM owns customer experience, Finance owns token and compute economics. But when everyone owns AI, no one truly does.

The AI value owner wakes up every day ensuring there is a coherent, enterprise-level AI roadmap, that functions are aligned and moving at a similar tempo — no jackrabbits sprinting ahead and no turtles dragging behind — and that the roadmap is updated frequently, at least quarterly and often continuously.

This person also helps every employee answer three questions: what future are we heading toward, what is our strategy to get from today to that future, and what is my role in making that strategy a success? In Li’s view, until leaders across the organization can clearly articulate these answers, the AI value owner has more work to do.

Part 2: From Data Frustration to Data Inspiration

In the second half of the episode, Sebastian Wernicke shifts the lens from AI back to its foundation: data and culture.

For years, leaders have been told that data is the new oil, and that big data, analytics, machine learning, and digitalization will transform everything. Yet survey results remain stubbornly consistent: nearly all leaders say data and AI are a top priority, and about two-thirds admit they are not satisfied with the value they’re actually getting.

This persistent gap breeds data frustration — we’re spending a lot but not seeing transformative impact — and data cynicism — we tried that, it never really worked for us. Wernicke argues this is not primarily a tooling issue. It is a cultural and decision-making issue.

Data's True Purpose: Change, Not Dashboards

Wernicke urges leaders to ask a simple but profound question: what is the purpose of data? The conventional answer is to generate insights we can use. His answer is more direct: the purpose of data is change — change in decisions, change in behaviors, change in how the business is run.

“If data isn't changing how you operate, you don't need the expensive tech stack, data cleaning, or analytics infrastructure you've built.”

He highlights a core misconception he calls the data deficit theory: the belief that if we just bring the right data to the right people at the right time, good decisions will automatically follow. Decades of psychology research say otherwise. When we’re deeply invested in a belief or direction, we do not naturally turn to data to disconfirm it — instead, we question the data’s quality, criticize the analyst, or rationalize the result. Without a culture of inquiry and safety, data rarely overturns strongly held assumptions.

Three Myths of Data That Undermine Decision-Making

Wernicke identifies three common myths that sit behind the dream of being data driven.

Myth one: data is objective. In reality, data is a compression of reality. Every metric — even something as simple as active customers — reflects choices about who counts as a customer, what active means, and over what time window. Those design decisions matter enormously when interpreting and acting on data.

Myth two: data speaks for itself. Even a simple question like which show is best, based on user ratings, has multiple defensible answers — the highest average rating, the most ratings overall for reliability, or the fewest worst ratings to minimize bad experiences. Each definition yields a different winner, all justified logically. Without clear definitions upfront, data-driven decisions can be steered toward any preferred outcome.

Myth three: data provides clear, definitive answers. In real analytics, you quickly leave the world of simple counts and enter statistics — probabilities, confidence intervals, likelihoods. Executives often want a binary answer, left or right, but data typically says something closer to option B is 70% likely to be better. If you’re not prepared to operate in shades of probability, data-informed decision-making becomes frustrating and underused.

These myths set organizations up for disappointment. The antidote is embracing nuance, defining terms upfront, and using data to support structured inquiry — not to rubber-stamp preconceived answers.

Creating a Culture Where Data Can Actually Change Minds

Afshar and Wernicke dive into the psychological safety and leadership behaviors needed to make data truly impactful.

Wernicke shares a simple three-question survey he gives audiences. Is your organization data driven? Most hands go up. In the last month, have you changed your mind based on data? Fewer hands. In the last month, has your manager publicly changed their stance because of new data? Almost no hands.

“People watch what leaders actually do, not what posters say.”

Several practical leadership moves emerged. First, ask junior people to speak first — Afshar cites Jeff Bezos’s practice of inviting the most junior person in the room to share their view first, reducing anchoring on the most senior voice and signaling that good ideas and strong data matter more than title.

Second, publicly model being corrected by data. Wernicke suggests leaders should actively seek out examples where data contradicts their stance, change their minds openly and visibly, and show that being wrong and adjusting is not punished, but respected.

Third, reward constructive dissent. Promotions, bonuses, and recognition should flow to people who use data to challenge assumptions productively, raise uncomfortable but important truths, and help the organization learn, not just comply. Without this, junior analysts quickly learn which kinds of numbers are career-enhancing and which are career-limiting — and they adjust accordingly.

Radical Data Integrity: Trustworthy, Not “Perfect”

On data quality, Wernicke introduces the idea of radical data integrity. Most organizations think good data means clean data — few missing values, numbers that tie neatly to the balance sheet. But for continuous, high-stakes decision-making, what matters more is trustworthiness.

Trustworthy data means you understand how it was generated, you know what it can and cannot be used for, you know who is accountable for it, and you can ask questions and get credible answers when something looks off. Cleanliness is useful, but integrity and transparency are critical if you want leaders to rely on data even when it challenges their instinct.

Most organizations struggle because they treat data as an annual spring-cleaning project: fund a one-time clean-up, declare victory, and watch entropy return over the next year. Instead, Wernicke calls for systemic and ongoing stewardship, where integrity is designed into processes and roles, not treated as a periodic initiative.

Bringing It Together: Winning with AI Because You're Data Inspired

By the end of the episode, the synergy between the two books is obvious. You can’t win with AI if you’re still frustrated with data. You can’t become data inspired without a culture of inquiry and psychological safety. And you can’t scale either without leaders owning AI as a strategic lever, not a technology project.

Charlene Li’s message: use AI to serve and accelerate your business strategy. Move beyond pilots, embrace speed, and invest in organizational readiness across mindset, skill set, tool set, and decision set.

Sebastian Wernicke’s message: make data’s purpose explicit — to drive change. Debunk the myths of objectivity and easy answers, build radical data integrity, and foster a culture where it’s safe, and expected, to let data challenge your beliefs.

Final Thoughts

DisrupTV Episode 443 makes an argument that cuts against a lot of conventional AI messaging: the hardest part of winning with AI was never the technology. It’s whether leaders are willing to do the harder, messier work of organizational change — owning strategy personally, killing the pilots that feel safe but go nowhere, and building a culture where data is trusted enough to actually change minds.

Charlene Li and Sebastian Wernicke approach the same problem from opposite ends and arrive in the same place. Li starts with AI and works backward to the organizational readiness required to use it well. Wernicke starts with data and works forward to the cultural conditions required to let it matter. Both conclude that the technology was never really the constraint — leadership, trust, and culture are.

For organizations willing to do this hard, human work — clarifying strategy, appointing real ownership, investing in fluency, and building a culture where being wrong in public is safe — AI stops being another wave of tech hype and becomes a genuine engine for transformation.

“Winning with AI starts with becoming data inspired — and that starts with leadership, not technology.”

Related Episodes

If you found Episode 443 valuable, here are a few others that align in theme or extend similar conversations:

Future of Work AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

From “activation energy” and agent orchestration to donkeycorns and relationship capital, DisrupTV 425 explains what actually separates AI hype from real business impact.

On DisrupTV <iframe width="560" height="315" src="https://www.youtube.com/embed/J5CupyHoVng?si=GHB_W8FIfV0UhJBW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
Subscribe to