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Why AI Pilots Fail, Why 2026 Matters, and How Entrepreneurs Win in the Age of Agents | DisrupTV Ep. 425

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… 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

Why AI Pilots Fail, Why 2026 Matters, and How Entrepreneurs Win in the Age of Agents

On DisrupTV Episode 425, co-hosts Vala Afshar, Chief Evangelist at Salesforce, and R “Ray” Wang, CEO and Founder of Constellation Research, tackled one of the most urgent questions facing leaders today:

Why does so much AI promise fail to turn into real business results—and what changes next?

Joining them were two voices with very different but highly complementary perspectives on the AI transition:

  • Vernon Keenan, founder of Keenan Vision and longtime industry analyst, known for his work advising enterprises and hyperscalers on AI strategy.

  • Nicholas Thorne, co-author of Me, My Customer, and AI and founder of Autos, focused on how AI is reshaping entrepreneurship, venture creation, and small business economics.

Together, the conversation moved beyond surface-level AI hype to unpack why 95% of GenAI pilots are labeled “failures,” what actually blocks adoption, and how AI is quietly reshaping jobs, consulting, and company creation—often without dramatic headlines.

Why 95% of GenAI Pilots “Fail” (And Why That’s Misleading)

A widely cited MIT statistic claims that 95% of enterprise GenAI pilots fail. According to Vernon Keenan, this number obscures more than it reveals.

Keenan challenged the methodology behind the study, noting its overreliance on balance sheets and survey instruments that fail to capture what’s happening inside organizations. His own research at UC Berkeley Haas, based on interviews with roughly 45 ecosystem participants, surfaced a different root cause:

The Real Problem: Activation Energy

Most enterprises underestimate the work required after deploying an LLM. Simply embedding a model into a chat interface doesn’t create value.

Enterprise AI success requires:

  • Orchestration patterns – agents coordinating tasks across systems, not just responding to prompts

  • Context assembly – harmonizing data across CRM, ERP, finance, support, and operations

  • Agentic processes – AI acting inside real workflows, not alongside them

The models aren’t the bottleneck. System design, integration, and organizational will are.

2026: The Year AI Gets Real (But Diffusion Still Lags)

Keenan described 2026 as “the year AI gets real”—not because the technology suddenly appears, but because economic pressure finally forces adoption.

Key dynamics shaping this next phase:

  • AI agents are already real and generating serious ARR at startups and AI-native vendors

  • Diffusion remains uneven, especially across the mid-market and small businesses

  • Enterprises are still learning how to operationalize agents at scale

Over the next five years, Keenan expects a full transition into what he calls the “age of the agent,” where embedded and overlay solutions unlock a new model of growth.

Once optimized, agents can be replicated infinitely at near-zero marginal cost—introducing virtual employee economics and forcing executives to rethink growth, productivity, and headcount entirely.

Quiet Erosion: How AI Reshapes Jobs Without Headlines

Rather than mass layoffs, Keenan warned of “quiet erosion”—the gradual hollowing out of entry-level and cognitive work.

Examples include:

  • Cognitive commoditization, where much of what MBAs and junior consultants know now lives inside LLMs

  • AI-first boutiques replacing traditional consulting leverage models

  • Small teams using agents to do the work of dozens

The real risk isn’t losing your job to AI—it’s losing your company to competitors who adopt AI faster and more effectively.

Tiny Teams, Donkeycorns, and a Million AI-Powered Businesses

Where Keenan focused on enterprise friction, Nicholas Thorne focused on what AI makes newly possible.

AI dramatically lowers the cost of starting a company—but it also raises the bar for differentiation.

Through his company Autos, Thorne uses agent orchestration to help founders:

  • Generate landing pages, videos, and lightweight apps

  • Set up CRM, lifecycle emails, and ad tests

  • Handle payments and operational workflows

His ambition is bold but grounded: enable one million people to build $1M/year businesses, which he calls “donkeycorns”—small, focused, profitable companies that grind like mules and party like unicorns.

Relationship Capital: The Only Durable Advantage Left

In a world where everyone has access to the same models, Thorne argued that relationship capital becomes the real moat.

Winning companies will:

  • Define themselves by who they serve, not just what they build

  • Maintain deep, continuous feedback loops with early customers

  • Use customer insight to prompt, iterate, and evolve faster than competitors

Your rate of innovation isn’t constrained by the model—it’s constrained by how well you understand your customers.

Key Takeaways from DisrupTV Episode 425

  • AI pilots fail due to lack of activation energy, not bad models

  • Orchestration and context matter more than raw AI capability

  • 2026 marks the start of a multi-year “age of the agent”

  • AI erodes jobs quietly through productivity, not mass layoffs

  • Tiny teams can now compete with legacy firms using agent leverage

  • Relationship capital is the most defensible asset in an AI-saturated market

Final Thoughts: AI as Electricity, Not Experimentation

Across enterprises, startups, and boardrooms alike, the message from DisrupTV 425 was clear:

  • AI is no longer a proof of concept

  • Leaders now demand outcomes, not demos

  • Agents, orchestration, and customer intimacy define winners

Like electricity before it, AI becomes invisible once it’s essential. Organizations that master activation energy, agent-driven workflows, and relationship-led innovation won’t just survive quiet erosion—they’ll define the next era of work and entrepreneurship.

Related Episodes

If you found Episode 425 valuable, here are a few others that align in theme or extend similar conversations:

 

Future of Work Data to Decisions Tech Optimization Chief Executive Officer Chief Technology Officer Chief AI Officer CIO CDO

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>

Memory price surge may crimp on-premise AI, data center deployments

Larry Dignan
Editor in Chief of Constellation Insights Constellation Research
About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

On-premise enterprise AI deployments and any hardware upgrades for servers, PCs and other devices in the works are going to become more expensive as memory prices surge.

Memory prices have been a concern for the technology industry for months. Trendforce is projecting 80% increases in PC and server DRAM. IDC said the memory shortage will affect smartphones and other electronic devices. In addition, enterprise vendors have already said they plan on passing along memory costs to technology buyers. You can thank the AI infrastructure boom for the memory squeeze.

Simply put, prices everywhere are going to go up for hardware and that reality is likely to crimp on-premises enterprise deployments and any tech purchase. For instance, Micron Technology already has agreements on price and volume for everything it can produce in 2026.

Micron CEO Sanjay Mehrotra said:

“Memory is now essential to AI's cognitive functions, fundamentally altering its role from a system component to a strategic asset that dictates product performance from data center to the edge. This structural shift means that system capabilities heavily rely on advanced memory for real-time contextual processing, which is vital for achieving autonomous and intelligent behaviors in AI data centers as well as in applications ranging from self-driving cars to advanced medical diagnostics.”

Pure Storage’s John Colgrove, Founder, Chief Visionary Officer & Director, said memory is an issue. Colgrove said that rising NAND prices will make it harder for enterprises to buy data center gear and he expects more interest in Pure Storage’s subscription plans. “I think as prices continue to go up on NAND as it becomes harder and harder to obtain new gear. I think that tends to move people more towards Evergreen/One,” said Colgrove, referring to Pure Storage’s subscription plan.

In December, Dell Technologies Jeff Clarke, chief operating officer, said the memory supply and demand mismatch is a big issue. Speaking at an investment conference, Clarke said:

“I've been at this for almost four decades. This is the most unprecedented mismatch in demand and supply that I've ever seen in the memory industry, which you're seeing correspondingly reflected in spot price. But there's an equal component is the material going to be available. We're out with our long-term agreements, our partnerships that we've had for many, many years. I've grown up in this industry, know most of those CEOs for a long period of time, and those relationships matter in these times.”

Clarke also said that these higher memory prices are ultimately pass along to the buyer. He said consumers will be more sensitive to the price increases, but enterprises will buy at market rates to solve a problem.

HPE CFO Marie Myers said at an investment conference in December that increased memory costs will be passed on. “Both DRAM and NAND are sort of leading (in price increases). A lot of this has been driven by just the tremendous demand pressure that's been out there with AI. And the last couple of months, I think we've all witnessed some pretty significant price changes in both NAND and DRAM,” said Myers. “We expect to pass on a lot of these increases in commodity costs to customers.”

Taiwan Semi CEO C.C. Wei said on the company’s earnings call that unit growth will be minimal for PCs and smartphones, but his company is benefiting from the AI infrastructure boom too. The takeaway is to stick with the high-end hardware. Taiwan Semi works with more high-end vendors and “the high-end smartphone is less sensitive to the memory price. So, the demand is still strong,” said Wei.

Bottom line: Hardware upgrades are likely to become more expensive for a wide swath of technology buyers.

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What's Agentic Commerce Without Infrastructure? (Distillation Aftershots)

Media Name: aftershots.png
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
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Welcome to a new edition of The Board: Distillation Aftershots (*).

This newsletter shares curious and interesting insights and data points distilled from enterprise technology to identify what’s notable.

In this issue, we have to talk about agentic commerce, given how much it was discussed at NRF this past week. Have to… even if we did a newsletter on it before (11/23 issue). The last time we addressed it was an intro to the topic; this time, we have more in-depth links for you to read.  First, my take. 

I said in the previous newsletter:

I have forever said commerce (or e-commerce as we called it originally) was not supposed to be an application.”

 

And that is also applicable to agentic commerce: it cannot function independently, it needs a strong infrastructure to support it, and that’s where vendors must provide value.  And it turns out most of them agree with that (and that drives me crazy) by releasing open-source, open-API, and easy-to-use resources as part of their agentic commerce offerings. Not a single vendor, as far as I can find, has said they are the only solution for agentic commerce; they all say, “We are part of the solution, check out how we integrate.”

During NRF (the National Retail Federation show in NYC happening every January), technology vendors went crazy to highlight how they are addressing agentic commerce, and how it is the latest (and most likely) way we will shop in the future.  Before committing to this vision, remember we said (and attempted) the same about social commerce. And I'm sure there were a few other smaller versions (metaverse, anyone?) of the same that bubbled up at NRF over the years. I am not demeaning the potential value of agentic commerce; I am saying there’s precedence for thinking the latest and greatest technology will replace previous iterations, when in reality it simply adds to the overall, integrated, not-a-standalone-solution model that enterprises run.

And so, the value of agentic commerce, as with past versions of x-commerce, is that by extending the ability to purchase via different interfaces, we extend the availability of the product to consumers.  Better ways to get to the product, faster ways to check out, an easier way to choose, pay, expect, and receive.  This will never change, even if brain-computer interfaces become the standard model for shopping.  Enterprises must ensure their commerce solutions are well-oiled, open, and easy to integrate into an agentic interface.  That’s the secret.  Always has, always will be: tune up your commerce infrastructure to be a platform for building an Agentic Commerce Ecosystem.

Here are some reading resources:

  1. Walmart, which is proving to be an avant-garde enterprise in AI adoption, announced it is integrating its shopping experience into Gemini. If you are not in Gemini, your commerce system may collapse to Walmart’s ambition. Our own Larry Dignan wrote about it in more detail. 
  2. Speaking of Gemini, Google puts its weight behind extending its AI solutions to accommodate all commerce platforms. As they say, “we are combining the best of Google Cloud's AI and infrastructure with a business's own institutional intelligence to power a truly agentic commerce journey” – it is about the customer journey. 
  3. While searching for standards, I came across the Agentic Commerce Protocol.  I have not seen it being adopted, nor do I think it’s necessary to be honest, but if you are seeking a way to integrate your commerce ecosystem with all frontier models and partners, it’s a place to start.
  4. A LinkedIn post from a vendor (read: take it with a grain of salt) claims a 54% higher conversion rate for agentic commerce versus traditional commerce, along with some ungodly explosive year-over-year growth rate for a solution that has been operating less than a year (here is where the grain of salt comes in handy). Also on LinkedIn: agentic payments are necessary for agentic commerce (hmm… maybe, more on this later).
  5. Before NRF, Bloomberg says ChatGPT enables connections with Spotify, Zillow, and Stripe(to address those LinkedIn comments). Published September-October 2025.
  6. And finally, a BCG research report that tells enterprises how to approach agentic commerce: with caution and consideration. Published October 2025.

What’s your take? We are fostering a community of executives who want to discuss these issues in depth. This newsletter is but a part of it. We welcome your feedback and look forward to engaging in these conversations.

If you are interested in exploring the full report, discussing the Board’s offering further, or have any additional questions, please contact me at [email protected], and I will be happy to connect with you.

(*) A normal distillation process produces byproducts: primary, simple ones called foreshots, and secondary, more complex and nuanced ones called aftershots. This newsletter highlights remnants from the distillation process, the “cutting room floor” elements, and shares insights to complement the monthly report.

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Agentic AI experiences will determine enterprise winners

Larry Dignan
Editor in Chief of Constellation Insights Constellation Research
About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Agentic AI to date has revolved around architecture, standards, use cases, process automation and productivity, but customer experiences and visions of reinventing categories are moving to the forefront.

Simply put, the agentic AI winners on both the buy side and sell side will likely be decided by customer experiences. This CX-meets-AI theme is already emerging on the consumer side. In recent weeks, we've seen the following:

  • Google said Gemini is getting personal with Personal Intelligence, a setting that connects your personal Gmail, YouTube, Google Photos and other apps to Gemini. It's an interesting move that highlights how AI agents are going to create personalized experiences.
  • OpenAI is planning on being a healthcare industry AI player with the launch of OpenAI for Healthcare, a HIPAA-compliant version of ChatGPT for clinicians, just days after debuting ChatGPT Health for consumers. The upshot here is that ChatGPT wants to be your healthcare front door. OpenAI also outlined its 2026 focus on practical AI.
  • Anthropic also launched Claude for Healthcare, which aims to be "a complementary set of tools and resources that allow healthcare providers, payers, and consumers to use Claude for medical purposes through HIPAA-ready products." In addition, Anthropic launched Claude Cowork, which is a research preview that enables you to bring agentic tools to the desktop.

A card carrying cynic (guilty as charged) would note that these efforts are really about stepping in front of value chains to be the gatekeeper. If Google, OpenAI and Anthropic platforms become the primary distribution platform, they'll reap the AI rewards. But the real game in the enterprise trenches will revolve around agentic experience.

At three conferences this week (two investment meetups and National Retail Federation), the CX end of the agentic AI equation started to surface for the enterprise. Retailers talked about integrating AI agents and vendors pitched multiple approaches. Google launched Universal Commerce Protocol (UCP), a standard for agentic AI commerce, checkout directly through AI mode along with direct offers and interactions to connect brands and shoppers. Google Cloud also unveiled Gemini Enterprise for Customer Experience, a suite that includes a shopping agent, customer experience agent studio, Vertex AI powered search and food ordering agent.

Other vendors also lined up a parade of AI agent announcements.

The real agentic AI magic will occur when the customer is delighted because an agent just gets you. This vision was outlined by Walmart's Daniel Danker, Executive Vice President of AI Acceleration, Product & Design. Speaking at the ICR Conference, Danker said Walmart isn't worried about being disintermediated. Its partnerships with OpenAI and Google are about growth.

The future is Walmart's agent working with other agents to provide a seamless customer journey. This agentic shopping journey will be practical, friction free and helpful. Walmart is positioning itself as the execution and fulfilment backbone of AI discovery and commerce.

"We understand customers so well that we know that they're probably running out of laundry detergent. It's also recognizing that because they tend to buy a gallon of milk, they're probably in a 4-person household, so they're probably going through laundry detergent at a certain pace. And so, we know the right moment to recommend that laundry detergent. Agentic AI will then just go ahead and send you the laundry detergent before you even run out of it. So that's the promise. We're on a journey toward it. We're not quite there yet, but we're moving fast," explained Danker.

Danker noted that consumers will always love shopping--it's a sport to many--but agentic AI can automate the mundane to improve experiences.

Walmart isn't the only giant thinking through agentic commerce experiences. Albertsons CEO Susan Morris said the company is partnering with the likes of Google, OpenAI and Databricks to ensure "every decision is smarter, every process is more efficient and every interaction is more seamless."

Morris said the biggest bet for agentic AI is digital customer experiences. "Our autonomous shopping assistants are meeting customers where they are and delivering frictionless personalized journeys, keeping our omnichannel customer experience modernized and on trend," said Morris.

Agentic CX will also move beyond retail. Healthcare is a major focus for both vendors and enterprises. Salesforce's Mark Sullivan, President of Salesforce, said at the JP Morgan Healthcare Conference that AI agents can become a healthcare concierge. He noted CVS, which recently outlined its engagement vision, and AstraZeneca were Agentforce customers.

CVS Health CEO David Joyner has said the company is focused on "building a simpler, more connected and more affordable health care experience for consumers, health care professionals, and payors." The goal is to be the "front door to healthcare."

Sullivan laid out how agentic AI can deliver care. "Finding care providers is more difficult than ever, and we think this can solve that problem. And then we think of any experience that you have with any procedure you might have, the prep, the post, the follow-up, we all depend on our spouses to do those things. And that's not necessarily the right way to do it. Why? Think of the old medicine where your doctor might call you before, make sure you did all the things that you need to do before your procedure and might follow up after to make sure you took the right medicine and didn't find your way back to the emergency room," explained Sullivan. "We think agents can do that. And we think we can do that better than anything that's ever been rendered before. And we think that keeps people out of the emergency room. We think that changes the health care economy in a meaningful way."

Veeva CFO Brian Van Wagener said the company is rolling out AI agents across its product line to better deliver care. Veeva is a top CRM provider for healthcare and life sciences companies. "We think the combination of those two things, AI in the platform and deep industry-specific agents is going to be transformational for the long term for our customers and is a place where we can bring a lot of value," said Van Wagener.

Michael Farrell, CEO of Resmed, said the theme of an AI-driven health concierge is critical for its core value, which sits at the intersection of health and sleep. "We also have a non-FDA-cleared product because it's just a concierge that I call our digital sleep health concierge. It's called Dawn. Not as in a woman, but as in the sun rising over the horizons that you wake up refreshed. And this is a digital sleep health concierge to help you with questions about exercise, drinking water, balancing your day life to have a better sleep life, stopping caffeine after sort of 5 clock, not having alcohol after 8," said Farrell.

 

The customer experience in industrial markets is different than retail or healthcare, but the role of agentic AI as an enabler is the same. Caterpillar launched its Cat AI Assistant and primary objective is to make it easier for customers to buy, maintain, manage and operate equipment. Speaking at CES 2026, Caterpillar Chief Digital Officer Ogi Redzic said "for a customer Cat AI Assistant is like a proactive partner. It flags machines that need attention, provides custom insights and makes actionable recommendations."

Under the hood, Cat AI Assistant "is a group of AI agents operating together on top of our digital ecosystem presented as a single, single assistant that's multimodal," said Redzic.

In finance, this agentic-AI-meets-CX theme is also powerful but has more of an employee spin. Bank of New York CEO Robin Vince said the company is focused on AI agents and digital workers that are primarily focused on its employee experience.

Google Cloud said in December that Gemini Enterprise is integrated into Bank of New York's Eliza AI platform. Robin Vince, President and CEO of Bank of New York, said agentic AI transforms the culture.

"We now have an enterprise AI platform, Eliza, that's general intelligence, model-agnostic and it supports this multi-agentic functionality that underpins the digital employees. Then we've put in place the resources to support that to really enable the scaling of it," said Vince. "We've got our own Nvidia hardware and tech, but we've also got the collaborations with Google, OpenAI and others. We're proud that in 2025 alone, we deployed over 130 digital employees, industry-leading multi-agentic AI capabilities. Our digital employees work alongside our people, supporting them with tasks like validating payment details and remediating code vulnerabilities, allowing teams to focus on higher value work and client outcomes."

Citigroup CEO Jane Frazer said the company's AI efforts have transformed operations and now it is focusing on client experiences. "We are shifting our focus to how we can use AI tools and automation to further innovate, reengineer and simplify our processes beyond risk and controls to improve client experience whilst reducing expenses," she said.

Data to Decisions Future of Work Next-Generation Customer Experience Innovation & Product-led Growth Tech Optimization Digital Safety, Privacy & Cybersecurity ML Machine Learning LLMs Agentic AI Generative AI Robotics AI Analytics Automation Quantum Computing Cloud Digital Transformation Disruptive Technology Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain Leadership VR Chief Customer Officer Chief Executive Officer Chief Information Officer Chief Marketing Officer Chief Revenue Officer Chief Technology Officer CEO CIO CTO CAIO Chief AI Officer CDAO Chief Data Officer CAO Chief Analytics Officer CISO Chief Information Security Officer CPO Chief Product Officer

ServiceNow expands OpenAI partnership

Larry Dignan
Editor in Chief of Constellation Insights Constellation Research
About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

ServiceNow said that it has expanded its multi-year partnership with OpenAI in a move that will revolve around computer-use automation, expanded AI tools and workflows using technologies from both companies and native voice interactions.

The partnership between ServiceNow and OpenAI was announced shortly after OpenAI outlined its 2026 focus on practical AI.

According to ServiceNow and OpenAI, the partnership will involve a hefty dose of co-innovation to deliver enterprise value. Under the terms of the deal:

  • OpenAI technical advisors and ServiceNow engineers will deliver custom enterprise AI applications without bespoke development.
  • ServiceNow will build direct speech-to-speech technology using OpenAI models. ServiceNow said it will work with OpenAI to deliver AI agents that can reason and respond without text use.
  • ServiceNow will use OpenAI models including GPT-5.2 with plans to use computer use models to deliver automation within the Now Platform.

Amit Zavery, president, chief operating officer, and chief product officer at ServiceNow, said in a statement that the partnership with OpenAI is designed to build unique AI experiences. "With OpenAI, ServiceNow is building the future of AI experiences: deploying AI that takes end-to-end action in complex enterprise environments," said Zavery.

For OpenAI, the win is getting its frontier models embedded into ServiceNow workflows.

Related:

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OpenAI’s 2026 focus on practical AI points to enterprise

Larry Dignan
Editor in Chief of Constellation Insights Constellation Research
About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

OpenAI has $20 billion in annual recurring revenue, but perhaps the bigger news is that the company's 2026 focus revolves around "practical adoption" in health, science and enterprise.

Sarah Friar, OpenAI's CFO, said in a blog post outlining a bit of the company's financial picture and infrastructure commitments.

"Our focus for 2026: practical adoption. The priority is closing the gap between what AI now makes possible and how people, companies, and countries are using it day to day. The opportunity is large and immediate, especially in health, science, and enterprise, where better intelligence translates directly into better outcomes."

Friar's missive is worth noting for the enterprise, which primarily sees Anthropic as the practical enterprise AI adoption play. Perhaps, there's a larger takeaway for software as a service vendors. The theory on Wall Street is that SaaS will be disrupted by AI agents and some of these vendors won't make the cut. For now, investors are voting with their dollars as enterprise software stocks are taking a hit early in 2026.

OpenAI can become "an operating layer for knowledge work," said Friar.

This disruption will take time to play out, but it's clear OpenAI sees itself as a leader going forward with a focus on AI agents and workflow automation to manage projects, coordinate plans and execute tasks.

Friar said AI's biggest constraint is infrastructure and OpenAI isn't shy about multi-year commitments. Friar's argument is that revenue goes along with computing power. The more compute, OpenAI has the faster it will grow. OpenAI's Friar noted that there will be new business models ahead.

"The business model closes the loop. We began with subscriptions. Today we operate a multi-tier system that includes consumer and team subscriptions, a free ad- and commerce-supported tier that drives broad adoption, and usage-based APIs tied to production workloads. Where this goes next will extend beyond what we already sell. As intelligence moves into scientific research, drug discovery, energy systems, and financial modeling, new economic models will emerge. Licensing, IP-based agreements, and outcome-based pricing will share in the value created. That is how the internet evolved. Intelligence will follow the same path."

Friar makes an interesting point, but outcome- and value-based pricing usually appears to vendors. Customers? Not so much.

 

OpenAI's post was making the case that the company's spending on infrastructure is disciplined with capital committed in tranches against "real demand signals."

According to Friar, OpenAI's business model will catch up to infrastructure with multiple revenue streams including commerce, ads, subscriptions and APIs.

Friar said compute grew 3X year over year from 0.2 GW in 2023 to 0.6 GW in 2024 to about 1.9 GW in 2025. Revenue in that time grew at the same clip with $2 billion in ARR in 2023, $6 billion in 2024 and more than $20 billion in 2025.

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DisrupTV Special Edition at Davos 2026: AI, Geopolitics, and the Race to Build Trust at Global Scale

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,&nbsp;Everybody Wants to Rule The World&nbsp;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… Read more
Elle DeRosa
Director of Marketing, Communications & Operations Constellation Research
Elle is the Director of&nbsp;Marketing&nbsp;at Constellation Research and producer of the weekly enterprise tech&nbsp;show,&nbsp;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,&nbsp;S50&nbsp;and&nbsp;ShortLists.&nbsp;She has previous experience in public relations and has a strong passion for the marketing world.&nbsp; &nbsp; Contact Elle at [email protected] Read more

DisrupTV Special Edition at Davos 2026: AI, Geopolitics, and the Race to Build Trust at Global Scale

Broadcast live from Davos during the World Economic Forum 2026, this special edition of DisrupTV brought together global leaders, technologists, and strategists to unpack one urgent question: How do we lead, govern, and collaborate in an AI-driven world defined by geopolitical uncertainty?

Co-hosted by Vala Afshar, Chief Digital Evangelist at Salesforce, and R “Ray” Wang, CEO and Founder of Constellation Research, the episode explored AI’s expanding role across healthcare, enterprise operations, national competitiveness, and global cooperation. Guests included Mark Minivich, Christian Limark, Dr. Travis Oliphant, Sandy Carter, and Jim Harris, each offering a distinct perspective on how AI is reshaping outcomes—and expectations—across industries.

Davos 2026: From Dialogue to Action

R "Ray" Wang opened the session by framing Davos 2026 around a clear mandate: fostering cooperation, broadening perspectives, and solving shared global challenges. Unlike previous years, this Davos carried a heightened sense of urgency—driven by geopolitical tensions, economic realignment, and the accelerating impact of artificial intelligence on GDP, labor, and national security.

Vala Afshar highlighted that while AI dominated conversations across Davos, the real differentiator was how leaders talked about trust, ethics, and implementation, not just innovation.

Geopolitics, Resilience, and AI as Economic Infrastructure

Mark Minivich, President of Going Global Ventures, emphasized that AI is no longer a future differentiator—it is fast becoming economic infrastructure. Nations that fail to integrate AI responsibly risk falling behind in productivity, resilience, and competitiveness.

Minivich noted that compared to prior years, Davos 2026 reflected sharper geopolitical realities:

  • Rising fragmentation between global power blocs

  • Increased focus on resilience over efficiency

  • The urgent need for public-private collaboration on reskilling and workforce readiness

As AI reshapes industries, governments and enterprises alike must rethink how they prepare workers—not just for new jobs, but for continuous adaptation.

AI in Healthcare: From Experimentation to Clinical Impact

Healthcare emerged as one of the most compelling domains for applied AI.

Christian Limark, CTO at Stanford Healthcare and Stanford School of Medicine, shared how Stanford is integrating AI directly into clinical workflows—not as experimental tools, but as decision-support systems embedded in daily practice. Stanford’s AI-powered chat and EHR platforms are helping clinicians:

  • Improve diagnostic decision-making

  • Reduce cognitive load

  • Deliver faster, more consistent patient care

Crucially, Limark stressed that AI succeeds in healthcare only when it augments human judgment—not replaces it.

Trust, Open Source, and Data Sovereignty in the Age of AI

Dr. Travis Oliphant, founder and chief architect of major open-source AI frameworks, underscored that trust is the currency of AI adoption. As organizations deploy AI at scale, questions of data sovereignty, transparency, and governance become existential.

Oliphant argued that open-source communities play a critical role in:

  • Enabling sovereign AI systems

  • Preventing over-reliance on opaque black-box models

  • Allowing nations and enterprises to maintain control over data and outcomes

Without trust, even the most powerful AI systems will face resistance—from regulators, workers, and citizens alike.

Ethical AI and the Reality Check Leaders Need

Sandy Carter, bestselling author of AI First and Unstoppable, brought a sobering reality check to the conversation: only 15% of the world’s information has been digitized.

This gap has major implications. Leaders often overestimate AI’s completeness while underestimating bias, data gaps, and ethical risks. Carter emphasized that ethical AI requires:

  • A clear understanding of AI’s limitations

  • Intentional governance and guardrails

  • Diverse voices shaping AI systems

At Davos, she also spotlighted the importance of inclusive leadership, hosting sessions on ethical AI and the “Unstoppable Women of Web3 and AI.”

Reinventing Business Processes with AI

Closing the episode, Jim Harris shared a powerful enterprise example from Ernst & Young, where AI transformed a 46-hour risk management process into a 15-minute workflow for new users.

The key lesson? AI value doesn’t come from automating broken processes—it comes from reimagining them entirely. Harris advocated for a “blank first” mindset:

  • Start with outcomes, not existing workflows

  • Design AI-native processes

  • Measure impact in speed, accuracy, and decision quality

Key Takeaways

  • AI is now geopolitical infrastructure, influencing GDP, competitiveness, and national resilience

  • Trust, transparency, and data sovereignty are foundational to AI adoption

  • Healthcare AI is moving from pilots to production, with real clinical impact

  • Ethical AI requires humility, especially given how little data is truly digitized

  • The biggest AI wins come from reinventing processes, not automating the past

Final Thoughts: Leadership in an Era of Converging Uncertainty

The DisrupTV Davos 2026 Special Edition made one thing clear: the future of AI will be shaped less by algorithms—and more by leadership choices.

As R "Ray" Wang noted, this is not a moment for incremental thinking. Leaders must balance innovation with responsibility, speed with trust, and ambition with cooperation. In a world where AI, geopolitics, and human systems converge, those who lead with intention will define what comes next.

Stay tuned for more DisrupTV insights from the world’s most influential conversations—where technology, leadership, and humanity intersect.

Related Episodes

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

 

Data to Decisions Tech Optimization Digital Safety, Privacy & Cybersecurity Future of Work Chief Executive Officer Chief Information Security Officer Chief Technology Officer Chief AI Officer CIO CDO

From healthcare to geopolitics, DisrupTV goes live from Davos to explore how AI, trust, and cooperation will shape the global economy.

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>

From Boardroom to Operations: Building Human-Machine Partnerships That Balance Speed with Wisdom

Media Name: pexels-olliecraig1-30131130.jpg
Dr. David Bray
Distinguished Chair of the Accelerator & Principal/CEO Stimson Center & LeadDoAdapt Ventures
Dr. David A. Bray is a Distinguished Fellow and Chair of the Accelerator with the Alfred Lee Loomis Innovation Council at the non-partisan Henry L. Stimson Center. He is also a CEO and transformation leader for different “under the radar” tech and data ventures seeking to get started in novel situations and an elected Fellow with the National Academies of Public Administration in addition to a lifetime member of the Senior Executives Association. He is Principal at LeadDoAdapt Ventures and has served in a variety of leadership roles in turbulent environments, including bioterrorism preparedness and response from 2000-2005. Dr. Bray previously was the Executive Director for the bipartisan National Commission for the Review of the Research and Development Programs of the U.S. Intelligence… Read more
1

In Part 1 of this 2026 Boardroom Decision series, I highlighted why corporate Boards must expand their fiduciary duty to encompass gray zone threats, hardware-level vulnerabilities, and the strategic asymmetries created by fragmented AI policies. I argued that traditional risk management frameworks are insufficient when facing adaptive, intelligent adversaries in rapidly changing environments. The question I left you with was this: how do we build resilient organizations that can thrive in a contested world while preserving human agency and ethical judgment?

The answer lies in operationalizing what I call decision elasticity through AI-augmented defense and human-machine partnerships. This is not about choosing between human judgment and machine speed. It is about architecting systems where both work together, each amplifying the other's strengths while compensating for weaknesses.

 

Why Cybersecurity and AI Budgets Must Rise Together

In a recent DisrupTV episode with friends Ray Wang and Vala Afshar, I joined Andre Pienaar, CEO of C5 Capital, to discuss leadership in the age of AI-driven cyber threats. Andre opened with a stark reality that every Board must internalize namely that cyberattacks are becoming more sophisticated, faster, and increasingly AI-driven. Threat actors no longer operate with manual tools. They are deploying automation, machine learning, and increasingly autonomous systems to exploit vulnerabilities at scale.

For Boards and executives, this means a fundamental shift in investment strategy. You cannot increase AI adoption without simultaneously increasing cybersecurity investment. Andre emphasized that AI expands the attack surface just as much as AI enhances productivity. Organizations deploying AI without upgrading security architectures are effectively widening the door for adversaries.

This connects directly to the gray zone threats I discussed in Part 1 of this series. Specifically, when nation-state actors spend 15 years systematically positioning themselves in your supply chain, they are not just waiting for you to deploy technological upgrades. They are counting on it.

Every new AI system, every cloud migration, every edge computing deployment creates new entry points if security is not architected from the ground up.

 

AI-Augmented Defense: Humans and Machines, Together

During the DisrupTV conversation, I reinforced a principle that has guided my work from the CDC to the U.S. Intelligence Community to the FCC to my current advisory roles: AI alone is not the solution, but neither are humans operating without it. Cybersecurity success depends on augmented intelligence, where AI detects patterns of life and anomalies at machine speed while humans provide the essential context, judgment, and ethical oversight.

I highlighted a sobering trend: ransom demands are increasing sharply, and AI-enabled attacks are lowering the cost and effort for bad actors. Defenders must respond with equal sophistication. The future of cybersecurity is not humans versus machines. The future is humans with machines.

This humans-with-machines partnership reflects what I call deployment empathy, the recognition that technological transformation is fundamentally about people. Leaders must create environments where teams feel psychologically safe to experiment, learn, and adapt alongside AI systems rather than feeling threatened by them.

Let me be concrete about what this looks like operationally. In my work advising organizations on AI adoption, I see three common failure modes that Boards must help their executive teams avoid.

Failure Mode 1: Treating AI as a Black Box. When security teams deploy AI-driven threat detection but cannot explain how the system reached its conclusions, they create two problems. First, they cannot improve the system because they do not understand its reasoning. Second, they cannot defend their decisions to regulators, customers, or juries when things go wrong. Boards must insist on explainable AI in security-critical applications.

Failure Mode 2: Over-Automating Decision-Making. Some organizations, in their enthusiasm for AI efficiency, automate responses to detected threats without human oversight. This creates catastrophic risks when AI systems misidentify legitimate activity as malicious or when adversaries learn to game the automated responses. Decision elasticity requires keeping humans in the loop for consequential decisions, even if AI provides the initial alert and analysis.

Failure Mode 3: Under-Investing in Human Capability. The most sophisticated AI security tools are useless if your team does not have the skills to interpret their outputs, tune their parameters, or integrate their insights into broader strategic context. Boards must ensure that AI investments are matched by investments in human capability development, not just technical training but also the critical thinking and ethical reasoning skills that machines cannot replicate.

 

Quantum Computing, Geopolitics, and Shortening Supply Chains

The DisrupTV discussion also explored the geopolitical implications of AI and quantum computing. Andre and I both stressed that quantum breakthroughs will eventually render today's encryption obsolete, making post-quantum cryptography a near-term planning requirement, not a distant concern.

This is where the hardware vulnerabilities I discussed in Part 1 of this 2026 Boardroom Decision Series become even more critical. If your supply chain is already compromised at the chip level, the transition to post-quantum cryptography will not save you. Adversaries with hardware-level access can simply intercept data before it is encrypted or after it is decrypted, rendering even quantum-resistant algorithms useless.

My recommendation to Boards is clear: shorten your supply chains to improve cybersecurity. This is not just about reshoring manufacturing, though that may be part of the solution. It is about reducing the number of handoffs, intermediaries, and black-box components between your organization and the foundational hardware and software you depend on. Every link in the supply chain is a potential compromise point. The shorter and more transparent the chain, the easier it is to verify integrity.

At the same time, AI policy and regulations are fragmenting globally.

I posited during the DisrupTV conversation that leaders must understand which geopolitical "technology matrix" they are operating within. To maintain resilience, organizations must be able to pivot as that matrix shifts.

 

Key Strategic Takeaways for Operational Leadership

Building on the Board-level governance principles from Part 1, here are the operational imperatives that CEOs, CISOs, and General Counsel must execute:

Harmonize Governance Internally Before External Mandates Force Your Hand. Do not wait for federal AI policy to resolve the 50-state fragmentation I discussed in Part 1. Establish internal AI governance frameworks now that can adapt to multiple regulatory regimes without requiring complete system redesigns. This means building modularity and flexibility into your AI architecture from the start.

Compartmentalize Experimentation While Maintaining Oversight. Create sandboxes where teams can experiment with AI capabilities without putting production systems or sensitive data at risk. But ensure these sandboxes have clear governance, defined success criteria, and pathways to production that include security reviews and ethical assessments.

Prioritize Pivotability Over Optimization. In a world of rapid technological change and geopolitical uncertainty, the ability to change direction quickly is more valuable than squeezing the last percentage point of efficiency from current systems. This is what I call maximizing pivotability. Avoid decision anchoring, where you double down on a technology or vendor relationship even when market signals suggest a need for change.

Embrace Different AI Flavors with Different Governance. Understand that computer vision AI operates deterministically while Generative AI is creative but unpredictable. Each requires different governance approaches and risk frameworks. Boards and executives must resist the temptation to apply one-size-fits-all policies to fundamentally different technologies.

Lead with Empathy and Courage Through Uncertainty. We need leaders who are not just technically literate, but also leaderships who lead with empathy and courage through unprecedented uncertainty. This means being honest about what you do not know, creating space for your teams to voice concerns and propose alternatives, and making decisions even when perfect information is unavailable.

 

The Operational Reality of Decision Elasticity

Decision elasticity is not just a conceptual framework. It is an operational discipline that requires specific organizational capabilities.

First, you need real-time situational awareness. This means AI systems that continuously monitor your environment, detect anomalies, and surface potential threats or opportunities before they become crises. But situational awareness alone is not enough.

Second, you need rapid sense-making capabilities. When AI surfaces an anomaly, your team must be able to quickly assess whether it represents a genuine threat, a false positive, or an emerging opportunity. This requires cross-functional collaboration, access to diverse expertise, and the ability to synthesize information from multiple sources.

Third, you need pre-authorized response options. In a crisis, you cannot afford to wait for Board approval or executive consensus on every decision. But you also cannot give mid-level managers carte blanche to make consequential choices. Decision elasticity means defining in advance what types of responses can be executed immediately, what requires escalation, and what triggers a full crisis response.

Throughout these three capabilities, you need continuous learning and adaptation. After every incident, whether it is a security breach, a compliance failure, or a missed opportunity, your organization must conduct rigorous after-action reviews that feed insights back into your AI systems, your processes, and your training programs.

 

Scaling Resilience Through Ecosystem Partnerships

The governance principles from Part 1 of this essay series, and the operational capabilities I have outlined here, collectively create the foundation for organizational resilience. No company can achieve resilience in isolation. In Part 3 of this series, I will explore how Boards can scale resilience through strategic ecosystem partnerships while maintaining the pivotability needed to adapt as the geopolitical and technological landscape shifts.

The key insight I will develop is this: scaling through partnerships is not just about growth. It is about building a resilient infrastructure that can pivot every three to six months as tech tectonics shift.

This requires moving away from top-down leadership and toward a decentralized, networked model that leverages collective intelligence while avoiding the decision anchoring that causes organizations to double down on failing strategies.

 

An Invitation to Operational Excellence

If your organization is struggling to balance AI adoption with cybersecurity, if your teams feel overwhelmed by the pace of change, or if you recognize that your current operational model is not built for the converged threats I have described, I invite you to engage in a deeper conversation about building human-machine partnerships that work.

My advisory work focuses on helping organizations develop the operational discipline and cultural foundations needed for decision elasticity. This is not about implementing a specific technology or following a compliance checklist. It is about building the organizational muscle memory to respond to ambiguous threats with speed and wisdom.

The stakes are clear. AI strategy is now inseparable from national security and economic competitiveness.

For operational leaders, the question is whether you will build the capabilities to compete in this environment or watch as more agile competitors and adversaries outmaneuver you. Given that fortune favors the brave, I strongly recommend a proactive leadership approach.

 

Dr. David Bray is both Chair of the Accelerator and a Distinguished Fellow at the non-partisan Stimson Center as well as Principal and CEO at LeadDoAdapt Ventures, Inc. He previously served as a non-partisan Senior National Intelligence Service Executive, as Chief Information Officer of the Federal Communications Commission, and IT Chief for the Bioterrorism Preparedness and Response Program. Business Insider named him one of the top “24 Americans Changing the World” and he has received both the  Joint Civilian Service Commendation Award and the National Intelligence Exceptional Achievement Medal. The U.S. Congress invited him to serve as an expert witness on AI in September 2025. He also advises corporate Boards and CEOs on navigating the convergence of AI, cybersecurity, and geopolitical risk.

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OpenAI launches ChatGPT Go, ad tests

Larry Dignan
Editor in Chief of Constellation Insights Constellation Research
About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

OpenAI launched ChatGPT Go in the US, an $8 per month subscription plan, and plans to launch ads in that plan and free versions.

The two efforts will give a boost to OpenAI's revenue and overall financial picture. After all, OpenAI has billions of dollars in AI infrastructure spending ahead with the latest being a deal with Cerebras.

In a blog post, OpenAI said ChatGPT Go launched in India in August and has rolled out to more than 170 countries. The US entry now gives OpenAI three tiers for its subscription plans.

  • ChatGPT Go is $8 per month.
  • ChatGPT Plus is $20 per month.
  • ChatGPT Pro is $200 per month.
  • ChatGPT Business is $25 per month for smaller companies. ChatGPT Enterprise pricing varies.

What remains to be seen is whether ChatGPT Plus subscribers trade down to ChatGPT Go, which carries ads. My working theory is that AI services may resemble streaming where customers realize ads aren't so bad if you can save money. ChatGPT Go has more messages, uploads and image creation tools than the free tier, but less than ChatGPT Plus. For many folks, that access may be fine. ChatGPT Plus will include GPT 5.2 Thinking, the ability to use legacy models and Codex. ChatGPT Plus can also retain details from past conversations longer.

More importantly to OpenAI's financial picture is its plans to roll out ads on the free tier and ChatGPT Go tiers.

OpenAI laid out its ad principles that include answer independence from advertising, conversation privacy and choice and control over data. Tests will roll out on ChatGPT and ChatGPT Go in the weeks ahead.

Here's a look at the test formats.

 

 

Data to Decisions Next-Generation Customer Experience Chief Information Officer

Disrupt Yourself: Personal Growth, Leadership, and Designing Work That Thrives | DisrupTV Ep. 424

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,&nbsp;Everybody Wants to Rule The World&nbsp;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… Read more
Elle DeRosa
Director of Marketing, Communications & Operations Constellation Research
Elle is the Director of&nbsp;Marketing&nbsp;at Constellation Research and producer of the weekly enterprise tech&nbsp;show,&nbsp;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,&nbsp;S50&nbsp;and&nbsp;ShortLists.&nbsp;She has previous experience in public relations and has a strong passion for the marketing world.&nbsp; &nbsp; Contact Elle at [email protected] Read more

Introduction: The Evolution from HR to Employee Experience

In the latest episode of DisrupTV, co-hosts Vala Afshar, Chief Digital Evangelist at Salesforce, and R “Ray” Wang, CEO and Founder of Constellation Research, led a timely discussion on how leadership, culture, and work itself are being redefined in the Age of AI.

The conversation explored a fundamental shift underway in organizations worldwide: the move from traditional Human Resources to employee experience (EX) design. As AI accelerates change, leaders must be far more intentional about how people experience work, growth, and belonging.

Joining the show were Whitney Johnson, CEO of Disruption Advisors, and Dean Carter and Mark Levy, former executives at Airbnb and Patagonia and co-authors of a new book on employee experience design.

Whitney Johnson: Personal Disruption and the S-Curve of Learning

Whitney Johnson reframed disruption not as a technology phenomenon, but as a human one.

She emphasized that real transformation happens when individuals are willing to disrupt themselves—learning new skills, adopting new identities, and stepping into uncertainty. Central to her thinking is the S-curve of learning, which maps growth across three stages:

  • Launch Point: Uncertainty, fear, and steep learning
  • Sweet Spot: Momentum, confidence, and rapid progress
  • Mastery: Competence, comfort—and the risk of stagnation

Johnson stressed that leaders play a critical role in helping employees navigate these transitions by creating psychological safety, encouraging experimentation, and responding constructively to new ideas. Growth, she noted, is humanity’s default setting—fear is simply the signal that learning is happening.

Designing for Agency, Not Compliance

A recurring theme throughout the episode was agency—the ability for employees to act with ownership, creativity, and purpose.

Johnson highlighted that resistance to change often stems from fear, not capability. Leaders who acknowledge this and design experiences that support small, behavior-based wins can help teams move forward with confidence. Immigrants, she observed, often excel at personal disruption precisely because they’ve already navigated profound life transitions.

Dean Carter and Mark Levy: From HR to Employee Experience Design

Drawing from their leadership roles at Airbnb and Patagonia, Dean Carter and Mark Levy described why traditional HR models are no longer sufficient.

Instead of managing policies and processes, organizations must design experiences that reinforce belonging, purpose, and trust—especially as companies scale.

Key principles they shared include:

  • Culture add over culture fit: Hiring people who strengthen and evolve values, not simply mirror them
  • Values-based hiring: Using core values interviews to assess alignment with mission
  • Vulnerable leadership: CEOs who model trust and openness set the tone for the entire organization

Their new book on employee experience design serves as a practical playbook for leaders seeking to preserve culture while growing rapidly.

AI and the Future of Employee Experience

AI was not framed as a threat—but as a design choice.

Carter and Levy emphasized that AI’s impact on work will depend on how intentionally leaders deploy it. While AI can automate tasks and surface insights, it cannot replace belonging, meaning, and human connection.

The challenge for leaders is to ensure AI augments human potential rather than diminishing it. That means investing in people, rewarding good thinking, and designing systems where technology supports—not substitutes—human judgment.

Key Takeaways

  • Employee experience design is replacing traditional HR as a strategic leadership priority
  • Personal disruption and the S-curve of learning provide a roadmap for sustainable growth
  • Psychological safety and agency are prerequisites for innovation
  • Culture add hiring strengthens values while enabling diversity and inclusion
  • AI must be implemented intentionally to preserve human agency and purpose at work

Final Thoughts: Intentional Leadership in a Disrupted World

As AI reshapes how work gets done, this DisrupTV episode made one thing clear: the future of work is not just about technology—it’s about design.

Leaders who succeed will be those who intentionally craft employee experiences that foster growth, trust, and meaning. By combining personal disruption, thoughtful culture design, and human-centered AI adoption, organizations can build workplaces that don’t just survive disruption—but thrive because of it.

Related Episodes

If you found Episode 424 valuable, here are a few others that align in theme or extend similar conversations:

 

Tech Optimization Digital Safety, Privacy & Cybersecurity Chief Executive Officer Chief Information Security Officer Chief Privacy Officer Chief Technology Officer
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>
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