| Constellation Research

IBM THINK 2026: Laser Focus on AI, Hybrid Cloud & Quantum

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…...

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]. ...

At IBM Think 2026, one theme emerged: AI, hybrid cloud, and quantum are no longer “emerging technologies” — they’re now as strategic as finance and defense.

As Constellation Research CEO R “Ray” Wang shared in his event debrief, IBM’s message this year wasn’t about chasing every possible innovation. It was about focus: choosing where not to play so you can go deeper where it truly matters for enterprises.

Strategy by Subtraction: What IBM Chose to Focus On

Over the last three years, IBM has made a deliberate pivot to support the AI race with a clear strategy:

Hybrid cloud as the foundation
AI at scale is ultimately a data problem. IBM’s bet is that a hybrid cloud architecture is essential to unify and govern data across complex, global enterprises. Without that connective tissue, AI initiatives stall.
AI models, tools, and operating blueprints
It’s not just about models. IBM is investing in the full AI stack — from infrastructure and GPU acceleration to tools, orchestration, and reference AI operating models that help organizations move from pilots to production.
Quantum and AI, hand in hand
Quantum isn’t a distant science project in IBM’s narrative. Ray points out that AI and quantum are tightly linked, especially for problems like protein modeling, optimization, and simulation — the frontier use cases where classical compute hits a ceiling.

The underlying message: strategy isn’t about doing everything. It’s about knowing what to double down on so you can deliver differentiated value for customers under intense pressure.

Deep Partnerships as the Real Moat

One of the strongest signals from Think 2026 wasn’t just the tech — it was who showed up.

Ray notes that around 120 public-company CEOs and board members were in the room, and more boardrooms are recognizing the need for a chief AI officer or equivalent accountable leader. This is a shift: AI is no longer only an IT or innovation topic; it’s a governance and strategy issue.

IBM’s long-term, “all-in” partnerships underscore that shift:

Saudi Aramco – a relationship since 1947
Decades-long collaboration with one of the world’s largest energy companies demonstrates IBM’s role as a strategic technology partner, not just a vendor. Ray calls out this continuity as “crazy” in the best way — proof that co-innovation at scale takes time and trust.
Relevance Health – modern healthcare experiences
With leaders like Ratnaker Lavu, IBM is working on healthcare transformation, using AI and data platforms to reimagine patient and member experiences.
Cleveland Clinic – quantum-powered protein modeling
One of the most compelling examples: Cleveland Clinic and IBM are modeling 12,635 proteins with quantum technology. The potential derivative capabilities — from drug discovery to personalized medicine — illustrate why quantum and AI matter together.

Across these accounts, the pattern is clear: IBM is going deep inside complex enterprises, acting as an embedded partner across infrastructure, data, AI, and now quantum.

Why AI Is the Edge in a World of Margin Compression

Ray highlights a sobering reality: 450 of the Fortune 500 are growing at single-digit rates.

In that context, margin compression is real. Companies are often “working 10x or 100x harder, but standing still.” Traditional levers — cost-cutting, incremental efficiency — are no longer enough.

This is where AI becomes an exponential advantage:

AI agents and automation can reconfigure workflows, not just speed them up.
Data-driven decisioning can optimize pricing, supply chains, and experiences in real time.
Hyper-personalization and new digital products can unlock revenue streams that didn’t exist before.

But that edge only appears when AI is supported by:

The right data architecture (hybrid cloud)
The right tooling and governance
The right operating model and talent
And critically, the right ecosystem partners

IBM’s pitch is that it can provide that end-to-end blueprint — from GPUs and infrastructure to models, agents, and orchestration layers that support real-world AI at scale.

From Legacy to “Next”: Blueprints for AI Operating Models

One recurring idea in Ray’s recap is the shift from legacy systems to a next-generation AI operating model.

IBM’s approach centers on:

Managing AI and agents at scale
Not just building one-off models, but managing fleets of AI agents that handle tasks across the organization — from IT operations to customer service and beyond.
Orchestrating processes end-to-end
Connecting AI to workflows so it isn’t just a bolt-on. This means orchestration across business processes, data pipelines, and customer journeys.
Bridging from where customers are today
Many IBM customers are large, regulated enterprises with decades of legacy infrastructure. The emphasis is on getting them from point A (today’s reality) to point B (AI-native operations) with “peace of mind” — a nod to security, compliance, reliability, and risk management.

Digitization, data quality, and integration across hybrid cloud, AI, and quantum form the backbone of that journey.

The Big Takeaway from IBM Think 2026

If you had to distill IBM Think 2026 down to a single takeaway, Ray Wang suggests this:

IBM is going deep in its relationships, across its entire portfolio, with customers who are betting on AI, hybrid cloud, and quantum as core to their future.

The customers IBM showcased — from long-time partners like Saudi Aramco to innovators like Cleveland Clinic — aren’t dabbling. They’re building AI-first operating models to break out of single-digit growth and margin pressure.

In a world where most organizations are still figuring out their AI strategy, IBM’s message from Think 2026 is clear:

Technology is now as strategic as finance and defense.
AI, hybrid cloud, and quantum are the pillars of that strategy.
And the winners will be those who combine deep data foundations, trusted partners, and clear focus on where AI can create exponential, not incremental, impact.

Data to Decisions Future of Work Tech Optimization

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Is the Worst Over for Saas? & Top 5 Technology Stories | CRTV Episode 129

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. ...

Hannah Mason

Marketing Director
Constellation Research

The SaaS apocalypse had a good run as a narrative. For a few months, the prevailing wisdom was that vibe coding, AI agents, and a generation of developers armed with Claude and Codex would roll their own software and walk away from the enterprise SaaS stack entirely. Episode 129 of ConstellationTV put that narrative in front of Constellation analysts Larry Dignan, Esteban Kolsky, and Martin Schneider, and the result was a nuanced, honest debate (in which they don't all agree).

The Debate: SaaS Apocalypse or SaaS Shakeout?

Esteban opened with the kind of directness that makes the CRTV debate worth watching. The SaaS apocalypse, he argued, was always a pricing concern dressed up as an existential crisis. Once vendors addressed the pricing anxiety, the narrative collapsed in about two days. The real SaaS companies, the ones with deep enterprise footprints, years of integrations, and the institutional knowledge that took a decade to build, are not going anywhere. You cannot replace three years and $25 million worth of legacy integration work with a vibe coding session.

Martin agreed on the broad strokes but pushed back on the edges. The established players with genuine systems of record are fine. The point products are not. The SMB-focused, single-function SaaS companies that never built deep integrations and never earned irreplaceable status in the enterprise stack are facing real margin compression, and AI is accelerating what was already on the horizon. The ones providing value will survive. The ones that were coasting on lock-in and switching costs without delivering real outcomes are already in trouble.

Larry added the observation that has been sitting under the whole conversation: zombie SaaS is real, most of it is owned by private equity, and whether AI is the cause or just the excuse for the shakeout is still an open question. It might not matter. The outcome is the same.

The Amazon angle was sharp. Larry raised AWS Connect's expansion into talent, supply chain, and healthcare as a sign of hyperscaler ambition in the SaaS space. Both Esteban and Martin were skeptical. The DNA of an infrastructure company is not the DNA of an enterprise software company, and that gap does not close easily. EMC tried it. CA tried it. The go-to-market motion, the customer relationship model, the way enterprise software gets sold and implemented, it is a different business. Google knows this about itself, which is part of why it has stayed in its lane more carefully than Amazon.

The bottom line from the debate: SaaS is not dead. But the shakeout is real, and it was coming with or without AI. The vendors that delivered genuine value and depth of integration will be fine. The rest are on borrowed time.

From Google Cloud Next: When Data Finds Its Meaning

In the next segment, Mike Ni caught up with Paul Pritchard, CEO of Overdose, a high-growth digital commerce agency, at Google Cloud Next, and the conversation cut to one of the most practical questions in enterprise AI right now. Most organizations do not have a data problem or an AI problem. They have a meaning problem. They have more data than they know what to do with, and none of it is telling a consistent story.

For Overdose, the turning point was Google Cloud's Looker semantic layer, built on BigQuery. Before, the agency was spending a month building dashboards that looked backward at the previous month's performance. Every channel claimed the sale. Every stakeholder had a different interpretation of the numbers. The CFO, the board, the marketing manager, all of them were looking at different truths and arguing about which one was right.

The semantic layer solved that. It established a single, grounded truth that every team, and every client team, could access in real time. From there, Overdose built a tool that takes a Looker insight, generates a creative brief, produces creative assets, and automatically publishes them to ad channels, with Looker tracking performance and triggering the cycle again when it is time for new assets.

The metric that matters: the business is now more profitable because the teams are focused on margin-oriented decisions rather than vanity metrics. Clicks and revenue are easy to chase. Profit is harder, and it requires knowing the truth first.

Paul's advice for any data or commerce leader starting this journey: begin at the data layer. Get to a single truth. Everything else builds on that foundation, and without it, everything you build on top is noise.

Larry's Five Tabs: What You Need to Know This Week

Larry's tabs covered five stories worth tracking across cloud, enterprise software, hardware, and an emerging pattern that deserves more attention than it is getting.

AWS Connect has expanded well beyond the contact center into talent, healthcare, supply chain, and workflow automation through Amazon Q. The strategic intent is vertical integration, and it is a direct challenge to the SaaS vendors in those categories. Whether it works is a separate question, but the ambition is clear.
Google Cloud is in the pole position on AI infrastructure. First quarter revenue was up 63%, putting the platform at an $80 billion annual run rate, roughly half of AWS but growing faster. The AI stack, models, TPUs, and tooling is the most fully built out of any hyperscaler right now, and Google Cloud Next made that case comprehensively.
ServiceNow's Knowledge conference introduced an AI control tower update worth watching closely. The new value calculator tracks AI spend, agent performance, and business outcomes in one place. For enterprises drowning in AI pilots and unclear on returns, a tool that actually tries to answer the ROI question is not a minor feature. It could be the killer app for the control tower.
Apple's earnings flagged something that is going to ripple across every hardware and device category: memory supply is getting tight, and costs are going up. Macs are particularly constrained because demand for Mac Minis and Mac Studios from developers building with local AI models is outpacing supply. Every device maker will face versions of this problem, and the question of what gets passed to consumers remains open.
The most interesting tab was the last one. GM and Rivian both reported earnings where software and services revenue is becoming a meaningfully larger share of the business. Rivian's software and services revenue rose 49% year over year, driven in part by its partnership with Volkswagen. Caterpillar is heading in the same direction. The pattern is clear: every company is becoming a software company, and AI is accelerating that transition faster than most investors are pricing in.

Why You Should Go to Canvas 2026

Constellation Research VP and Principal Analyst Chirag Mehta closed the episode with a question that cuts across every conversation about enterprise AI: Is AI making everyone faster, or is it making teams better?

Individual productivity gains from AI are real and measurable. A product manager can pressure test an idea in minutes. A designer can generate concepts almost instantly. An engineer can explore multiple implementation paths in the time it used to take to explore one. But faster individuals do not automatically create better-aligned teams. In fact, AI can amplify the small misalignments that already exist in an organization. More ideas, more plans, more prototypes, and the same fundamental questions left unanswered about what the team is actually building and why.

That is the collaboration challenge Miro is addressing with Canvas 2026, and it is why Miro is on the Constellation ShortList. The category is moving beyond digital whiteboards into platforms that help cross-functional teams structure work, apply AI, and move from ideas to coordinated outcomes. Shared context is becoming infrastructure. It is how strategy connects to execution, and how organizations ensure AI produces better decisions, not just more output.

Canvas 2026 is built around that conversation. If you are a product, engineering, design, or technology leader working through the same challenge, it is worth your time. Register here before May 19th.

Watch the full episode on YouTube and subscribe to Constellation Research for weekly enterprise technology debates, customer stories, and the five things you need to know.

Future of Work Tech Optimization Data to Decisions Digital Safety, Privacy & Cybersecurity Innovation & Product-led Growth New C-Suite Chief AI Officer Chief Data Officer Chief Executive Officer Chief Information Officer

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Why Chirag Mehta is going to Miro's Canvas 2026

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…...

AI is making everyone faster. That part is largely solved. A product manager can pressure test an idea in minutes. A designer can generate concepts almost instantly. An engineer can explore multiple implementation paths faster than ever before. Individual productivity gains from AI are real and measurable.

The harder problem is what happens next. Faster individuals do not automatically create better-aligned teams. In fact, AI can amplify the small gaps that already exist in an organization. Teams can generate more ideas, more plans, more prototypes, and more analysis, and still struggle with the same fundamental questions: What are we actually building? Why does it matter? Who needs to be involved? What trade-offs are we making? How do we keep everyone moving in the same direction?

That is the question Miro is putting at the center of Canvas 2026, and it is the right question to be asking right now.

The Collaboration Gap Is a Structural Problem

The productivity conversation around AI has focused heavily on individual output. That is where the early wins are easiest to demonstrate and easiest to measure. But the enterprise does not run on individual output. It runs on coordinated team outcomes, which require something AI cannot provide on its own: shared context.

Shared context is how product, engineering, design, and technology leaders connect strategy to execution. It is how teams move from isolated AI experiments to coordinated product outcomes. It is how organizations make sure AI helps teams make better decisions, not just produce more output. Right now, that shared context layer is underdeveloped in most enterprises, and the gap is becoming more visible as AI accelerates everything around it.

Why Miro Is on the Constellation ShortList

Miro has been on the Constellation ShortList, and the reason is consistent with what Canvas 2026 is trying to address. The category is moving beyond digital whiteboards. What matters now is whether a platform can help cross-functional teams structure work, apply AI to that work, and move from ideas to outcomes with less friction.

That is a different value proposition than shared canvases and sticky notes. It is a platform question about how teams operate when AI becomes part of everyday work, not just a productivity tool sitting alongside it.

What to Watch at Canvas 2026

The most useful conversations at events like this tend to happen when vendors stop talking about capabilities and start talking about operating models. The question I will be bringing to Canvas 2026 is not whether AI can make individuals faster. That is settled. The question is how leading organizations are redesigning the way teams work together so that faster individuals actually produce better collective outcomes.

That means looking at how shared context is structured and maintained across functions, how decisions are made and documented when AI is in the workflow, and what it actually takes to move from an AI-assisted prototype to a shipped product.

If those are the questions your organization is working through, Canvas 2026 is worth your time. Register here before May 19th.

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CR CX Convo: What’s Next With AWS - A Sit Down with Amazon Connect’s Pasquale DeMaio

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…...

Hannah Mason

Marketing Director
Constellation Research

For anyone who follows Constellation analyst Liz Miller's research, the premise is not new. She has been talking about Amazon Connect as an engagement backbone for years, but her position has remained the same: it is more than a contact center platform. It is the infrastructure layer that enables enterprises to engage with the humans they serve, whether those humans are customers, employees, candidates, or patients.

When Liz sat down with Pasquale DeMaio, VP of Amazon Connect, for this CR CX Convo, the takeaway was confirmation that the floodgates have officially opened, and enterprises still thinking of Connect as a call center tool are already behind the curve.

Deflection Is Dead. Full Stop.

Pasquale said it better than most analysts have. If your CX strategy is still built around deflection, containment, and average handle times, you are not solving a customer problem. You are solving a cost accounting problem. In a world where a customer can open ChatGPT and ask it to solve their issue, the moment you deflect them, you have handed them off to someone who actually wants to help. How long before your business is just option C on a list?

The shift Pasquale is describing is from optimization to problem solving. Not how do we get customers off the phone faster, but how do we actually resolve what they came for? That sounds obvious. It is not obvious in practice. It requires a complete rethinking of what success looks like, from metrics to incentives to how you design the experience itself.

The People Problem Was Never the People

This is the thread Liz keeps pulling on in every conversation about enterprise AI, and it showed up here in a big way. For years, the dominant approach to CX technology has been to treat people as the bottleneck. Agents talk too long. Recruiters take too long. Doctors spend too much time on documentation. The hammer has been pointed at the humans.

What Amazon Connect is doing differently is designing around what people are actually good at. Not replacing them. Not deflecting around them. Getting the technology out of their way so they can do the thing they showed up to do.

The story Pasquale told about the heart rate monitor study is the one that stuck. They asked agents to wear monitors while testing new software designed to reduce the administrative burden of their jobs. Heart rates went down. Stress levels dropped. And a less stressed agent does what? They focus on the customer. They solve the problem. They do the job they actually want to do.

That is not a small thing. That is the entire design philosophy, and it is the right one.

Hiring Is One of the Best Use Cases You Are Not Talking About

The talent product deserves more attention than it is getting in the broader conversation about where agentic AI delivers enterprise value.

Think about what traditional recruiting looks like at scale. Eighty candidates to make one offer. Thirty days on average to close that process. Meanwhile, the best candidates are hearing back from five other companies, and the window to make an offer they will accept is closing fast. Layered on top of all of that is the reality that human beings carry preconceived notions into every decision. Accents. Zip codes. Whether the recruiter slept well the night before. All of it seeps into decisions that are supposed to be about whether someone can do the job.

What Connect's talent solution does is restructure that entirely. The assessment is about capability. The interview is about fit. The recruiter gets clean, structured data without the noise, and they can move. Amazon Connect is running this process in under a day. For any enterprise dealing with mass seasonal hiring, that is not an incremental improvement. That is a completely different model.

The Real Opportunity Is in the Connections

This is where CxOs need to pay attention, because it is a conversation that hasn't happened enough yet.

Each of these use cases, contact center, hiring, healthcare, and supply chain, operates as its own intelligent system. But the moment you start connecting them, the value compounds in ways that are hard to overstate. Supply chain decisioning tells you that Christmas demand is starting two weeks earlier than last year. That changes your seasonal hiring timeline. Hiring data flows into your HCM. Customer profile data unifies across every touchpoint, so that marketing, operations, and the contact center all learn from the same signal.

Pasquale made a point worth underscoring: this is the moment to bring the CMO into the Connect conversation. Not because Connect is a marketing tool, but because the data flowing through this ecosystem is exactly what marketing needs to be genuinely proactive. And proactive does not mean a chatbot popping up on a website. It means understanding what a customer needs before they have to ask.

The enterprises that figure out how to string these pieces of intelligence together will have a structural advantage that is very hard to replicate.

Questions You Should Ask Right Now

If there is one question worth walking away with, it is this: where in your organization are people still being treated as the bottleneck, and what would it look like to redesign that?

Not with AI for the sake of AI. Not with deflection dressed up as self-service. But with technology that actually gets out of the way so people, customers, candidates, and patients can do what they came to do.

That is the premise on which Amazon Connect has been building for years. The difference now is the scale and the specificity of what is possible. The floodgates are open. The question is whether your organization is ready to walk through them.

Marketing Transformation Future of Work Innovation & Product-led Growth New C-Suite AI Agentic AI Chief Information Officer Chief Data Officer Chief AI Officer Chief Marketing Officer

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AI Governance and Innovation: From Data Center Risk to Exponential Leadership | DisrupTV Ep. 437

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

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] ...

AI Governance and Innovation: From Data Center Risk to Exponential Leadership

As enterprises accelerate their adoption of AI, the conversation is rapidly evolving. It’s no longer just about capability—it’s about resilience, security, and governance at scale.

In this episode of DisrupTV, Vala Afshar and R “Ray” Wang are joined by Bob Gourley, David Bray, and Andrea Bonime-Blanc to explore what it really takes to lead in the age of exponential technologies.

From data center vulnerabilities to AI-driven cyber risk and board-level governance gaps, the message is clear: AI is forcing a complete rethink of leadership.

Data Centers: The Physical Reality of AI Risk

AI may feel abstract, but it runs on physical infrastructure—data centers, energy grids, and network systems that are increasingly exposed.

Modern data centers are often built for efficiency, not defense. Their proximity to public roads and urban environments introduces new risks, especially from drones and other low-cost attack vectors.

At the same time, physical and cyber threats are converging. Communication jamming, infrastructure disruption, and hybrid attacks are no longer theoretical scenarios. They’re becoming part of the operational threat landscape.

Layer in energy constraints, geopolitical dependencies, and grid instability, and a new board-level question emerges: what happens when your infrastructure can’t be powered or protected?

The “Messy Middle” of AI and Cybersecurity

AI is both a solution and a stress test for cybersecurity.

As Bob Gourley notes, we’re entering a phase where AI will surface vulnerabilities faster than organizations can fix them. This doesn’t mean systems are getting worse—it means visibility is improving dramatically.

The result is a “messy middle”:

A surge in discovered vulnerabilities
Increased pressure on security teams
Short-term degradation in traditional KPIs

But this phase is necessary. It forces organizations to confront years of accumulated technical debt.

The organizations that succeed won’t be those that avoid the spike—they’ll be the ones that:

Prioritize effectively
Combine human expertise with AI-driven analysis
Measure response speed instead of raw vulnerability counts

Over time, this leads to a stronger, more resilient baseline.

Cloud, Edge, and the Rise of Simulation

Security is increasingly driving architectural decisions.

Cloud platforms offer resilience through scale, while edge computing enables localized, resilient AI capabilities when connectivity is disrupted.

At the same time, tools like digital twins are emerging as critical assets. By simulating real-world environments—supply chains, infrastructure, threat scenarios—organizations can test resilience before crises hit.

This is a shift from reactive security to proactive, scenario-based planning.

Exponential Governance: Leading in Pandora’s Era

Andrea Bonime-Blanc frames today’s moment through a powerful metaphor: Pandora’s box has been opened.

AI, along with other exponential technologies, is now widely accessible. There’s no going back—only forward with better governance.

She outlines five pillars for what she calls an exponential governance mindset:

Integrated governance across the full tech lifecycle
A culture that rewards responsibility, not silence
Deep awareness of stakeholder impact
Resilience built for compounding crises
Structured foresight to anticipate what’s next

This isn’t about slowing innovation—it’s about steering it.

Bridging Builders and Guardians

A recurring tension in AI is between speed and safety.

On one side are the innovators pushing boundaries. On the other are the risk leaders focused on long-term consequences.

The organizations that win won’t choose one over the other. They’ll integrate both:

Engineers working alongside ethicists
Product teams aligned with governance leaders
Innovation informed by accountability

This collaboration turns risk into a design input—not a post-launch constraint.

The New Mandate for CEOs and Boards

Across the discussion, several leadership imperatives stand out:

Build an AI strategy grounded in strong data foundations
Get hands-on with AI tools to understand their impact
Encourage dissent and “responsible heretics”
Rethink outdated assumptions (like “data is the new oil”)
Embed governance into innovation from day one

Perhaps most importantly, boards must evolve.

Curiosity, humility, and situational awareness are no longer optional traits—they’re core competencies in an AI-driven world.

Key Takeaways

AI infrastructure is a physical risk surface, not just a digital one
Cybersecurity will get harder before it gets easier as AI exposes hidden vulnerabilities
Resilience—not prevention—must become the primary security mindset
Governance must evolve from reactive oversight to proactive design
The future belongs to organizations that integrate innovation with responsibility

Final Thoughts

AI is not arriving in a vacuum. It’s landing in a world already shaped by geopolitical tension, infrastructure fragility, and systemic risk.

That’s what makes this moment different.

The organizations that treat AI as just another technology upgrade will struggle. The ones that treat it as a catalyst to rethink security, governance, and leadership will gain a real advantage.

Because in the end, AI doesn’t just scale systems—it exposes them.

And the question leaders must answer now is simple: are your systems, your governance, and your leadership ready for what AI will reveal?

Related Episodes

If you found Episode 437 valuable, here are a few others that align in theme or extend similar conversations:

Tech Optimization New C-Suite Digital Safety, Privacy & Cybersecurity AI Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Experience Officer

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

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From Data Chaos to Confident AI: How Overdose Built a Semantic Foundation on Google Cloud

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…...

Hannah Mason

Marketing Director
Constellation Research

Most enterprises don't have a data problem. They have a meaning problem.

That's the framing Mike Ni, analyst at Constellation Research, brought to an interview with Paul Pritchard, CEO of Overdose, at Google Cloud Next. Throughout their conversation, Pritchard unpacked exactly what it takes to get from scattered data sources to AI that actually moves the business forward.

The answer, it turns out, starts well before the AI.

The Real Problem: Everyone's Fighting Over the Truth

Overdose works with merchants globally as a high-growth digital commerce agency operating across dozens of client relationships simultaneously. The challenge they kept running into wasn't a lack of data. It was that every team had a different version of it.

"With those disparate data sources comes a whole bunch of confusion," Pritchard explained. "Every channel claiming the sale meant that there was a fight every day to figure out what the truth was."

CFOs were looking at one number. Marketing managers at another. Board members at a third. And the agency was spending enormous energy just reconciling the differences before anyone could get to the actual work of growing the business.

The turning point came when Overdose built a semantic layer powered by Looker and Google BigQuery. Not another dashboard. Not another BI tool. A single, grounded source of truth that every team, client, and agency alike could work from.

"The ability to now sit down with a client and understand what the imperative metrics are (profit, effective movement of stock, customer acquisition that turns into lifetime value) and give everyone the same information at the same time has been so impactful," Pritchard said.

From Month-Long Dashboards to Real-Time Action

Before Looker, Overdose's process looked like this: spend a month building a dashboard, present it at the end of the month, and spend the meeting looking backward at what had already happened.

"You can imagine what you're doing at that stage," Pritchard said. "Looking back on the performance of that month and comparing it year on year."

Now, that same insight is available in real time. Teams don't wait for a monthly review to act. They build action-oriented plans based on what's happening now. And when an insight surfaces, generative AI is there to do something with it immediately.

Overdose built a tool that takes the semantic layer's output and runs it through an autonomous action loop: insight → creative brief → creative assets → published to ad channels → performance tracked back through Looker → new assets created when ready.

"Our merchants can move faster to engage with their customers," Pritchard said. "That's the best thing that can come out of it."

AI That's Actually Grounded

This is where the conversation gets pointed. Pritchard is not shy about his view on AI deployed without a solid data foundation.

"If it's not grounded in data, you get confident hallucinations," he said. "And if you get confident hallucinations, your brand is at risk."

It's a line worth sitting with. Confident hallucinations (i.e. AI that sounds certain while being wrong) are arguably more dangerous than obvious errors, because they're harder to catch and easier to act on. For a commerce agency making real-time decisions about campaigns, creative, and spend, that's not a theoretical risk.

This is also, Pritchard argued, why so many AI proofs of concept never make it to production. The semantic foundation wasn't there from the start. The AI had no solid ground to stand on.

"Intent is everything," he said. "A POC gives you some sort of intention. But the reality is, unless it's in the real world, connecting up all of those parts of your business to allow you to act on the fly in real time, that is the magic."

The People Question

One of the most common concerns when AI enters a business is what it means for the people doing the work. Pritchard's framing is worth noting.

"Instead of thinking about the reduction that everyone talks about, we think about the empowerment it brings to our teams," he said. "Our teams are now talking to clients about impactful metrics, rather than thinking about operational toolsets."

The shift isn't fewer people, but better conversations. Teams that used to spend time wrangling data are now spending time on strategy. And as the AI handles more of the execution layer, the human value moves further up the stack.

The Advice for Leaders Starting This Journey

Ni asked Pritchard what data and commerce leaders need to know before embarking on this path. His answer was direct: Start with the truth.

"What we knew is that disparate data sources leading to inconsistent reporting. Every channel claiming the sale meant there was a fight every day. When you get to that truth, what's beyond it is absolutely exponentially beneficial. Start by trying to get to the truth. Once you've got that, anything's possible."

For Overdose, the partnership with Google Cloud and the GCP team was central to getting there. Looker's price point made it accessible to the mid-market merchants it serves. And the consistency it created across every client relationship became the foundation for something bigger: an agency whose institutional knowledge and context compound over time.

"The information we retain over time should be able to be used to continually inform, elevate, and create innovation for those businesses," Pritchard said. "Through our partnership with Google Cloud, we've been able to do that in a consistent way — generating more revenue, more margin, and more customer acquisition for our merchants."

Watch the Full Interview

Catch the full conversation between Mike Ni and Paul Pritchard from Google Cloud Next below.

👉 https://youtu.be/w-0yDAaqyXQ

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Read more about From Data Chaos to Confident AI: How Overdose Built a Semantic Foundation on Google Cloud

Built to Last in the Age of Agentic AI: Why Technology Is Ready—but Leadership Isn’t | DisrupTV Ep. 436

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

Elle DeRosa

Director of Marketing, Communications & Operations
Constellation Research

Built to Last in the Age of Agentic AI: Why Technology Is Ready—but Leadership Isn’t

On this episode of DisrupTV, hosts R “Ray” Wang and Vala Afshar sit down with Joe Kim, CEO of DRUID AI, and Paul McCarthy, author of The Fired Leader, to unpack a hard truth:

AI is ready. Most organizations are not.

The conversation explores two forces colliding in real time—agentic AI and disruptive leadership—and why the gap between them is now the biggest risk (and opportunity) for enterprises.

AI Has Moved From Experiment to Executive Mandate

AI is no longer a side project or innovation lab exercise. It’s now a CEO-level priority shaping strategy, operations, and competitive advantage.

As Joe Kim explains, the shift from proofs of concept to production changes everything:

AI decisions now impact revenue, cost structures, and governance
Agentic systems introduce autonomy into workflows
The stakes move from experimentation to accountability

Even more surprising: in some cases, government agencies are advancing faster than the private sector in adopting agentic AI.

The takeaway is clear—this is no longer about testing AI. It’s about running the business with it.

Agentic AI Changes the Rules Entirely

Traditional enterprise systems are deterministic: same input, same output.

Agentic AI is not.

These systems reason, decide, and act—often without direct human intervention. That means organizations must rethink how they design, manage, and secure technology.

Instead of tools, agents must be treated like operators:

They have defined responsibilities
They require oversight and training
They can be manipulated or exploited

This shift introduces new risks, especially around security and control. Social engineering won’t just target humans—it will target AI agents.

To manage this, organizations need a control plane above the models: a layer that governs decisions, monitors behavior, and enforces guardrails.

What “Built-to-Last” AI Actually Looks Like

According to Joe Kim, durable AI systems share a few critical characteristics:

Modular architecture
AI stacks must be flexible. Models, agents, and data layers should be interchangeable—not locked together.

Data-first design
Clean, governed, and well-routed data remains the foundation of performance.

Observability and control
Organizations must be able to track, evaluate, and adjust agent behavior in real time.

Lifecycle management
Agents need versioning, testing, and promotion pipelines—just like software.

Cost discipline
Token usage must translate into measurable business outcomes, not just experimentation.

Guardrails by design
Without strong controls, agentic systems can behave unpredictably or unsafely.

In short: AI infrastructure is no longer just about capability—it’s about control, economics, and trust.

AI Is Becoming a Commodity—Execution Wins

As Ray points out, AI itself is rapidly commoditizing.

The advantage no longer lies in access to models, but in:

Distribution
Customer relationships
Integration into workflows

Incumbents now have a real opportunity to reinvent themselves—if they move fast.

If they don’t, the same forces will disrupt them.

The Real Bottleneck: Human Decision-Making

AI accelerates everything—except leadership.

With answers now generated in seconds, the limiting factor becomes:

How quickly leaders can interpret and decide
How comfortable they are with uncertainty
How well they manage non-deterministic systems

This shift is already creating pressure at the top. Many leaders are unprepared for the speed and ambiguity of AI-driven decision environments.

Transformation Is Not Technical—It’s Relational

Vala highlights a critical point: AI transformation is fundamentally about people.

Organizations adopting hundreds of agents are also:

Reskilling employees
Redesigning workflows
Reallocating talent internally

This isn’t just automation—it’s a redefinition of how humans and machines work together.

And that’s where leadership becomes the real constraint.

The FIRE Framework: What Future Leaders Need

Paul’s FIRE framework defines the traits organizations must cultivate:

Fresh thinking – challenging outdated assumptions
Inquisitiveness – asking better, deeper questions
Real accountability – authenticity with ownership
Expressiveness – willingness to challenge the status quo

These are exactly the qualities needed to lead in an AI-driven world—and the ones most organizations suppress.