AI Productivity Debate, Revenue Intelligence, and Claude Mythos | CRTV Episode 131

Martin Schneider

Vice President and Principal Analyst
Constellation Research

Martin Schneider has had a unique career that has spanned both analyst and marketing practitioner roles, focused on high technology and related industries. The unifying factor has always been both a keen analysis of go-to-market trends, while also having achieved success as a marketing leader. Schneider started his career as a journalist covering B2B technologies, and quickly transitioned into a leading analyst covering application software for the 451 Group in NYC, where he specialized in CRM, marketing automation, and business intelligence/analytics technologies. After analyzing various go-to-market strategies of dozens of technology vendors, Schneider made the move to the vendor side, where he led successful go-to-market teams for several startups and established tech providers,…...

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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]. ...

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Enterprise technology is at a crossroads. Budgets are tighter, growth is harder to find, and the pressure to show real returns on AI investment has never been greater. Episode 131 of ConstellationTV tackles three of the biggest questions facing technology and business leaders today.

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Is AI at an inflection point? The debate continues.

Host Martin Schneider opened the episode with a Great Debate featuring Holger Mueller, Esteban Kolsky, and Larry Dignan. The question: has AI actually hit an inflection point, or are organizations still waiting on the productivity payoff?

The consensus leaned toward yes, with important caveats. Holger Mueller argued that inflection is happening one use case at a time, not company-wide, and that the real shift will come as enterprises gain better control of their data architecture and begin building or customizing their own agents. Esteban Kolsky was more direct, calling the inflection point for generative AI a done deal while noting that CRM may not be the right home for it. Martin pointed to the quote-to-cash cycle and CPQ workflows as strong near-term candidates in which deterministic, high-value tasks can be effectively handed off to AI.

The group also took on token economics. The emerging view: as enterprises move toward proprietary and domain-specific models, the token conversation shifts from cost-per-query to infrastructure investment and operational efficiency. Tokens, as Holger noted, are simply another form of elastic cost, and enterprises that treat them like cloud spend will be better positioned to manage them.

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Revenue intelligence: the execution engine for growth

R "Ray" Wang sat down with Martin to discuss his newly published market overview on revenue intelligence, a category gaining significant momentum as organizations scramble to find profitable, scalable growth.

Revenue intelligence is not pipeline inspection or sales coaching in isolation. It is the full orchestration of the revenue lifecycle, from acquisition and activation through retention, renewal, and expansion, all aimed at optimizing customer lifetime value. Martin noted that 85% of the Fortune 500 are growing in the single digits, meaning the pressure to do more with existing customers and data has become a strategic priority.

Key trends in the space include the rise of purpose-built AI and domain-specific models tailored to revenue processes, the expansion of data acquisition beyond the CRM to include call records, meetings, email, and third-party signals, and a shift from DIY AI approaches toward vendor-led, outcome-driven implementations. Chief revenue officers and chief growth officers are emerging as the primary buyers, and they want results without having to build from scratch.

Vendors covered in the report include Clari, Salesloft, Gong, Salesforce, Outreach, ZoomInfo, Conga, SugarCRM, and Zoom Revenue Accelerator. The full report is available on constellationr.com.

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Claude Mythos and the new cybersecurity operating model

Chirag Mehta closed the episode with a walkthrough of his new Big Idea report on Claude Mythos, Anthropic's advanced AI system currently available only through a restricted, defender-focused program.

Chirag's central argument is that Mythos is not primarily a threat to enterprise security; it is a signal about where the work of security is heading. The challenge for most organizations is not finding vulnerabilities. It is what happens after: validating whether an issue is real, understanding whether it can be chained or exploited, engineering a safe fix, and shipping that fix before the risk compounds.

Mythos points to meaningful capability in exactly that space, combining long-context code understanding, reasoning, tool use, and cybersecurity-specific workflows. The implications for CISOs, product leaders, and engineering teams are practical: security programs will need tighter connections between discovery, validation, engineering, and release management. Organizations that can validate faster, fix faster, and maintain control over disclosure and production stability will have a structural advantage.

Chirag was clear that the public record does not show Mythos autonomously compromising well-defended enterprises from scratch. Its value depends heavily on the operating environment around it, including test harnesses, human review, and engineering processes. The report is available now on constellationr.com.

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Episode 132 will be hosted by Holger Mueller. Watch Episode 131 now on the Constellation Research YouTube channel and at constellationr.com.

Data to Decisions Digital Safety, Privacy & Cybersecurity Future of Work Revenue & Growth Effectiveness Agentic AI AI Generative AI Chief Information Officer Chief Technology Officer Chief Information Security Officer Chief Revenue Officer On ConstellationTV

SAP's Big Bet: Autonomous Enterprise or Overpromise? | 2026 Q2 SAP Coffee Corner

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.  Coverage Areas: Future of Work Tech Optimization & Innovation  Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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SAP Sapphire 2026 stood out. Not for a single headline announcement, but for something rarer: a coherent, end-to-end vision that actually held up across keynotes, analyst sessions, and conversations on the floor.

In the Q2 episode of Coffee Corner Radio, Martin Fischer and I covered what mattered most from this year's event. Here are the highlights.

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The Autonomous Enterprise Vision

For the first time since mySAP.com and the Enjoy initiative in the late nineties, SAP presented a vision compelling enough to anchor the entire conference. The autonomous enterprise narrative, centered on the new SAP Business AI Platform, brought together Business Data Cloud, Joule, the agentic framework, and BTP under one roof. Is it mostly a rebundling of existing capabilities? To a degree. But the commitment that these pieces work together and the platform-first framing of the main keynote signal a meaningful shift in how SAP thinks about its role for customers.

Build Your Own Agents

With 30-plus packaged agents announced and roughly 100 more expected this year, SAP's message was clear: the platform is there, but customers need to own their automation destiny. Packaged agents are a starting point, not a finish line. The key takeaway for any SAP customer right now is to evaluate which agents you need, assess the headroom in your backend systems, and start building.

Acquisitions That Signal Intent

Three acquisitions stood out. Veltio, for MDM capabilities that strengthen the case for an open data layer. Dremio, for data federation capabilities, though federation's track record in high-performance transactional scenarios warrants healthy skepticism. And Prior Labs, a two-year-old Freiburg AI startup acquired to advance SAP's tabular AI capabilities, with the notable decision to keep it as an independent research lab rather than absorb it into the product organization.

SAP is investing in the German software ecosystem, and that matters for the European installed base.

The API Policy Controversy

The pre-Sapphire API policy announcement landed with the subtlety of a dropped piano. The initial version read as broadly restrictive, suggesting third-party and custom integrations might no longer be permitted. Two updates and a 17-page FAQ later, the situation is clearer but not entirely settled. The policy reads stricter than the FAQs, and customers are right to want a concrete roadmap: which APIs, on what timeline, at what price.

The underlying direction is architecturally sound. SAP's final move toward a centralized, vendor-supported API framework is what the agentic era requires. But the execution gap between policy and delivery is the real question, and it needs an answer before customers can plan confidently.

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What Comes Next

The vision is the strongest SAP has put forward in this century. Now comes the hard part. Customers doubling down on SAP have a clearer path forward than they did a year ago. But the roadmap for APIs, the scalability of backend systems for agentic workloads, and the delivery of industry-specific capabilities will all be tested in the months ahead.

Listen to the full Coffee Corner Radio Q2 episode on the Constellation Research YouTube channel and all major podcast platforms

Future of Work Tech Optimization Data to Decisions Agentic AI AI Automation Enterprise Acceleration Chief Information Officer Chief Technology Officer Chief Operating Officer On CR Conversations

Scaling AI from Experimentation to Impact with IBM Consulting

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…...

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I had a great conversation at IBM Think 2026 with Javier Cassini, Global Managing Partner for Hybrid Cloud and Data at IBM Consulting. We'd just come off Constellation's Futures Forum conference, where I'd spent two days with 120 CEOs and board members. The theme was unmistakable: the metrics are real now, and the pressure to scale AI is no longer theoretical.

Javier confirmed what I was hearing on the ground. The question has fundamentally changed. Clients aren't coming to IBM asking for use cases anymore. They want to capture the dividend, and they want to do so at an industrial scale.

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Three Patterns Driving Real Results

Javier outlined three profiles he's seeing consistently across IBM's customer base.

1. The first is productivity at scale. Not 10-20% efficiency gains... we're talking 40-60% reductions in operating costs, depending on the domain. The key insight here is that you can't get there by layering AI on top of existing steps. You have to re-engineer the entire workflow from first principles. Finance transformation, order-to-cash, supply chain, and software development lifecycles. All of it gets rethought around AI, not retrofitted with it.
2. The second is decision velocity. I love this framing because velocity isn't just speed; it has direction. It means collapsing decision trees, automating steps that previously required a committee meeting every Friday, and doing it with better context and data than a human could have managed alone. So many processes aren't expensive in terms of headcount, but they're slow due to friction at specific points. That friction is where the opportunity is.
3. The third, and arguably the most strategic, is net-new revenue. Pearson was one of the examples on stage at Think: how do you build entirely new AI-native products and business models? Javier is clear that you typically need the second pattern to fund the third. But boards increasingly see this as existential. If you're not learning and building the flywheel now, you may be behind your competitors before you realize it.

Why Sophisticated Teams Still Struggle

This is the part of the conversation that I think gets glossed over too often. Even teams with strong data foundations and clear executive sponsorship hit walls. Javier's take: it starts with security, trust, and governance. If you don't invest in those early on, you'll hit problems at scale that you didn't anticipate in a clean MVP environment.

He also walked through a data readiness framework that I thought was genuinely useful. Most teams think about data in terms of cleanliness — is it accurate, is it structured? That's layer one. But you also need a semantic layer (what does the data actually mean?), a context layer (what does it mean in this specific situation, in this market, with these rules?), and a decision layer where feedback loops capture what actually happened and improve the model's autonomy over time. That last piece, the learning loop, is what separates organizations that get compounding returns from those that plateau.

The Operating Model Is the Real Bottleneck

Javier said something near the end of our conversation that I've been thinking about since. The bottleneck has moved. It's no longer a technology problem. It's an operating model problem. The question isn't whether the models are good enough; they are. The question is how quickly you can transform your organization, how quickly you can bring your team to new ways of working, and whether leadership understands the art of the possible well enough to provide real direction.

CEOs are feeling this acutely. At our Futures Forum conference, two things were top of mind beyond the standard profitability and productivity goals: whether I have the right people and whether my organization can actually evolve. Javier sees a lot of use-case proliferation that never scales because there's no single top-down commitment to a single vision. Organizations get attached to what they've already built, even when it hasn't reached scale, and it waters everything down.

What Success Actually Looks Like

One of the metrics Javier tracks is the level of agency: how much work can you delegate to agents versus keeping a human in the loop? The model he described is humans at the edge: setting direction, defining what they want, delegating execution to agents, and expanding that surface over time as trust and observability increase. Autonomy isn't a switch. It's a dial. And you build toward it through evals, feedback loops, and deliberate governance, not by hoping the models figure it out.

Interestingly, the organizations getting there fastest tend to be in regulated industries. They already know exactly what humans can and can't do. That clarity turns out to be a competitive advantage when you're designing AI systems with the right guardrails.

The conversation could have gone another two hours. We barely scratched the surface of governance trade-offs and the shift from automated decisions to trusted execution. More on that soon.

Future of Work Data to Decisions Digital Safety, Privacy & Cybersecurity Agentic AI AI Automation Chief Executive Officer Chief Operating Officer Chief Information Officer Chief Digital Officer On Event Update

What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

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

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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] ...

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What Happens When AI and Geopolitics Become Inseparable | DisrupTV Ep 440

Artificial intelligence is no longer just a technological shift — it is a structural force reshaping geopolitics, economic power, corporate governance, and leadership itself.

In DisrupTV Episode 440, hosts Vala Afshar and R “Ray” Wang were joined by an exceptional panel:

• Malcolm Turnbull, former Prime Minister of Australia
• Lucy Turnbull, former Lord Mayor of Sydney
• David Bray, Distinguished Chair of the Accelerator, Stimson Center & Principal/CEO, LDA Ventures Inc.
• Sheri Jacobs, Author of The Unexpected Power of Boundaries: Rethinking The Rules, Risks And Real Drivers Of Innovation

Across geopolitics, infrastructure, media systems, and organizational design, a single theme emerged:

In the AI era, trust is the currency — and boundaries are the operating system.

AI, Geopolitics, and the New Balance of Power

Malcolm Turnbull set the stage at the highest level. AI isn't changing everything — but it is profoundly amplifying existing strategic dependencies, particularly around energy and compute.

He identified two "currencies of the future": data and electricity. China, he argued, has positioned itself as an "electro-state" — with massive grid investment, leading global spending in renewables, and the capacity to power AI data centers at scale and lower cost than most Western competitors.

The implication for business leaders is sobering: choosing your technology stack is no longer a procurement decision. It's a geopolitical one. Organizations must ask who controls the platforms they're integrating into their operations, whether those operators are trustworthy partners, and what happens if access is suddenly constrained or weaponized.

Turnbull's warning was blunt: a three-to-six-month lead in frontier model capability is trivial compared to long-term structural advantages in energy and infrastructure.

From Open Internet to Centralized AI: A Very Different Playbook

Both Lucy Turnbull and David Bray drew a sharp contrast between the early internet era and today's AI landscape — and the differences matter enormously.

The early internet was decentralized, open, and permissionless. Barriers to entry were low. Garage innovators could build meaningful companies. Infrastructure and services spread globally with relatively few geopolitical constraints.

Today's AI landscape is a different beast: capital-intensive, compute-heavy, dominated by a handful of frontier model providers, and increasingly shaped by both corporate strategy and national policy. For the first time in three decades of digital globalization, we are seriously entertaining hard boundaries around access to models, data, and infrastructure.

Bray added a striking data point: by 2030, over 40% of the world's data is expected to be AI-generated — raising deep questions about authenticity, decision-making, and who controls the filters that shape what AI systems are allowed to say or show.

His prescription: decentralization. He sees genuine hope in edge AI and on-device models, alternative AI toolkits beyond today's dominant generative systems, and open-source and open-weight models that enable local innovation and community-level filtering.

Fragmented Realities, Media Silos, and the AI Filter Problem

One of Turnbull's deepest concerns is that AI may turbocharge an already fragmented information environment.

Digital tools and low-cost content production have already atomized media. People increasingly inhabit echo chambers, no longer sharing a common factual baseline. What once required major broadcast infrastructure can now be done with consumer electronics — from anywhere, by anyone.

In that environment, AI systems configured with ideological or commercial filters — especially those controlled by a single platform — could quietly shape what people see, learn, and believe at a population scale. The risk isn't limited to authoritarian states. Turnbull explicitly warned that illiberal manipulation of AI filters is a live risk inside democracies.

The leadership takeaway: AI content and filter governance must be treated as a core risk and ethics domain, not a technical side issue delegated to engineering teams.

What Boards Should Be Asking Right Now

The panel zeroed in on the boardroom — and offered some pointed provocation.

David Bray's question for CEOs: "What happens if our company becomes the target of a conspiracy theory or disinformation attack?" This is no longer hypothetical. Disinformation can move markets, damage brands, and undermine trust in leadership overnight.

Lucy Turnbull's question: "How can we increase shareholder returns while being a civil, ethical, and humane enterprise?" The premise is that profit and principles are not in tension — they must be integrated.

Malcolm Turnbull's question — deceptively simple, deeply technical: "Who has administrative privileges on our company's network and systems, and who are these three people?" That single question cuts straight to cybersecurity, insider risk, and operational resilience.

Bray also surfaced lesser-known but very real risk chains that boards are largely ignoring: helium shortages linked to Middle East geopolitical tensions that spike chip production costs two-to-four times and disrupt medical imaging operations; infrastructure constraints limiting the US's ability to add new data center power connections at the pace AI workloads demand; and growing Gen Z unease about AI's impact on employment — a source of genuine intergenerational tension if left unaddressed.

The bottom line for executives: AI risk is not just about algorithms. It is supply chain risk, infrastructure risk, reputational risk, and social stability risk — all at once.

Leadership in an Age of Turbulence

When asked what qualities leaders need most right now, Malcolm and Lucy Turnbull converged on fundamentals that become more critical, not less, in an AI-driven world.

For Malcolm, it comes down to character and trust. Trust is built by telling the truth, being transparent, and maintaining consistency — not just when it's convenient. He offered a practical example from his time as Australia's communications minister, when he inherited a problematic broadband rollout. Rather than obscure the problems, he published a simple weekly spreadsheet: how many premises were passed, how many were connected, broken down by technology. The act of sustained, transparent accountability — even when few people checked — rebuilt trust over time.

He also invoked a surfer's metaphor for navigating change: you either catch the wave and ride it, or fight against it and get dumped. Openness to innovation isn't optional. Just because something was done a certain way yesterday doesn't mean it should be done that way tomorrow.

Lucy Turnbull emphasized the combination of passion, purpose, and judgment — the ability to avoid reckless decisions even when outliers seem to be profiting from them. She pointed to her father, a trial lawyer who practiced into his 80s, as a model of focus, commitment, and treating every case as the most important one.

David Bray shared a personal story of enduring a multi-year disinformation campaign, ultimately emerging vindicated. His lesson: do not adopt the tactics of those who lack integrity, even when they appear to be winning. In an era of polarization and information manipulation, staying grounded in integrity and taking the next right step is itself a leadership strategy.

The Unexpected Power of Boundaries

The second half of the episode shifted to Sheri Jacobs, whose book — The Unexpected Power of Boundaries — provided a perfect thematic bridge from geopolitics to personal leadership.

Jacobs' central argument is counterintuitive: many leaders misuse the mantra of "thinking outside the box." They call for unlimited freedom in brainstorming and innovation, but teams lacking clarity on constraints default to safe ideas, incremental thinking, and compliance with the status quo. Real creative freedom requires knowing where the edges are.

She draws a meaningful distinction between constraints — imposed by outside forces like time, money, or regulation — and boundaries, which are chosen limits we set for ourselves and our organizations. Boundaries are not limitations. They are sources of power because they provide focus, clarify priorities, accelerate execution, and build trust by making expectations explicit.

One of her most resonant lines from the episode: "Business grows at the speed of clarity, and clarity is boundaries."

That clarity matters for customers (what you do and don't do), employees (what's expected and off-limits), partners (where you play and how you operate), and investors (what risks you will and won't take). Without it, organizations overcommit, burn out their teams, dilute focus, and stall innovation. With it, they run more experiments safely, learn faster, and allocate energy where it matters most.

Jacobs connected this directly to AI: trust will be the currency of the AI economy. Speed matters, but trust scales. Boundaries — around data use, model behavior, human oversight, and customer commitments — are how organizations signal and sustain trust at machine scale.

Operating at Machine Scale in a Human-Scale World

Ray Wang tied the conversation together with a challenge many leaders feel acutely: machines can run 24/7, humans cannot. AI raises expectations around responsiveness, volume, and velocity in ways that are genuinely unsustainable without intentional design.

Jacobs' framework offers the corrective. The answer isn't to match machine speed — it's to define clear human boundaries around availability, focus, and priorities, aligned with organizational purpose and long-term sustainability.

For boards and senior leaders, this also means being honest about whether existing leadership patterns are fit for a new era. Old playbooks don't apply when a significant portion of content, analysis, and decision support is machine-generated. Building cultures where people have permission to experiment, failure is treated as learning, and boundaries are explicit, fair, and regularly revisited isn't just good leadership hygiene — it's a competitive strategy.

Key Takeaways

• AI is a geopolitical lever, not just a technology. The choice of AI platform is now a strategic decision about national alignment, access risk, and long-term infrastructure dependency.
• Energy is the new frontier advantage. Nations with the cheapest, most abundant power will dominate AI infrastructure — and China is aggressively positioning itself to win that race.
• Trust is the decisive competitive currency — between nations, companies, customers, and teams. Speed gets you noticed; trust keeps you in the game.
• Boundaries enable innovation, they don't limit it. When people know where the edges are, they're freer to explore bold ideas within that frame. Clarity is the accelerant.
• Boards are underestimating AI risk. Disinformation, insider threats, supply chain disruptions, and workforce instability are all AI-adjacent risks that demand board-level attention now.
• The information environment is fragmenting fast. AI-powered filter systems, in the wrong hands, could quietly reshape what millions of people believe — including inside democracies.
• Operating at machine scale requires human-scale boundaries. Leaders must define what they will and won't do — for their organizations and themselves — before AI speed burns them out.

Final Thoughts

DisrupTV Episode 440 was, at its core, an argument for a different kind of AI leadership — one grounded not in velocity but in integrity.

The panel made clear that the technical dimensions of AI — model capability, compute infrastructure, data volume — are only part of the challenge. The harder and more consequential questions are human ones: Who do we trust? What do we stand for? Where are our edges? What do we owe our teams, our customers, and the broader world as we integrate these systems into the fabric of how we operate?

Malcolm Turnbull's surfer metaphor is worth sitting with. The wave is coming regardless. The leaders who will shape what's on the other side are not those who paddle hardest — they're those who read the water honestly, stay grounded in their values, communicate with transparency, and build the kind of trust that can withstand speed, pressure, and disruption.

In the AI age, speed gets you noticed. Trust keeps you in the game. And boundaries — chosen, communicated, and honored — are what make that trust real.

Related Episodes

If you found Episode 440 valuable, here are a few others that align in theme or extend similar conversations:

• AI Governance and Innovation: From Data Center Risk to Exponential Leadership | DisrupTV Ep. 437
• What Do You Really Stand For in the Age of AI? | DisrupTV Ep. 438
• The Human Edge in an Age of Agentic AI | DisrupTV Ep. 439

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

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

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

Cornerstone Workforce AI: What It Is, What It Does, and Why You Should Start Now

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the&nbsp;cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.&nbsp; Coverage Areas: Future of Work Tech Optimization &amp; Innovation&nbsp; Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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Holger Mueller sat down with Cornerstone OnDemand's Suchi Upadhyayula in New York, on the day they launched Cornerstone Workforce AI. Here is what customers need to know.

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What Cornerstone Workforce AI Actually Delivers

Let's start with what is new for existing customers. If you are on Cornerstone's learning platform today, meaningful AI innovation is arriving for you right now.

1. First, there is a course assistant. This is built directly into the learning experience and helps learners go deeper on topics they care about, complete quizzes, and engage with content in a more interactive way than was previously possible. It is a better learning experience, not just a chatbot bolted on top.
2. Second, there is an adaptive learning agent with role-play capabilities. This is significant. The ability to practice what you have just learned in a simulated scenario within the same platform closes a gap that traditional learning management systems have never been able to address. You learn. You practice. You retain.
3. Third, for customers on the talent platform, there are new assistive features for performance reviews. Nobody loves writing performance reviews. Managers dealing with 360 feedback across large teams lose meaningful hours every cycle. Cornerstone is now helping surface that work faster with AI assistance, both for the person writing the review and for the manager synthesizing feedback.

The Intelligence Plus Package

These capabilities land differently depending on your current setup. If you are a Learn Plus customer, you get meaningful new functionality out of the box at no additional cost. If you are looking for more agentic, outcome-based capabilities, that is where Intelligence Plus comes in. The tier structure is logical, and the on-ramp is designed to let customers build from where they are rather than rip and replace.

Custom Workflows and Forward Deployed Engineers

Beyond the packaged capabilities, Cornerstone is now offering something more bespoke. If you have a specific outcome in mind, say, a custom onboarding flow tailored to your organization's brand and business processes, Cornerstone will send forward-deployed engineers to build it with you. This is a meaningful shift. It is not a vendor handing you a product and leaving. It is a co-creation model that takes your nuances seriously.

This approach is gaining momentum across enterprise software, and Cornerstone is leaning into it correctly.

MCP Support and the Open Platform Play

One of the more technically interesting announcements is MCP support. Cornerstone has built a trusted data platform as its foundation, layered strong APIs on top of it, and is now exposing those APIs as MCPs. In practice, this means that if your organization is already running Claude, OpenAI, or another agent platform of your choice, you can connect directly to Cornerstone's data sources and capabilities via standard MCP protocols.

The result is a flexible, composable architecture. Your enterprise decides which agent platform to use. Cornerstone's data and functionality connect to it. The learning content meets employees where they already are, in Teams, in Slack, in whatever tools they use every day. That is the right design principle, and it is good to see it in practice.

Skills Without the Spreadsheet

One of the sharpest observations in the conversation was about how skills are actually built and detected. Nobody wakes up in the morning thinking about which skills they need. They wake up, go to work, and do tasks. A Java developer who needs to learn Python is not thinking about skill acquisition. They are thinking about getting their work done.

Cornerstone's approach to skills detection is task-based rather than declarative. Instead of asking employees to fill out yet another skills inventory spreadsheet, the system infers skills from what people are actually doing. That is the right direction, and it sidesteps one of the oldest failure modes in HR technology.

Making Your Content AI-Ready

Most Cornerstone customers have rich learning catalogs assembled over the years. The challenge is that a catalog sitting in a system is not the same as that catalog being accessible to AI. Cornerstone is launching assistive tools to help customers curate, select, and vectorize their existing content to make it AI-ready. User-generated content can be promoted to AI visibility with a single step. It is a practical, low-friction path to getting value from what already exists.

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Start Now

The most important piece of advice from this conversation is simple: start the journey now.

There are still many organizations sitting on the sidelines, waiting to understand exactly how the AI inference works, whether compliance requirements are met, and whether the timing is right. Cornerstone's message is that they are here to help navigate those internal approval processes. But the deeper point is this: the innovation cycle in this space is no longer yearly. It is quarterly at best and monthly at worst. If you wait for a perfect moment, you will spend the rest of the year catching up to organizations that started six months ago.

You do not need to have it all figured out. You need to get started, learn from it, and build from there. That is exactly what Cornerstone is building toward with its co-creation model, and it is the right instinct for where enterprise AI is right now.

Human to the power of AI. That is the frame Suchi closed with, and it is a good one.

Future of Work Tech Optimization Data to Decisions AI Machine Learning Agentic AI Digital Transformation Customer Experience Chief Human Resources Officer Chief Information Officer Chief Technology Officer Chief Operating Officer Chief Executive Officer On CR Conversations

The Future of Enterprise Resource Planning (ERP) in the Age of AI

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

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

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A lot of folks think they can slap an agentic AI on top of an Enterprise Resource Planning (ERP) and get to modernization. They're wrong. And we know this because we've been talking to over a hundred early adopters who are in the middle of this transition right now, and they're telling us directly: it doesn't work that way.

Here's what's actually going on.

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The Myth of Easy AI Modernization

You can't bolt AI onto a system without expecting something to break. The questions that matter are deeper than the AI layer itself. Where does the data connect? Where does the business logic live? How do you get to the last mile of experience? These are hard questions, and they don't get answered by adding an agent on top.

AI expertise is now a commodity. Everyone has it. The real differentiator is what you're using AI for, specifically building experiences, connecting value chains, and designing for both human and machine scale. That's the work. That's where the value is.

ERP Systems of Record Are Gold

Here's what the SaaSpocalypse crowd keeps getting wrong. ERP systems of record are not going away. They are the core of your business information and logic, and they are not easily replaced. The distribution, the SaaS delivery, the global systems integrators, the client relationships, and the support infrastructure built around these systems, that is a moat. A real one.

Constellation's position is clear: core ERP systems are not only going to survive AI displacement, but also serve as the foundation for the agentic AI revolution. The modernization happening right now is being built on top of ERP, not in spite of it.

What Modern ERP Actually Looks Like

The shift is real, but it takes time. Here's how it maps out.

Traditional ERP is screen-based. Modern ERP is headless and machine-to-machine. Decisions today are human-heavy. In the future, they will be highly machine-automated. Logic today is heavily deterministic. In the future, it will be probabilistic and eventually counterintuitive. Data today lives in static schemas and large databases. In the future, it will be ontology-driven and microservices-based.

These are not incremental changes. They are architectural ones. And that is exactly why you cannot just slap an agent on top and expect things to improve. We learned this lesson with RPA. The same mistake is being made again. If the core is not modernized, nothing built on top of it will reach its potential.

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Three Big Shifts to Watch

First, the machine-to-machine world is here. Whether it is deterministic automation for known workflows or probabilistic decision intelligence for more complex choices, the question of when and where a human is inserted in the process is becoming the most important design decision in enterprise technology. We learn from those insertions. We train on them. We improve.

Second, OT and IT are converging. Operational technology and information technology are coming together, with decisions happening at the edges. This is going to change quality, supply chains, and safety in ways we are only beginning to understand.

Third, security is not optional. We are building for a post-quantum world. Anthropic's research into finding software vulnerabilities is just one signal that the threat surface is expanding. Token economics and security concerns are also driving a meaningful shift back toward on-premises deployments, and zero-trust security protocols are being reexamined hard.

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The Bottom Line

ERP is not dead. It is not being replaced by AI. It is being transformed by it, slowly and carefully, on top of a foundation that took decades to build.

The organizations that understand this, that invest in modernizing the core rather than papering over it with agents, will be the ones that reach the autonomous enterprise. The ones that don't will find themselves rebuilding from scratch, just like the companies that bolted RPA onto broken processes and wondered why nothing got better.

These are the trends shaping the next era of ERP. We are watching it all closely.

Tech Optimization Data to Decisions Future of Work Agentic AI AI ERP Enterprise IT Enterprise Acceleration Quantum Computing Cloud Chief Information Officer Chief Technology Officer Chief AI Officer Chief Executive Officer Chief Financial Officer Chief Operating Officer On CR Conversations

Tokenmaxxing, DAM, and the AI Blind Spot in Professional Services | ConstellationTV Episode 130

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…...

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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]. ...

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Episode 130 of Constellation TV covers a lot of ground. Hosted by Liz Miller, VP and Principal Analyst at Constellation Research, this one moves fast and pulls no punches. Here's what we covered.

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Tokenmaxxing: Right or Wrong?

Liz opened the debate with a term she made clear she does not enjoy, and invited Holger Mueller and Mike Ni to weigh in.

Holger framed it as a potential democratization of business automation. For the first time, individual employees can experiment with AI without filing a change request or waiting on IT. That's new, and it matters.

Mike's take centered on the hero. The employees who go deep and push the limits of what AI can do are the ones who create the cultural shift that eventually brings the rest of the organization along. Token usage as a metric is blunt, but unleashing the right people isn't.

Liz pushed back on both. The problem isn't experimentation. It's when maxing becomes the measure of success. Usage without purpose isn't a strategy, and organizations that treat it like one will end up farther behind, not further ahead. The right measure isn't how much. It's what changed because of it.

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Constellation ShortList: Digital Asset Management

DAM doesn't get the attention it deserves. Liz made the case that digital asset management is the last mile of experience delivery and the first technology that actually brings experience teams together in a way that works.

She walked through the three maturity stages: brand assurance, where it's about storage and findability; brand security, where controls, versioning, and metadata come in; and brand safety, where AI becomes the autonomous force keeping assets usable, rights-compliant, and built for delivery at scale.

Smartsheet's Brandfolder stood out on the shortlist for its seamless integration with work management, its clean UI, and its ability to serve both power users and occasional visitors without making either group feel like they're climbing a hill just to find a logo. If you're still treating your DAM like a big bucket of folders, it's time to rethink it.

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The AI Problem PSOs Keep Getting Wrong

R "Ray" Wang sat down with Robert Cesafsky, COO of Certinia, off the back of his recent HBR piece, and the conversation cuts to the heart of why so many AI deployments in professional services are underperforming.

Most PSOs are putting their AI investment into service delivery. Client-facing work. Research, content, project execution. That makes sense on the surface. But the services management side, estimation, quoting, resource management, billing, revenue recognition, is being almost entirely ignored. And it requires a fundamentally different approach.

Service delivery can tolerate probabilistic AI. Services management cannot. When you're doing ASC 606 revenue recognition, you need deterministic AI. Rules-bound, auditable, traceable. The mistake PSOs keep making is collapsing both sides into one strategy and wondering why it isn't working.

The other insight worth sitting with: where you insert the human in the loop is more important than the automation itself. Expertise is becoming a commodity. Experience is not. We are the last generation of managers to only manage humans. The organizations that build their AI architecture around that reality will be the ones that win.

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Buzzword Bingo with Larry Dignan

Liz wrapped the episode by lobbing the worst buzzwords in enterprise tech at Larry Dignan to get his unfiltered reaction. Automagical. Agentrification. Customer obsessed. IMHO. And of course, tokenmaxxing.

Larry's take on token maxing: nothing is more painful than a 60-year-old CEO saying maxing. It sounds absurd, it proves nothing, and a donkey can eat a lot of hay and still not win the Kentucky Derby. If your barometer for AI success is how many tokens you burned, you are measuring the wrong thing.

Watch the full episode and subscribe to Constellation Research YouTube for Episode 131, hosted by Martin Schneider, in two weeks.

Tech Optimization Data to Decisions Future of Work Agentic AI AI ERP Quantum Computing Digital Transformation Cloud Chief Information Officer Chief Technology Officer Chief Financial Officer Chief Operating Officer Chief Information Security Officer On ConstellationTV

Project Management vs. Project Delivery: Why the Difference Finally Matters

R "Ray" Wang

Principal Analyst and Founder
Constellation Research

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

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The age of AI has created an unprecedented opportunity for professional services organizations to transform how they work. Yet most PSOs are making a critical mistake in how they deploy AI, and it's costing them the exponential gains they're chasing.

R "Ray' Wang recently sat down with Robert Cesafsky, COO of Certinia, who just published a sharp piece in Harvard Business Review on exactly this problem. PSOs are solving the wrong AI problem, and until they fix the framing, no amount of investment will get them to the outcomes they want.

The Two Sides of Every Services Business

Every professional services organization runs two fundamentally different businesses under one roof.

The first is service delivery. This is the client-facing work. Research, content synthesis, content creation, unstructured data management, and project execution. This is where most AI investment is going today, and understandably so. Large language models are genuinely powerful here, and the ROI is visible.

The second is services management. Estimation, quoting, resource management, project financial management, forecasting, billing, and revenue recognition. This is the business of running the business. And this side is being almost entirely ignored.

Probabilistic vs. Deterministic: The Distinction That Changes Everything

Here is the core of Robert's framework, and it matters enormously.

Services delivery can tolerate probabilistic AI. Outputs vary. Creativity is welcome. Some hallucination risk is manageable when a human is reviewing the work.

Services management cannot work that way. Full stop.

Try running ASC 606 revenue recognition on a probabilistic model. Try invoicing a client or reallocating resources across a project portfolio without auditability. It does not work. These workflows require rules, enterprise context, and a clear record of exactly what the AI did, when, and why.

The mistake PSOs keep making is collapsing both sides into one AI strategy. They deploy an LLM across the enterprise and expect it to handle delivery and management equally well. The outputs look plausible right up until they create a compliance problem or a billing error that nobody can explain.

The right architectural move is to harness probabilistic AI within a deterministic, rules-bound, auditable framework for anything that touches service management. That is the approach Certinia is taking with its Veda platform, and it is the right instinct.

The Convergence Is Already Underway

Here is where the urgency compounds. The line between service delivery and service management is disappearing fast.

Think about what an end-to-end services workflow looks like in the AI era. A client conversation happens. Unstructured data. That conversation should flow directly into a statement of work, a services estimate, a staffed project with both human and digital workers, and ultimately into billing and revenue recognition in real time. Delivery informs management. Management informs delivery. The feedback loop is continuous.

Organizations that have built these two sides as separate AI strategies will never close that loop. They will never reach the autonomous enterprise. They will have two half-built systems producing noise while their competitors are compressing project timelines from months to weeks.

Where You Put the Human Is the Whole Game

As AI compresses decision timelines, the human is in the critical path of almost every significant outcome. That means more decisions, faster, with less time to deliberate. And it means that knowing exactly where to insert human judgment in the workflow is more important than the automation itself.

Expertise is becoming a commodity. Experience is not.

We are the last generation of managers to only manage humans. The leaders coming up behind us will manage mixed teams of human and digital workers as a matter of course. Services firms that build their AI architecture around that reality, pairing human ingenuity with the scale of digital labor, will be the ones to achieve 10x, 100x, and even 1000x gains.

The ones that don't will find themselves on the wrong side of two S-curves at once.

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The Bottom Line

PSOs need to get three things right to compete in the AI era.

1. First, recognize that service delivery and service management require fundamentally different AI approaches. Probabilistic for delivery. Deterministic and auditable for management.
2. Second, stop treating AI as a single strategy across the whole business. Break it apart. Solve for each side correctly before trying to connect them.
3. Third, connect them. The autonomous enterprise is only possible when delivery and management are in lockstep, sharing data, informing each other, and closing the loop in real time.

Read Robert's HBR piece. It came out on May 5. Then look hard at where your AI investment is actually going and ask whether you are solving the right problem. Most organizations are not. Yet.

Future of Work Tech Optimization Data to Decisions Agentic AI AI Enterprise Service Digital Transformation Enterprise Acceleration CCaaS Chief Operating Officer Chief Information Officer Chief Financial Officer Chief Executive Officer On CR Conversations

How Conversational AI is Transforming Db2 Management

Holger Mueller

Vice President and Principal Analyst
Constellation Research

Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the&nbsp;cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.&nbsp; Coverage Areas: Future of Work Tech Optimization &amp; Innovation&nbsp; Background: Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business Process as a…...

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Constellation Research VP & Principal Analyst Holger Mueller sat down with Miran Badzak, IBM's executive director of databases, at IBM Think for a timely conversation very relevant to enterprise computing. IBM is making a serious move in the database management space with Genius Hub, a new AI-powered product built specifically for Db2 DBAs, and the timing could not be more relevant.

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What Is Genius Hub?

Genius Hub is a conversational AI interface designed to help database administrators manage large fleets of Db2 databases, whether they run on-premises, in the cloud, or across hybrid environments. At its core, it handles the undifferentiated work that consumes DBA time: patches, updates, maintenance, tablespace management, anomaly detection, and more.

Think of it as a hyper-specialized assistant that knows your specific version and distribution of Db2, has access to your telemetry data, and can walk you through issues in plain language.

From Recommendations to Actions

Earlier this year, Genius Hub launched with AI-powered recommendations. You flag an issue, the AI tells you what it thinks you should do, and you decide whether to act. That was already useful.

What IBM announced at Think takes it a step further. Genius Hub can now take actions on your behalf. You review the proposed steps, approve, and the agent executes. It is a meaningful leap from advisory to agentic, and IBM has been deliberate about keeping humans in the loop throughout.

As Miran put it, the AI won't delete your database. That may sound like a low bar, but in mission-critical environments running banking applications and complex enterprise workloads, it matters more than it sounds.

Flexible Inferencing, On Prem or Cloud

One thing worth noting for enterprise buyers is the flexibility in inferencing. Genius Hub supports cloud-based inferencing across IBM Cloud, AWS Bedrock, Microsoft AI Foundry, and Google Vertex. It also supports fully air-gapped on-premises deployments running on AMD Instinct and, newly announced, Intel Gaudi chips.

For heavily regulated industries where data cannot leave the building, that on-prem optionality is not a nice-to-have. It is a requirement.

MCP Support Opens Up Integration

IBM also announced this week that it will support MCP servers for Genius Hub. This means Db2 management can now be integrated into broader fleet management workflows and tooling. The example Miran gave was instructive: with a single command, you could turn off all SAP instances running on Db2 across your environment. That kind of cross-system orchestration is where agentic AI starts to show its real enterprise value.

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Getting Started

A free trial is available at ibm.com/db2. The install takes a few minutes, you connect it to the databases you want to manage, and you're running. No need to bring your own inferencing infrastructure. Genius Hub is also included in the new AI editions of Db2 for existing customers upgrading.

Bottom Line

IBM is moving with real urgency here, shipping updates multiple times a month and rapidly expanding capabilities. Genius Hub is not a bolt-on feature. It is a purpose-built AI layer for one of enterprise computing's most foundational workloads. Worth watching closely.

Data to Decisions Tech Optimization Future of Work Agentic AI AI Data to Decisions Cloud Enterprise Acceleration Chief Information Officer Chief Technology Officer Chief Information Security Officer On Event Update