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

Liz Miller

Vice President & Principal Analyst
Constellation Research

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

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

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Outputs Are Not Outcomes

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

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

The Martech Stack Is Finally Shrinking

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

It Was Never a Data Problem

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

Compliance as an Enabler, Not a Blocker

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

The Secret Sauce AI Still Can't Replicate

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

Stop Talking About Speed

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

The Bottom Line

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

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

Michael Ni

Vice President and Principal Analyst
Constellation Research

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

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

The Context Washing Debate

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

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

OpenText: AI Agents as Network Managers

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

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

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

AI in Marketing: Less Stack, More Process

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

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

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

Qualcomm's Data Center Bet

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

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

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

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

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

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

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

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

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

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

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

The Action Trap: Burning Tokens Without Outcomes

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

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

From Selling Tools to Selling Outcomes

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

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

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

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

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

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

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

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

Data: Important — But Don’t Wait Forever

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

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

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

The 90/10 Rule: Where the Real Moat Lives

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

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

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

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

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

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

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

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

Final Thoughts

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

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

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

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

Related Episodes

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

• Winning with AI by Becoming Data Inspired | DisrupTV Ep 443
• How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441
• Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

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

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

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

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

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

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

Nobody should be surprised that AI is expensive

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

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

Who built the leaderboards, and why

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

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

AI is a utility, so treat it like one

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

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

Right-size the model to the task

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

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

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

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

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

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

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The takeaway

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

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

Chirag Mehta

Vice President and Principal Analyst
Constellation Research

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

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

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

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The problem business networks solve

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

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

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

Onboarding and the build-versus-buy question

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

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

Managing at scale

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

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

From acceleration to transformation with AI

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

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

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

One piece of advice

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

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

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

Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

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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Winning with AI by Becoming Data Inspired | DisrupTV Ep 443

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

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

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

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

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

Kill Your Pilots: Why “Pilot Purgatory” Destroys Momentum

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

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

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

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

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

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

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

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

Beyond Cost Cutting: AI as Value Reclamation and Capability Expansion

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

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

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

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

Building an AI-Fluent Workforce

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

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

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

The Case for an AI Value Owner

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

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

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

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

Part 2: From Data Frustration to Data Inspiration

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

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

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

Final Thoughts

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

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

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

Related Episodes

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

• What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440
• How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441
• Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

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>

On-Prem or Cloud for AI? Outcome-Based Pricing and Spotting Agentic AI Washing

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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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 AI strategy keeps circling back to the same hard questions: where should workloads actually run, how should vendors get paid for outcomes, and how can buyers tell real AI capability from a marketing label. Episode 132 of ConstellationTV takes on all three.

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On-Prem or Cloud? The Debate Continues

Host Holger Mueller opened the episode with the Big Debate, joined by Michael Ni and Esteban Kolsky, on whether AI has to run in the cloud or can live on-premises. Michael argued that AI follows its own kind of gravity, pulling compute toward wherever inference demand and real-time decisions actually happen, which points toward a distributed model spanning cloud, edge, and on-prem layers. Esteban pushed back with a data-gravity and elasticity argument, comparing cloud economics to the historical shift away from companies generating their own electricity. Holger argued for the edge on cost and efficiency grounds, citing Apple's on-device AI push as evidence that enterprises don't need to run everything through the server. The debate landed where these things usually do: it depends on the layer, and the next few years will be about getting that placement right rather than picking a single side.

Personalization Is a Means, Not the Outcome

Liz Miller used her Marketing Minute to share takeaways from Pega World, Pegasystems' annual event. Her core point: personalization is a tactic, not the business outcome itself. The real goal is a more durable, profitable customer relationship, and Pega's customer decision hub has been quietly doing that work for years, well before "agentic" became the term of the moment. Liz also referenced comments from Pegasystems CEO Alan Trefler on deliberately testing and deploying agentic AI, without losing sight of why the work matters in the first place.

Outcome-Based Pricing: Great on the Whiteboard, Hard in Practice

Larry Dignan covered the renewed push toward outcome-based pricing, with Oracle, Pegasystems, HubSpot, and UiPath all referencing it recently. His take is skeptical. Outcome-based pricing runs into the same problems revenue-share models always have: nobody agrees on how to measure the outcome, nobody wants to give up their share once there's something to share, and it's unclear who bears the cost if an outcome takes more time or tokens than expected. Larry expects the topic to come up heavily on Q2 earnings calls, but his advice for now is to tread carefully.

Five Ways to Spot Agentic AI Washing

Holger closed the episode with a preview of his new Best Practice report on agentic AI washing, which extends the same skepticism that applied to cloud washing a decade ago. Of the 18 questions in the full report, he highlighted five: whether the capability existed before generative AI took off in 2023, whether it's just a chatbot with a new label, whether it actually runs in the cloud and can scale elastically, whether it can access third-party data rather than just the vendor's own, and whether the underlying capabilities are extensible rather than fixed. Vendors that fail these tests, in Holger's view, are most likely repackaging something that already existed.

Tech Optimization Data to Decisions Next-Generation Customer Experience Marketing Transformation Agentic AI AI Quantum Computing Chief Information Officer Chief Technology Officer Chief Marketing Officer Chief Revenue Officer On ConstellationTV

Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

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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Will the Future Like You? Finding Purpose in the Age of AI | DisrupTV Ep 442

As AI learns to imitate us better than we know ourselves, two big thinkers ask the question that matters most: who are we becoming, and what are we actually here to do?

Part 1: Persona Fog — When the Performative Self Takes Over

Patricia Martin opens with a striking reframe of the last decade. We have moved, she argues, from the search for self to the performance of self. After years of curating, posting, and optimizing ourselves for algorithms, many of us are now experiencing what she calls persona fog — a civilizational malady of the psyche.

Persona fog emerges when our persona — Jung’s term for the social mask we wear — becomes load-bearing, carrying weight it was never designed to hold. We invest in multiple, fragmented personas in search of validation and attention, while the true self remains underneath, increasingly hard to access, like a landscape obscured by fog.

The symptoms, Martin says, are everywhere: chronic stuckness, persistent self-doubt, an inability to craft a compelling future story for yourself, and a deeper confusion that goes beyond what to do next — a confusion about who you fundamentally are.

Algorithms, Intermittent Reinforcement, and the Fragmented Self

Martin connects this directly to how social media and algorithms shape identity. Drawing on B.F. Skinner’s concept of intermittent reinforcement, she describes how content creators and everyday users experience wild, unexplained swings in reach — millions of views one day, a fraction the next. Over time, this erodes a stable sense of who we are and what works.

The result is a kind of self-editing by algorithm: we lean into the parts of ourselves that get rewarded and quietly delete the parts that fall flat. We accumulate multiple personas optimized for attention but disconnected from our core self — fueling an addiction-like cycle of posting more, tweaking more, and chasing the next hit of validation, fragmenting further with every round.

AI as the “Most Colossal Persona Fog Machine”

Martin has been working with AI since 2016, and her outlook is sober. AI, she says, is going to be the most colossal persona fog machine we’ve ever encountered. It will get you — deeply understand your patterns and preferences. It will amplify you — enhance your content and presence. And it will optimize you — performing your style better than you do.

The end result, in her words, is that AI will author versions of you that you never intended.

In a world where the self is both a unit of measure and a unit for sale, and social media is the supply chain, AI doesn’t slow that system down — it accelerates it.

The Radical Act of Self-Determination

If AI, algorithms, and platforms are pushing us toward fragmentation, Martin’s counter-move is self-determination and integration. Consciousness, she argues, is the core tool we all have — a powerful, built-in guidance system that starts sending symptoms like stuckness, doubt, and discontent when it’s buried under layers of false selves.

We are entering an era, Martin says, where the most crucial work is integration — becoming the editor-in-chief of who you really are. This isn’t about quick hacks. We are inside a systemic problem, and systemic problems require self-development, step by step. A core practice is simple to state and hard to do: decide what is really you and what is not, and discard what isn’t true.

In this environment, Martin argues, true leadership will be defined less by hard technical skills and more by clarity of self, sovereignty over one’s identity, and the capacity for integration — including the shadow, in Jungian terms. These qualities will be rare, and therefore immensely valuable.

Reclaiming Attention and Transcendence

Martin cites her interview with Mihaly Csikszentmihalyi, author of Flow, who distinguishes two levels of consciousness: attention, the lower grade, easily hijacked by platforms and notifications, and transcendence, the higher state where peak performance and deeper meaning reside. As our ability to control our focus erodes, so does our access to transcendence — with profound implications not just for performance, but for human flourishing itself.

Becoming more conscious about what we post, why we post it, and how it shapes us, Martin argues, is itself a way of reclaiming both attention and self.

Building Identity in Others: The “Good Enough” Parent, Partner, and Leader

Martin also stresses the constructive side: how we can help others build a stable sense of self in an age of attention scarcity. Drawing on psychiatrist Donald Winnicott’s idea of the good enough parent, she notes that identity is reinforced when someone who matters puts the phone down, pays real and undivided attention, and witnesses you at your most alive, engaged, and curious.

This applies to children, romantic partners, friends, coworkers, and team members alike. Moments of being truly seen — without performance, cameras, or algorithms — are now rare, and therefore deeply powerful. In this age, simply witnessing someone’s core self is an act of identity-building.

Martin prefers the term core self over authentic, noting how authenticity has been commoditized and is often performed online. The task, she says, is to recognize that each of us has an innate kernel, like the seed of a tree, develop reverence for that core in ourselves and others, and support growth that is consistent with that kernel — because a maple tree will always be a maple tree.

Part 2: What’s the Point? Turning Purpose into a Daily Superpower

If Martin explores how we’re losing ourselves, Tom Rath focuses on how we can rebuild purpose — not as a grand, once-in-a-lifetime revelation, but as something we manufacture daily.

Rath is the author of multiple bestsellers — including How Full Is Your Bucket, StrengthsFinder 2.0, Eat Move Sleep, and Life’s Great Question — with over 10 million copies sold. His new book, What’s the Point? Turning Purpose into Your Daily Superpower, takes direct aim at the conventional wisdom of follow your passion.

Final Thoughts

As the episode wrapped, Vala and Ray reflected on the privilege of spending an hour each week with thinkers who have written more than ten books and sold millions of copies, yet remain willing to share deeply personal stories.

Two big threads tie this episode together. The first is a question of identity: who are we becoming in a world of AI and algorithms? Patricia Martin warns of persona fog, AI-driven amplification, and the risk of losing our core self, and calls for integration, self-determination, and a renewed reverence for the core self in ourselves and others.

The second is a question of direction: how do we build purpose amid disruption? Tom Rath reframes purpose as something we build daily, not passively discover. He challenges us to replace follow your passion with follow your contribution, and offers a simple compass — what’s the point? — for structuring days, careers, and lives that matter.

In Ray’s words, we are in an age where we must question what it means to be human in the age of AI. If we lose sight of that, we risk losing sight of where humanity is going.

Read together, Will the Future Like You? and What’s the Point? offer a powerful dual lens. Martin helps us see how we’re being manipulated, fragmented, and performed. Rath helps us rebuild our lives around contribution, daily choices, and enduring relationships.

Related Episodes

If you found Episode 442 valuable, here are a few others that align in theme or extend similar conversations:

• What Happens When AI and Geopolitics Become Inseparable? | DisrupTV Ep 440
• How Market Engineering and Mission Guardians Will Shape the Next Era of Business | DisrupTV Ep 441
• 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>

Revenue Intelligence: The Execution Engine for Growth

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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85% of Fortune 500 companies are growing at single digits, and the pressure to find profitable growth has never been higher. In this segment, analyst Martin Schneider breaks down revenue intelligence, why it's one of the hottest categories in enterprise tech right now, and what it actually takes to optimize customer lifetime value across the full sales journey.

Martin covers the key players in the space, from RevOps platforms to legacy CRM providers, what buyers (CROs, CMOs, and emerging chief growth officers) should be asking vendors, and why agentic, workflow-driven tools are the ones to watch.

Read Martin's full market overview report: https://www.constellationr.com/research/revenue-intelligence

Revenue & Growth Effectiveness Growth Chief Revenue Officer Off