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Experience on Our Minds: How Cross-Functional Partnerships Accelerated AI Success in CX at IBM

Experience on Our Minds: How Cross-Functional Partnerships Accelerated AI Success in CX at IBM

Discover how cross-functional partnerships between customer experience leaders and developers are turning AI promise into real-world impact. Liz Miller, VP & Principal Analyst at Constellation Research, shares insights from IBM TechXchange—a “Customer Experience Day” hiding in plain sight among builders and developers.

Liz unpacks:

  • The “two polls” that reveal what developers really worry about with AI (accuracy, training data, and outcomes)
  • Why customer and employee experience is the #1 place builders want to apply AI
  • The challenges teams face with data access, quality, and legacy systems
  • How Scuderia Ferrari and IBM are using AI and up to 10,000 data points per second to transform fan engagement
  • Why CX, data, and AI must become a team sport across marketing, service, sales, commerce, and development

If you’re a customer experience, marketing, service, or product leader wondering how to work better with your builders—and how to turn operational data into unforgettable experiences—this talk is for you.

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Cisco AI Summit 2026 Key Takeaways | With R "Ray" Wang & Chirag Mehta

Cisco AI Summit 2026 Key Takeaways | With R "Ray" Wang & Chirag Mehta

Cisco AI Summit 2026 communicated a clear message through its powerhouse lineup of speakers: AI is real, it’s here, and the gap will widen quickly between those who experiment boldly and those who wait.

Here are key takeaways from CR analysts R "Ray" Wang and Chirag Mehta:

  • OpenAI vs. Anthropic: Two very different cultures and go-to-market motions—consumer-first vs. enterprise-first—but both pushing the frontier fast.
  • Metrics matter (from AWS’s Matt Garman): Most generativeAI POCs fail not because of the tech, but because they lack clear success criteria.
  • Context is everything: Teams that started with A#I from day one are seeing 100x gains vs. ~10x for those retrofitting AI onto existing workflows.
  • Prompt is the craft (from Dylan Field): AI doesn’t replace designers and developers—the value shifts to how well you frame problems and prompts.
  • Beyond digital intelligence (from Fei Fei Li): The next frontier is physical and spatial intelligence—how AI perceives and interacts with the real world.
  • Cisco’s three big gaps: Infrastructure, data, and trust will define who actually wins with AI at scale.

As R "Ray" Wang summarized: we’re entering a world of margin compression, exponential scale (10x/100x/1000x), and seemingly infinite possibilities, but only for those who apply AI with precision and clear order of operations.

Looking forward to continuing these conversations at the Constellation's AI Forum on March 19, 2026, in Menlo Park, CA, where we’ll focus on real-world lessons from Chief AI Officers and operators in the trenches. 

Learn more here: https://www.constellationr.com/event/2026/constellations-ai-forum-2026

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SaaSpocalypse, Cisco AI Summit & Agentic GTM KPIs | ConstellationTV Episode 123

SaaSpocalypse, Cisco AI Summit & Agentic GTM KPIs | ConstellationTV Episode 123

crtv-episode 123

In ConstellationTV episode 123, the Constellation analyst team tackles a question many enterprise technology leaders are quietly asking: Is SaaS over in an AI-first world, or are the rules simply changing? Here is a recap of the main episode takeaways with co-hosts Larry Dignan and Martin Schneider.

“SaaS Apocalypse”: Market Reset, Not Extinction

Analysts Martin Schneider and Larry Dignan address the narrative that “SaaS is dead.” Their view: while the delivery model survives, the business model is under massive pressure.

  • From Record to Reasoning: Core systems like CRM and ERP aren't disappearing, but they must become AI-enabled, AI-embedded, or AI-native to justify their cost.
  • The "NFL Roster" Approach: CXOs should treat their SaaS estate like a pro sports team—identifying which vendors are past their prime, which upstarts have genuine upside, and where to create "cap space" through consolidation.
  • Ownership Risk: Private equity–backed software firms caught in "death spiral flywheels" of debt and cost-cutting may struggle to produce real AI differentiation.

The New Infrastructure Oligarchs

The shift toward AI has triggered a massive capex cycle among hyperscalers.

  • Proprietary Silicon: Leaders like Amazon and Google are investing hundreds of billions to build their own silicon rather than renting margin from others.
  • The Utility Gilded Age: Much like the railroad magnates of the past, cloud leaders who own the infrastructure stack will become the new network oligarchs.
  • Strategy for CXOs: Assume a small number of strategic hyperscaler partners and a broader ecosystem of domain ISVs and agents layered on top.

Dispatch from the Cisco AI Summit

R "Ray" Wang and Chirag Mehta provided ground-level insights from the Cisco AI Summit, highlighting a "capability overhang" where model potential outpaces enterprise readiness.

  • OpenAI vs. Anthropic: OpenAI is moving at a breakneck speed that some customers struggle to absorb, while Anthropic is positioning itself as a more enterprise-centric partner with a heavy focus on Claude in production and roadmap clarity.
  • Context is King: AWS’s Matt Garman noted that teams building greenfield workflows optimized for AI see ~100x improvements, compared to only ~10x for those bolting AI onto existing patterns.
  • The Frontier: Visionaries like Fei-Fei Li (World Labs) are pointing toward physical and spatial intelligence—AI that understands and acts within 3D environments.

From Expertise to Experience

In a panel with Larry Dignan, Esteban Kolsky, and Michael Ni, the team explored the boundary between machine and human.

  • Machines as Expertise Engines: AI excels at rules, facts, and probabilities—codifying structured expertise.
  • Humans as Experience Engines: Humans lead in judgment under constraints and trade-offs across ambiguous signals.
  • The Context Gap: Context is the critical bridge. A model might suggest an order based on demand, but a human knows the SKU is being discontinued. Bridging this requires managing life cycle context and explicit decision rights.

Measuring Agentic GTM: KPIs That Matter

To close, Martin Schneider introduced his new Big Idea report on measuring AI agents in go-to-market motions. As organizations deploy dozens of agents, a small number of well-scoped agents with clear KPIs will outperform "agent sprawl."

  • Automation Multipliers: Measuring lead volume and handling capacity per human FTE.
  • Conversion Quality: Tracking if AI-augmented motions improve win rates and drive higher-margin, strategic deals rather than just increasing speed.

As AI moves from a "capability overhang" to a functional reality, the organizations that thrive will be those that stop bolting AI onto legacy patterns and instead start redesigning their workflows, infrastructure, and GTM metrics around the new rules of agentic execution.

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Beating the Odds: AI Leadership, Enterprise Advantage, and Probability Hacking | DisrupTV Ep. 427

Beating the Odds: AI Leadership, Enterprise Advantage, and Probability Hacking | DisrupTV Ep. 427

Beating the Odds in an AI Era: Leadership, Probability Hacking, and the Power of Kindness

On the latest episode of DisrupTV, co-hosts Vala Afshar and R "Ray" Wang welcomed two guests who approached success and leadership from very different—but deeply complementary—angles: Kyle Young, author of Success Is a Numbers Game, and Jon Reed, co-founder and Editor-in-Chief of diginomica.

Together, they explored a central question facing leaders today:
How do you improve your chances of success—and lead responsibly—when AI, uncertainty, and constant disruption are reshaping work and business?

The answer, it turns out, lies at the intersection of probability, enterprise AI leadership, and human-centered values.

Success Is a Numbers Game: Kyle Young on Probability Hacking

Kyle Young’s message challenges a common misconception: that success is primarily about motivation, grit, or positive thinking. In reality, he argues, success is governed by probabilities—and most people dramatically overestimate their odds.

Big goals often fail because too many things must go right at once. We tend to average our confidence instead of multiplying risk. When success depends on ten or more conditions, even small weaknesses compound quickly.

Young introduced probability hacking, a disciplined approach to improving outcomes by identifying potential bad outcomes (PBOS) and deliberately reducing their likelihood. His tool, the success diagram, maps:

  • The goal

  • Everything that must go right

  • Everything that could go wrong

  • Concrete actions to de-risk each step

Rather than relying on optimism, probability hacking forces leaders and individuals to confront risk head-on and transfer probability from failure to success.

Young shared how this approach shaped his own career—from recovering after multiple layoffs to intentionally building the credibility, platform, and relationships needed to become a published author. The lesson: success improves when you stop guessing and start designing your odds.

The Paradox of AI Leadership

The conversation then shifted from personal success to enterprise leadership, with Jon Reed offering a grounded and often contrarian view of AI.

Reed described today’s paradox of AI leadership: organizations feel intense pressure to “move fast” on AI, yet the consequences of getting it wrong—on people, trust, and outcomes—have never been higher.

He emphasized that real AI leadership requires:

  • Transparency about where AI is headed and how it affects employees

  • Outcome-driven thinking, not tool obsession

  • Technical and data literacy at the leadership level

Reed drew a clear distinction between consumer AI—impressive but fragile—and enterprise AI, where reliability, governance, security, and context are non-negotiable. Flashy demos may inspire, but enterprise value comes from systems that work consistently, explain decisions, and integrate with real business processes.

Rather than mandating AI usage, Reed argued leaders should mandate AI understanding—especially when AI influences hiring, performance, compensation, or customer outcomes. The goal is not adoption theater, but sustainable value.

Creating Space for Experimentation

A recurring theme was culture. Reed stressed the importance of safe experimentation—sandbox environments where teams can explore AI responsibly, test ideas, and bring back improvements organically.

When AI genuinely helps people do their jobs better, adoption follows naturally. When it doesn’t, mandates only deepen resistance. The role of leadership is to create the conditions for curiosity, learning, and trust.

Kindness as a Leadership Practice

In one of the episode’s most human moments, the conversation turned to kindness. Reed reflected on how intentional kindness—especially during moments of stress and disruption—can change how leaders show up.

Vala Afshar shared a simple but powerful reframing: instead of asking, “How was your day?” ask, “Who did you help today?” Over time, that question builds empathy, purpose, and a culture where helping others becomes part of identity—not an afterthought.

In an era where AI accelerates change and anxiety, kindness isn’t a soft skill—it’s a stabilizing force.

Final Thoughts

This episode delivered a clear message for leaders navigating the AI era:

  • Don’t rely on hope—design your odds. Use probability thinking to de-risk success.

  • Lead AI with clarity and literacy. Focus on outcomes, trust, and understanding—not hype.

  • Anchor leadership in humanity. Kindness, practiced intentionally, strengthens cultures under pressure.

Beating the odds in an AI-driven world isn’t just about smarter models or bigger investments. It’s about how thoughtfully leaders manage risk, how responsibly they deploy technology, and how intentionally they choose to show up for the people around them.

Related Episodes

If you found Episode 427 valuable, here are a few others that align in theme or extend similar conversations:

 

Future of Work Tech Optimization New C-Suite 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.

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How AI Will Transform Business Models, Leadership & Growth

How AI Will Transform Business Models, Leadership & Growth

Live from Davos 2026 at the IBM House, Constellation Research’s Ray Wang sits down with IBM’s Neil Dhar to explore how AI is moving from proof-of-concept experiments to real, measurable business transformation.

In this conversation, they discuss:

  • How IBM combines a world-class consulting firm with a world-class technology platform
  • The “client zero” story: $4.5B in run-rate savings and how those funds innovate
  • IBM’s new AI transformation platform and ready-made agents that help clients drive ROI
  • Key insights from IBM’s Enterprise 2030 thought leadership report
  • What organizations will look like in 2030: self-learning, AI-infused, and continuously evolving
  • The new demands on leaders: from custodians to transformers
  • Themes from Davos: business model reinvention, scenario planning, and managing fear in times of rapid change

If you’re a CEO, business leader, or transformation executive wondering how to put AI to work—beyond the hype—this interview breaks down the roadmap from experimentation to real value creation.

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Why Your AI Fails Without Data & Workflows Private

Why Your AI Fails Without Data & Workflows Private

At AWS re:invent, R "Ray" Wang talks with Caroline Roche of IBM about how IBM and AWS are partnering to help enterprises move from AI experiments to real business outcomes.

They explore:

  • Why agentic AI is changing the focus from processes to end-to-end workflows
  • How to break down functional silos and drive cross-department collaboration
  • Why AI success depends on data investment, structure, and orchestration across ERP, CRM, and business apps
  • Real examples of decisions that can be delegated to agents, like pricing and travel approvals
  • The mindset shift leaders need: stop “buying AI” and start designing workflows and outcomes
Data to Decisions Future of Work Next-Generation Customer Experience AI Data to Decisions Cloud LLMs business Chief Data Officer Chief AI Officer Off

IBM’s Advantage Platform: Killing Technical Debt and Scaling AI on AWS

IBM’s Advantage Platform: Killing Technical Debt and Scaling AI on AWS

How do you take projects that used to take 10 months and deliver them in 8 weeks? In this AWS re:Invent conversation, IBM’s Javier Olaizola Casin explains how IBM and AWS are helping enterprises accelerate with data, AI, and hybrid cloud.

We discuss:

  • How IBM's Advantage agentic framework reduces cycle times from months to weeks
  • Tackling technical debt and large-scale application modernization (including VMware exits)
  • Why data curation, governance, and compliance are the real enablers of AI at scale
  • The role of hybrid cloud and AI in transforming workflows and business processes
  • How to move from incremental efficiency gains to step-change business impact
  • Rethinking organizational design for an “always-on,” agentic enterprise
  • Scaling backend systems for a world where every human has multiple AI agents

If you’re working on enterprise AI, modernization, or hybrid cloud strategy, this interview offers a practical view on what it really takes to move faster than your market.

Data to Decisions Future of Work Next-Generation Customer Experience AI Data to Decisions Cloud LLMs Chief Data Officer Chief AI Officer Off

Autonomous Security for Cloud by IBM Consulting

Autonomous Security for Cloud by IBM Consulting

Cloud security was never meant to be manual—but for most enterprises, it still is. In this video, we explore how IBM Consulting’s Autonomous Security for Cloud (ASC), co-developed with AWS, tackles the growing gap between fast-moving cloud environments and traditional, rule-based security operations.

Learn how ASC:

  • Translates industry regulations, client policies, and real-time cloud 
  • Metadata into enforceable AWS-native controls
  • Delivers continuous compliance instead of point-in-time audits
  • Uses Gen AI and fine-tuned LLMs to keep security configurations aligned as workloads, policies, and risks change
  • Embeds controls directly into cloud landing zones with infrastructure as code

If your teams are drowning in alerts, managing exceptions, and chasing configuration drift across AWS accounts and regions, discover how autonomous security can help you move from reactive controls to continuous assurance.

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AI, Critical Thinking, and Geopolitical Risk: Inside DisrupTV’s Deep Dive on Gemini, Multimodal AI, and Global Resilience | DisrupTV Ep. 426

AI, Critical Thinking, and Geopolitical Risk: Inside DisrupTV’s Deep Dive on Gemini, Multimodal AI, and Global Resilience | DisrupTV Ep. 426

AI, Critical Thinking, and Geopolitical Risk: Inside DisrupTV’s Deep Dive on Gemini, Multimodal AI, and Global Resilience

On the latest episode of DisrupTV, co-hosts Vala Afshar, Chief Evangelist at Salesforce, and R "Ray" Wang, CEO and Founder of Constellation Research, convened a timely conversation at the intersection of AI innovation, critical thinking, and geopolitical risk.

Joining them were Peter Danenberg, Distinguished Software Engineer at Google and a key contributor to the Gemini AI platform, and Dr. David Bray, Distinguished Chair at The Stimson Center and CEO of LDA Ventures. Together, they explored how multimodal AI, community-driven innovation, and geopolitical awareness are becoming essential capabilities for leaders navigating the Age of Intelligence.

Inside Google Gemini: From Demos to Developer Communities

Peter Danenberg offered a behind-the-scenes look at Google’s Gemini AI platform, including emerging capabilities like Code Canvas and Computer Use, which move AI beyond chat interfaces and into real-world workflows.

A central theme of Danenberg’s work is community engagement. What began as a small Gemini Meetup with roughly 20 attendees has grown into a thriving forum of more than 600 participants—developers, builders, and AI practitioners experimenting at the edge of what’s possible.

These meetups aren’t just technical demos; they serve as a feedback loop between users and platform builders, allowing insights from real-world experimentation to flow directly back to Google’s leadership. According to Denenberg, this user-driven model is critical for shaping AI tools that are both powerful and practical.

Multimodal and Ambient AI: The Next Evolution

Looking ahead, Danenberg highlighted the shift toward multimodal and ambient AI systems—models that can process text, images, sound, and contextual signals simultaneously, and operate continuously in the background of human activity.

These systems aren’t meant to replace human judgment, but to augment decision-making, creativity, and problem-solving. The challenge, he emphasized, is ensuring that humans remain active participants rather than passive recipients of AI-generated outputs.

AI and the Risk to Critical Thinking

Drawing from his widely viewed TED Talk, Denenberg addressed a growing concern: the potential erosion of critical thinking in an era of increasingly capable large language models.

He cited research comparing brain activity when people rely on AI tools versus when they actively create or reason through problems themselves. The takeaway isn’t to avoid AI—but to design systems that challenge users to think, test assumptions, and maintain a sense of ownership over their work.

His experiments with Socratic-style AI learning environments reflect this philosophy: AI should ask better questions, not just provide faster answers.

Geopolitical Risk, AI, and the New Reality for Global Enterprises

Dr. David Bray expanded the conversation beyond technology into geopolitical and cybersecurity realities facing enterprises today. As global supply chains become more fragmented and nation-state actors increasingly weaponize AI, companies must rethink how they manage risk.

Bray emphasized that AI-driven cyber threats now operate at machine speed, requiring equally adaptive and responsive defenses. Traditional, static security models are no longer sufficient when adversaries can rapidly tailor attacks using AI tools.

AI, Cybersecurity, and Board-Level Accountability

One of Bray’s strongest messages was the need for board and executive awareness. AI risk is no longer confined to IT departments—it spans legal, operational, geopolitical, and reputational domains.

He stressed tighter collaboration between CIOs, CISOs, and General Counsel, particularly for organizations operating across borders. Boards must understand not just where AI is deployed, but how geopolitical shifts can amplify technical vulnerabilities.

Human–AI Collaboration as a Competitive Advantage

Despite the risks, both speakers were clear: the future belongs to organizations that master human–AI collaboration.

Denenberg envisions AI systems that help organizations model worldviews, anticipate risk, and explore scenarios—enhancing human foresight rather than automating it away. Bray reinforced this view, noting that resilience comes from pairing machine-scale intelligence with human judgment, ethics, and strategic context.

Key Takeaways from DisrupTV Episode 426

  • AI is moving beyond chat into multimodal, ambient systems embedded in daily workflows

  • Community-driven AI development accelerates innovation and improves real-world adoption

  • Critical thinking must be protected through intentional AI design, not blind automation

  • Geopolitical risk and AI security are inseparable, especially for global enterprises

  • Human–AI collaboration, not replacement, is the defining advantage in the Age of Intelligence

Final Thoughts: Intelligence With Intention

This DisrupTV episode made one thing clear: AI’s true value isn’t found in raw capability alone, but in how thoughtfully it’s integrated with human expertise, organizational culture, and global awareness.

As Vala Afshar and R "Ray" Wang underscored in closing, leaders who invest in community, critical thinking, and contextual intelligence won’t just keep pace with AI—they’ll shape how it responsibly transforms business and society.

In an era defined by rapid technological change and geopolitical uncertainty, intelligence with intention may be the most important innovation of all.

Related Episodes

If you found Episode 426 valuable, here are a few others that align in theme or extend similar conversations:

 

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Meta vs. Microsoft: Michael Ni on the AI CapEx Divergence | With Mike Ni

Meta vs. Microsoft: Michael Ni on the AI CapEx Divergence | With Mike Ni

In our latest segment on the Schwab Network, Michael Ni, Vice President and Principal Analyst at Constellation Research, breaks down the contrasting AI investment stories of two tech giants. 

While both are spending heavily, the market is rewarding them very differently based on how that capital translates to the bottom line.

The Tale of Two CapEx Strategies.
According to Ni, the divergence comes down to immediate margin contribution versus long-cycle platform discipline:

  • Meta's AI Monetization Loop: Meta put a 70% increase in CapEx on the board, but they successfully showed how that investment directly contributed to margin.
  • Efficiency in the Ad Economy: By embedding AI deeper into ad productization and auto-generation, Meta achieved an 18% lift in impressions and a 6% increase in pricing power.
  • Microsoft’s Long-Term View: Microsoft saw a 66% CapEx increase, but Ni notes the market has "punished" them for their platform discipline, even as Azure continues to turn AI infrastructure into durable margins.
  • Beyond the Chatbot: Ni emphasizes that enterprise buyers are ramping up spend because AI is now showing real ROI within core business processes, not just simple chat interfaces.
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