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Mastering AI Content Revolution: Strategy for the Future

Mastering AI Content Revolution: Strategy for the Future

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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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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The world of content is in the midst of a seismic shift, driven by the transformative power of artificial intelligence (AI). Constellation Research analysts Ray “Ray” Wang and Liz Miller deep-dive into the monumental changes brought by large language models (LLMs) to the ways we create, consume, and optimize content. If you’re a brand, a content creator, or anyone invested in the digital landscape, this breakdown gives you actionable insights to thrive in this new AI era.

The Game-Changing Role of Content

Liz opens by cutting straight to the point: “Content in the age of AI is fundamentally changing. Right? Because it's not only feeding our experiences, our engagements, it's feeding LLM answers.” Let that sink in—content today doesn’t just engage users on websites or social platforms; it directly impacts what AI models are trained and optimized to deliver. Content is no longer passive. Instead, it actively shapes answers generated by LLMs like ChatGPT, Google Gemini, or other AI-driven tools.

This evolution means that the importance of content has magnified exponentially. The way you craft your narratives, keywords, and overall messaging doesn’t just affect human audiences—it influences AI responses, which increasingly dictate user behavior.

A World of Scale: Exponential Production and Consumption

Ray elaborates on the enormity of what these models bring to the table: “You're both a content producer and a content consumer, but I'll do this at 10x, 100x, 1000x scale because that's what the LLMs are gonna be doing.”

AI operates on a mind-boggling scale—producing and consuming content at rates we’ve never seen before. For brands, this opens up profound implications. Whether you realize it or not, your content can now be optimized, replicated, and expanded at massive scales, often without direct human intervention. What you produce today might inform answers across millions of AI interactions tomorrow.

Challenge Ahead: With such an unprecedented scale, questions of authority and ranking take center stage. If every brand is creating thousands upon thousands of pieces of content optimized for AI, what determines visibility and credibility?

The Growing Anxiety Among Brands

As exciting as it is, Liz acknowledges the flip side: “People are starting to freak out about it already. You're starting to see things like, Why does ChatGPT show up in my traffic all of a sudden? Why are all the bots starting to answer things differently?”

The explosion of AI usage adds to brand concerns of visibility and influence. Why is AI favoring one answer over another? How do these bots interact with our content? For businesses, it means asking more complex questions:

  • How do we ensure our content influences AI-generated responses?
  • Are bots accurately understanding our brand essence and messaging?
  • How do we avoid being drowned out by poorly optimized or generic answers fed into LLMs?

These are critical challenges brands must address in the evolving AI environment.

New Optimization Strategies: GEO and Visibility

Liz introduces a strategy that feels groundbreaking yet inevitable: “That's when you're gonna start to look for things like GEO or generative engine optimization… But you're also gonna look for things like AI visibility and LLM visibility.”

Let’s pause to unpack this. GEO—short for Generative Engine Optimization—may soon become a fundamental approach for brands. Much like SEO (Search Engine Optimization), GEO focuses specifically on ensuring content reaches and resonates within generative AI models. This isn’t just about appearing in search engines; it’s about helping your content surface prominently in AI-driven interactions.

Liz further discusses the importance of concepts like "AI visibility" and "LLM visibility." These ideas extend traditional optimization methods into the AI realm, where brands must consider:

  • How crawlable is your content for AI algorithms like OpenAI's?
  • Are LLMs interpreting your intent correctly?
  • Does your content "answer" user queries better than competitors'?

Tools to Lead the Way

Thankfully, brands aren’t being left to navigate this uncharted territory alone. Liz pointed out an incredible advancement to help: “The newly released Adobe LLM Optimizer… is now generally available. But this is really a tool that, if you are on AEM, guess what? You've got it. Go check it out.”

Adobe LLM Optimizer is poised to play a pivotal role. If you’re using Adobe Experience Manager (AEM), this tool is a must-check-out resource. It provides critical insights into how LLMs view, crawl, and interpret your content—and helps guide adjustments to maximize visibility in generative AI responses.

This marks the beginning of an era where tools like Adobe Optimizer will become essential for brands aiming to outpace their competitors in AI-powered ecosystems.

Why This Moment is the Most Exciting Time

Ray closes the discussion with a refreshing burst of optimism: “We are living in the most exciting time. It was boring before when just one search engine dominated.”

In his view, the emergence of an AI-driven world means all bets are off. Gone are the days when a single search engine (read: Google) dictated the rules of engagement. Today, the playground is more dynamic, more complex, and, frankly, more exhilarating. If you’re ready to embrace change, there’s opportunity everywhere—whether you’re a small creator or a global brand.

But the excitement comes with a challenge: staying ahead of the curve. AI isn’t waiting for anyone to adapt. It’s moving fast, and those who understand its implications first will reap the rewards.

Actionable Takeaways

  • Rethink Content Strategies: Your content doesn’t just serve human audiences—it now directly impacts LLM-generated answers. Craft messaging and insights strategically to shape AI responses in your favor.
  • Scale with Precision: Embrace the exponential production and consumption power of AI. Build systems to scale your content while retaining authority.
  • Prepare for GEO: Generative Engine Optimization could soon dominate the marketing playbook. Invest early in strategies that enhance your visibility in AI-driven ecosystems.
  • Explore Tools for LLM Visibility: Technologies like Adobe LLM Optimizer can transform how you cater to AI models. Take time to experiment and integrate such tools into your workflow.
  • Keep Up the Pace: Change is constant, and the pace of development is astounding. Stay informed and proactive about emerging trends, tools, and systems shaping the AI landscape.

Why Brands and Creators Should Pay Attention

The conversation between Ray and Liz underscores this singular truth: we’re on the brink of an entirely new digital era. AI isn’t just another technology trend; it’s the force reshaping how humans interact with brands, content, and the world at large.

For businesses, this means adapting. It’s not simply about maintaining a presence but about ensuring your content actively shapes AI interactions. The brands that figure this out first—those who navigate GEO, embrace AI visibility, and leverage emerging tools—will emerge as the leaders in the age of AI-powered content.

In short, if you’re looking for the golden ticket to future-proofing your brand, it lies in understanding and mastering AI’s influence on content creation and consumption. As Ray and Liz reminded us, embrace this transformative moment—it’s about to redefine everything.

Data to Decisions Digital Safety, Privacy & Cybersecurity Future of Work Innovation & Product-led Growth Marketing Transformation Matrix Commerce New C-Suite Next-Generation Customer Experience Tech Optimization ML Machine Learning LLMs Agentic AI Generative AI AI Analytics Automation business Marketing SaaS PaaS IaaS Digital Transformation Disruptive Technology Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain CRM ERP finance Healthcare Customer Service Content Management Collaboration Chief Analytics Officer Chief Customer Officer Chief Data Officer Chief Digital Officer Chief Executive Officer Chief Financial Officer Chief Information Officer Chief Information Security Officer Chief Marketing Officer Chief People Officer Chief Privacy Officer Chief Procurement Officer Chief Product Officer Chief Revenue Officer Chief Supply Chain Officer Chief Sustainability Officer Chief Technology Officer Chief AI Officer

Content Creation to LLM Optimization: Navigating the New AI Frontier

Content Creation to LLM Optimization: Navigating the New AI Frontier

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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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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Hear from Constellation analysts R "Ray" Wang and Liz Miller as they explore how AI and LLMs are transforming content creation, search authority, and brand visibility.

Learn about innovative strategies like generative engine optimization, discover how tools such as Adobe LLM Optimizer can help you stay ahead, and gain actionable advice for navigating today’s rapidly evolving digital landscape. Don’t miss this expert conversation!

Data to Decisions Future of Work Innovation & Product-led Growth Marketing Transformation New C-Suite Next-Generation Customer Experience Tech Optimization Chief Customer Officer Chief Executive Officer Chief People Officer Chief Marketing Officer Chief Digital Officer Chief Technology Officer
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Fix Your Blindspots. Unleash RevOps. Scale Smarter. | DisrupTV Ep. 415

Fix Your Blindspots. Unleash RevOps. Scale Smarter. | DisrupTV Ep. 415

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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Fix Your Blindspots. Unleash RevOps. Scale Smarter. | DisrupTV Ep. 415

This week on DisrupTV, R "Ray" Wang and Vala Afshar caught up with innovators shaping the next chapter of business and technology:

  • Amy Osmond Cook, PhD, and Ryan Westwood, Co-authors of The RevOps Advantage: How To Maximize Your Revenue Team’s Potential
  • Marty Dubin, Author of Blindspotting: How to See What’s Holding You Back as a Leader

In this episode, we dive into The RevOps Advantage, and uncover how aligning revenue operations can turn chaos into clarity. Our guests break down why RevOps is every company’s “pit crew,” how it helps organizations adopt AI strategically, and what leaders can do to eliminate the blindspots holding their teams back.

We explore the FACTOR framework, leadership awareness, and how RevOps as a mindset drives real growth in times of change. Whether you’re a CEO, RevOps pro, or just trying to run smarter, this episode will help you see what’s been hiding in plain sight.

The Role of RevOps in Business Success

Amy Osmond Cook, a seasoned marketing, revenue operations, and communications executive, emphasizes that RevOps is not just a back-office function—it’s a business growth engine. When implemented correctly, RevOps aligns marketing, sales, and customer success around a shared view of data, enabling faster decision-making and more scalable growth.

Ryan Westwood, a tech entrepreneur and board director, adds that CEOs must commit to collaboration, transparency, and data centralization to make RevOps effective. When leadership prioritizes RevOps, silos are removed, and data becomes the backbone of growth strategy.

Bala Baskaran notes that alignment and transparency are critical to unlocking AI’s potential within organizations. RevOps helps businesses harness automation while maintaining the right level of human oversight—ensuring decisions remain strategic and empathetic.

AI, Data, and Automation: The RevOps Accelerator

AI’s integration into RevOps has transformed how companies operate. The panel discusses how AI-powered analytics, predictive modeling, and data enrichment can help leaders make smarter, faster decisions.

However, the challenge of data hygiene remains front and center. Businesses often struggle to keep information current and relevant—a barrier that technology must continue to solve.

The recent acquisition of Copy.ai was highlighted as a milestone moment for the ecosystem, adding deeper automation and intelligence capabilities to RevOps platforms. As Bala notes, “the next phase of RevOps is about intelligent alignment—connecting data, decisions, and human insight.”

Building a World-Class Revenue Operations Function

The group agrees that a world-class RevOps organization requires more than just software—it requires a CEO who believes in it.

RevOps leaders are increasingly reporting directly to the CEO, reflecting the function’s growing influence in shaping strategy, optimizing performance, and improving customer experiences.

For RevOps to succeed at scale, leaders must build teams grounded in trust, radical transparency, and accountability, ensuring alignment across departments and functions.

Leadership Blind Spots and the Path to Self-Awareness

The conversation transitions as Marty Belden joins to discuss his book Blind Spotting—a framework designed to help leaders uncover the blind spots that limit performance and growth.

Drawing on his experience as a psychotherapist and executive coach, Marty outlines six key blind spots that can derail leadership effectiveness, including emotional biases, flawed assumptions, and identity misalignment.

He explains that even high-performing CEOs can suffer from blind spots when they fail to see how their self-perception differs from others’ experiences of them. These gaps can lead to poor communication, resistance to change, and missed opportunities for innovation.

  • “Self-awareness isn’t a soft skill—it’s a leadership discipline,” Marty explains. “You can train it through science, structure, and consistent feedback.”

Identity, Imposter Syndrome, and Growth

The discussion touches on imposter syndrome, a common blind spot among accomplished leaders. Even successful CEOs can feel undeserving of their achievements, leading to overcompensation or self-doubt.

The key to overcoming these challenges lies in acknowledging personal blind spots, seeking feedback, and consciously aligning one’s identity with evolving leadership roles.

One speaker notes that leadership transformation often begins with humility—being open to small, consistent changes that compound into lasting behavioral growth.

Belonging, Curiosity, and Listening in Leadership

The panel emphasizes that effective leadership isn’t about having all the answers—it’s about creating space for others to contribute.

Leaders who ask thoughtful questions, reflect before reacting, and foster belonging build teams that feel valued and heard. This emotional connection drives innovation and retention far more effectively than command-and-control leadership.

As R "Ray" Wang closes, he reminds listeners that self-awareness and curiosity are foundational to leading in an age of rapid technological change. Science-backed methods, coaching, and reflection help leaders stay adaptive as they scale businesses in an AI-driven world.

Key Takeaways

  • RevOps drives alignment and scalability by centralizing data and promoting cross-functional transparency.
  • AI amplifies RevOps through automation and analytics—but human judgment remains critical.
  • Data hygiene is essential for AI-driven organizations; clean, enriched data powers better decisions.
  • Blind spots hinder leadership effectiveness, but self-awareness can be developed through feedback and reflection.
  • Leaders must align identity with evolving roles to maintain authenticity and confidence.
  • Curiosity and belonging are the new cornerstones of modern leadership.

Final Thoughts

DisrupTV Episode 415 highlights a powerful truth: scalable growth in the modern enterprise is driven by alignment, self-awareness, and intelligent technology.

Revenue Operations is no longer a niche discipline—it’s the central nervous system of every data-driven organization. And as AI reshapes how we work, the most successful leaders will be those who remain human at the core—curious, self-aware, and committed to transparency.

In the end, business transformation isn’t just about systems or automation. It’s about leaders who can see their blind spots, empower their teams, and build cultures where data, empathy, and innovation thrive together.

Related Episodes

If you found Episode 415 valuable, here are a few others that align in theme or extend similar conversations:

 

Future of Work Marketing Transformation New C-Suite Tech Optimization Chief Executive Officer Chief People Officer Chief Marketing Officer Chief Revenue Officer Chief Technology Officer
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Oracle AI World 2025: Shifting left AND right into Oracle’s suite spot

Oracle AI World 2025: Shifting left AND right into Oracle’s suite spot

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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We’re no longer in the “let’s try a model” era. We’ve crossed into the territory of “make the model do your work.” For Oracle, that’s exactly where AI World 2025 landed.

This week, Oracle went beyond unveiling products by doubling down on its brand promise: reduce complexity so enterprises don’t have to stitch the stack. It’s a shift from experimentation to embedding, from model tuning to decision automation.

Why This Matters to CDAOs and AI Leaders

Let’s be real: most data and AI teams are buried under too many tools, too many silos, and too much governance debt. The promise of AI is huge … but but but the complexity of getting there is overwhelming for even the enterprise fast-followers.

That’s why this move from Oracle hits a nerve. The company’s DNA has always been about centralizing complexity -> integrating the dominant innovations/design of the moment so enterprises don’t have to stitch them together. The promise is engineered together + removing abstractions = performance, manageability, and extensibility. 

Let's look at some of key announcements.

Data Foundation Layer: From Data Silos to Self-Governing Intelligence

Oracle’s Lakehouse isn’t a new product, it’s the convergence of several mature ones:

  • Autonomous AI Lakehouse: Oracle merges its Autonomous Database with Iceberg support, offering query acceleration, multicloud availability (OCI, AWS, Azure, Google), and zero-ETL / zero-copy flows.
  • A “catalog of catalogs” for unified governance of data and model assets across environments.
  • AI Database 26ai: enhancements include vector/hybrid search, support for Model Context Protocol (MCP), built-in “AI Private Agent Factory,” and open agent interoperability.

Think of it as Oracle teaching its database to govern itself across clouds, formats, and workloads.

For data teams, that means fewer pipelines across silos to manage. For CDAOs, it means centralized metadata governance, policy management, and context-rich retrieval for RAG and reasoning agents. Tradeoffs of buying into the Oracle governance and architectural choices always to be considered.

Intelligence Layer: From Insights to Agents

Then came the bigger reveal … 

  • Oracle AI Data Platform: blends Oracle’s cloud, database, and GenAI services into one environment for a full-stack environment uniting data, AI frameworks, and agent lifecycle management. Don't move data to the AI, but move AI to the data (for Oracle, in the DB, of course)!
  • Built-in Agent Studio/Agent Hub providing a developer workbench and business-user cockpit for building, orchestrating, and governing AI agents. The Agent Studio is importantly expanding observability, lineage, evals, human-machine interface controls, and policy enforcement across data and models.
  • Fusion Data Intelligence / Embedded Agents: Oracle embeds reasoning into finance, HR, supply chain apps.
  • … all supported by expanded partner commitments with Global SIs (Accenture, Cognizant, PwC) pledging over $1.5B in investment in capabilities and industry use case building.

This isn’t just about running models. It’s about running the business with AI. TK Anand reiterated Larry Ellison’s point around Oracle’s advantage of “bringing your enterprise data and foundation models together to build agentic experiences.” Implication = enterprises need to play the long game, where value is captured by models that reason over private, proprietary data in context, in real time, and absolutely inside enterprises. 

The Bigger Shift: Start of Decision Automation era

This year marks a turning point: The good: We’re climbing the next S-curve of enterprise AI adoption and moving intelligence into the enterprise. The not so good: most enterprises are still on the last S-curve of moving data and apps to the cloud.

For CDAOs, what will define who wins this next wave? You are already seeing vendor innovation around the next area of innovation.

  • Governance becomes a differentiator. Trust will separate pilots from platforms. Those who can convincingly deliver reasoning without sacrificing privacy or performance will capture the gatekeeper role in the enterprise AI stack.
  • Context, context, context. The winners will be those who can ground every model, prompt, and agent in a business context — connecting metadata, semantics, and decision logic so AI acts with understanding, not hallucination. Context is how insight becomes action, how automation earns trust, and how data turns into decisions.
  • Decision-centric architecture takes center stage. We’re moving from shared data to contextualized intelligence, from dashboards to decisions, from human control to adaptive collaboration and decision automation.

I’ll be writing more in the coming month on the rise of the decision-centric architecture, how it is forming, and what it means for CDAOs designing for the next decade of AI.

What do you think? Will enterprises favor vertically integrated stacks or open, federated ecosystems? Which domain (HR, supply chain, customer, finance) is your “first agentic battleground”? How will you measure decision velocity in your organization?

Drop your thoughts below 👇 — let’s debate where the next S-curve leads.

#OracleAIWorld #DecisionVelocity #CDAO #DataToDecision #AgenticAI #AIInfrastructure #Analytics #ConstellationResearch

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7 takeaways on enterprise AI projects from Infosys, Wipro Q2 results

7 takeaways on enterprise AI projects from Infosys, Wipro Q2 results

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Infosys and Wipro executives said that enterprises are starting to operationalize artificial intelligence projects, scale agentic AI and build a new software stack as pilots move to production with industries such as banking and financial services leading the way.

Those were the highlights from a pair of earnings reports from the Indian services firms that landed within minutes of each other. Infosys reported second quarter earnings of $839 million on revenue of $5.08 billion, up 2.9% from a year ago. Wipro reported second quarter earnings of $368 million on revenue of $2.56 billion, up nearly 2% from a year ago.

Earnings results from services firms are a handy way to get a high level view of what's happening with AI efforts. Both Infosys and Wipro provided color on AI implementations, but the former delivered named references including ABN Amro, Mastercard, Telstra, HanesBrands and Agco Corp.

Here's a look at the takeaways from the Infosys and Wipro results.

Enterprise AI is being scaled by customers.

Infosys CEO Salil Parekh said that "client interactions show strong focus on deploying AI across the enterprise for growth and on cost efficiency programs." He said clients are leveraging Infosys' platforms for AI agents and AI overall.

Wipro CEO Srinivas Pallia said customers are prioritizing cost optimization, vendor consolidation, modernization and scaling AI agents.

On the delivery side, both companies said they are leveraging AI agents more.

AI-driven productivity pays the bills, but growth matters too.

Across industries, AI investments are justified based on cost savings, but customer experience and growth is becoming a bigger part of the ROI mix. Infosys CFO Jayesh Sanghrajka said:

"Clients are actively planning modernization and AI-driven initiatives with a clear focus on cost efficiency, enhanced customer experience and strategic transformation."

Pallia said Wipro's customers are "clearly looking for cost optimization."

The broader theme for both companies is that enterprises aren't into AI pilots as much as they are business outcomes.

Can you monetize trust?

Questions from analysts around agentic AI and automation frameworks often revolved around accuracy and hallucinations. What happens if an AI agent goes rogue?

Both companies highlighted guardrails, years of experience automating processes and industry knowhow. Wipro's Hari Shetty, chief strategist, said Wipro Intelligence is a layer for responsible AI guardrails built into the platform.

Meanwhile, Infosys Chief Delivery Officer Satish H.C. said: "Our Responsible AI office ensures clients have the defense and technical guardrails to address AI-related risks. We are among the first certified on ISO 42001:2023."

The questions were notable since they raised more queries. Can Indian IT giants monetize trust in AI? And are vendors on the hook if AI agents wind up hurting a client's business?

Banking, financial services and insurance lead AI spending.

The fact that financial companies lead the way on AI spending isn't surprising to anyone keeping tabs on earnings reports. The financial sector is one of the most mature industries for AI adoption. Both Infosys and Wipro noted that financial firms are scaling AI pilots.

Sanghrajka said:

"In financial services, clients are actively planning modernization and AI-driven initiatives with a clear focus on cost efficiency, enhance customer experience and strategic business transformation. We see strong momentum in mortgages, capital markets commercial banking and wealth management areas. Banks have spent significantly to build AI infrastructure. Many initiatives are progressing from proof of concepts to full-scale projects with notable traction in Agentic AI."

Healthcare transformation

Healthcare was cited as a strong vertical. Infosys cited strength in healthcare with clients deploying AI and automation. Wipro's Pallia cited a mega deal with a healthcare client. Palia noted:

  • Healthcare is going through structural changes and companies are navigating various policy changes.
  • Payers are trying to accelerate and transform contact centers.
  • Wipro healthcare clients are looking for real-time claim processing, efficiencies in pre-authorization and enhanced transparency.

Healthcare was Wipro's best vertical in the second quarter with revenue growth of 3.9%.

Manufacturing and energy mixed.

Infosys' Sanghrajka said manufacturing "continues to face trade and macro uncertainties, which is creating pressure on discretionary spend specifically in the automotive sector."

However, Sanghrajka said manufacturers are spending on the following:

  • Application and infrastructure rationalization.
  • Leveraging AI for automation.
  • Predictive maintenance.
  • Resolving supply chain bottlenecks.

As for energy, Sanghrajka said utilities are trying to leverage new technologies to meet data center demand. "Utility companies are looking for partners to meet the accelerating electricity demand, creating opportunities in areas like renewable integration, grid modernization, AI-driven optimization, et cetera," he said.

Industries are aiming AI spend at specific issues. Telecom, technology and communication are focused on using AI for customer experience, but cost optimization remains critical. Retailers are also mixed as they wrestle with tariffs and consumer demand.

The new AI stack

Other nuggets worth noting include:

  • Infosys and Wipro both said that big deals are signed due to vendor consolidation plans. Enterprises are looking for end-to-end services and horizontal plays.
  • That vendor consolidation is leading to large scale generative AI and agentic AI deployments.
  • Forward deployed engineers are being used for AI transformation deals.
  • Indian IT services firms are increasingly developing AI stacks for enterprises instead of deploying packaged applications. Infosys' Satish HC said:

"The opportunity with AI is that we are developing a new breed of services stack or software stack for our clients, which wasn't done before. This is a reimagining how we run business or how we even deliver services. The onus is on co-creation with our clients, which is why I think there is a sharper pivot to the leverage of forward-deployed engineers. As we move from POCs to enterprise scale adoption, we see that there will be a lot more need for forward-deployed engineers to work with our clients to drive this co-creation of the new breed of software stack."

 

Data to Decisions Chief Information Officer

Zoho details Indian government migration, expands roster of AI agents

Zoho details Indian government migration, expands roster of AI agents

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Zoho has moved 1.1 million Government of India users to Zoho Workplace in a move that highlights how the vendor, known for its disruptive pricing and reach with smaller enterprises, can scale.

In addition, Zoho, which has more than 130 million users, rolled out a series of AI agents for its collaboration, human resources and customer experience apps across its platform.

Regarding the India government migration, Zoho Chief Evangelist Raju Vegesna said during a briefing that the 1.1 million users is just the first installment of the migration. "There are a few other million users coming," said Vegesna. "It's an ongoing process."

Zoho said India's National Informatics Center (NIC) asked for proposals in 2023 to shift collaboration services to a dedicated, secure environment. Zoho was selected to manage and migrate official email and office suite services.

The custom rebranded version of Zoho Workspace will be available for government departments for email, files, documents and communications. India's government is specifically using Zoho Mail, WorkDrive, Meeting, Cliq, Mobile Device Management, Writer, Sheet and Show, OneAuth and Contacts.

Zoho's apps are hosted in a secure data center under NIC's control and includes a complete cybersecurity stack.

As for the agentic AI additions, Zoho said its latest AI agents will be available at no charge across Zoho Collaboration, Customer Experience and Human Resources.

The AI agents from Zoho are a fast follow from the launch of Zia Hubs and Zia LLM, its proprietary B2B model.

Among the agents launching:

  • Lead Generation Agent works across Zoho Mail and CRM to go through unread messages, identify sales queries and converts them into a lead in Zoho CRM.
  • AI Base Creation enables customers to create a use case with a prompt. Zia will create a base with relevant tables, sample data and linked fields. The agent works within Zoho Tables.
  • Resolution Expert, which works within Zoho Desk, documents ticket resolutions for other agents to resolve similar issues.

  • Agreement Intelligence, which is used in Zoho Sign, generates drafts of contracts, agreements and documents.
  • HR agents in Zoho Recruit include Candidate Matches, Job Matches and AI-Assisted Assessment Generation.

Philip Edey, Business Analyst at Arctic Spas Inc., said in a briefing that Zoho's AI tools are enabling the company to scale and manage nearly 1,000 leads a week, up from 300 leads. Edey said:

"Integrating AI and agentic systems into our workflow is key for the next six months to the year and going forward. With 14 corporate stores and a huge network of dealers, we want to try to make these tools work for us and work for our staff to make it a better user experience. Zoho's AI driven suite can help us get there."

Vegesna said Zoho is rolling out agents and new Zia capabilities. The approach is "to open it up to a limited set of people, and as we gain feedback, refine it," said Vegesna, who noted cybersecurity is a key area.

Data to Decisions Future of Work Next-Generation Customer Experience Innovation & Product-led Growth Tech Optimization Digital Safety, Privacy & Cybersecurity New C-Suite Marketing Transformation I am Team Leader at the Nominee Organization (no vendor self nominations) Distillation Aftershots zoho ML Machine Learning LLMs Agentic AI Generative AI Robotics AI Analytics Automation Quantum Computing Cloud Digital Transformation Disruptive Technology Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain Leadership VR Chief Information Officer Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Data Officer Chief Analytics Officer Chief Information Security Officer Chief Product Officer

Salesforce, Google comingle Agentforce 360, Gemini models

Salesforce, Google comingle Agentforce 360, Gemini models

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Salesforce and Google expanded their existing partnership in a move that brings Google Gemini models to Agentforce 360, integrates Agentforce into Google Workspace for sales and IT service and brings Gemini Enterprise to Slack.

The addition of Gemini to Salesforce's Agentforce 360's Atlas Reasoning Engine goes along with support for models from OpenAI and Anthropic.

Google Cloud announced Gemini Enterprise ahead of Dreamforce and joint customers are likely to endorse the connections between Agentforce 360 as they stitch together AI agents.

Dreamforce 2025

Key parts of the Salesforce-Google expanded partnership include:

  • Gemini users can deploy AI agents in Salesforce for multistep enterprise workflows.
  • Salesforce and Google will expand capabilities in large action models, which automate processes, with fine-tuned Gemini models.
  • The AI agents on the respective platforms will be tied together via Model Context Protocol (MCP) and Agent2Agent (A2A).
  • Users can access Salesforce Customer 360 apps like Agentforce Sales and Agentforce Service from Google Workspace apps like Gmail and Meet.
  • Salesforce customers can access and view data and insights across the Gemini app and Workplace tools such as Sheets, Docs, Drive and Meet.
  • Agentforce IT Service will have integrations with Google products including Workspace, ChromeOS and Looker.
  • Gemini Enterprise will be integrated into Slack's Real-Time Search API. Gemini Enterprise users will be able to ground responses within the most current conversational data and files.
  • Gemini Enterprise agents can be used directly within Slack.
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Salesforce sets $60B revenue target: Will unlimited Agentforce plans drive growth?

Salesforce sets $60B revenue target: Will unlimited Agentforce plans drive growth?

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Salesforce executives said that growth is reaccelerating due to Agentforce deployments and it is projecting a long-term revenue target of $60 billion by fiscal 2030 excluding the Informatica purchase. The big question is whether new pricing plans designed to meet customers where they are will drive the growth.

The revenue target assumes more than 10% of organic compound annual revenue growth from fiscal 2026 to fiscal 2030. Salesforce also said it aims to have the sum of subscription and support constant currency growth rate and non-GAAP operating margin total 50 by the end of fiscal 2030. Software vendors have typically made the "Rule of 40" as a key metric.

"We are confident in our ability to reach $60-plus billion by FY '30. In addition to that, as you know, we are very focused on profitable growth. One of my key goals as CFO is to deliver Salesforce is not only an agentic enterprise, but a lean agentic enterprise," said Salesforce CFO Robin Washington.

During the Wall Street analyst session, Salesforce executives, notably Chief Revenue Officer Miquel Milano, walked through the pricing and packaging of Agentforce. "We are meeting customers where they are," said Washington, who noted Salesforce is focusing on industries and segments as well as geographies.

Salesforce CEO Marc Benioff said "what we've seen in the last 3 years is that the speed of innovation has outpaced the speed of customer adoption." That situation is changing though.

Benioff said:

"There's only two things that we do. One is building that product. The second thing is now selling it. So now on a global stage, across every geography, every language and across all six of those segments, we have to deliver the goods, and we have to deliver the growth. And we have to get to those -- the numbers are off the screen, but then we're trying to get to these very high numbers."

Pricing matters

Milano outlined an Agentforce pricing strategy that appears to be more flexible to entice enterprises to bet on Agentforce 360 as a platform play. The unlimited plans for Agentforce and Data 360 may give CFOs and CIOs more predictable costs. 

“Customers don’t want to count tokens. They want predictability. The Agentic ELA gives them unlimited use for two or three years,” said Milano.

New monetization models to “meet customers where they are” include:

  • Seat-based editions (e.g., Agentforce for Service/Sales): upgrades existing licenses.
  • Consumption-based credits for flexible AI use.
  • Flex Agreements: reallocate seat spend toward AI consumption.
  • Agentic Enterprise License Agreements (ELAs): flat-fee, unlimited use of Data 360 and Agentforce for predictable costs — already a dozen signed and more than 150 accounts in negotiation.

Milano said:

“CEOs for the most part, they're tired. They're saying I just want to transform. I just want to use AI everywhere. We are clear that there are very few technology vendors that can do this for us. We want Salesforce to do it, but we are worried about the pricing. Predictability of cost was very important for CEOs. So we put together something very simple: Flat fee, unlimited usage of Data Cloud and Agentforce for our customers. They can deploy any use case they want for 2, 3 years. They can ingest as much data as they need for those Agentic use cases.”

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Snowflake, Palantir forge data integration partnership

Snowflake, Palantir forge data integration partnership

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Snowflake and Palantir said they will integrate Snowflake AI Data Cloud with Palantir Foundry and Palantir Artificial Intelligence Platform (AIP).

The two companies said the partnership will enable joint customers to build data pipelines, analytics and AI applications faster. Eaton, which is a power management company, is an early customer of the integration.

For Palantir, the company has been rapidly building its ecosystem and partnerships. Systems integrators have also been betting on Palantir's AIP platform. For instance, Accenture acquired Decho, an AI consulting firm focused on scaling Palantir across health and public sector. In July, Deloitte and Palantir announced a partnership.

Snowflake and Palantir said the integration between Foundry and Snowflake Iceberg Tables will lead to bidirectional zero-copy interoperability.

Using Eaton as the example, the customers said the partnership:

  • Will improve customer experience.
  • Connect engineering to manufacturing with supply chain orchestration with agentic AI. That connection should improve inventory, on time delivery and quality.
  • Enhance visibility across Eaton's technical landscape.

Constellation Research analyst Michael Ni said:

"This partnership is a win-win: Palantir gets a scalable data backbone, and Snowflake gains a front-row seat in some of the world’s most mission-critical AI deployments. It’s not just about data storage, but increasingly operationalizing data to decisions, as well as ensuring insights are available across the enterprise.

This announcement isn’t just about helping existing customers. For both, this deal is a land and expand play. Snowflake gains an AI decisioning layer solution partner it didn’t have, and Palantir can now offer unified data, governance, and AI workflows without data duplication. That’s a major productivity and compliance gain that both can offer their customers."

Related:

Data to Decisions snowflake Chief Information Officer

HPE has big AI infrastructure plans that are 'going to take a little bit of time'

HPE has big AI infrastructure plans that are 'going to take a little bit of time'

Larry Dignan
Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Read more

Hewlett Packard Enterprise gave an upbeat presentation on taking networking share and capitalizing on the AI infrastructure buildout, but its fiscal 2026 revenue growth forecast was lower than expected.

At its Security Analyst Meeting, HPE projected fiscal 2026 revenue growth of 5% to 10% and analysts were expecting about 17%. HPE projected adjusted earnings for fiscal 2026 of $2.20 a share to $2.40 a share compared to estimates of $2.42 a share.

HPE also projected a 5% to 7% compound annual revenue growth rate with non-GAAP earnings of at least $3 a share for fiscal 2028. HPE did raise its dividend 10% and announce a $3 billion share repurchase plan.

CEO Antonio Neri emphasized that HPE was focusing on its operating margins and capturing profitable share of AI infrastructure. Neri was joined by Rami Rahim, who gave an overview of the networking plan.

"HPE strategic priorities over the next 3 years are clear. We will build a new networking industry leader, capture AI infrastructure growth profitably, with a focus on sovereign and enterprise customers, accelerate our high-margin software and services growth through our HPE GreenLake Cloud, capitalize on unstructured data market growth with our own IP through Alletra MP Storage and drive customer transition to next-generation server platforms," said Neri.

Neri said the company will deliver annual run rate savings by 2028 of at least $600 million through the Juniper integration and another $350 million from its efforts to optimize efficiency under a program called Catalyst.

"HPE is playing to win in 3 strategic IT markets: networking, cloud and AI, each one serves as an essential building block for modern IT. These markets are all growing at an accelerated pace, driven by advancement in AI and the swift expansion in data center build-outs," said Neri. "We anticipate the overall total addressable market across our portfolio will increase to over $1.1 trillion by fiscal year 2028."

Rahim added that networking will be a critical part of HPE's growth engine for AI infrastructure. "In this new AI era, networking has become mission critical. And as a result, we expect to capture market share in key network product segments that are both large and growing," said Rahim.

Rahim added that HPE's networking business will focus on data center needs for industries, locations, enterprise and AI factories. He said data centers will become more diverse and the era of one-size-fits-all is over.

HPE's presentation was bullish, but the guidance from CFO Marie Myers didn't line up with the expansion plans. Myers explained that HPE is looking to lower its debt load, invest in its products and increase capital returns.

Wall Street analysts weren't thrilled about the outlook, but Neri and Rahim explained that the sales cycles for networking are longer, neo cloud providers are a new market and reference accounts take time.

"We did not bake revenue synergies into 2026 for a specific reason. I have a lot of work in 2026 to integrate 2 businesses together without disrupting any customers. I think I can do that. So we were a bit cautious about expecting too much in that time frame. I think over time, through both commercial and technical integrations, we can see revenue synergies, but that's just going to take a little bit of time," said Rahim.

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