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Category: Industry Analysis

Market trends, acquisitions, and competitive landscape

  • What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI

    What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI

    ServiceNow just spent $7.75 billion to solve a problem most enterprises don’t know they have yet.

    In January 2026, ServiceNow announced its largest acquisition ever: Armis, a cyber exposure management platform, for $7.75 billion in cash. But this wasn’t an isolated move. It was the culmination of an acquisition strategy that signals a fundamental shift in how the enterprise software market views AI governance.

    When a $200 billion platform company makes its largest purchase in history, it’s worth paying attention to what they’re buying—and why.

    The Acquisition Timeline

    ServiceNow’s 2025 spending spree tells a coherent story. In January 2025, they acquired Cuein, an AI-native conversation data analysis platform. In April, they announced the acquisition of Logik.ai, an AI-powered configure-price-quote solution. Then came Moveworks for $2.85 billion, Data.World for data governance, and Veza for identity security.

    The Armis deal dwarfs them all. At $7.75 billion in cash—more than twice the Moveworks price—it represents a massive bet on the convergence of AI, security, and operational technology. Combined with the earlier acquisitions, ServiceNow is assembling capabilities that span AI conversation analysis, data governance, identity management, and now comprehensive exposure management across IT, OT, and IoT environments.

    This isn’t a collection of opportunistic purchases. It’s a deliberate construction of an AI governance stack.

    The AI Control Tower Vision

    ServiceNow has been explicit about their strategic direction. They’re positioning themselves not just as an AI platform, but as what they call an “AI Control Tower”—a unified system that governs and manages AI across the enterprise.

    In the Armis announcement, ServiceNow President Amit Zavery stated it directly: “In the agentic AI era, intelligent trust and governance that span any cloud, any asset, any AI system, and any device are non-negotiable if companies want to scale AI for the long-term.”

    That framing matters. ServiceNow isn’t just saying AI governance is important. They’re saying it’s non-negotiable for scaling AI—and they’re willing to spend nearly $8 billion to prove the point.

    The Armis acquisition specifically addresses a visibility gap that most organizations haven’t fully reckoned with. Without knowing what’s connected across IT, operational technology, IoT, and physical environments, ServiceNow argues that “workflow automation, AI governance, and risk prioritization all collapse into theatre.” You can write policies all day, but if you can’t see what’s actually happening across your technology footprint, those policies are aspirational at best.

    Why This Matters for Every Enterprise

    ServiceNow’s acquisition strategy validates a market reality that’s been emerging for the past two years. AI governance isn’t a nice-to-have feature for compliance teams to worry about later. It’s becoming a core enterprise capability—one that established platform companies are racing to own.

    Consider what this signals. A company with ServiceNow’s market intelligence—they see how their 8,100+ enterprise customers are actually deploying technology—has concluded that AI governance is worth a multi-billion dollar bet. They’re not experimenting. They’re going all-in.

    This has several implications for enterprise leaders.

    First, the governance problem is real and urgent. If you’ve been treating agentic AI governance as a future concern, the market is moving faster than that timeline allows. ServiceNow, Microsoft, Salesforce, and other major platforms are all investing heavily in AI governance capabilities. They’re building for a future where governance is expected, not optional.

    Second, visibility is the foundation. Every acquisition ServiceNow made connects to visibility in some way—seeing AI conversations, understanding data flows, tracking identities, monitoring connected devices. You can’t govern what you can’t see, and the platform leaders are racing to be the ones who provide that visibility layer.

    Third, the vendor landscape is consolidating. When large platforms acquire specialized governance capabilities, they’re signaling an intent to own that layer of the stack. Organizations that wait too long may find themselves choosing between platform lock-in and building custom solutions from scratch.

    The Broader Pattern

    ServiceNow isn’t alone in this recognition. Microsoft has been embedding governance capabilities across its Copilot ecosystem. Salesforce is building AI controls into its platform. AWS, Google Cloud, and Azure are all developing AI governance tooling.

    The pattern is clear: every major platform company has concluded that AI governance will be a battleground for enterprise relationships. They’re not just selling AI capabilities—they’re selling the ability to control, secure, and measure those capabilities.

    This creates both opportunity and risk for enterprises. The opportunity is that governance capabilities will become more accessible as platform providers compete to offer them. The risk is that governance becomes another vector for platform lock-in, with organizations finding themselves dependent on a single vendor not just for AI capabilities but for their ability to manage and measure those capabilities.

    What This Means for Your AI Strategy

    The ServiceNow acquisitions should prompt several strategic questions for enterprise leaders.

    If you’re still waiting for AI governance, the market isn’t. The leading platform companies are spending billions to build governance capabilities. They’re doing this because they see demand from their largest customers—the enterprises that are furthest along in AI deployment. If you’re behind the curve on AI governance, you’re increasingly in the minority.

    Enterprise-grade governance is becoming table stakes. Two years ago, AI governance was a differentiator. Organizations that had it were ahead. Today, it’s moving toward baseline expectation. The question is shifting from “Do you have AI governance?” to “How mature is your AI governance?” Organizations without any governance infrastructure will increasingly struggle to pass security reviews, satisfy regulators, and win enterprise deals.

    You don’t need $8 billion to get started. ServiceNow is building for a world where they’re the governance layer for their entire customer base. Your organization has different needs. You need visibility into what AI is doing, measurement of what value it’s delivering, and controls that scale with your risk profile. That doesn’t require a platform acquisition strategy—it requires the right tools applied to your specific environment.

    The Vendor-Neutral Alternative

    Olakai was built on the same insight that’s driving ServiceNow’s acquisition strategy: enterprises need unified visibility, governance, and ROI measurement across their AI deployments. The difference is in how we deliver it.

    Rather than locking customers into a single platform, Olakai provides a vendor-neutral control plane that works across AI tools, models, and infrastructure. We integrate with whatever AI systems you’re using—whether that’s chatbots from one vendor, copilots from another, and agent frameworks from a third. The goal is the same governance visibility and ROI measurement that ServiceNow is assembling through acquisitions, without requiring you to commit to their ecosystem.

    This matters because most enterprises don’t have a single-vendor AI environment, and they’re unlikely to in the foreseeable future. Different teams have different needs. Different use cases have different requirements. A governance layer that only works within one platform leaves gaps that shadow AI will fill.

    Looking Ahead

    The ServiceNow acquisition spree marks a turning point. AI governance has moved from emerging concern to validated market category, with billions of dollars of M&A activity confirming its importance.

    For enterprise leaders, the message is clear. The organizations that figure out AI governance in 2026 will have a significant advantage over those that don’t. They’ll scale AI programs faster because they can prove value and manage risk. They’ll win more enterprise deals because they can satisfy security and compliance requirements. They’ll retain talent because they can offer AI tools with appropriate guardrails rather than blanket prohibitions.

    ServiceNow is betting that AI governance will be non-negotiable for enterprises that want to scale AI. Based on what we’re seeing in the market, that bet looks correct.

    The only question is whether you’ll build that governance capability before your competitors do.

    The market has validated AI governance. Schedule a demo to see how Olakai delivers it without platform lock-in.

  • AI Predictions for 2026: What Enterprise Leaders Need to Know

    AI Predictions for 2026: What Enterprise Leaders Need to Know

    As 2025 draws to a close, enterprise AI has reached an inflection point. Chatbots and copilots proved the technology works. Agentic AI is demonstrating the power of autonomous action. But the gap between AI experimentation and AI value remains stubbornly wide for most organizations.

    The stakes are higher than ever. According to Gartner, 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. But the same Gartner research warns that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. The difference between the winners and the laggards won’t be who has the most AI—it’ll be who extracts the most value from it.

    Here are the trends we see shaping enterprise AI in 2026—and what they mean for business leaders.

    1. From Pilots to Production

    2026 will be the year enterprises finally move beyond pilot purgatory. Organizations that have been experimenting for 2-3 years will face a “ship or kill” moment: either prove ROI and scale, or acknowledge the experiments failed. The era of open-ended experimentation is ending.

    This shift has real consequences. Expect pressure to quantify AI value in business terms, not just technology metrics. Governance and measurement become requirements, not nice-to-haves. Vendors will face harder questions about real-world results, not demo magic. According to McKinsey, high-performing organizations are three times more likely to scale agents than their peers—but success requires more than technical excellence. The key differentiator isn’t the sophistication of the AI models; it’s the willingness to redesign workflows rather than simply layering agents onto legacy processes.

    If you’ve been running pilots, define success criteria and set a deadline. Either demonstrate value or reallocate resources to use cases that can. For a structured approach to proving value, see our AI ROI measurement framework.

    2. The Rise of Multi-Agent Systems

    Single-purpose agents will give way to coordinated multi-agent systems. Just as microservices transformed software architecture, agent ecosystems will transform how enterprises automate complex workflows. Gartner reported a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025—a clear signal that enterprises are thinking beyond standalone agents.

    This shift enables complex processes like order-to-cash and hire-to-retire to become fully autonomous. Agents will hand off work to other agents, creating agent-to-agent workflows that mirror how human teams collaborate. But governance complexity increases as agent interactions multiply—you’ll need visibility not just into individual agents but into the handoffs and decisions across the entire system.

    Forrester predicts that 30% of enterprise app vendors will launch their own MCP (Model Context Protocol) servers in 2026, enabling external AI agents to collaborate with vendor platforms. Gartner outlines five stages in enterprise AI evolution: Assistants for Every Application (2025), Task-Specific Agents (2026), Collaborative Agents Within Apps (2027), Ecosystems Across Apps (2028), and “The New Normal” (2029) where at least half of knowledge workers will be expected to create, govern, and deploy agents on demand.

    Design your agent architecture with coordination in mind now. Establish standards for how agents communicate and hand off work before the complexity becomes unmanageable.

    3. Governance Becomes Competitive Advantage

    Organizations with mature AI governance will scale faster than those without. While governance has been seen as a brake on innovation, 2026 will reveal it’s actually an accelerator—enabling confident deployment of higher-risk, higher-value use cases that competitors can’t touch.

    Companies with governance in place can move to production faster because security and compliance aren’t blocking deployment at the last minute. Regulatory pressure will increase with the EU AI Act fully in effect, state laws emerging in the U.S., and industry standards solidifying. Customers and partners will ask about your AI governance posture. Forrester predicts 60% of Fortune 100 companies will appoint a head of AI governance in 2026—organizations ramping up agentic exploration will especially benefit from this increased focus.

    Build governance foundations now. Start with visibility (what AI is running?), then add controls (who can do what?), then measurement (is it working?). Our CISO governance checklist provides a comprehensive framework.

    4. The ROI Reckoning

    CFOs will demand clear AI ROI numbers. The days of “we’re investing in AI for the future” are ending. 2026 will require concrete evidence that AI investments are paying off.

    McKinsey estimates generative AI could add between $2.6 and $4.4 trillion annually to global GDP, with AI productivity gains in areas like security potentially unlocking up to $2.9 trillion in economic value by 2030. But that’s the macro picture. At the individual enterprise level, AI leaders will need to connect AI metrics to business outcomes. Activity metrics like conversations and completions won’t be enough—you’ll need cost savings, revenue impact, and time-to-value calculations. Some AI projects will be cut when they can’t prove value.

    Establish baselines before deploying AI. Define what success looks like in business terms. Track outcomes, not just activity.

    5. Shadow AI Backlash

    A major data breach or compliance violation caused by shadow AI will force enterprises to take unauthorized AI use seriously. What’s been tolerated as employee experimentation will become a recognized security risk.

    Enterprises will invest in shadow AI detection and governance. Policies will shift from “don’t use AI” (which doesn’t work) to “use approved AI” (which gives employees a sanctioned path). Security teams will add AI-specific controls to their toolkit. Gartner’s warning about “agent washing”—vendors rebranding existing products without substantial agentic capabilities—adds another dimension: you’ll need to distinguish real AI tools from marketing rebadging.

    Understand your shadow AI exposure now. Provide sanctioned alternatives that meet employee needs. Build detection capabilities before an incident forces your hand.

    6. Industry-Specific Agents Emerge

    Vertical AI solutions will outperform horizontal ones. Pre-built agents for specific industries—healthcare claims processing, financial underwriting, legal document review—will deliver faster time-to-value than general-purpose platforms that require extensive customization.

    Industry expertise becomes as important as AI capability. The build vs. buy calculus shifts toward buy for common workflows, with differentiation coming from proprietary data and processes rather than technology. Gartner estimates only about 130 of the thousands of agentic AI vendors are real—the rest are rebranding without substance.

    Evaluate industry-specific AI solutions for common workflows in your sector. Reserve custom development for truly differentiating use cases where your unique processes create competitive advantage. The Future of Agentic use case library provides examples across industries.

    7. The Talent Shift

    AI will change the skills organizations need—but not in the ways people expect. Demand will grow for AI governance, integration, and change management expertise. Pure AI/ML research talent will remain concentrated at large labs; most enterprises won’t build models, they’ll integrate and govern them.

    Change management and training become critical for adoption—technology that people don’t use delivers zero value. New roles are emerging: AI Ethics Officer, AI Governance Lead, Agent Operations. Gartner predicts that through 2026, atrophy of critical-thinking skills due to GenAI use will push 50% of global organizations to require “AI-free” skills assessments. The top five HCM platforms will offer digital employee management capabilities, treating AI agents as part of the workforce requiring HR oversight.

    Invest in governance and integration capabilities. Build change management into every AI project. Upskill existing staff on AI governance rather than competing for scarce model-building talent.

    8. Cost Optimization Pressure

    AI costs will come under scrutiny. Early implementations often over-spend on model API calls, infrastructure, and maintenance. 2026 will bring focus to AI unit economics and cost optimization.

    Cost per transaction becomes a key metric alongside accuracy and time savings. Model selection will consider cost/performance tradeoffs—not every task needs the most powerful model. Right-sizing becomes standard practice: using simpler, faster, cheaper models where appropriate, reserving expensive frontier models for tasks that truly require them.

    Track AI costs at the use-case level so you understand where money is going. Experiment with smaller models for routine tasks. Optimize prompts and workflows for efficiency—often the cheapest improvement is making fewer API calls through better prompt engineering.

    The Path Forward

    2026 will separate AI leaders from AI laggards. The difference won’t be technology—it will be execution. Leaders will prove ROI, scale successful pilots, and build governance that enables rather than blocks. Laggards will remain stuck in experimentation, unable to prove value or manage risk.

    Gartner’s best case scenario projects that agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion—up from 2% in 2025. By 2028, Gartner predicts 90% of B2B buying will be AI agent intermediated, pushing over $15 trillion of B2B spend through AI agent exchanges. The enterprises that build the capabilities to participate in that future will thrive; those that don’t will struggle to compete.

    The enterprises that succeed will treat AI not as a technology project but as a business transformation. They’ll measure what matters, govern what’s risky, and scale what works. The future of enterprise AI is measurable, governable, and valuable. 2026 is the year to make it real.

    Ready to move from experimentation to execution? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and scale AI with confidence.