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Physical AI and Robotics Take Center Stage at Computex Taipei 2026 for Semiconductor Vendors Trump's Quantum EO: What It Means for the U.S. Market The sovereignty conversation vendors need to have - but rarely do Q&A: Your Pipeline Conversion Questions, Answered by IDC Analysts The Intelligence Gap Is Widening. Here's What the Data Says. Anthropic, Trump, and Fable 5: The Dispute That Makes the Case for Frontier AI Studies IDC Quanta: The Next Era of Tech Intelligence The $22.5 Trillion AI Opportunity Why the memory market is still tight: what comes next Indonesia PC Market Grows 9.4% in Q1 2026 Despite Component Pressures But Headwinds Are Building The Dawn of Just-in-Time Software SpaceX, Cursor, and the Race to Build the Best Coding LLM in the World NVIDIA Becomes #1 in Datacenter Ethernet Switching as 1Q26 Market Surges 39.8% to $15.4 Billion Wi-Fi 7 Captures 44% of Enterprise WLAN Dependent Access Point Revenue as 1Q26 Market Grows 15.9% to Nearly $2.7 Billion AI Is Ready. Enterprises Are Not. Vendors Need to Fix It. Smart Glasses Surge: The XR Market Is Rewriting Its Own Rules WWDC 2026: Apple’s AI Credibility Test AI Is Making MSPs More Efficient. Here’s How to Share in the Gains. The Dark Funnel: How Answer Engine Optimization is Reshaping B2B Brand Visibility Agentic AI Is Breaking Your ROI Model. Here’s How to Fix It When the Question Can’t Wait: How Kyndryl Delivers Enterprise Intelligence at Speed Why the Grey Market Won’t Disappear in the Middle East and Africa: The Case of Notebooks Print, AI, and the expanded buying committee: highlights from IDC’s Print Executive Dinner in London AI in telecommunications: Why it is becoming an infrastructure priority Do AI Markets Face a Circular Financing Problem? Enterprise Applications Will Determine the Answer. From AI pilots to business value: what EMEA digital leaders are doing differently in 2026 Why India’s PC Market Surged 31.1% in Q1 2026 and What Comes Next PC Market Enters Volatile Territory as Memory Shortage Persists Through 2027 Agentic AI Ecosystems: Navigating the Megatrend That’s Reshaping Enterprise Technology Markets Leaning into disruption: How Perficient delivers real outcomes in an AI-first world Google’s Fitbit Air and Google Health: The Software Platform Play That Matters More Than the Hardware Why Fast and Trustworthy Aren’t Mutually Exclusive in AI Research The Middle East Conflict Just Rewrote the Rules of Business Continuity Ecosystem strategy in 2026: turning AI disruption into partner-led growth Worldwide Smartphone Market to Decline 13.9% in 2026 as Memory Crisis and US-Iran War Constrain Growth The AI Supercycle Has Started. Where Does Asia/Pacific Stand? Devices at Google I/O 2026: Android XR Glasses, Googlebooks, and Gemini Intelligence Why Research Alone Isn’t Enough: The Delivery Problem No One Is Talking About Digital Accessibility in the Workplace: From Compliance to Competitive Advantage When Pinocchio Became a Real Boy: Security Platforms Have Grown Up Plotting a Future-Proof Course: How The Resorts Companies Turned a Decade of IDC Partnership into a Competitive Edge European CISO priorities in 2026: AI agents, platformization, and sovereignty Why IDC Directions Is Coming to Hangzhou — and Why You Should Be There Telcos’ Next AI Revenue Play: Monetizing Orchestrated Digital Infrastructure The Workforce Skills Gap That AI Can’t Solve for Itself The Agent Takeover: What Happens When AI Becomes the Primary User of Enterprise Software The AI Answer Gap: Why Fast Answers and Defensible Ones Are No Longer the Same Thing The Market Every Western Executive Should Be Watching. IDC’s CEO Already Is. Ecosystem Strategy in 2026: Why AI Is Rewriting Partner-Led Growth Anthropic, SpaceXAI, and the New Compute Race in AI Navigating a Market Where the Wrong Bet Has Real Consequences Renuka Drummond Named Top 10 Corporate Counsel Worldwide by OnCon Icon Awards From Labor Arbitrage to Platform-Led Outcomes: How Agentic AI Is Rewriting the IT Services Playbook EMEA IT Market 2026 – Q2 Updates: 5 Key Takeaways on AI, IT Spending and Market Trends Japan’s AI Infrastructure Market Is Heading for ¥1 Trillion and Here Is What Comes Next Asia Pacific IT Spending Outlook 2026: How the Middle East War Is Reshaping IT Budgets From Digital Access to Accessibility with AI Enablement From Cost Optimization to Continuity: What IT Buyers Need Now and How Suppliers Must Respond AI as an Organizational Stress Test: What Will Break First in Your Operating Model? Japan’s ¥2.1 Trillion IT Modernization Wave: The Race Has Already Begun Semiconductor Market to Surge Past the Trillion-Dollar Threshold: AI Infrastructure Drives Market Growth Most AI Investments Don’t Deliver Value – Here’s What EMEA Leaders Are Missing Hannover Messe 2026: 7 Insights and 3 Pieces of Advice on Industrial AI, Manufacturing, and the Future of the Factory IDC Directions 2026: The AI Conversation Has Shifted. Here’s How to Catch Up. Coding by Prompt Is Coming to Your Business Units: What CIOs Should Do Next FutureScape 2026: Building Trust, Resilience, and Prosperity in the Agentic Future FutureScape 2026: Charting the Path to Enterprise-Wide Orchestration From Task Execution to Value Creation: What the 2026 Humanoid Robot Half Marathon Reveals About Industry Progress Europe’s AI Story Has a New Problem: It’s Actually Working AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion Digital Sovereignty: Why “Sovereign” Is No Longer Enough FutureScape 2026: Navigating the Crosscurrents of Disruption Huawei and Apple Support China Smartphone Market Resilience as Shipments Decline 3.3% in Q1 2026 The Dirty Secret of AI in ITSM: Why Bad Data Wins Every Time Wholesale telecommunications: How platform models are reshaping the market Beyond LLMs: Why AI Strategy Now Requires Multi-Model, Multimodal, and Multi-Agent Architectures It’s a Data Thing: Retail and Restaurant AI Investments Will Miss the Mark if Not Led by Data Modernization Inference to Overtake Training by 2027 – Why Japanese First Movers Are Betting on Sovereign AI Infrastructure The productivity plateau: Why efficiency gains no longer differentiate Why I’m Excited about IDC Directions 2026 GTC 2026: Workstations enter the sidetop era Dispelling the myth of a silver bullet in sovereign AI MacBook Neo: Apple’s strategic play to disrupt the PC market AI sovereignty risk: A five-step agenda for CIOs Data pricing for AI is being negotiated without a stable model From cyber risk to business risk: How CISOs should engage the board in 2026
Agent Supplier or Featureware: The Choice Every SaaS Vendor Faces Now
Bo Lykkegaard · 2026-06-18 · via IDC

In two earlier posts I argued that SaaS is being disrupted rather than killed, and that the per-seat revenue model is on its way out. The dramatic decline in market capitalization of SaaS vendors over the past 9 months, sometimes referred to as the SaaS-pocalypse, is partly about investors pricing in the fear that SaaS applications will recede behind an AI agent layer. This would make the SaaS applications become invisible “featureware,” with the agent capturing the user relationship, the workflow, and, eventually, the budget. 

This post will show that enterprise buyers now expect their software vendors to supply the agents, and to serve as the trusted source of data and context for custom-built and third-party agents. This is the natural next progression for SaaS vendors and represents an attractive market opportunity. 

To avoid confusion, here are IDC’s AI-related definitions. An AI assistant is conversational and works as a tool for a human. An AI agent is autonomous, uses other tools, and carries memory and context across tasks. An agentic workflow is a business process that an agent executes end-to-end with limited human intervention. 

SaaS vendors are faced with a dual threat, hence SaaS-pocalypse 

The first threat is that organizations will simply write their own applications instead of buying standard SaaS applications. This threat triggered the dramatic devaluation of SaaS stock in February as Anthropic released its Code capabilities. Conversations with CIOs in enterprises partly validate this fear. While they are not contemplating creating core accounting, HRIS or workforce management applications, they might build rather than buy auxiliary applications in areas such as planning, performance management, compensation management, logistics planning, etc. Application areas where customer requirements vary widely and where legal complexity is low are moving gradually from buy to build. 

The second threat is that agentic overlays will increasingly handle the user interaction while AI agents access SaaS applications via API interfaces to carry out transactions on behalf of the human user. The implication is key price justifiers of SaaS applications, such as the interface, the feature breadth, and the brand gradually disappears from the users. This also implies that the per-seat user pricing stop making sense, when users are no longer the primary users of SaaS applications. 

AI agent adoption is already past the tipping point 

In IDC’s April 2026 Future Enterprise Resiliency and Spending survey of organizations with 500 or more employees, 74% had already deployed at least one agent, 15% were piloting, and only 1% reported no use and no plans. The same respondents expect the number of agent types in production to roughly triple, from 24 in March 2026 to 62 by 2027. Buyers are not evaluating whether to enter the agentic era. They are deciding which parts of their operation to hand over first, and operational and core business data are their top target. 

The big question is who will deliver these AI agents. Most organizations have deployed standard AI tools and run pilots, but few have redesigned core processes to use AI at scale. The survey results shows that most organizations deploy ready-made agents wherever they can, rather than build their own. 

Where Vendor-Supplied Agents Win 

IDC distinguishes four kinds of agent by who builds them and how the buyer obtains them. In-application agents come packaged inside the application and the buyer simply adopts them. Low-code / no-code agents are configured by the buyer in a visual builder the vendor provides. Standalone agents are third-party products the buyer implements alongside existing applications. Custom-built agents are assembled by internal teams using full-stack orchestration frameworks. 

The survey data, which focuses on AI agent quantities as opposed to spend, points in one direction. The two types that vendors supply directly, in-application agents and low-code or no-code builders, are growing fastest in numbers and from the largest installed base. Custom-built agents show the slowest growth, because the orchestration they require is more difficult and require more inhouse skills to build. Buyers prefer to adopt or configure an agent that already understands their data and respects their permissions over building one from scratch. 

SaaS applications as trusted data and context for custom-built agents 

IDC also sees a massive demand for custom-built AI agents, especially for core business processes that are unique to an industry or an organization. Standalone products and custom-built agents will operate inside the same enterprise, and they will need data and context that lives inside your application. 

A custom-built or third-party agent that accesses data “from anywhere” still needs a place where the data is correct, the process is compliant, the permissions are enforced, and the transaction is guaranteed to execute. A SaaS vendor can offer vetted business processes, governed data, and audit trails for custom AI agent consumption. This is, in IDC’s view, a key future role for today’s SaaS applications. 

What the AI-pivoted SaaS application looks like 

The interface stops being a single screen and becomes several modes serving the same processes: the traditional UI, a conversational UI, a flow-of-work UI embedded where the user already operates, and machine interfaces so external agents can call the application directly and safely. 

The AI-pivot requires SaaS vendors to rethink the workflow from the ground up, which touches the full stack: foundation models, an embedding layer, a vector database, retrieval-augmented generation, an orchestration layer, guardrails, monitoring, and version management. The vendor also has to give buyers an agent toolkit of their own, so that the low-code and no-code configuration buyers increasingly demand happens inside the vendor’s governed environment rather than outside it. 

European vendors carry an additional set of requirements that, handled well, become a selling advantage. Compliance with GDPR, NIS2, and the EU AI Act (still there despite the recent delay), data residency and sovereign cloud guarantees, genuine multi-language model performance, and transparency in how the AI reaches a decision are all conditions of sale to compliance-sensitive European buyers. 

SaaS is not dead, and the incumbents are not doomed. But the asset that justifies a vendor’s existence is shifting from the screen the user looks at to the agents the vendor supplies and the governed data those and other agents rely on. Your customers already expect you to be their agent supplier. The only question is whether you are ahead of that shift or reacting to it. 

So what do you actually do with this? There are three concrete moves software vendors need to make in the near term. They are specific, they are sequenced, and the window to move first is closing. I will walk through all three in a focused 25-minute webcast, grounded in IDC survey data from more than 1,000 enterprise organisations: where your customers sit on the AI maturity curve, why vendor-supplied agents will dominate enterprise deployments through 2030, and which ERP and SaaS processes are attracting the most AI investment right now. Secure your spot here. 

Bo Lykkegaard

Bo Lykkegaard - Associate VP for Software Research Europe

Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis,…