AI Agent: How to profit from the $58 Billion disruption!

You know what a chatbot does. You ask it something. It answers. You ask again. It answers again. The conversation is yours to drive and the AI is a passenger. Now, an AI agent is something entirely different.

An agent doesn’t wait to be asked. It receives a goal, breaks it into steps, executes those steps autonomously across multiple systems and applications, makes decisions along the way and reports back when the job is done. It books the meeting, writes the brief, updates the Customer

Relationship Management (CRM) system, generates the report and sends the follow-up – without a human managing each step.
That difference, between answering and doing, is the most commercially significant transition in the AI cycle so far. And it’s happening right now.

The scale of what’s coming in AI

Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% in 2025. 50% of enterprises using generative AI are expected to deploy autonomous AI agents by 2027, doubling from 25% in 2025.

The agentic AI market expanded from $7.6 billion in 2025 to a projected $10.8 billion in 2026, outpacing early cloud adoption. By 2035, the market is projected to reach between $182 billion and $221 billion. That’s a compound annual growth rate approaching 50%.

Even more interesting, the IDC forecasts global enterprise AI agent spend reaching $1.4 trillion by 2027, with McKinsey’s range landing between $1.2 and $1.6 trillion.

The last time software changed this fast

The $58 billion shake-up referenced in the briefings from Gartner and Forrester refers specifically to the productivity software market – the category dominated for 35 years by Microsoft Office, Google Workspace and the suite vendors that built their business models around humans doing work inside applications.

By 2028, Gartner expects agent ecosystems to enable collaboration across multiple applications and functions, with a third of user experiences shifting from native applications to agentic front ends.

In plain language: instead of a human opening Salesforce, navigating to a screen and entering data, an agent does it autonomously, across multiple systems simultaneously, faster than any human could.

The last time the enterprise software market faced a disruption of this magnitude was the shift from on-premise to cloud. Before that, it was the shift from mainframe to desktop. Q1 2026 venture funding for agent-native startups reached $4.7 billion. Annualised, that implies a $20 billion-plus 2026 cohort, the largest software vertical funded since cloud-native in 2015–2017.

The money that funded Salesforce and Workday in the cloud era is now funding the agent layer that will sit above them.

Who loses?

The most exposed category is the broad suite of point solution SaaS products – standalone tools that do one thing for a human user. Task management. Scheduling. Data entry. Report generation. Workflow routing. Every category of enterprise software that exists to help a human do a task manually is now competing with an agent that can do that task autonomously.

Over 80% of organisations believe AI agents are the new enterprise apps, triggering a reconsideration of investments in packaged software. That reconsideration is already reducing renewal rates for point-solution SaaS vendors whose products are being displaced by agent-native alternatives.

Salesforce’s Agentforce reached $1.4 billion in ARR and surpassed 9,500 paid deals. Microsoft has embedded Copilot agents across its entire 365 suite. ServiceNow, Oracle and SAP are racing to agent-enable their platforms before third-party agents make the platforms themselves irrelevant.

The companies that successfully pivot from application vendor to agent orchestration layer will survive. The ones that don’t will face the same fate as the on-premise vendors that couldn’t make the cloud transition.

Who wins?

The most accessible and most durable investment opportunity in the agent revolution isn’t in the agent makers themselves. It’s in the infrastructure that every agent needs to function.

Three layers matter.

The first is the model layer. Every AI agent runs on a foundation model. As agent deployment scales from tens to hundreds of agents per enterprise, model consumption scales proportionally. The companies providing model infrastructure – Anthropic, OpenAI, Google DeepMind, and the cloud platforms hosting them (AWS, Azure, Google Cloud) – are the direct beneficiaries of every new agent deployment.

The second is the orchestration and tooling layer. Agents need to connect to enterprise systems, execute actions across APIs, manage memory between sessions and coordinate with other agents. The companies building the connectors, the memory systems, the monitoring tools and the governance frameworks are the unsexy but indispensable picks-and-shovels of the agent era.

The third is the compute layer. The average Fortune 500 organisation runs 3.4 distinct AI agents today, projected to reach 6–8 by 2027. Each agent is making real-time decisions across enterprise systems. That inference compute demand flows directly to Nvidia, AMD and the data centre operators running the infrastructure.

A word of caution

The agent revolution has a failure rate that deserves acknowledgement. Gartner expects over 40% of agentic AI projects to be cancelled by 2027 driven by unclear business value, escalating costs and inadequate governance.

Only about 130 of the thousands of companies marketing agentic AI are genuine, with Gartner coining the term “agent washing” to describe vendors rebranding existing chatbots and automation tools as AI agents without real agentic capability.

The investment implication is the same as in the early cloud era: the vendors selling real capability with real production deployments will separate sharply from the ones riding the narrative. The infrastructure layer (compute, models, orchestration) faces less of this risk because it serves every agent deployment, successful or not.