Agentic AI Is Easy. Until the Work Matters
SMB is Where it begins. Maturity Defines What it Becomes
Agentic AI works today. But for most organisations it remains in a narrow band of work. The net result is that the market is misreading the signal. Because it is focused on the destination, the level 5 or 6 maturity benchmark, and not the entry point or the path to get there. Enter Salesforce’s SMB play.
Agentic AI looks like it’s working, especially in smaller organisations. It is quick to deploy. It is easy to demonstrate. And it can deliver immediate gains in productivity. It also fits neatly into some workflows where speed matters more than consequence. It’s the kind of model I wrote about in TechnologyOne’s PLUS Moment.
And if you read that as the starting point, not the end state, the current wave of success tells you something important. It does not tell you that agentic AI is solved. But it does tell you we’re seeing the earliest stage of a much longer progression. One that will take years to unfold as organisations move from low-consequence efficiency into work that carries real consequence and accountability.
I feel like this is the part of the story the market continues to gloss over. Not just because we’re misreading the results, but because of how the story is being told. Vendors are conditioned to sell the future. The fully autonomous state. End-to-end orchestration. Agents operating confidently across the business. That’s what I refered to as Level 5 and 6 maturity in an article last year called Have You Tried Our New AI Agent?
And it’s not just a marketing choice. It’s a market expectation. Boards want market share, and category leadership and to know they are going to win. So the narrative gets pushed forward where the destination is clear and the vision is compelling. But it sits orders of magnitude ahead of where most organisations actually are. And in doing so, the middle gets lost.
We jump from aspiration to demonstration, without spending enough time on what meaningful entry points look like. Where do you actually start? What kinds of work can be trusted first?
That’s where the confusion creeps in. The harder the market pushes the future, the less clear the starting point becomes.
Early success is too often interpreted as capability (for both the customer and AI solution provider), when in reality we’re just seeing what happens when you apply autonomy to low-authority workflows. It’s not that every AI technology being pitched is mature. It’s that the client work isn’t demanding enough to expose where it fails (at scale or higher levels of workflow complexity). Now, after a few years of headline-grabbing announcements, it feels like things are starting to settle down in 2026.
In a recent briefing with Salesforce, what stood out was their emphasis on strong AI adoption in the CRM SMB segment. Even allowing for some variation between Salesforce and ABS definitions, that’s a TAM of over 2.5 million businesses in Australia.
The smaller deal sizes in this segment make it easy to dismiss, especially for enterprise-focused sales teams. But that misses the point. If agentic capabilities are landing quickly and sticking, that’s a signal, not something to write off.
What if this wasn’t just the easy version of the problem, but the honest one? What if it shows where agentic AI actually works today, and not where a keynote says it should.
In smaller organisations, most workflows sit in a low-authority band. Fewer systems. Lower consequence. Lighter governance, often implicit rather than defined. That’s the space where agents work easily. They can summarise, draft, trigger, and respond. They can take small actions without needing a deeply structured model of the organisation behind them (see Whatever-as-a-Service). Identity exists, but it doesn’t need to carry much meaning. It’s present, but it’s very thin. In that technical environment, agentic AI feels easy. But what if we stop seeing this as a weakness and start seeing it as a starting point?
With its latest SMB offerings, Salesforce is demonstrating through commercial expression, something much of the market is missing. Agentic AI is not something you install. It is something you grow into. It is a maturity curve. And one of the most important of this time. Yet vendors continue to sell the destination. Yes, it’s technically valid and an exciting place to be, but also practically out of reach for most organisations today. Largely because we are in a transitional age of computing, and once again in too much of a rush to get there.
What’s missing is simply clearer starting points. And I can’t for the life of me understand why more vendors don’t take a beat and realign around this fundamental assumption. It’s time to rebalance the rhetoric. Less about where the plane or train is going and more clarity on the airport or platform where I can step on.
When it comes to AI, it’s more useful to think of SMB not as a cut-down version of the enterprise, but as the starting point of one. That matters even more today, as enterprise buying centres have become increasingly fragmented. That is why the AI platforms that will define the next operating model are the ones built to carry organisations through that journey, as their work gains complexity and consequence.
Take identity as an example. A business might begin with a simple login, minimal governance, and little or no integration. At that stage, identity doesn’t meaningfully constrain agentic capability. Because both are naturally limited to low-consequence workflows. So you can adopt a solution that will allow you to perform Level 1 or 2 AI.
Now as the organisation grows, so does the complexity of its work. More systems are added (swivel chair work begins). More customers. More transactions. More exposure. Identity starts to evolve. From simple login, to managed access, to SSO, to a fully governed identity platform. Workflow becomes more structured. Decisions begin to carry a lot more consequence. That requires AI at a Level 3 or 4 maturity, perhaps now in the form of an Agent, to remain useful, and evolve with it. Ideally without changing the underlying AI platform.
That is a pathway. From point of entry to escalating maturity.
And I think this is the quiet logic behind Salesforce’s tiered SMB model. From free to starter, to professional, to enterprise, to more advanced agentic capability, the platform and its licensing model, is not just scaling features. It is scaling the step conditions, up to a tipping point, from which AI in the form of agents, can be trusted to execute.
For big or small organisations, that journey can take years and reflects something deeper about Agentic AI. That it is not just about what the agent can do but about what the organisation is ready to let it do.
This is where the broader market narrative starts to quickly break down. Because we’ve become conditioned to look for immediate proof of value of high-maturity capabilities. Where only fast adoption and instant capability can deliver visible impact within a reporting cycle.
But that’s not how platforms like Salesforce create value. They create it over time. As organisations grow into them. As their work becomes more structured. As identity matures. As governance strengthens. As more consequential workflows move onto the platform. The value compounds. Not because the technology changes overnight, but because the conditions for using it evolve.
That kind of progression doesn’t show up cleanly in quarterly narratives. But it is exactly the kind of pattern long-term investors have always looked for. The ability to build with the customer, not just sell to them. In that sense, the slower journey is not a delay. It has always been the model.
In the early stages of that journey, the scope of what can be trusted remains narrow. Which is why most of what we are seeing today is still operating in the efficiency layer. AI can remove some friction and speed up some navigation. Maybe even reduce the effort required to move between systems. But it is still operating around the edges of work.
This doesn’t change the view that the real AI shift begins when agents move into workflows that carry consequence. At this level of maturity (5 or 6) a payment is no longer just a transaction, but financial authority. A service request is no longer just a ticket, but a commitment tied to service levels, risk, and compliance. And a system change is no longer just an update, but a controlled alteration to a live operating environment (managed behind the scenes by a service ontology).
Now the agent is no longer assisting. It is participating. And participation requires structure. This is where identity changes form. It stops being about access and becomes about authority. Who can act, under what conditions, with what level of accountability. Without that, the agent can suggest but it cannot execute. This is the boundary most organisations, nor technology vendors, especially legacy ERP vendors, have not yet crossed.
Not because they lack technology, but because they have never fully defined how work actually happens. It sits fragmented across systems, buried in process documents, or held in the heads of individuals. More mature Agentic AI exposes that immediately. It doesn’t fail because the model is weak but because there is nothing coherent for it to act within.
But nobody starts there. And this is why the SMB success stories matter right now. Not because they prove agentic AI is simple. But because they show where it starts. The real test is for both the vendor and the client is still going to be what happens as those organisations grow. Because that is where agentic AI stops being easy. And starts becoming accountable.
So the winners will not be the platforms with the simplest generative interface or the most capable agents. They will be the ones that understand where to start. The ones that can meet organisations at their point of entry and grow with them over time. The ones closest to the flow of work. The ones that can bind workflow, identity, and data into something that can actually hold responsibility, without requiring constant reinvention (or system replacement) as that work matures.