Agentforce and Data 360 ARR Grew Over 200% Last Quarter. The Companies Winning Aren’t the Ones with the Best Agents — They’re the Ones with the Cleanest Data.
Salesforce’s first quarter fiscal 2027 results, reported in late May, showed Agentforce and Data 360 annual recurring revenue approaching $3.4 billion, up more than 200% year over year. A meaningful share of that growth came through Informatica, which Salesforce acquired for $9.6 billion and folded into the Data 360 platform. Read past the headline number and a clear signal emerges: Salesforce isn’t just selling agents, it’s
selling data unification as the prerequisite for agents to work at all — and the market is paying for that prerequisite at scale.
This matters because it contradicts the way most organizations are currently sequencing their AI investment. The instinct, understandably, is to start with the agent: pick a use case, configure Agentforce, point it at your Salesforce org, and measure results. When that pilot underperforms — and a large share of them do — the diagnosis usually focuses on the agent’s prompt design, its training examples, or its scope. The data underneath rarely gets blamed first, even though it’s usually the actual cause.
Here’s why. An agent is only as good as its ability to retrieve a complete, accurate, current view of the customer or case it’s acting on. In most enterprises running Salesforce for five-plus years, that view is fragmented across Sales Cloud, Service Cloud, a separate marketing platform, and frequently a homegrown or legacy billing
system that was never properly integrated. The same customer might exist as three different records with three different identifiers, none of which reconcile automatically.
An agent querying that environment doesn’t fail loudly — it fails quietly, by giving a confident answer based on an incomplete picture, which is arguably worse than failing loudly because nobody catches it until a customer complains.
Data Cloud — and now Data 360, with Informatica’s data integration and quality capabilities built in — exists to solve exactly this problem before an agent ever gets involved. The core function is identity resolution: stitching together every interaction, transaction, and case associated with a single customer into one coherent, harmonized profile, regardless of which Salesforce cloud or external system originally captured it.
Layered on top is governance — defining which agents can access which fields, under what conditions, with what audit trail — which matters enormously once agents start taking actions rather than just answering questions.
The practical implication for any organization currently planning or running Agentforce pilots is to reverse the usual diagnostic order. Before concluding an agent isn’t smart enough, audit whether it’s even working from complete data. A Data Cloud readiness assessment typically surfaces three categories of problems: duplicate or fragmented customer identities across clouds, inconsistent field-level data quality (missing values, conflicting values, stale values), and access governance gaps that either over-expose sensitive data to agents or under-expose the data an agent actually needs to do its job.
Each of these is fixable, but none of them get fixed by tuning the agent itself.
There’s a sequencing argument here that’s easy to underrate: the organizations posting the strongest Agentforce results aren’t necessarily the ones with the most sophisticated gent configurations. They’re disproportionately the ones that treated data unification as phase one and agent deployment as phase two, rather than running both
simultaneously and hoping the data caught up. Salesforce’s own ARR numbers support his — Data 360 growth and Agentforce growth are climbing together, not because customers are buying two separate products, but because the products are functionally one investment with two visible price tags.
For Salesforce customers operating Revenue Cloud, Service Cloud, and Experience Cloud across multiple business units — a common Selectiva client profile — this dynamic is especially pronounced, because the more clouds in play, the more identity fragmentation accumulates by default. The good news is that a data readiness assessment is a contained, fast-moving engagement compared to a full agent rollout, and it produces a concrete list of fixes rather than abstract recommendations. Selectiva Systems brings two decades of experience untangling exactly this kind of fragmented Salesforce data landscape, from Sales and Service Cloud through Revenue Cloud and Experience Cloud implementations. Before recommending any Agentforce expansion to a client, our team runs a structured data audit first — because an agent built on bad data doesn’t just underperform, it actively erodes trust faster than no agent at all.
There’s a budgeting implication too. Informatica’s $428 million contribution to Data 360 revenue in a single quarter shows enterprises are now willing to fund data quality and integration work as its own line item, not as a rounding error inside a larger AI initiative.
If your organization is still treating data cleanup as a side task for whoever has spare time, that’s no longer how the market — or your competitors — are pricing the problem.
Request Selectiva’s Data Cloud Readiness Assessment to find out if your data can actually support the AI agents you’re planning to deploy.
Why Data Cloud Is the Real Bottleneck in Your Agentforce Strategy
