The AI challenge facing enterprise customers is how to turn fast-moving capability into trusted, organization-wide value without being overwhelmed by uncertainty around models, data, regulation, sovereignty, cost and the future of work.
That was the message from Mick Costigan, VP of Salesforce Futures, speaking with CX Today at Salesforce Agentforce World Tour London. Costigan leads a team inside Salesforce’s strategy organisation that helps the company’s leaders and customers “anticipate, imagine, and shape the future”.
As Costigan put it, this means tracking what is changing outside Salesforce across technology, customer behaviour, geopolitics, and the wider business environment and turning those signals into useful stories about what could come next.
“Most of the stories about AI come from science fiction, unfortunately, and they’re not particularly useful,” Costigan said.
“We’re trying to get people beyond the suspicion that Skynet is about to take over, and we’re all going to be human slaves in a robot future, and think about things that are actually going to be meaningful for how we need to make decisions.”
It’s a task that has become more urgent as customers try to understand what Salesforce calls the “agentic enterprise,” an organization where AI agents can reason, act, access tools and support or automate work across business functions.
The Three Challenges Customers Face
Costigan framed the problem around three basic questions customers are asking about AI, with agents having emerged as the “killer app.”
“How good are agent capabilities going to get, number one. Number two, how do I bring them into my organization, and what does it mean in terms of changes for how the organization works? And then point three: what do humans do?”
Those three questions sit at the heart of enterprise AI adoption.
The first is about capability. Customers can see models improving quickly and agents becoming more powerful, but it is unclear how far that will go, how quickly, or which types of models will matter most. The second is about implementation. Even if AI agents become highly capable, enterprises still need to connect them to real data, tools, processes, systems of record, permissions, governance frameworks and user interfaces. The third is about people. If AI can take on more work, companies must rethink jobs, skills, workflows, accountability and the human role in decision-making.
Costigan said customers are often caught between the promise of frontier AI and the reality of their existing organizations. The answer is to pursue both near-term ROI from practical AI use cases and more ambitious innovation around how the business itself might be restructured where frontier models may play a larger role. “My advice is to kind of do both things simultaneously.”
The Gap Between AI Capability and AI in Use
Costigan made a clear distinction between the pace of model development and the pace at which enterprises can actually use AI effectively.
Model capability may be improving at what feels like “the beginning of an exponential rate”. But enterprise adoption is slower because it depends on far more than model intelligence, Costigan said.
“Getting data right, getting access to tools right, getting the interface right, so that people can work with it, generating productivity gains that aren’t just at the level of an individual, but are actually across the organization, is more challenging.”
Enterprise agents need to be able to operate inside complex business environments with trusted context, access to the right data and guardrails that ensure their outputs are auditable.
Salesforce is increasingly focused on what it calls the “agentic harness” to make AI viable for the enterprise.
“When we started building Agentforce, one of the first things we said was models are great, but you need this layer around that model,” Costigan said. Salesforce has worked with frontier model labs on requirements such as zero data retention, which is an enterprise requirement.
“It’s way beyond just trust,” Costigan added. “It’s access to data, everything around context engineering… How do we bring better access to data? That challenge is huge. Access to tools; how does all of this integrate?”
Why Salesforce Believes the Harness Matters
To explain the importance of the harness, Costigan compared today’s AI models with the earliest cars, recalling seeing an 1886 Benz motor wagon at a car museum comprising an engine, “three bicycle wheels and a garden bench, basically” as an analogy for large language models.
“That’s like an LLM in 2021, where the engine is the whole thing, and there’s these flimsy things around it.” The evolution of cars, Costigan pointed out, was not just about engines. “We added a whole bunch of other ingredients… to make it more beautiful… but also safer, more aerodynamic, more efficient and engines less likely to blow up.”
In the same way, AI models may be the engine, but enterprises need brakes, steering, dashboards, safety systems, interfaces and rules of the road. This is key because models can still behave unreliably if they are not properly constrained or connected to trusted information, Costigan noted.
“We still have this issue of [models responding] just straight up, ‘I should have checked that file you shared with me instead of just making it up or winging it. These models will still continue to do that unless you have the right harness.”
Here is where Salesforce sees Agentforce playing a role.
“You can think about Agentforce as a really robust enterprise-grade harness. That’s really important in government or regulated industries where you really can’t afford to have… any of the challenges that we’ve seen with models.”
Regulation and Sovereignty Are Moving Up the Customer Agenda
The challenge becomes even more acute when regulation enters the picture as authorities struggle to catch up with what models can do, Costigan said, noting that discussions around global AI regulation are complicated by geopolitics.
“There’s a role for us in terms of the harness—of that trust layer that we talked about—being able to provide that certainty, particularly for regulated industries, where they don’t want to take on all the risk of trying to figure these things out, not to mention keeping up with regulation as it’s changing.”
Costigan argued that regulation is not only about existential risk or high-level alignment debates.
“We’ve had AI in use for the last 15 years. We’ve seen some negative consequences… so we’ve gotten better at figuring out how to protect people from some of the biases that we’re importing from humans—let’s be clear—into these models.”
Costigan added that data sovereignty has become a major issue for Salesforce customers, particularly in Europe.
While Salesforce may have begun with a story of a VP of sales buying software on a credit card, Costigan said the company now works on “multi-year, multi-jurisdiction agreements with large global enterprises”.
Customers are increasingly asking Salesforce for clarity and future-proofing around sovereignty, data location and regulatory compliance. Costigan pointed to Salesforce’s EU Sovereign Cloud and its work with infrastructure partners such as AWS. “We’re both being led by our customers and also working with our partners to help deliver what people want in terms of sovereignty.”
But he also warned that sovereignty comes with trade-offs. Globalized access to technology has allowed innovation to flow quickly from where it starts to users around the world. Restricting that flow may meet certain policy objectives, but it can also create costs and slow access to innovation.
“The benefits of technology being able to flow around the world in terms of giving people access to innovation are important,” Costigan said. “And any attempts to limit sovereignties are going to have consequences.”
“There is a tension sometimes between the ambition for sovereignty that some governments have, versus their ability to bear the costs and their willingness to bear the costs. We’re going to be responsive to our customers, which include many governments, and we’ll adapt. But we’ll also advocate for what we think is going to be the best way of delivering innovative new technology to most people around the world.”
The Real Enterprise AI Test
Costigan’s message was that customers are not short of AI ambition but they are short of certainty.
They can see models advancing quickly and AI-native startups rethinking work from the ground up. They can see boards asking why the “year of agents” has not yet translated into measurable results. But they also face hard questions about data, context, tool access, regulation, trust, sovereignty, costs, and the human role in the organization.
For Salesforce, that is why the future of enterprise AI will be determined by the systems around AI models; the harnesses, governance layers, workflows, partner ecosystems and platforms that make AI usable in real businesses.
As Costigan put it, customers do not necessarily want to become AI scientists inside their own organizations, they want the technology to work and deliver value.
