AI’s Enterprise Test Is Shifting From Speed to Results

As soon as model providers like OpenAI and Anthropic went live a few years ago, many established and new companies rushed to build applications on top of them, hoping to harness the power of the most transformational technology in recent memory. At the time, the conversation focused on which AI application was the most impressive based on its speed and ability to reduce the time and cost of legacy workflows.

However, the conversation has since shifted from most powerful to the most impactful. Proofs-of-concept that wow the C-suite aren’t enough for Fortune 500 buyers anymore because while they may showcase impressiveness, that’s not the same as actually driving real business results if that power isn’t applied strategically.

So, what can young AI companies do to actually operationalize the technology into durable, revenue-generating products across real business environments instead of coasting on hype?

The old criteria

In the early days, buyers and investors were evaluating criteria that today are considered table stakes: making things faster and cheaper. ROI was measured in speed and cost savings, with accuracy as the burden of proof. Discussions that acknowledged AI’s flaws focused on hallucinations and data trustworthiness.

As such, enterprise organizations quickly realized that fast and cheap AI-generated outputs don’t actually make their jobs any easier. From market research and coding to healthcare, supply chain, legal, and finance, professionals in numerous industries have to spend hours reworking AI outputs to make them production-ready. At this point, the review process is just as (if not more so, in some cases) tedious as the work itself without AI.

The new criteria

This is why investors now look for assured impact. They assess whether a product or solution will demand laborious hours of review, resulting in frustrating bottlenecks, or if it’s trustworthy enough to deliver on its promises the first time.

That’s the new bar. The most effective vendors evolve beyond a mere software layer into an ‘outcome layer’ to help organizations solve mission-critical problems, become deeply embedded in their customers’ operations, and create more value as the technology grows.

Enterprise buyers have wised up to young AI companies that promise to automate work but ultimately are only surface-level and cost more time and money than what they delivered is worth.

Context is key

To reach this bar, AI solution providers must understand their targeted industries’ complex operational nuances and weave together a system of agents that handle isolated steps into a single end-to-end automated workflow.

The key to this level of comprehensiveness is context. The problem is, a tremendous amount of business context is never written down; it resides within human minds, especially at the enterprise level. LLMs can’t act on what they don’t know, so real, expert practitioners need to translate their contextual knowledge into agentic systems that ensure the AI fits naturally into the end user’s daily routine.

Think of it like this: Deploying a set of new AI agents is similar to hiring a new employee. They have the right work ethic, knowledge, and skills, but lack the deep amount of context necessary to perform optimally. As you train them with context and your business’s unique approach to your industry, this employee can go from ramping with oversight to completing the entire job with little to no supervision.

Human-in-the-loop

This is why human-in-the-loop applications are typically the most successful of AI projects. They translate nuanced context into the system of agents by providing a judgment layer that AI alone can’t replicate, ensuring the system produces impactful results and eliminates the end-user’s need for final-mile rework.

Consider a global accessories brand that was expanding into new licensed partnerships with a major entertainment company. The team was aiming to build a multi-season roadmap across several IP properties, each with its own consumer base and aesthetic language, in a retail environment that no longer allows time to launch, learn, and refine.

Rather than relying on instinct or a months-long traditional study, the brand used an agentic research system grounded in its category context, with experienced researchers providing the judgment layer throughout. The team validated the full roadmap in six weeks, with clear direction and no final-mile rework, before committing to multi-year deals. The difference in this case was the context and judgment wrapped around it.

Enterprise organizations aren’t buying AI for its own sake. They’re buying results. Vendors that embrace human-in-the-loop models will be the ones to advance into enterprise AI’s next phase because the more context they can bring to a problem, the more effective their solutions can be.