Every week, a new AI tool claims to reinvent the enterprise. But beneath the hype, many enterprises are grappling with a sobering reality: the GenAI solutions they've deployed are falling far short of expectations.
According to McKinsey, nearly eight in 10 enterprises deploying GenAI are still seeing no meaningful bottom-line impact. More recently, MIT found that 95% of AI pilots fail in the enterprise. The culprit? The unpredictable nature of GenAI itself.
The Trust Problem with GenAI
While GenAI can be a powerful tool for creativity and ideation, it is inherently unpredictable with randomness baked into the algorithm, and that is the one thing enterprises can't afford — especially at runtime. Organizations need reliability, transparency, and control to survive, particularly in highly regulated industries like healthcare, finance and insurance. If operations rely on free-form GenAI, the results can be chaotic.
One incorrect AI output can erode customer confidence, trigger compliance violations, or create significant financial risk. Take banking, for example: if a customer applies for a loan, the institution must ensure the decision is both accurate and repeatable. That means if the customer applies again tomorrow with the same information, they should receive the same outcome. Without that consistency, you undermine the trust of your organization.
The Path to Predictable AI Agents
The answer to predictable agents lies in the less glitzy, tried-and-true approach used by nearly all organizations in some form: the common workflow.
Enterprises typically leverage hundreds of different types of approved processes and workflows to get tasks done. This ensures consistency in outcomes no matter who or what is completing the work. Examples include the process for processing a loan, running a credit report inquiry, or resolving a customer service complaint.
When AI agents are grounded in these workflows, the results can be truly transformative. This ensures the agents follow approved procedures step by step, so they don't go off track and perform unexpected actions. This turns unpredictable AI agents into fully reliable teammates, which allows organizations to effectively scale agentic deployments across the enterprise.
Using the Right AI at the Right Time
AI definitely has a role in helping enterprises transform their workflows.
Deploying the right AI at right time can inject innovation and accelerate transformation. For example:
- Design time is when enterprises create the optimal workflows and logic that run their organizations. This is when GenAI's powerful creative capabilities can really shine as the team brainstorms and explores different possibilities. At this ideation stage, you want all the reasoning power of GenAI to stimulate thinking, foster collaboration, and bring in the wisdom of the internet together with your own experiences.
- Run time is when enterprises actively engage with customers or employees in live conversations. This is not the time for AI creativity or improv — when they need predictability and reliability at every turn. In run time, enterprises should leverage semantic AI, a specialized AI that understands context and follows the right workflow to ensure consistency, transparency, and control.
A Win-Win for Agentic AI
By separating creative reasoning AI at design time from contextual semantic AI at run time, organizations get the best of both AI worlds while eliminating the risk.
When agents get a request from a user or customer, they use the language power of AI (these are Large Language Models after all) to search through the library of workflows and find the right one. These agents don't guess their way forward — they simply follow the best available workflow for the job. This delivers real outcomes for customers, which engenders trust in the organization's agents.
Reasoning AI is better left to design time, when creativity is a benefit, not a detriment. Ultimately, a human will decide which workflow idea best serves the company's needs. The winning workflows are then approved and discoverable by the AI agents to follow when the appropriate run-time situation arises.
Another way to think of this: consider the difference between practice time and game time. At practice time (or design time), the coach can devise a number of creative plays (or workflows) to try with his players (or agents) until they land on the right ones that will go into their playbook. Then in a live game time situation (run time), the coach assesses the situation, calls the right play for the moment, and the players execute it to perfection — or at least that's how we're used to it happening here in Boston. (Sorry, not sorry.)
This approach gives regulators and an organization's customers confidence that outcomes are fair and consistent. Workflows have always defined how businesses run, and now they can make AI agents work smarter. If two customers come to an agent with the same problem, they should get the same treatment every time. Only agents grounded in workflows can guarantee this outcome.
Moving Beyond Pilots and Hype
Real GenAI in the enterprise isn't about flash, it's about making organizations more dependable.
The winners will be those with predictable agentic solutions that customers and employees can trust. That's how you move beyond hype to real business transformation.
A new era of reliable, workflow-driven agentic AI is within our reach. Resist falling into the trough of disillusionment and focus on building systems that deliver consistent results at scale. Start by evaluating your current AI strategy, identifying where reasoning AI can bring creative ideas to light and where semantic AI can bring greater transparency and control. By doing so, your enterprise can move from hype to lasting impact and become a true leader in the predictable agentic AI revolution.