Entrepreneurs have heard for years that artificial intelligence will change how companies operate. In many ways, that prediction has already come true. AI can draft emails, summarize research, generate code, and identify patterns in data faster than any human team. Yet many organizations are discovering an unexpected tension: despite having more intelligence at their fingertips, decision-making does not always become easier. The real challenge is coordination.
At Domopalooza 2026, Domo’s annual gathering of data and technology leaders, a different kind of AI conversation was taking shape. The focus was no longer on whether AI models are capable, but on whether companies are structured in a way that allows those capabilities to translate into consistent action.
Many companies can now generate answers quickly. Fewer can act on those answers in a coordinated way across departments.
For years, enterprise software has largely functioned as a system of insight. Dashboards and reports help leaders understand what is happening in the business. They identify trends, highlight inefficiencies, and point to potential opportunities. But insight alone does not create alignment. Someone still needs to interpret the information, communicate it across teams, and decide what to do next.
AI is beginning to change that dynamic, but not simply because models are becoming more advanced.
Instead of existing as standalone tools, AI is increasingly being embedded directly into workflows. These systems, often referred to as agents, are designed to perform specific operational tasks. Some assist with onboarding employees, ensuring required steps are completed across multiple systems. Others monitor compliance requirements, flag anomalies in financial reporting, or route customer requests to the right department. Rather than simply highlighting an insight or even producing one-time outputs, these processes operate continuously in the background, helping organizations maintain consistency in everyday decisions. The shift is subtle but important: from systems that help companies understand what’s happening to systems that help them act on it.
In practical terms, this means companies are beginning to build collections of narrowly defined AI processes that work together. The emphasis is shifting from isolated tools toward coordinated systems embedded into real business operations.
A simple example illustrates the difference. In several organizations, onboarding a new employee requires input from HR, IT, operations, legal, and finance. Even when each team has access to good information, the process can still become fragmented because responsibilities and definitions vary across departments. AI can help identify missing documentation, notify the appropriate team, and update internal records automatically. The benefit is not simply faster analysis, but fewer gaps between teams.
What many leaders are discovering is that the constraint is no longer access to intelligence (AI models and processing speeds). It is whether the underlying data and workflows are aligned well enough for intelligence to be applied consistently.Without shared context, even accurate insights can be difficult to use.
Imagine how differently teams may define terms like customer, revenue, or completion. One department may track revenue by invoice date, while another tracks it by payment date. One team may consider a customer active after a single transaction, while another requires a longer relationship. When definitions vary, AI systems can produce outputs that look reasonable on the surface but still drive conflicting decisions.
As Ben Schein, chief analytics officer at Domo, put it: “The bottleneck in enterprise AI isn’t model access anymore. Most companies can get to intelligence. The harder part is making sure that intelligence is grounded in shared definitions, trusted data, and workflows people can actually act on.”
This challenge is leading companies to focus more attention on the structure of their data and the clarity of their decision processes. In technical terms, this shared structure is sometimes called a semantic layer, but the idea itself is straightforward. Organizations need a common framework that allows information to be interpreted consistently across teams. Without it, automation often reinforces silos instead of reducing them.
Companies like Domo, founded by Josh James, who previously built Omniture before its acquisition by Adobe, are orienting their platforms around this coordination challenge. Earlier generations of business intelligence tools focused primarily on visualizing data. Today, a new class of enterprise software is evolving to support operational decisions more directly, helping teams move from reviewing information to acting on it.
This evolution reflects a broader shift taking place across enterprise software. The question is no longer simply whether AI can produce useful outputs. It is whether those outputs can be trusted and applied consistently across functions. For entrepreneurs, this distinction is significant.
Introducing AI tools often exposes inconsistencies that were previously manageable but largely invisible. Differences in terminology, reporting structures, and approval processes can become obstacles when companies attempt to automate decisions. AI does not remove operational complexity. In many cases, it makes that complexity more visible.
Organizations that see the strongest results tend to invest early in aligning definitions, clarifying workflows, and improving data quality before expanding automation efforts. These steps may appear less exciting than adopting new tools, but they often determine whether those tools produce meaningful outcomes.
Another theme that emerged from discussions at the conference was the quiet transition of AI from experimentation to infrastructure. Instead of testing isolated use cases, companies are beginning to build internal libraries of specialized processes designed to support recurring operational needs. Some organizations described managing dozens, or even hundreds, of narrowly scoped AI functions that help maintain consistency across reporting, customer service, and compliance activities.
What stood out was how many of these examples had moved beyond prototypes and into production. The measurable time and cost savings they described offered a useful counterpoint to the growing narrative around unproductive AI experiments and “AI slop” that creates more technical debt than business value.
As several customer examples at the conference made clear, the goal is not to replace human judgment. It is to reduce friction around routine decisions so teams can focus attention where judgment matters most.
For many founders, the pattern may feel familiar. Early cloud software promised efficiency gains, but delivered the most value only after companies adapted their processes to take advantage of it. AI appears to be following a similar trajectory. The technology is advancing quickly, but the organizations that benefit most are those willing to clarify how decisions are made.
One practical question founders may consider before adopting new AI tools is whether key metrics are defined consistently across teams. If different parts of the organization interpret the same data differently, automation can amplify confusion rather than reduce it.
The next phase of AI adoption may depend less on breakthroughs in model capability and more on whether organizations can create environments where information flows coherently across functions.
AI is no longer just a question of what machines can understand. It is becoming a question of whether organizations understand themselves well enough to use it. The companies that benefit most may not be those with the most advanced models, but those that have done the quieter work of aligning how well their data, definitions, and decisions-making.
