Information is not enough to effect change.
Decision Intelligence is about optimizing the processes by which decisions get made, governed, monitored, and constantly iterated upon.
Among other considerations, these include:
- What decisions are most impactful for the business?
- Where are the operational bottlenecks?
- What processes require human decision-making?
- Where does AI decision support play a role?
- What actions are safe to automate?
- What are new governance requirements?
Such considerations become especially pertinent with increasing AI agent involvement.
If proper workflow re-design is not accompanied by governance, there is a risk of automating tasks without improving overall performance.
This is an issue that has been increasingly voiced by industry analysts. In this regard, Gartner has indicated that many of the AI agent projects within the enterprises could fail to deliver the desired results without putting into place governance and controls. This is because AI agents will be increasingly responsible for the coordination of tasks in the system, and hence, it becomes necessary to put in place some guardrails as far as decisions are concerned.
I’ve worked with successful companies that managed to lower their service resolution times and increase operational agility only once they focused their AI-powered processes directly on key business metrics like cycle time reductions, escalations avoidance, margins improvement, or customer retention.
That shift — from experimentation to measurable operational impact — is where many enterprises are now focusing their attention.
Fragmented AI creates fragmented outcomes
One of the key operational challenges that I keep running into is fragmented intelligence within the enterprise.
Sales use one set of AI solutions. Customer Service uses another set of AI solutions. Supply Chain uses yet another set of forecasting models. Financial analysis works within an entirely different set of AI workflows.
While each solution might make some progress locally, integration at an enterprise level is often a challenge.
For example, while working with one organization focused primarily on retail, marketing optimization drove more promotional demand than inventory and staffing were able to meet. Each of those areas had its own intelligence, but there was no enterprise-level coordination of intelligence.
The consequence was friction within operations instead of acceleration.
In order for enterprise applications to be ready for the future, this fragmented approach to AI will not work. Enterprise apps have to become systems that integrate signals, workflows, decision-making and execution.
That is essentially the difference between AI being adopted and transformed by an enterprise.
Leadership priorities for the AI-agent enterprise
But as AI agents integrate into enterprise systems, the focus of corporate leaders also needs to shift.
No longer should leaders only think about what kind of AI technologies are going to be deployed.
Instead, they need to ask themselves:
- What outcomes need better performance?
- What processes have too much friction?
- What decisions are best left to humans?
- Where does AI fit in for safe coordination?
- Who will govern and oversee how things work?
- How will success be tracked and measured?
And generally speaking, organizations that are progressing well tend to have an operational approach to AI versus a testing one.
They do not focus on using cutting-edge AI but more on operational efficiency, coordination, governance, and value.
Such transformation is part of a bigger picture. Today’s companies realize that the way to gain any competitive edge does not lie in merely having AI systems, but rather in establishing an “AI Operating Model” as proposed by IBM, in which AI agents work together with company data, automation systems, governance, and human decision-making. As AI capabilities become more prevalent, the competitive factor will be found in the way companies design their operations around intelligent execution.
Practically, the best operating model I’ve observed combines human decision-making with AI coordination. In some processes, humans take the lead. In other processes, AI makes suggestions, but the manager makes the final decision. Finally, there could be certain repetitive operations that eventually run independently but with guardrails.
It’s all about intentionality.
The future enterprise will operate differently
Over time, all organizations will gain access to AI models, cloud computing, and enterprise software systems comparable to those used by others.
The difference lies in how well organizations embed intelligence within their workflows.
Organizations that thrive will be those that can develop systems that do all of the following:
- Sense changes early in their operations
- Make decisions rapidly
- Reduce workflow frictions
- Learn continually based on results
- Embed their investments in AI directly within their business processes
AI agents are helping make this happen.
However, the bigger challenge goes beyond using even more AI.
The challenge involves changing the way enterprises sense, decide, execute, and learn operationally.
This is the evolution currently underway, which will transform enterprise application software and enterprise work in general.
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