How AI agents are turning enterprise apps into decision systems

Last year, I worked with an enterprise leadership team that had made significant investments in its piloting of generative AI in areas such as customer service, IT operations, and productivity workflows. On paper, the organization appeared ahead of the curve. Employees were using copilots. Business units were experimenting with AI assistants. Executives were tracking AI adoption metrics across departments.

But when we looked at operational performance, very little had actually changed.

Approvals remained slow among different teams. Customer escalation was reliant on manual intervention. There was also still time wasted in resolving disparate data sets prior to making a decision. The use of AI in the environment has been optimized, but not its intelligence within the processes and functions of the enterprise itself.

I have seen this pattern in multiple enterprises in the last year, where these organizations are pursuing AI with vigor but cannot move any faster in their business performance.

The question isn’t one of commitment. Most enterprises already have some form of AI initiative.

The problem here is that most organizations continue to use AI technology as a supporting layer and not as an embedded intelligence in their enterprise operations and applications.

That is precisely why there is a much bigger paradigm shift in AI agents than merely automated processes.

They have started to bring change by converting the enterprise systems into something beyond just systems of records to systems of action coordination.

Enterprise applications are evolving beyond systems of record

Enterprise applications have traditionally been transaction systems for decades.

ERP systems have standardized financial processes, procurements, and supply chains. CRM applications have helped organize information about customers and their interactions. HR systems have streamlined employee-related operations.

All these applications provided a robust basis for operations management.

Yet, they required extensive human involvement in interpreting the information, deciding, coordinating, and responding to any changes.

What is changing now is the involvement of AI agents in the processes described above.

AI-enabled enterprise applications are capable not only of reporting and visualizing but also of:

• Detecting operation irregularities
• Interpreting the situation in the broader context of different systems
• Suggesting next best actions
• Coordinating workflows
• Learning

During an operational analysis conducted during my practice, a procurement team faced significant challenges because of supply disruptions and manual workflow coordination.

People had to spend hours looking through ERP, inventory, logistics, and finance systems to find appropriate sourcing alternatives and make a decision.

This organization introduced an AI application that detected supply risks, proposed sourcing alternatives, and launched relevant approval procedures according to business logic defined beforehand.

It is essential to note that time savings were achieved not just due to automation.

Many organizations still consider the application of AI to be confined to support for productivity. The real potential lies in making enterprise systems capable of intelligent execution.

Why many AI initiatives stall before delivering business value

One consistent lesson that has been learned throughout the years is that AI implementation is not synonymous with operational transformation.

Companies have tended to implement copilot capabilities relatively easily since they involve providing employees with the capability of assisting them with their tasks like creating content or retrieving knowledge.

However, it is common that such bottlenecks stay the same.

Approvals may still traverse many different systems. Decisions continue to be dependent on disparate data sources. Collaboration between departments remains manual. Information still needs substantial verification prior to taking any action based on a recommendation provided by artificial intelligence.

This problem is increasingly being understood in the industry context. It has been termed “the Gen AI Paradox” by McKinsey. In its analysis of agentic AI, McKinsey observes that despite the rapid proliferation of generative AI adoption among firms, many firms still have difficulty leveraging their adoption of this technology to make a tangible impact on business outcomes. The deployment of enterprise copilots and AI assistants has outpaced the need for changing operations for improved decision making, coordination, and execution.

In many cases, the primary problem did not come from the model used for AI. The challenge was to incorporate intelligence into the operations process.

This is where Enterprise Intelligence comes into play.

Enterprise Intelligence does not necessarily mean just implementing another form of artificial intelligence technology. It implies the organization’s ability to link AI, enterprise data, workflows, governance, and human decision-making into an effective operation model.

It has been found that successful organizations did not necessarily conduct the most pilots. They focused on optimizing workflows so that the intelligent capabilities reach the point of decision-making.

AI agents are changing how enterprise decisions get executed

The growing emergence of task-specific AI agents is speeding up this trend.

Unlike legacy automation platforms, AI agents are able to be contextually aware within and across business systems and workflows. AI agents are becoming more sophisticated at coordinating actions instead of completing specific, isolated tasks.

This trend becomes particularly apparent in operational systems where decision-making needs to cut across multiple teams and systems.

In ERP systems, for example, AI agents can:

• Detect procurement irregularities
• Evaluate risks associated with suppliers
• Suggest procurement options
• Initiate approval processes
• Coordinate activities between procurement, financial and operations teams

Within CRM systems, companies are starting to use AI agents to:

• Prioritize customers based on purchase signals
• Suggest next best actions in sales
• Personalize customer interaction
• Automate customer recovery workflows without escalation

IT operations represent another domain where this trend is rapidly gaining momentum.

An IT operations team I worked with was able to significantly reduce alert fatigue by implementing an incident coordination process with support from AI assistance, where incidents were prioritized, correlated signals within the infrastructure were detected, and partial remediation tasks were automated. The engineers retained control over decision-making, yet response times got faster since teams did not waste time filtering operational noise.

These examples illustrate a broader point: AI agents are not simply automating tasks. They are reshaping how enterprise decisions are coordinated and executed.

Why decision intelligence matters

With increased AI agent deployment in workflow processes, yet another consideration comes up — ensuring the AI-generated recommendations result in enhanced organizational effectiveness.

This is where the concept of Decision Intelligence plays a crucial role.

For decades, enterprises have believed that more dashboards and analytics automatically equate to better decisions. The opposite has been true in my experience – decision-making gets slowed, fractured, and inconsistent amid an abundance of data.

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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