Workflows Are the Real Battleground for AI

In many organizations experimenting with artificial intelligence, the first improvements tend to appear in places where knowledge is the main bottleneck. Systems based on large language models can summarize documents, retrieve information from internal databases, and assist employees with research tasks that previously required considerable time. These capabilities have proven valuable, particularly in environments where large volumes of information must be processed quickly. Yet despite these gains, the day-to-day mechanics of business operations often remain largely unchanged. Even companies that have adopted AI tools widely still rely heavily on human coordination to move work through their operational processes. The reason usually becomes clear once attention shifts from individual tasks to the workflows that connect them.

Where operational complexity actually lives

Enterprise processes rarely consist of a single action. A typical customer interaction may require retrieving account information, verifying eligibility, updating records, triggering downstream actions, and notifying other systems. Each step may involve different applications and different operational rules. A straightforward insurance request might touch a CRM platform, a policy database, a billing system, and a document management system. In healthcare, scheduling an appointment can involve eligibility checks, physician availability, insurance validation, and regulatory documentation. Workflows exist precisely because organizations rely on multiple systems to operate - systems that were often implemented years apart and rarely designed with one another in mind. In large enterprises it is not unusual for a single process to pass through software introduced in completely different decades. Automating a single step rarely eliminates the need to coordinate the rest of the process.

Why traditional automation struggles

Traditional automation technologies were designed for predictable environments. Robotic process automation (RPA) tools, for example, perform well when the steps of a process are fixed and the inputs remain consistent. Operational workflows rarely behave this way. Requests vary, information may be incomplete, and exceptions occur frequently—often at the exact moment a carefully designed automation script expects everything to behave predictably. Even small deviations, such as missing data or an unusual customer request, can interrupt rigid automation flows. This explains why many organizations still rely heavily on human operators to coordinate operational processes even when parts of those processes have already been automated.

AI changes how workflows can be coordinated

Artificial intelligence introduces a different capability: interpreting context. Instead of executing a fixed sequence of actions, AI systems can evaluate a request, determine the relevant information required to resolve it, and interact with the systems needed to complete the task. This makes it possible to automate processes that previously required human judgment simply to coordinate the workflow. Earlier automation tools focused on individual steps within a process. AI systems increasingly make it possible to coordinate the workflow itself. Economists studying technological change have long observed similar patterns. Research by MIT economist David Autor shows that new technologies tend to automate tasks within workflows, rather than replacing entire occupations. Artificial intelligence appears to be following the same trajectory.

The rise of agentic workflow systems

Recent advances in AI architectures have accelerated this development. Agent-based systems can interpret a request, determine the actions required to resolve it, and interact with multiple systems in order to complete the process. This model is already appearing across several industries. Telecommunications providers use AI-driven systems to diagnose network problems and initiate remediation workflows. Insurance companies increasingly rely on automated agents to verify policies and process parts of claims handling. Logistics companies use AI coordination systems to adjust routes and schedules across complex operational networks. In each of these examples, the real value of artificial intelligence lies not in answering questions but in resolving operational situations.

Governance becomes central

As artificial intelligence begins executing operational workflows, governance quickly becomes critical. Organizations need to know what actions were taken, why they were taken, and whether those actions comply with internal policies and regulatory requirements. Once automated systems begin making operational decisions, the ability to explain those decisions becomes just as important as the ability to execute them. In regulated sectors such as healthcare, insurance, and finance, these requirements are reinforced by legal frameworks governing automated decision-making. The European Union’s AI Act, for example, introduces transparency requirements for systems that interact with customers or influence operational outcomes. Operational AI systems therefore require architecture that integrates with enterprise systems, applies governance rules consistently, and maintains clear records of the actions performed by the system.

Elba and workflow orchestration

This is precisely the type of operational environment for which platforms like Elba were developed. Rather than focusing solely on conversational interaction, Elba operates as an agentic, omnichannel orchestration layer capable of translating requests - whether received through voice, messaging, or other channels - into workflows executed across enterprise systems. Once intent is identified, the platform retrieves context, interacts with connected applications, applies business logic, and completes defined operational processes within governance boundaries established by the organization. From the user’s perspective the interaction often begins with a simple request. Behind the scenes, however, the system coordinates the sequence of operational actions required to resolve that request across the organization’s digital infrastructure.

Where AI adoption is heading

The first phase of enterprise AI adoption focused largely on information. Systems answered questions, summarized documents, and assisted employees with knowledge tasks. The next phase is unfolding inside operational workflows. Organizations that successfully integrate AI into the processes coordinating work across systems will see the largest operational impact. As artificial intelligence continues to mature, its most meaningful contribution may not be generating better answers but enabling organizations to execute workflows more efficiently and reliably.