Stuck on AI? Fix Your Outdated Workflow to Get Results

Many organizations find their AI initiatives stalling not due to a lack of technological capability, but because their underlying workflows remain trapped in a bygone era. The real breakthrough comes from redesigning these processes to provide trusted data and new workflows, enabling intelligent systems to act with true agency rather than just analyzing information. This shift moves the focus from isolated AI tools to integrated organizational systems that can handle today’s relentless pace of change.

We’ve entered a period defined by agentic AI, where systems can reason and act across complex operations. The future belongs not to those with the most advanced algorithms, but to companies that rebuild their workflows, allowing intelligence to operate with proper context and authority. This Cognitive Industrial Revolution augments human reasoning, mirroring how steam power once augmented muscle and data augmented memory. We are transitioning from Systems of Record, which document the past, to Systems of Agency, which actively shape the future.

Uncertainty has become the standard operating environment. Prices for energy and materials swing wildly, while geopolitical shifts and new tariffs reshape cost structures almost overnight. A recent survey of supply chain leaders found that over eighty percent reported their operations were already affected by new tariffs, impacting a significant portion of their activity. At the same time, expectations for real-time transparency, tighter margins, and faster execution have skyrocketed. The core problem is structural: many organizations still rely on decision-making processes built for a slower, more predictable world.

A major bottleneck exists in commercial operations. While production and logistics have seen decades of automation, the critical connective tissue between sales, planning, delivery, and payment often remains a manual assembly line. Customer data sits in one system, fulfillment in another, with critical functions like pricing and forecasting managed through a fragile patchwork of spreadsheets and emails. This is where AI projects often hit a wall. Research reveals a puzzling gap: while most companies have moved beyond pilot programs, only a small fraction see meaningful financial returns. The reason is that many use AI to speed up individual tasks without fixing the fragmented processes underneath.

In manufacturing, this creates an “impact gap” where AI cannot deliver value because it’s stuck between disconnected planning and execution silos. For years, companies hired teams to do this coordination work, sales chasing confirmations, planners reconciling numbers in meetings. AI changes the economics of this arrangement. When digital labor can handle routing, summarizing, and reconciling, the inefficiencies that were once hidden become painfully clear.

When AI initiatives falter, the instinct is to blame the model. More often, the issue is that AI is being layered onto an operating model that cannot provide a continuous flow of context. AI assumes demand signals flow smoothly into planning and that commitments are visible across departments. When these assumptions break, AI doesn’t quietly adapt like a human would; it highlights the gaps. This explains why impressive pilot programs fail to scale. Studies consistently point to operating models, leadership, and governance, not the algorithms themselves, as the primary constraints to success.

The leaders pulling ahead are those shifting focus from individual productivity boosts to systemic agency. They are building integrated data foundations and trust layers that allow intelligent agents to act with a complete, enterprise-wide understanding. Agentic systems without trusted context do not create leverage; they amplify fragmentation. This underscores that AI is less a technology upgrade and more an organizational redesign.

History is filled with examples of organizational design failures masquerading as technical problems. Henry Ford’s production system was a masterpiece for its time, but it became a constraint when the market demanded variety. Ford’s crisis wasn’t a lack of skill but an information flow optimized for a reality that had vanished. Similarly, in the 1980s, many manufacturers responded to lean competition by simply automating rigid processes faster, rather than fixing broken workflows. Today, we risk the same mistake with AI: investing in cognitive technology while clinging to operating models built for human-mediated, sequential handoffs.

The next frontier moves beyond physical automation into cognitive simulation. Platforms now allow companies to create full digital twins of their factories and supply chains. Within these simulated environments, they can test thousands of autonomous agents interacting simultaneously. This means leaders can refine decision logic at scale in a digital world before deployment, turning dark factories into simulated, optimized operations. If the last industrial wave was about automating the “hands,” this cognitive wave is about automating the “heads”, orchestrating intent through an organization at machine speed.

The quickest path to failure is pursuing full autonomy too soon. Autonomy isn’t a product you install; it’s an outcome you earn through clarity. Manufacturers must progress step-by-step. First, achieve system-level visibility by making commitments, plans, and execution signals visible across all functions with a single source of truth. Next, deploy contextual intelligence within the workflow, using agents to summarize documents and surface relevant information, freeing people for judgment calls. Only when visibility and context are reliable should event-driven orchestration expand, where systems act on events and escalate only the exceptions requiring human trade-offs.

The coming era’s true challenge is organizational design, not model selection. Leaders must define where human judgment is essential, ensure customer needs translate seamlessly into system action, and eliminate work that only exists because systems are uncoordinated. In this revolution, the technological platform’s role is fundamentally changing. It is evolving from a passive system of record into an active system of agency, providing the trusted context and execution power that standalone AI lacks. This foundation is what allows digital labor to move safely from simple assistance to genuine autonomy.

This shift is not a distant future goal; it is the pressing competitive reality of today. As old digital structures break down, leadership faces a critical choice: will you continue to act as the digital glue holding fragmented systems together, or will you become the architect of a new, coherent system? Is your organization designed for the modern flow of work, or is it merely a faster digital version of the silos built for a past era?

(Source: ZDNET)