The article correctly shifts attention from isolated AI productivity gains to workflow redesign, but that shift has a major management consequence: the unit of value is no longer the prompt or even the assistant license, but the end-to-end process. For CIOs, that changes where to look for returns. The best candidates are not the most visible tasks; they are workflows with high handoff friction, repeated reconciliation, policy checks and cross-system latency. If those dependencies are not mapped first, enterprises risk funding impressive demos that never change operating performance.
This also reframes architecture decisions. Workflow AI depends less on model sophistication than on identity, permissions, data quality, system integration and exception handling. An agent that can act across finance, procurement and compliance introduces a control problem as much as an automation opportunity. Leaders should therefore evaluate AI initiatives using a simple test: what systems can the agent read, what actions can it take, what approvals remain human, and how will every decision be logged, reviewed and reversed when needed?
The overlooked challenge is operating-model design. If AI removes coordination work, managers must decide whether the benefit becomes headcount reduction, cycle-time compression, better controls or capacity for higher-value work. Those are different strategies and require different metrics. A practical next step is to target one workflow where value can be measured across cost, speed and risk simultaneously, then use that pilot to establish reusable governance, integration and change-management patterns before scaling agentic automation more broadly.
Too many conversations about AI today focus on individual productivity and routine vs. strategic tasks, or opine on evolving skill sets. These are important items, but business leaders miss a key point.
AI’s value in the enterprise won’t come from boosting task speed or outcompeting human creativity, but from something much bigger: enabling organization-wide exchange, intelligence and analysis, and freeing the talent that organizational drag has held back.
It’s natural to focus on how AI can empower people to complete tasks more quickly, whether that’s creating recipes and travel itineraries at home or summarizing PDFs and analyzing data sets at work. But companies aren’t just buildings filled with "solopreneurs." They reflect integrated and organized systems, protocols, know-how, tech and more in a way that scales to continually achieve more with less.
Scaling AI beyond the individual
Organizational drag isn’t a minor issue. The collective weight of internal bureaucracy, redundant processes and other friction can dramatically slow a team. As one indicator, McKinsey found in November that 57% of the work hours in the U.S. could be automated using technologies available today.
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Consider a procurement analyst reconciling purchase orders against invoices. Currently, the team spends hours matching line items, checking vendor terms and resolving discrepancies across systems. It’s low-value work that actively saps time, energy and the capacity for deeper, purposeful work.
Now, imagine a scenario where the entire process is largely handled by agents. The analyst uploads the invoice or flags a purchase order. AI takes over, populating cost estimates, matching line items and providing administrators with AI-powered insights for faster decisions and error corrections. Once reconciled, the record is automatically updated across finance systems. There is no duplicate entry, no manual follow-ups, no wasted time.
This is when the real impact can be felt across the workflow. It’s not just one analyst who is saving time. With reconciliation automated, the procurement, finance and compliance teams can now redirect their time toward higher-value outcomes. It optimizes what gets done to cut operating costs, accelerating revenue growth by increasing the speed and quality of internal processes and managing risk by ensuring each step of the workflow follows the policies, permissions and governance. These three buckets — cost optimization, growth acceleration and risk management — are where enterprise AI delivers returns that far exceed individual productivity gains.
Related:Latest AI research puts CIOs and CTOs in the driver’s seat
Building a foundation
Scaling AI across the enterprise effectively requires more than an individual using a new tool. It requires an enterprise-level AI stack that allows users to find the right agents, access enterprise information at their fingertips and generate insights that support team strategy. When paired with organizational culture change, these workflow-level capabilities are what start to bring the true value of AI within reach.
This is more than just task automation; it’s workflow orchestration. It is AI operating on a foundation of organized data, clear permissions and strong governance. This helps serve as an organization’s connective tissue, managing tasks across different applications.
In the case of our procurement analyst, that means understanding who is authorized to view data and approve actions, and moving information from a request portal to an expense system accordingly. Clean, integrated AI systems let the analyst quickly find the right agents or access any of them through a unified portal.
As she considers a new project, an interactive AI agent can respond to any questions she has with trusted data according to permissions, guiding her thinking. If she needs to engage different team members, the agent can create personalized communications based on the individual, team, geography and other categories. Using AI allows workers to quickly and productively drive strategic growth in a business.
Related:Why IT leaders should unpack AI before they buy
From working with AI to workforce AI
This isn’t just a tech transformation. Once AI is embedded across the enterprise, companies will need to reexamine their processes and culture to ensure they are a fit for the new way of doing things. The result won’t just be quick emails and content generation. We’ll see more information exchange, creativity, strategy and mission-critical work by employees unleashed to spend their time on what they do best.
The stakes are enormous, with McKinsey estimating that AI has the potential to deliver $4.4 trillion in value. We’re already seeing this at IBM, where we’ve realized $4.5 billion in savings even in these early innings of AI. By embracing organizational change management practices, enterprises can optimize how work gets done to reduce costs, accelerate revenue growth and manage risk.
Now is the time to prepare for and capitalize on this opportunity. After rolling out individual AI tools, companies need to focus on enterprise workflows that are ripe for agentic transformation. They should adopt a unified platform that lets AI be created, reused, scaled and governed consistently — and securely — across the enterprise. And, as they uplevel their enterprise AI use, they need to bring employees along for the ride. Push them to not just use new tools but also reimagine workflows. Taken together, these steps will kick-start the "flywheel" of AI, unlocking space for continuous innovation and, ultimately, driving meaningful business value.
What are your tips for scaling AI across the organization? Let us know: [email protected].
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