You’ve successfully rolled out AI tools across your organization. Your L&D team has gamified adoption, usage metrics are strong, and employees are genuinely more productive. You’ve done everything the experts told you to do: “Start with the business need.”

So why won’t your executives sponsor the next phase of your AI initiative?

If you’re among the leaders who’ve achieved grassroots AI adoption but are struggling to secure executive buy-in forscaling, you’re not alone. I recently spoke with a large tech company facing this exact challenge—impressive tool adoption and measurable individual productivity gains, but executives remain unconvinced about continued investment.

The root issue isn’t technological—it’s conceptual. The real value of AI isn’t unlocked by creating a workforce ofindividually empowered employees, each running faster in their own silo. Think of it like your organization’s humanworkflow: you can make each person incredibly efficient at their individual tasks, but if the handoffs between people are broken—if information gets lost in email chains, if approvals sit in queues for days, if coordination requires endlessmeetings—work still piles up and customers still wait. The transformation happens when AI becomes your workflow orchestrator, eliminating the human bottlenecks andcoordination delays that have constrained your business for years.

Why Individual Productivity Gains Don’t Move the Needle

We’ve all seen the demos. An employee uses an AI tool to write an email in seconds, summarize a long document, orgenerate a week’s worth of social media content in an hour. The productivity gains are real, measurable, and exciting. The problem? They rarely, if ever, translate into a meaningful impact on the company’s P&L. This phenomenon, which IndustryWeek calls”productivity leakage”—when efficiency gains at the individual level don’t add up to clear business value¹—is thefrustrating reality for most organizations.

You’ve achieved what many organizations struggle with: widespread AI adoption. Your training programs worked, your gamification strategies drove engagement, and your usage dashboards show impressive metrics. Employees are genuinely more productive, and satisfaction scores are high.

But when you present these wins to your executive team, you’re met with polite acknowledgment and questions about “real business impact.” Your CFO wants to know how this translates to revenuegrowth or cost reduction. Your CEO asks when they’ll see the ROI that justifies continued investment.

This disconnect isn’t a failure of your implementation—it’s a fundamental misunderstanding of what executives value. Individual productivity gains, no matter how widespread, don’t automatically translate into organizational effectiveness.

This is hat I call the AI productivity paradox: your people are orking faster, but the business isn’t getting better.

Recent research confirms this disconnect—while AI adoption has risen 50% among workers and daily AI usage has increased by 233%, most companies report that these gains haven’t translated intorevenue growth or margin improvement². As the MIT study on AI pilot failures reveals, 95% of generative AI initiatives fail not because of technology limitations, but because organizations optimize existing broken processes rather than questioning why those processes exist in the first place³.

From ‘Moving the Needle’ to ‘Finding the Dial’: Short-Circuiting the Human Workflow Maze

The real value of AI isn’t found in making individual employees faster; it’s found in making the entire human workflow smarter. It’s about deploying AI not as a personal productivity tool, but as a workflow orchestrator that eliminates the coordination delays between your teams and departments. Consider the all-too-common patient experience when seeking prior authorization for a medical procedure—a perfect example of how broken human workflows create unnecessary delays. Based on industry research, here’s what actually happens:

• Day 1: Patient calls their healthcare provider’s call center with a question about coverage for a recommended The call center agent logs the inquiry and explains that prior authorization is required.
• Days 2-4: The provider’s office submits a prior authorization request to the insurance company, includingmedical documentation and justification for medical necessity. This requires coordination between the clinical team, medical records, and administrative staff.

• Days 5-7: The insurance company’s medical review team evaluates the If additional information isneeded, they send it back to the provider, restarting the clock.
• Days 8-10: Once approved (or denied), the decision is communicated back to the provider’s office, which thenmust contact the patient to explain the outcome and next steps.
• Days 11-14: If approved, the patient can finally schedule their If denied, the appeals process begins, adding another 2-3 weeks.

Each of these handoffs adds days of delay, not because the individuals are inefficient, but because the human workflowitself is broken. Each department operates in its own silo, with its own queue and its own SLA. The result is a two-weekordeal for the patient and a mountain of unnecessary operational costs for both the provider and the payer. According to the American Medical Association, practices complete an average of 39 prior authorization requests per physician per week, with each request taking substantial administrative time⁴.

Now, imagine equipping each of these employees with an AI assistant. The call center agent might log the call faster, theclinical staff might complete forms more quickly, but the fundamental delays—the handoffs, the waiting, the inter-departmental coordination gaps—remain.

This is here the “start ith a business case” advice becomes real.

Instead of optimizing the individual steps, a true AI strategy questions the human workflow itself. What if an AIworkforce could handle the entire prior authorization workflow from end to end? What if it could access patientrecords, review medical guidelines, coordinate with insurance systems, and provide real-time decisions—all while the patient is still on the initial call?

The Impact of Short-Circuiting the Human Workflow Maze:

• Cost of Goods Sold (COGS): Eliminating 10-14 days of administrative overhead could reduce operational costs by 60-70% per authorization
• Patient Experience: From a two-week anxiety-inducing process to a same-day resolution, dramatically improving satisfaction and health outcomes
• Competitive Advantage: Healthcare systems that can provide instant authorization decisions become thepreferred choice for both patients and referring physicians
• Scale Unlocked: Instead of hiring more administrative staff to handle volume, the same AI workforce can process 10x the authorizations without additional headcount

This is the power of workflow orchestration. It doesn’t just make the existing process faster; it eliminates the human coordination delays entirely.

Beyond Productivity Theater: The Two Paths to Sustainable Value

As I explored in my previous article on the CTO’s evolution from builder to business architect, the most successful technology leaders understand that true value creation requires fundamental rethinking of business processes, not just technological augmentation.

When executives ask about AI’s business case, they’re usually thinking about one path: cost arbitrage—the promise ofdoing the same work for less. This approach often leads to the question: “Do we need to reduce headcount to realize AI’s value?” The uncomfortable answer is that headcount reduction is indeed the shortest path to immediate ROI, but it’s also the fastest diminishing return.

Marc Benioff’s recent announcement illustrates this reality. Recently, he revealed that Salesforce had reduced its customer support staff from 9,000 to 5,000, a 44% reduction, because AI agents were now handling 50% of the customer support workload. “I need less heads,” he stated plainly. While this approach deliversimmediate cost savings, it’s a one-time benefit that quickly becomes the new baseline.

But there’s a more sustainable path: leveraged groth. Instead of reducing costs, the goal is to keep operational costs steady while dramatically scaling your top line. This is where AI’s true transformational power emerges—not in replacing people, but in removing the capacity constraints that limit your ability to grow.

A great example of this is a mid-market company whose growth was tethered to their operational capacity. To expand, they faced the prospect of hiring more people for manual processes, diverting resources from strategic roles. By shifting their focus from scaling headcount to scaling theirteam’s impact and streamlining complex back-office workflows, their existing talent is now able to operate on a new level. The same team that supported $500M in revenue now skillfully manages a $1.5B pipeline. This is thoughtful growth: investing in thecapacity of your people, enabling them to drive a 3x increase in business without getting bogged down in operational drag.

This is the workflow orchestration principle in action: instead of making individual people faster, we eliminated the handoffs that created the capacity constraints in the first place.

How to ‘Find the Dial’: A Clear Path to Transformational Value

As I outlined in my analysis of why 95% of AI pilots fail³, successful AI implementations share common characteristics.They don’t focus on “moving the needle”—they find the dial and discover how much they can turn it up.

Here’s your actionable framework:

Step 1: Identify Processes Hindered by Capacity Constraints Look for workflows where human limitations—not business logic or regulatory requirements—create bottlenecks. Ask: “What would this process look like if we had unlimited, instant human capacity?”

Step 2: Map the Soul-Crushing Work Gather the people who actually participate in these processes. Document the time spent on:

• Data entry and form completion
• Searching for information across multiple systems
• Waiting for responses from other departments
• Creating status updates and progress reports
• Coordinating schedules and handoffs
• Following up on pending items

Step 3: Design the AI Workflow Orchestrator Instead of augmenting individual roles, design an AI workforce that caneliminate handoffs entirely and coordinate the entire workflow from end to end. This AI orchestrator should:

• Access all necessary systems and data sources
• Coordinate between teams in real-time
• Handle stakeholder communication automatically
• Escalate only true exceptions that require human judgment

Step 4: Measure Impact, Reach, and Scale Track three critical metrics:

• Impact: How much faster/better is the end-to-end outcome?
• Reach: How many stakeholders (customers, employees, partners) benefit?
• Scale: What new capabilities does this unlock that were previously impossible?

Step 5: Turn Up the Dial Once you’ve proven the concept, ask: “What becomes possible now that this constraint isremoved?” Often, eliminating one bottleneck reveals the next one, creating a continuous improvement cycle that compounds value over time. Like optimizing a human workflow, each coordination delay you remove increases the throughput of the entire organization.

But here’s where it gets interesting: when you turn up the dial, entirely new questions emerge.

• What impact can you have now that you’re not constrained by human coordination limits?
• What reach becomes possible when your operations can scale without linear cost growth?
• What markets, products, or customer segments were previously impossible but are now within grasp?

Success in AI isn’t only about making individuals more productive; it’s about making organizations more effective. It’s about moving beyond productivity theater and designing AI workflow orchestrators that eliminate the handoffs and coordination delays that have constrained yourgrowth for years. The companies that master this distinction will achieve leveraged growth—scaling their top line while keeping operational costs flat—while others remain trapped chasing individual efficiency gains that never materialize on the balance sheet.

Your dial is waiting. The question isn’t just how much you’re willing to turn it up—it’s what new world of possibilities you’re ready to unlock when you do.

References:

¹ IndustryWeek, “AI and ROI: Translating Time Saved to Business Gains,” May 28, 2025

² Various industry reports on AI adoption and productivity metrics, 2024-2025

³ MIT study on AI pilot failures, referenced in “Recognizing Patterns in AI Use Cases: Why 95% of AI Pilots Fail”

⁴ American Medical Association, “Prior Authorization Practice Resources,” 2025