A full AI investment is surging, but are you seeing the returns you expected? Many organizations are finding that despite significant investments in automation and artificial intelligence, transformational, enterprise-wide value remains elusive. Faster reports and minor productivity gains are a far cry from the revolutionary impact AI promises. What's the missing piece? According to Bain & Company, it's a people-first approach that inextricably links workflow modernization to workforce modernization.

In their recent analysis, Bain highlights that treating technology's impact on people as a downstream change management challenge is a recipe for AI stagnation. Companies that fail to prioritize workforce adaptation will likely settle for less ambitious goals, limit creative technology deployment, and ultimately face lower adoption rates, disappointing ROI, workforce disengagement, and technology skepticism.

The core problem lies in the fragmented approach many companies take. Finance, tech, operations, and HR often operate in silos, leading to duplication, missed interdependencies, resistance, and uneven adoption. The result can be a demoralized workforce facing constant disruption without a clear path forward.

Why a People-First Approach?

Bain's research underscores that human capital is more crucial than ever in the age of AI. Humans remain the primary source of innovation and the creators of intangible assets that drive a significant portion of market value. Companies that prioritize technological innovation alongside unlocking the productive power of their people will see the greatest gains.

Companies embracing this approach are demonstrating a 10% to 15% productivity lift, translating to 10% to 25% EBITDA gains. This scale of impact delivers significant return on investment, proving that when AI is aimed at the right problems, it can fuel a human-centric productivity engine that drives transformation at scale.

The Four High-Gain Moves

Forward-looking companies are converging on four key strategies that link workflow and workforce modernization:

End-to-End Workflow Rebuilds: Move beyond scattered AI pilots and focus on redesigning workflows from beginning to end, using fit-for-purpose technologies that balance off-the-shelf solutions with custom-built applications. The question shouldn't be, "What's the AI use case?" but rather, "What work should stop, simplify, or move to better serve customers?" and "What can AI make 10 times better?" This approach forces strategic choices and avoids the trap of automating broken processes. Prioritize a few critical end-to-end workflow rebuilds with clear outcomes and dedicated ownership.
Data Pragmatism: Stop waiting for perfect data. Ship fit-for-purpose data products for the workflows that matter most. Default to buying or partnering first, only building bespoke solutions when they truly differentiate your offering. This pragmatic approach allows you to iterate and improve data quality over time, rather than getting bogged down in lengthy and expensive data preparation projects.
Clean-Sheet Thinking: Don't simply optimize the current state. Start with a clean sheet and fundamentally rethink how work is done. Remove low-value tasks, collapse handoffs, reset decision rights, and cap exceptions. Replace committee vetoes with tiered risk guardrails. This radical redesign can unlock significant efficiencies and streamline processes.
Integrated Workforce Strategy: Reskilling, redeployment, incentives, and trust are crucial components of a successful AI implementation. Invest in robust training programs to equip employees with the skills they need to thrive in an AI-powered workplace. Create clear pathways for redeployment and offer incentives that align with the new workflow. Most importantly, build trust by communicating transparently about the impact of AI on jobs and providing support for employees navigating these changes.

Example in Action

Bain cites the example of a leading global bank that redesigned its workflow and roles to compress a 60- to 100-day process with multiple handoffs into a single-day cycle. This dramatic acceleration was achieved by redesigning the workflow and roles together. The streamlined process not only increased efficiency but also accelerated learning and adoption.

Moving Forward

To capitalize on the true potential of AI, business leaders must move beyond a purely technological focus and embrace a people-first strategy. This requires a fundamental shift in mindset, a willingness to challenge existing processes, and a commitment to investing in the skills and well-being of the workforce.

Here are some actionable steps leaders can take:

Conduct a Workflow Audit: Identify key workflows that are ripe for AI-driven transformation. Focus on processes that are complex, time-consuming, and involve numerous handoffs.
Engage Employees: Involve employees in the workflow redesign process. Their insights are invaluable in identifying pain points and developing solutions that work in practice.
Develop a Reskilling Plan: Assess the skills that will be needed in the future and create a comprehensive reskilling plan to equip employees with those skills.
Communicate Transparently: Be open and honest about the impact of AI on jobs and the company's plans for managing the transition.
Measure and Iterate: Track the results of your AI investments and make adjustments as needed. Continuous monitoring and iteration are essential for maximizing ROI.

By embracing a people-first approach, businesses can unlock the full potential of AI and create a future where technology and human talent work together to drive innovation and growth. The organizations that prioritize their workforce will not only achieve greater ROI on their AI investments but also build a more resilient, engaged, and future-ready workforce. The time to act is now – don't let a lack of focus on human capital be the reason your AI investments fall short.