為何 AI 轉型要從重新設計工作開始

Team of 4 employees in a conference room with charts and laptops

Even in today's AI-infused business world, most organizations continue to modernize workflows and workforces through disconnected, one-off efforts. Finance and procurement rethink sourcing and shoring; technology teams focus on automation; operations pursues lean efficiency; and HR tackles reskilling, right-sizing, and engagement.

The result is fragmentation: duplication, missed interdependencies, resistance, and uneven adoption. In the worst cases, workforces face a succession of destabilizing changes.

But there is a better path. Frontier companies are deploying AI to move beyond scattered pilots and redesign workflows end-to-end, using fit-for-purpose technologies that balance off-the-shelf and custom-built applications.

Questions leaders should ask about AI workflow redesign

Executives at these frontier companies don't start by asking, "What's the AI use case?" They start with the work itself: What work should stop, be simplified, or move to better serve customers? Where could AI produce an order-of-magnitude improvement?

Those questions force choices. Instead of funding scattered pilots, leading companies concentrate on a critical few end-to-end workflow rebuilds with clear outcomes and single-threaded ownership. They don't wait for perfect data or build everything custom. They use fit-for-purpose data products for the workflows that matter, buying or partnering first and building bespoke technology where it truly differentiates their offering.

Most importantly, they don't optimize the current state. They start with a clean sheet.

Why workflow and workforce redesign must happen together

Workflow and workforce modernization should be synchronized across five areas:

Equip leaders and cross-functional teams. Few leaders possess bold vision, deep process knowledge, technology fluency, and workforce planning experience. Cross-functional teams spanning technology, business process owners, finance, and HR must therefore become the core unit of change.

Don't layer AI on top of problematic processes — redesign instead. Most companies carry substantial "workflow debt" — unnecessary work that has accumulated around meetings, approvals, handoffs, exceptions, and one-off policies, making even simple tasks hard to execute. AI amplifies whatever system it's dropped into. If workflow debt isn't addressed, AI and automation multiply complexity instead of productivity.
Humans can work around fuzzy rules; AI agents can't. They need clear rules, stable handoffs, and aligned decision rights. Getting process and work design right is a prerequisite to deploying AI agents at scale, not a problem to fix afterward.
Organizations that get real returns from AI start with a clean sheet for their most critical workflows. Rather than tweak existing processes or chase small efficiency gains, they set integrated targets for customer experience, service quality, cost, and speed, then work backward to design the workflows that can deliver those outcomes. They determine what must be done by humans, what should be done by humans assisted by AI, and what can be run autonomously by AI agents.

Enable the hybrid workforce. Humans, AI-assisted "super humans," autonomous agents, and robots will increasingly operate in integrated workflows. This requires upgraded capabilities in workforce planning, learning and development, and change management.

Build trust and clarity. Adoption depends on trust in technology, leadership, and employer intentions. Reskilling, redeployment, and clear communication help employees see AI as an augmentation rather than a replacement.

Embed and scale the value. Introduce new workflows, staffing rules, and performance standards in enterprise systems to enable continuous improvement.

What does end-to-end workflow redesign look like in practice?

A leading UK banking group shows the scale of change possible. Complicated internal processes meant testing or deploying a new customer-engagement idea took 60 to 100 days and involved more than 10 handoffs.

Rather than try to whittle a few days off the process, leaders reframed the challenge: Could the team do it in one day?

Within four months, data-products and digital-channels teams had built a new workflow that combined the bank's technology foundation with agentic AI capable of complex reasoning and problem-solving.

To run the new workflow, they needed a redesigned workforce, too. The bank replaced large, specialized teams of up to 40 people with three- to four-person groups of full-stack problem solvers, reducing handoffs and tapping talent with broader skills and experience.

New roles consolidated work previously spread among multiple specialists. Finding these full-stack problem solvers required both reskilling and hiring, but employees embraced the project in part because the bank emphasized training and empowerment rather than head count cuts.

The changes are helping the bank's marketing evolve into a continuous learning system in which AI uncovers insights into customer behavior and humans discover what resonates with customers.

The lesson is broader: It's not the number of AI use cases that matters so much as redesigning high-value workflows end-to-end while modernizing the workforce in parallel.

Explore more of Bain's insights on the impact of AI on organizations, business transformation, and talent.

This sponsored post was supplied by Bain.