Two numbers neatly sum up the AI dilemma. According to BCG’s 2026 AI Radar survey, 82% of CEOs are more optimistic about AI ROI than they were a year ago. So far, so good. But a mere 6% of companies are seeing meaningful value from AI as measured in reduced costs and increased revenue.
What do the organizations in the successful 6% get right? One element is prioritization. Rather than experimenting everywhere, they focus on a small number of initiatives that have the biggest potential impact. Equally important, they go deep in these areas. They don’t simply automate existing workflows and activities. Instead, they redesign end-to-end processes around AI-enabled decision making.
That’s where the economics change. BCG estimates that AI-enabled transformations can reduce generation and administrative costs by 25% to 35%, R&D costs by 20% to 30%, and sales, marketing, and portions of the cost of goods sold by 15% to 35%.
The Problem with Pilots
AI is deceptively easy to deploy. With a little effort, companies can launch pilots everywhere and see something that resembles progress. Employees work faster, developers produce more code, and marketing teams create more content. But in many cases, beneath the surface, the cost structure of the business remains fundamentally unchanged, for three key reasons:
- This approach to AI deployment focuses on automating existing tasks, rather than optimizing cross-functional processes or the overall operating model. If processes are unnecessarily complex before AI implementation, the technology will simply solidify that complexity instead of eliminating it. Approvals, decisions, and friction will remain in place. For example, one company used AI to reduce a task that had taken ten days to a single day, but customers still had to wait ten days for a response because the surrounding business process had not changed. Only after the company began measuring customer outcomes—not merely the speed of the underlying task—did it eliminate committees, remove intermediate steps, and redesign the process for a quicker turnaround.
- Efficiency gains typically get reabsorbed into the system. If AI enablement makes an activity happen 20% faster, the resulting 20% gain in employee time needs to be reallocated to other tasks or the benefit disappears.
- Companies commonly measure results in terms of activity (hours saved, tasks automated) instead of explicit financial value that shows up in the P&L. Unless companies link productivity gains to specific actions, such as reducing spending on external providers, increasing throughput, or removing organizational capacity, the value remains theoretical.
Fundamentally, the problem with AI pilots is that they lead to incremental changes to the status quo. Companies think that they’re transforming, but in reality they’re just achieving marginal gains by doing all of the same work slightly faster. Meanwhile, the cost base remains too high.
The Companies Winning with AI Aren’t Doing More—They’re Going Deeper
Experimentation can be a starting point for some companies, but the real value from AI comes from going deeper on a smaller number of initiatives.
Prioritization
The strongest organizations do not scatter AI investment across disconnected pilots. Instead, they focus aggressively on the core areas that can give them a competitive advantage. They determine where human judgment creates the most value, and then they automate everything surrounding it, including data gathering, approvals, reporting, forecasting, and execution. And they say no to smaller-scale initiatives that won’t produce a meaningful change. This kind of organizational focus enables organizations to harvest the biggest yield from their resources of capital, capabilities, and talent.
In one manufacturing example, a company considered applying AI to reduce production waste. But waste represented less than 1% of the company’s cost of goods sold. Even eliminating all of it would not have generated enough value to pay for the AI solution. The use case was technically feasible but economically immaterial.
Organization Redesign
The organizations that generate outsized returns redesign how work gets done end-to-end. They focus on workflows and decisions rather than isolated tasks, and they’re willing to take a clean-sheet approach to big-picture organizational elements such as spans of control, management layers, governance models, workflows, and decision rights. They simplify before they automate, clarify decision rights, and remove low-value work before embedding AI into the process.
Critically, these top performers also use financial metrics to gauge the performance of their AI initiatives. They tie structural metrics directly to the P&L, including measured reductions in management layers, spending on outsourcing, process complexity, and organizational overhead.
Together, prioritization and organization redesign create a compounding cycle of advantage. Early cost savings fund larger AI investments. Those investments unlock deeper operational redesign measures. And deeper redesign generates additional efficiency, speed, and cost-out. Over time, organizations become leaner, faster, and structurally stronger.
AI in Action
A few recent examples show what’s possible:
- One global consumer company generated €250 million in cost savings and gains of 15% to 20% in P&L efficiency by redesigning marketing workflows around AI and reducing agency spending.
- A food and beverage company transformed its procurement through AI to reduce costs by $500 million while streamlining supplier negotiations.
- A global technology company automated more than 1 million back-office activities while targeting an annual operating expense reduction of roughly 30% on a $15 billion cost base.
These organizations were not simply deploying AI tools. They were redesigning core operating models and workstreams to dramatically reduce costs.
The companies winning with AI today are pushing beyond pilots and going deeper on a smaller number of initiatives. They are concentrating investment in the core. They are redesigning workflows around decisions. And they are recognizing that sustainable cost advantage comes from operating model transformation, not technology deployment alone. Depth, not breadth, is the differentiator.
To explore the full findings, benchmarks, and transformation insights behind these shifts, read BCG’s “Executive Perspective: Driving Sustainable Cost Advantage with AI.”
