How Four Companies Capitalize on AI to Deliver Cost Transformations

Companies increasingly use AI to improve productivity, but they also need to take on the next challenge: using the technology to deliver gains to the bottom line. Cost remains a critical strategic priority for C-suite executives, with more than 90% recognizing AI’s pivotal role in reducing costs over the next 18 months. But translating productivity gains into lasting financial value is tough. It’s costly and complicated to implement AI at scale. Often, companies try to automate existing workstreams and processes—using AI to do the same work faster—instead of rethinking those processes to capitalize on the technology and generate real savings.

That is a missed opportunity. Building AI into broader cost transformations can generate sizable benefits—accelerating the overall timeline, eliminating low-value work, delivering a better experience for employees and customers, and future-proofing the organization. To capture these benefits, companies need to go beyond mere efficiency and instead rethink how the organization operates.

Over the past few years, BCG has helped companies launch successful AI-enabled cost transformations based on this type of holistic, comprehensive mindset. In all of them, three drivers of success have been critical:

• They strategically leverage AI to optimize cost transformation programs.
• They reshape functions and processes from the ground up.
• They rigorously measure the value and cost reduction targets in their budgets to ensure bottom-line impact.

Here’s how four companies moved beyond mere cost cutting to fundamentally transform the organization and create a lasting cost advantage.

Leverage AI in Conjunction with Traditional Cost-Savings Measures

A leading energy provider in Germany wanted to automate payment reviews as part of a comprehensive effort to streamline its cost base. While procurement is a common cost lever, the company set itself apart by creating a custom GenAI tool to automatically identify potential overpayments.

Launched within a rapid ten-week development window, the tool scans incoming invoices and compares the amounts the utility was charged against the pricing terms in contracts and purchasing orders. It flags meaningful discrepancies and alerts claims managers to review them; it even drafts messages that managers can send to suppliers in order to reconcile the differences. The application has the potential to generate tens of millions of dollars in value for the company. 

Reshape Functions and Processes from the Ground Up

A global biopharma company wanted to become an industry pioneer in the use of GenAI. Rather than simply making existing processes more efficient, it evaluated those processes, determined how GenAI could improve them, and ran “mirror” processes—the existing processes and the new GenAI approach, side-by-side—to validate results. In sprints, it refined the mirror processes based on initial results until it could determine whether the GenAI-enabled process would succeed. Once it was sure the outputs were reliable, the company shifted to scaling up successful programs.

The company focused on three use cases in three functions:

• Marketing. In the first wave, the company used GenAI to develop marketing content (primarily text and images) for existing campaigns, which reduced agency costs by 20% to 30%. Developing an unbranded website article formerly cost more than $20,000, but it is now nearly free. Teams also used GenAI to adapt content for specific markets. In the past, localizing marketing assets took two months, but GenAI reduced that time to just a day, including a human-in-the-loop review. The team then began using GenAI to generate faster and more accurate insights about marketing content, which it used to refine and improve that content over time. The company was thereby able to produce more content faster and at lower cost, dramatically increasing the ROI on campaigns. The projected savings from marketing alone totaled $80 million to $170 million.
• Research and Development. Drug development and manufacturing processes are heavily documented, creating a sizable opportunity for GenAI to improve efficiency. Initially, the company ran a pilot test to draft clinical-science and operations documents, leading to efficiency gains of 30% to 40%. For example, reports on clinical studies used to take about 17 weeks from start to finish. GenAI reduced drafting time by about 35%, to 10 to 12 weeks, and that will soon go down to just 5 weeks. Summarizing a medical study previously took 20 to 25 hours, whereas GenAI solutions can do it nearly instantaneously. As a result, the overall timeline for drug development will be shorter and the company will be able to get new products to the market three to six months faster, boosting revenue. Total potential cost reduction in R&D: more than $45 million.
• Manufacturing and Supply Chain. The company used GenAI to draft product quality reviews—a necessary step in verifying production processes, identifying trends, and ensuring that drugs comply with regulatory standards. In the past, those reviews typically took about 20 days to complete, including data collection and analysis, content drafting and iteration, review, and final approval. The GenAI-enabled process takes two to six days, depending on the complexity of the product. Data collection is automated and ongoing. The tool can generate a first draft in minutes, and human-in-the-loop reviews take several days (though most reports do not need any changes). Total reduction in drafting time is about 70% to 90%.

Rigorously Measure the Value and Cost Reduction Targets

A consumer packaged goods company invested in an enterprise-wide GenAI platform to increase efficiency, reduce costs, and build competitive advantage. The company identified the marketing function as its highest priority for transformation and focused on three categories of tasks in which GenAI could generate the biggest potential gains in both efficiency and effectiveness.

• Insights. GenAI now supports teams by transforming unstructured data into new and marketable insights and ideas.
• Content Generation. The platform helps marketers develop new content more rapidly, accelerating the process by approximately 40%.
• Business Performance and Reporting. In its most impressive contribution, GenAI issues monthly and annual business plans and helps teams track shifts in consumer preferences and overall market conditions. In the past, a team looking to improve a marketing campaign needed to look up and aggregate performance data manually, write a report with recommendations, and align stakeholders—a process that took six people roughly a week to accomplish. In contrast, the GenAI tool automatically captures data in real time and generates a preformated report with performance information and recommendations in less than an hour. All recommendations are vetted by a human operator before being implemented.

In all, the program has led to efficiency gains of approximately 60%—and up to 90% for some tasks—even as the overall quality of outputs has improved. The company is now expanding GenAI applications into other parts of the business.

Apply All Three Drivers of Success

Each of the three drivers mentioned above is important, but companies that apply them collectively can generate even bigger benefits. For example, IBM wanted to boost overall growth in a challenging economic environment. It launched an ambitious transformation program with the goal of streamlining its business operations and unlocking significant productivity and cost savings.

A comprehensive design stage helped IBM identify productivity benchmarks and the areas where it could make the biggest improvements in performance. Critically, the leadership team focused on AI to boost the gains from traditional cost reduction levers. They concentrated on support functions—legal, IT, procurement, and HR—reshaping processes to leverage AI and automation. The company also focused on the people aspect of the change program. (See “The Power of People.”)

In the past two years, IBM has unlocked roughly $3.5 billion in cost savings and a 50% increase in the productivity of enterprise operations. It has invested that capital in innovation and growth opportunities, particularly AI, automation, and hybrid cloud offerings. Moreover, the company now has immense credibility in supporting enterprise customers that want to embark on similar cost efforts.