January 5, 2025
Managing Partner
Generative AI holds the potential to significantly enhance productivity and cost savings, but realizing these benefits requires strategic alignment and process redesign.
Deploying generative AI for efficiency gains alone won't deliver the financial returns companies expect. Executive teams have grown accustomed to ambitious targets for enhancing productivity through generative AI, which are achievable. The initial phase of deploying generative AI has only hinted at its potential to improve the speed and quality of work significantly. This is especially true in previously tricky areas to automate, such as decision-making, content creation, and complex data analysis.
Take customer service, for example. Companies can cut the time employees spend on manual responses by 20% to 50%. In product development, software engineering can become 15% to 30% more productive, while HR teams could see a 40% reduction in the time required to craft a job posting. As executive teams scale their generative AI experiments, they witness firsthand the technology's ability to deliver on its promise of increased productivity. These tools enable employees across various functions to work faster, often with enhanced quality. Yet, despite the initial excitement, many companies across different industries are experiencing a disconnect. Their executive teams and boards are noticing that the productivity gains from generative AI have not yet translated into significant cost savings.
However, leading companies are on track to achieve up to 25% in cost savings by integrating end-to-end process redesign with generative AI tools. This strategic approach provides them with the means to expand their operations and boost profits. Conversely, companies experiencing the productivity-savings gap most acutely often see savings of just 5% or less, with results typically linked to isolated experiments. This struggle to surpass minimal savings places these companies in a challenging position, especially if their investors are eager to see early profit and loss impacts from generative AI. Nonetheless, executive teams and boards should resist the urge to scale back their generative AI ambitions. While doing so might relieve short-term profitability pressures by reducing investments in the technology, it could undermine long-term gains.
Instead, these companies need to understand why there is a gap between productivity and savings. They must find a strategic way to align generative AI productivity improvements with their cost management objectives. Fortunately, executive teams can leverage a more familiar innovation to tackle this strategic alignment: zero-based redesign (ZBR).
This approach can help bridge the gap and ensure productivity gains translate into meaningful cost savings. When generative AI emerged in business, companies couldn't afford to be passive observers. The technology's rapid evolution and immense economic potential compelled executive teams to dive into experimenting with it or risk falling irretrievably behind. More than a year later, this decision has proven wise, as generative AI has exceeded initial expectations, offering vast growth opportunities. However, early adoption's swift and experimental nature has inadvertently created three temporary barriers to achieving cost savings with generative AI. These challenges are explored below.
The first obstacle is the absence of a day-one-cost mission. Successful performance improvement initiatives often begin with a clear cost objective. When deploying new technology or similar tools for cost transformation, this mission should include an ROI target and a specific timeline for achieving it. Such upfront commitments help teams focus on the desired outcomes, even if the anticipated savings fluctuate. Yet, in the haste to gain firsthand experience with generative AI, aligning use cases with cost-saving goals or finding ways to hold teams accountable for translating increased productivity into reduced costs wasn't always feasible from the start.
Another obstacle is insufficient internal sponsorship. Implementing generative AI requires diverse expertise from technology teams and specialists across business units and functions. It also demands sponsorship from an executive leader responsible for reducing costs within the function. Unfortunately, this crucial buy-in has not always been secured. As a result, technology and analytics teams may circulate forecasts of significant cost savings, only to be met with surprise from functional executive leaders who should have been involved from the beginning.
The third obstacle is outdated end-to-end processes. Cost reduction is challenging and rarely occurs naturally, such as through spontaneous experimentation. It necessitates a committed management team ready to fundamentally redesign workflows, often requiring organizational restructuring, targeted headcount reductions, and comprehensive change management.
These hurdles are prompting some executive teams and boards to question the value of their ongoing commitment to and investment in generative AI, especially in enhancing business efficiency. While such concerns are understandable, they overlook a straightforward solution within reach.
As companies seek to enhance productivity and translate those gains into cost savings, it becomes imperative to rethink the nature of work and the methods used to accomplish it. Zero-based redesign (ZBR) is a formidable strategy to address these challenges. Unlike conventional cost-cutting techniques that impose blanket reductions, ZBR aligns with a company's strategic priorities, fundamentally reconstructing business processes from scratch. This approach simplifies end-to-end processes by removing complexities from high-value tasks, streamlining or eliminating low-value activities, and eradicating redundant work. ZBR becomes even more impactful when combined with generative AI, facilitating transformative cost savings while boosting operational efficiency.
Generative AI opens new avenues for the radical simplification central to ZBR. This doesn't imply indiscriminate automation of entire processes laden with inherent inefficiencies. Instead, it involves reimagining processes in the context of generative AI, leveraging the technology's novel tools, and retaining only the critical steps in workflows.
Take, for instance, the manual data collection involved in financial planning. Often, some of this data is utilized while the rest remains unused. Rather than mindlessly automating everything, a genuine AI-driven cost transformation would focus solely on automating the collection of relevant data, thereby eliminating unnecessary labor.
Across various functions, generative AI is enhancing the capabilities of human workers. In financial planning, professionals can now delegate the creation of initial forecasts to AI tools designed for complex model handling, allowing them to concentrate on refining subsequent drafts—a more value-added task. Such advancements steadily increase a company's capacity to achieve its objectives. Once a generative AI-driven cost transformation identifies essential tasks and their execution methods, the final step involves recalibrating organizational capacity accordingly.
Leading companies are already witnessing substantial cost savings—up to 25%, as demonstrated in prior examples of best practices—thanks to generative AI-powered cost transformations. For high-growth businesses, this approach enables revenue growth to outpace cost increases by curbing unnecessary new expenditures.
After lackluster outcomes from isolated generative AI trials, one wealth and asset management firm is now pursuing $1 billion in annual savings, equating to about 20% of its total cost base. This initiative follows a cost transformation roadmap that merges end-to-end process redesign with generative AI tool implementation. In finance and compliance, the dual approach automates reporting and analysis, cutting workloads by over 40%.
Crucially, executive teams can integrate ZBR with generative AI seamlessly. Many can establish a feasible plan within six months, realizing up to half the anticipated value within the first year. This innovative cost transformation positions businesses for a prosperous future and wins employee support, evidenced by significant improvements in their willingness to recommend the company, as measured by NPS (Net Promoter Score). Realigning productivity with cost savings is still feasible, even as the pressing demands of generative AI experimentation add complexity to the task. Before diving into the meticulous process of zero-based redesign, executives should candidly evaluate their generative AI initiatives and the initial stages of scaling them.
In doing so, they must reflect on several crucial questions regarding their progress.
The responses to these inquiries serve as a foundation for unlocking the full potential of generative AI. By addressing these aspects, organizations can reaffirm that productivity should inherently translate into savings.
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