The conversation around artificial intelligence has evolved quickly. What began as a race to “do AI” has given way to a more practical realization: Experimentation alone is not enough – you need a strategy. Organizations moved fast to deploy tools and launch pilots, often measuring success by activity. Over time, executives started asking better questions: What are we getting in return? Where can AI create measurable value? Increasingly, the answers come from understanding how work actually gets done, where time is spent and where inefficiencies exist.
The value of AI is less about scale and more about where and how deliberately it is applied, driving a new way of thinking about use cases and the value of taking a strategic bottoms-up approach.
Moving beyond the use-case checklist
By now, most organizations have no shortage of AI ideas. The challenge isn’t about generating them – it’s determining which ones are worth pursuing. Instead of beginning with technology, organizations are stepping back and looking at the work itself. Where is time spent, where does effort build up, and what drives outcomes? This bottoms-up approach offers a much clearer picture of where AI can have the greatest impact, helping prioritize which tasks to automate, enhance or rethink altogether.
What becomes increasingly clear is that the biggest opportunity extends beyond repetitive low-value work. While those areas offer quick wins, a data-driven approach enables organizations to target AI development and implementation precisely and strategically. The real payoff comes from applying AI to complex, high-impact work, helping organizations move faster, scale more efficiently and execute with greater precision.
Use cases: From efficiency gains to strategic advantage
To make this more concrete, it’s helpful to look at how this plays out in practice. The following examples highlight three distinct types of use cases, each reflecting a different way organizations are applying AI to drive value and illustrating how a more targeted, bottoms-up approach can deliver strategic impact.
In the pharmaceutical industry, speed to market is a competitive advantage. When a new drug is ready for launch, pharmaceutical companies often rely on partners to quickly stand up a dedicated workforce. This means recruiting, onboarding and training hundreds of representatives, each of whom must be prepared to engage with physicians as an expert resource on the drug’s use, benefits and risks. This is a complex, time-sensitive process that requires each step to be coordinated and done consistently and to scale. By introducing a Multi-Agent System (MAS) across the launch lifecycle, these organizations can accelerate key activities like candidate screening, onboarding and training into integrated, responsive steps. For one client, Protiviti strategically applied AI to compress the launch timeline from 12 months to six months. This enabled the company to gain faster market entry, stronger positioning and meaningful financial upside. In this case, AI served as a true force multiplier for strategic initiatives.
IT environments are becoming increasingly complex. Teams are not just tracking hardware and software for tax and capitalization anymore. Now they are managing an expanding ecosystem of applications, licenses and AI agents, many of which enter the environment outside standard procurement channels. Employees may purchase tools independently, licenses renew automatically, and new AI agents are deployed with limited centralized oversight. Over time, visibility starts to erode and items are no longer accounted for, even though they may have been expensed.
Organizations struggle to answer basic questions like: Which assets are in use? Which licenses are expiring or underutilized? Where are risks or gaps emerging?
By applying AI to analyze expense data, contracts and system activity, organizations can build a more complete and dynamic view of their technology landscape, identifying discrepancies, flagging expirations and surfacing issues in real time. The result is stronger visibility, improved cost control and more effective governance in an increasingly complex environment.
In more data-intensive environments, AI is enabling organizations to address challenges that have long been difficult to solve at scale.
For a global logistics and package delivery company, route optimization is a constant challenge. Over time, drivers rely on familiar routes, which may not be the most efficient. Across a large network, even small inefficiencies can add up to millions of unnecessary miles.
The issue isn’t a lack of data – it’s the ability to act on it. Analyzing traffic patterns, delivery volumes and timing constraints at scale is simply too complex to manage manually. By applying AI to continuously optimize routing decisions, organizations can reduce miles driven, improve efficiency and lower fuel costs, potentially saving millions of dollars.
Across each of these examples, a common theme emerges: The highest-value opportunities are rarely the most obvious. They are often embedded in the complexity of how work gets done.
Reframing value: The human factor
These examples point to a broader shift in how organizations are realizing value from AI. In our work helping clients adopt AI technologies, we have not seen AI drive net headcount reductions. Instead, organizations are using AI to expand capacity, enabling teams to handle greater volumes of work, move faster and pursue opportunities that were previously out of reach.
AI allows organizations to accelerate key initiatives, improve speed to market, optimize throughput and redirect human effort toward higher-value work. It’s less about doing less, and more about doing more – with greater focus and effectiveness.
But realizing that value depends on more than technology. Adoption is where many efforts succeed or stall. The benefits of AI are realized when employees understand it, trust it and incorporate it into how they work. This requires a deliberate focus on effective change management, supporting employees through the transition – not only in learning new tools, but also in understanding how their roles evolve.
As the AI landscape continues to shift, the organizations that will pull ahead will be those that move with clarity and intent, with a focus on ROI that separates the business from peers.
