From Buzzword to Bottom Line: Bridging the AI Implementation Divide

In the current business landscape, there's no shortage of excitement around generative artificial intelligence (AI). Conference keynotes, industry publications and every consultant seem to paint visions of revolutionary transformation. Yet amid this enthusiasm, a concerning pattern has emerged: despite significant investments, many organizations struggle to capture tangible returns from their AI initiatives.

The problem often lies in what I call the "Big P vs. little p" approach to AI implementation. Companies frequently pursue ambitious, transformative "Big P" problems – complete departmental overhauls, enterprise-wide initiatives or complex challenges requiring sophisticated customization. While these moonshots capture executive imagination, they also demand substantial resources, face implementation hurdles and often deliver delayed returns.

Meanwhile, the most successful organizations are discovering that AI's immediate value comes from addressing the "little p" problems – those everyday workflow inefficiencies that collectively drain productivity across the organization. These smaller challenges – repetitive data processing, routine documentation and standard communications - represent the perfect starting point for AI integration.

The "little p" approach offers several advantages:

• Quick implementation with minimal disruption

• Faster time-to-value and measurable ROI

• Improved organizational trust and competence with AI

• Increased momentum for broader adoption

Beyond the Hype: Solving "little p" Problems with AI for Real Business Impact

In our nuclear business, we were approached by the license renewal department at a major nuclear operator. They had a specific problem: each license renewal engineer spends thousands of hours reviewing operating experience from the past 10 years looking for age-related condition reports (CRs). This can mean reviewing upwards of 100,000 CRs, with less than 1.5% that are actually age-related. 

We developed a solution using results from previously completed reviews to develop an AI model that would automatically screen CRs that are not age-related. The model reduced the amount of manually reviewed CRs by 80% and saved 2,000 hours per application. The same model has been used for other plants in the fleet, increasing ROI on our “little p” solution with outsized results.

Beyond the Hype: Using RAG, LLMs and Chatbot to Deliver Results

One of our biggest hurdles for internal efficiency is giving staff access to all of our collective knowledge, data and expertise in one location. Jensen Hughes is a company made of many acquisitions over the past decade and with each acquisition came experts, but also another way of organizing data – a “Big P” problem became apparent. 

We are a company of seller-doers. That means our staff who perform the work for clients are the staff who write the proposals and complete administrative work to initiate new jobs. As such, Jensen Hughes project managers (PM) write roughly 24,000 proposals a year with a staff of about 1,700. No matter how you look at that ratio, proposal writing consumes a significant portion of our technical experts’ time and resources each year – a classic “little p” problem that could be solved practically with retrieval augmented generation (RAG), large language models (LLMs) and agentic AI applications. 

Enter ChatAdvisr – our in-house RAG-bot platform that harnesses the power of LLMs to help individuals and teams work smarter and faster. We’ve developed a specialized workflow for proposals that allows users to upload an RFP, past proposal or simply ask for a new proposal to be written. ChatAdvisr then searches our curated archive of past proposals to find those with similar scopes of work or the same client. From there, it drafts a new scope based on a combination of the RFP/prompt and past scope items, then generates a cover letter in our “style” and exports a formatted word document. It streamlines research, writing and formatting into a single step, saving PMs significant time.

Our team made an intentional decision to focus on internal processes when developing ChatAdvisr. This allowed us to address our unique challenges while designing an architecture that keeps our data safe. In a landscape where AI tools are often rushed to market, we’ve remained focused on data privacy, security and transparency – critical to building trust with both employees and leadership. By establishing a strong foundation of control and protection, we’ve enabled ChatAdvisr to scale responsibly and earn widespread adoption across the company.

Balance is Important: You Can’t Solve Everyone’s Individual Problems

Even though this blog advocates for focusing AI on more specific problems, it’s unrealistic to build complex, custom solutions for every individual employee. That’s why it’s so important to work with stakeholders to identify and understand where your company or team’s biggest time vampires lie. Creating a dynamic team of contributors to consult and inform on the most impactful AI workflows will yield great results in shorter timeframes. It’s also important to recognize when a company does not have experience with AI technology, tools and implementations. Bringing in external experts can significantly reduce the learning curve and accelerate understanding of what AI can deliver.

Just remember, the most effective AI strategy combines both perspectives: pursue "little p" wins today while thoughtfully planning for "Big P" transformations tomorrow. This balanced approach builds the organizational capabilities, confidence and culture needed for larger AI initiatives.  As you evaluate your organization's AI approach, ask yourself: Are you overlooking valuable "little p" opportunities in pursuit of "Big P" transformations? The fastest path to AI value might be hiding in those everyday workflows just waiting to be optimized.