Insights
The hardest part of implementing AI and Agentic AI isn’t the technology: it’s figuring out where to start. Many organizations feel pressure to move quickly, yet most efforts stall early. The reason? Unclear problem definition. Without a shared understanding of what needs solving, teams often default to vague use cases or overengineered solutions that struggle to gain traction.
At Chai, we’ve found that the real foundation for meaningful AI work begins with identifying the right problem and doing so systematically. That’s where expertise in behavioral science plays a critical role.
By analyzing how decisions are made, how workarounds emerge, and where friction exists in daily operations, we help teams uncover patterns that point to high-impact AI opportunities. This approach isn’t about guessing; it’s about diagnosing and finding the right use cases where AI and Agentic AI can help businesses thrive.
In a landscape saturated with AI buzzwords and generic solutions, companies increasingly seek partners who don’t just offer technology, but bring the frameworks and methodologies to surface real problems and solve them the right way. Our approach combines behavioral science, design, and strategic tools like the AI Canvas and our executive-focused AI ebook to guide organizations toward clarity and impact. It’s a method designed to move beyond the noise, so teams can act with greater precision and confidence.
Why Most AI Efforts Fail to Launch
Despite the widespread interest in artificial intelligence, most enterprise AI initiatives struggle to get off the ground or fail to scale. This isn’t due to a lack of tools or talent. The issue lies in how these initiatives begin.
Tech-first syndrome is a common trap. Organizations often start by selecting a tool, maybe a powerful model or a newly released platform, before identifying a specific problem it should solve. This technology-first mindset pressures teams to retrofit a use case onto a solution they’ve already bought into, which rarely leads to long-term value.
Vague or overly broad objectives are a significant reason AI projects fail to gain traction. Many organizations start with general aspirations like “we want to automate more” or “we need to use AI,” but these goals quickly lose momentum without a specific problem to solve. Without clarity, defining scope, assessing feasibility, or measuring outcomes becomes difficult, leaving teams unsure of what they’re building and why.
This ambiguity leads to scattered efforts: product teams don’t know which workflows to target, data teams lack direction, and leadership can’t track ROI. As a result, what starts as innovation often becomes a disconnected proof of concept that never scales.
McKinsey reports that companies with clearly defined AI use cases, such as reducing lead times or improving service levels, see real outcomes, like up to 35% inventory reduction and 65% service level improvement.
Broad goals like “improve customer service” must be broken down into precise, observable problems, such as reducing response time or automating repetitive tickets. Without that decomposition, AI will likely solve the wrong problem, or none at all.
Lack of internal alignment is another frequent obstacle. When there’s no consensus on success across leadership, operations, and technical teams, measuring progress, building momentum, or justifying continued investment becomes challenging. AI projects require a clear problem definition and a shared understanding of expected outcomes, data availability, and user impact.
These issues reflect a broader need: AI initiatives can’t thrive in ambiguity. They require precision, coordination, and a human-centered understanding of where value hides. Even the most sophisticated tools struggle to make a real-world difference without that foundation.
The Blind Spot in AI: What Science Reveals That Brainstorming Doesn’t
When companies gather to discuss AI opportunities, the first instinct is often to brainstorm, ideate possible use cases, debate priorities, and identify processes that “feel” inefficient. But there’s a limit to what intuition can uncover. In complex organizations, the real obstacles to performance are rarely visible on a whiteboard. They live in the behaviors of teams, the invisible workarounds, and the friction points embedded in daily decisions.
This is where behavioral science becomes essential. It doesn’t rely on guesses. It observes how people work, navigate systems, bypass bottlenecks, and compensate for broken processes. It’s a diagnostic tool that uncovers what brainstorming misses.
The core idea is straightforward: wherever people struggle, slow down, or invent workarounds, there’s a signal. These signals often point to where AI can add real value by automating routine tasks, surfacing hidden risks, or supporting human judgment. But spotting these signals requires a behavioral lens, not just a list of potential use cases.
This principle is at the heart of how Chai works. We don’t assume AI is the solution; we start with the question: Where does the real problem live? We reveal the tangible frictions that matter through structured behavioral observation and analysis. From there, we apply tools like the AI Canvas to frame opportunities precisely, before any technology decisions are made.
In a landscape crowded with AI pilots and overpromised outcomes, the real differentiator isn’t the technology, it’s the clarity behind its use. Understanding where friction exists and why it matters separates meaningful innovation from wasted effort.
That’s the shift: from guessing to knowing, from abstract ambition to targeted action. The most successful AI systems aren’t the ones that impress in slide decks, they’re the ones people trust, use, and rely on every day to solve real problems. And that only happens when you start by understanding the behavior behind the business.
External Examples That Began with Behavior, Not Tech
Logistics (Uber Freight)
“Empty miles comprise 20–35 % of U.S. trucking miles… our analysis suggests network optimization can reduce empty miles by as much as 64 %, leading to a 23 % reduction in drivers’ overall miles.”
Furthermore, since launching AI-driven routing, Uber Freight reports that empty miles fell by 10–15 %, using real-time machine learning to match drivers with continuous loads and reduce inefficiency.
Supply Chain Optimization
McKinsey’s research highlights a clear pattern: AI delivers the greatest impact when it’s tied to well-defined, behavior-informed use cases. In their global operations study, companies that leveraged insights into real-world behaviors—such as purchasing habits, approval bottlenecks, or manual workarounds—saw up to 35% reductions in inventory levels and 15% cost savings across their supply chains.
Rather than treating AI as a plug-in solution, these companies began by diagnosing where decisions were slow, processes were repeated, or exceptions were the norm. That behavioral clarity allowed them to deploy AI in ways that didn’t just automate tasks, they optimized systems. The gains weren’t theoretical. They were measurable, scalable, and sustained.
Security & Fraud Detection
“Machine learning models can spot stolen credit cards quickly and accurately by detecting purchase behavior that doesn’t match a customer’s previous buying history.”
Meanwhile, IBM notes that AI fraud detection “learns to recognize the difference between suspicious activities and legitimate transactions… even catching trends that a human agent might miss.”
These systems succeed not because they rely on predefined rules, but because they continuously learn from patterns in human behavior. They detect anomalies not in the code, but in context: how users act, when they act, and what changes in that behavior over time. It’s a prime example of AI that works because it’s tuned to how people operate, not in spite of it. And it reinforces the larger point: real value from AI emerges when behavior is the input, not an afterthought.
Source: Canva
Why Your Team and Partners Want a Thoughtful Approach
In today’s rapidly evolving AI environment, internal teams and external partners aren’t just looking for cutting-edge solutions but clarity, confidence, and credibility. An organization's approach to AI sends a strong signal about its ability to execute change thoughtfully and sustainably.
Clarity builds trust. Teams are more likely to support and engage with AI initiatives when they see that the problem being solved is real, relevant, and well-understood. When partners lead with investigation—not assumptions—they earn the confidence of both technical and non-technical stakeholders. Chai’s behavioral-first methodology ensures that alignment happens early, before resources are committed to the wrong solution.
Defined problems lead to faster adoption. When use cases are grounded in observable behavior and structured frameworks, teams see the value more quickly. Implementation becomes less about proving a concept and more about accelerating outcomes people recognize and care about.
Durability over quick wins. Many AI projects solve surface-level symptoms but miss the underlying issue, resulting in short-term success and long-term friction. By starting with human-centered analysis, Chai helps organizations build lasting AI solutions. The result it’s real operational change supported by the people who rely on it.
This matters more than ever. In a competitive landscape where talent is selective and AI and Agentic AI innovation fatigue is real, companies want to work with partners who take the time to understand their world. It is not about implementing AI but applying it where it moves the needle.
Conclusion: Start with Behavior, Scale with AI
In an AI-saturated market, the fastest way to move from idea to impact isn’t more tools; it’s better understanding. Companies that succeed with AI and Agentic AI don’t start with the tech. They start with the problem. To do that, they choose partners who truly understand their business, their workflows, and their friction points.
That’s where behavioral science becomes essential. In complex environments, guessing where AI might help is costly. But by systematically analyzing how people work, where decisions break down, and where inefficiencies emerge, organizations can surface real opportunities, clearly defined, measurable, and ready for intelligent automation.
At Chai, we’ve spent over 15 years applying this evidence-based approach to technology adoption. We don’t just talk about behavioral science; we’ve embedded it into how we uncover use cases, frame projects, and guide AI implementation. Tools like our AI Canvas and strategic guidance materials help translate insight into action.
Before investing in AI, companies need to determine what needs solving. This is the only way to avoid wasted pilots, misaligned priorities, and low adoption. Skipping this step doesn’t save time, it creates rework.
With Chai, you start with clarity, so AI doesn’t just get implemented, it makes your business work better.
Next Steps
- Download Chai’s AI Ebook — A guide to align your team around Agentic AI potential.
- Book a discovery call - Let’s explore how we can help you identify and prioritize the AI use cases that will drive the greatest impact in your organization.
