Define and Conquer
How PSA is helping organizations move faster by transforming vague AI ambitions into clear, actionable project definitions.
At Gridlocked & Co., a fictional urban mobility analytics firm, Maya had built a reputation for making messy transportation data make sense as its fictional director of data science. Her clients (mostly mid-sized city transit departments), relied on her team to optimize routes, streamline operations, and introduce AI-based tools to ease commuter frustrations. But lately, something had shifted. Instead of winning praise for predictive models or performance dashboards, her projects were bogging down in endless meetings, unclear requests, and skeptical stakeholders.
The latest challenge came from Midvale Transit, a long-time client and emblem of everything that made public-sector innovation both exciting and exhausting. Their problem was maddeningly vague: riders were frustrated, buses were late, and public trust was slipping. Maya asked for data; they sent spreadsheets that hadn’t been updated in months. She requested specific pain-points; they replied, “We just want it to work better.” The AI team back at Gridlocked & Co. didn’t know where to start. And worse, they didn’t know if they should start.
What was once a data-driven, problem-solving consultancy had turned into a strategy-by-committee holding pattern. Maya wasn’t just stalled; she was worried. The tools she had weren’t broken. What she was missing was clarity.
When Pressure Mounts From All Sides
This wasn’t just a client problem; it was also an industry pattern. Across the urban planning sector, cities were under immense pressure to modernize. Federal and state agencies were offering “smart city” grants with AI mandates baked in. Mayors were promising intelligent transit systems in campaign speeches. And private competitors (think of companies like SpeedyLoop and BusByte, also fictional) were pitching slick presentations with vague but compelling-sounding AI pilots.
But Maya knew the dirty truth: most of these so-called innovations were little more than smoke and mirrors. Under the hood, many were hastily patched systems or glorified dashboards wrapped in buzzwords. Her team at Gridlocked & Co. prided itself on being different. They didn’t just sell AI; they also solved problems with it.
And yet, Maya’s team had hit a wall. Their core bottleneck wasn’t technical. It was conceptual. Without a clearly scoped problem, no amount of data science could help. Her engineers were burned out from trying to reverse-engineer solutions from incoherent client goals. Project timelines stretched into quarters, and decision-makers lost faith long before prototypes saw daylight.
What Maya really needed wasn’t another AI model. She needed to figure out what problem was actually worth solving (and fast).
When Nothing Changes, Everything Gets Worse
The costs of inaction were mounting. If Maya couldn’t cut through the ambiguity, her client (Midvale Transit) would walk. Worse, they’d leave with the impression that AI can’t deliver results—souring future opportunities across the entire sector. Internally, her own leadership team was growing skeptical. They had invested heavily in building an AI consultancy practice, and expectations were high.
More personally, Maya felt her credibility slipping. She wasn’t just a project lead; she was the connective tissue between client needs and AI solutions. If she couldn’t define a clear problem, she couldn’t do her job. And if she couldn’t do her job, the company couldn’t deliver.
There was also the matter of competition. While Maya’s team was caught in scoping purgatory, competitors were skipping straight to flashy demos … never mind that those demos rarely led to implementation. And in the world of city procurement, perception was half the battle.
Doing nothing meant losing more than a project. It meant losing trust, talent, and time (resources that were far harder to rebuild than any technical system).
So Maya asked the hard question that every strategic leader eventually must: What’s blocking us from even starting?
The answer, it turned out, wasn’t a lack of ideas or effort. It was a lack of focus. And that was the first thing that had to change.
Curious about what happened next? Learn how Maya leveraged a recently published AI research on Problem Scoping Agent (PSA), turned insight into a strategic edge, and achieved meaningful business outcomes.