The missing middle

Companies adopting AI tend to land in one of two places.

The first group goes big. Enterprise transformation. A six-figure consulting engagement that produces a strategy deck. An ambitious pilot that touches five departments, requires new infrastructure, and needs a vendor relationship complex enough to have its own project manager. Eighteen months later, the pilot is still a pilot. The deck is still a deck. The AI mandate from the board sits where it started: unanswered.

The second group goes small. Someone on the marketing team starts using ChatGPT. A developer experiments with Copilot. An operations manager builds a quick automation that saves her team two hours a week. These scattered experiments produce genuine value for the individuals running them, but they never connect to anything larger. No one evaluates which experiments matter most. No one sequences them so early wins build toward later ones. The company accumulates tools and anecdotes, not a strategy.

Both groups fail for the same reason. They're missing a pipeline.

Not a strategy document that lives in a slide deck. An actual operating system for moving AI initiatives from "that's interesting" through evaluation, testing, and into production, with a review rhythm that prevents good ideas from stalling indefinitely.

Faisal Hoque, writing in Fast Company, put the core problem plainly: "Technology is rarely the constraint. Most companies can access impressive AI tools. What they lack are the management systems needed to deploy those tools strategically, build repeatable capabilities, and create sustained value."

The pipeline is that management system. Five steps, designed for mid-market companies that have budget and ambition but no dedicated transformation department.

Step 1: Know where you stand

Before building a pipeline, you need to know what you're working with. This step is an honest inventory, not an aspirational one.

Three dimensions matter.

What's been tried. Most organizations have more AI history than they realize. Someone bought a tool. A team ran a pilot. An executive attended a conference and came back with a vendor recommendation that went nowhere. Catalog all of it. What was the initiative? What happened? Why did it succeed, stall, or die?

The patterns in your failures tell you more than any vendor's pitch deck. If three pilots died because nobody on the business side owned the outcome, that's not bad luck. That's a structural problem you need to fix before initiative number four.

What capabilities exist. Not what you wish you had. What you actually have today. Is your data clean and accessible, or scattered across systems with no API? Does anyone on staff understand machine learning at a practical level, or is your AI knowledge limited to people who've used ChatGPT? Do your core systems talk to each other?

These aren't judgment questions. They're inventory questions. The answers determine what's realistic in the next twelve months versus what requires foundation work first.

What risk looks like. Every company says it wants to move fast with AI. Few have defined what that means for their specific business. A financial services firm has different guardrails than a manufacturing company. A company that just dealt with a data breach has a different appetite for third-party AI tools than one that hasn't. Define the boundaries before the pressure to move fast pushes you past them.

The baseline will surface uncomfortable truths. You might find that your data infrastructure can't support the initiatives your leadership wants. You might discover that previous pilots failed not because of the technology but because nobody owned the outcome. You might realize the team's AI literacy is lower than assumed.

Good. Those truths are the foundation. The companies that skip the baseline and jump straight to buying tools are the ones that end up in pilot purgatory, where everything is "in progress" and nothing is in production.

Step 2: Generate and score

With the baseline established, the next step is structured ideation. "Structured" is doing all the work in that sentence. Most companies generate AI ideas through some combination of vendor demos, executive enthusiasm, and whichever process annoys the most people. That produces a list ordered by excitement, not by value.

A better approach: assemble cross-functional teams and change the question. Not "what can we do with AI?" but "what problems are preventing us from hitting our goals, and could AI help solve any of them?" The first question generates moonshots. The second generates targets.

Those teams will produce more ideas than you can pursue. Good. The pipeline's job is to narrow, not to encourage. Every idea gets scored against five criteria on a simple 1-to-10 scale.

Strategic priority. How directly does this initiative support your core business goals? A project that marginally improves an internal report scores lower than one that cuts customer response time in half. Be ruthless about the connection between the initiative and something your business actually measures.

Risk. What's the downside if this fails after deployment? A bad internal tool wastes money. A bad customer-facing tool damages trust. A bad tool that touches regulated data creates legal exposure. Score accordingly.

Expected value. What's the realistic return? Not the theoretical maximum in a best-case scenario. The honest return based on what you learned in the baseline. If your data isn't clean, discount any initiative that depends on clean data. Companies are remarkably good at optimistic projections and remarkably bad at honest ones. Try for the second.

Cost. What does it take to reach production? Include everything: software, integration, training, change management, ongoing maintenance. Companies consistently underestimate this number because they price the technology and forget the people. Research shows organizations spend 93% of their AI budgets on technology and 7% on people. The companies getting value have those numbers closer to inverted.

Implementation difficulty. How hard is this to actually deploy in your environment? A tool that plugs into existing systems scores differently than one requiring new infrastructure. An initiative one team can own scores differently than one needing five departments to coordinate.

You're not building a precise financial model. You're creating a structured way to compare initiatives and surface the ones worth deeper evaluation.

When you rank the results, patterns will emerge. Some initiatives cluster near the top across all five criteria. Those are your candidates for step three. Some score high on value but low on feasibility because they require capabilities you don't yet have. Those go in the backlog, tagged with what needs to change before they become viable. Some score high on excitement but low on everything else. Kill those early, before they consume resources.

The scoring also surfaces dependencies you wouldn't see otherwise. One initiative might depend on data generated by another. A customer-facing tool might require an internal infrastructure project to ship first. These dependencies become your sequencing logic. They tell you what has to happen before what, and they're the reason the pipeline produces better outcomes than picking projects in isolation.

If you've worked through our AI readiness scorecard, the approaches complement each other. The scorecard evaluates whether a specific workflow is ready for AI. This scoring model evaluates whether a specific initiative deserves your resources. Use both, and you spend money on the right initiatives applied to the right workflows.

Step 3: Check the architecture

The initiatives that survived scoring now face a harder question: do they actually fit your technical environment?

This is where ambition meets infrastructure. A project can score well on strategic priority and expected value while being architecturally impossible in your current setup. The point of this step is to find that out before you've committed budget and people.

Four dimensions.

Purpose alignment. Does this initiative, as currently scoped, advance the specific goal it claims to? It's common for initiatives to drift during ideation. A project that started as "reduce invoice processing time" morphs into "build an intelligent document management platform." The first is an initiative. The second is a multi-year program. Catch the drift here.

People readiness. Can the team that will use this system absorb the change? If they're running at full capacity with no room to learn new tools, the system will sit unused regardless of how well it works. A Section AI survey of 5,000 knowledge workers found that 40% of non-management employees reported saving zero time from AI tools, and nearly 70% described feeling anxious or overwhelmed by the technology. That's not a technology problem. That's a readiness problem. Score it honestly.

Process fit. Does this initiative integrate with how work actually flows through the organization? Or does it require redesigning a process nobody has time to redesign? The answer determines whether you're deploying a tool or launching a change management program. Both are valid, but they need different timelines and different budgets.

Technical feasibility. Can your systems support this? Are the APIs available? Is the data in the right format? Does the integration path exist, or does someone need to build it? The most common surprise here is discovering that a "simple" integration touches six systems, three of which have no API and one of which runs on software older than half the team.

Map each surviving initiative against all four dimensions. Some pass cleanly and move to development. Some reveal fixable gaps: data that needs cleaning, a team that needs training, a process that needs restructuring. These stay in the pipeline but with prerequisite work identified and scoped. Some fail on dimensions that aren't easily fixed. Kill those. They'll come back when conditions change.

The architecture check also reveals sequencing opportunities. One initiative often generates data or capability that makes subsequent initiatives easier. A quality inspection system that produces structured defect data creates a foundation for predictive maintenance. A support tool that classifies customer interactions feeds analytics that inform product decisions. Sequence your pipeline so early deployments build the foundation for later ones. The Fast Company case study of a midsize manufacturing company showed exactly this pattern: their first initiative (visual quality inspection) generated structured data that made their second and third initiatives viable.

Step 4: Ship to production

This is where most pipelines break.

The initiatives that passed scoring and architecture review enter active development. The technical work isn't usually the problem. The organizational work is. Pilots fail to reach production for three predictable reasons: nobody defined success before launch, nobody on the business side owns the outcome, and nobody built a feedback loop that determines whether the thing is working.

Fix all three before you write a line of code.

Define success criteria before launch. Not "this will save time" but "this will reduce invoice processing from 160 manual hours to under 10" or "this will handle 80% of tier-one support tickets without human intervention." The criteria need to be specific enough that three months after launch, you look at a number and know.

Assign a business owner. Not the IT team. Not the vendor. A person on the business side whose performance is tied to whether this initiative delivers. When AI success is measured in model accuracy, it stays in the lab. When it's tied to a metric someone's bonus depends on, it ships.

Build the feedback loop. How will you know if the AI's output is good? How quickly? Who reviews it? What happens when it's wrong? Systems with tight feedback loops improve fast. You can check the output within minutes or hours, correct errors, and feed the corrections back. Systems with loose feedback loops don't improve. They generate plausible output that nobody can evaluate until months later, if ever.

Then ship. Not to a sandbox with synthetic data. To production, with real users, real inputs, and real consequences. Start within a defined scope. One production line. One team. One customer segment. Measure against your success criteria. Talk to the people using it daily. What's working? What's failing? What do they wish the system did differently?

Expand based on evidence, not enthusiasm. If the numbers support expansion, expand. If they don't, you've learned something valuable before scaling a problem.

Step 5: The quarterly review

The pipeline isn't a project with an end date. It's an operating rhythm. The quarterly review is the mechanism that keeps the whole system from decaying into the familiar pattern of initial excitement followed by quiet neglect.

Three questions structure every review.

What's the status of active initiatives? Are experiments meeting their milestones? Are production systems delivering the value they promised? Does anything need more resources, a different approach, or mercy killing? This is triage, not celebration. The goal is an honest assessment, not a progress report designed to make leadership comfortable.

Is the pipeline balanced? As initiatives advance, ship, or die, the pipeline needs replenishment. Look across every stage: what's in ideation, what's being scored, what's in architecture review, what's in development, what's in production? If everything is clustered in one stage, something is stuck. If the ideation pipeline is empty, you've stopped looking for new opportunities. If nothing has shipped in two quarters, something systemic is broken.

Has the world changed? Markets shift. Technology matures. Competitors move. Your own organization learns things that change what's possible. The scoring criteria you set six months ago might not reflect today's priorities. An initiative you killed might be viable now because a prerequisite shipped. One you funded might be irrelevant because a competitor got there first, or because AI costs dropped so far that a different approach makes more sense. Recalibrate against current reality, not the reality that existed when you started.

The quarterly review prevents the single most common failure mode in enterprise AI: the perpetual pilot. Pilots survive indefinitely because nobody asks the hard question. Does this graduate to production, or does it die? Without a forced rhythm, pilots accumulate. They consume budget. They occupy headspace. They create the illusion of movement while nothing ships.

Set the cadence. Protect it. This is not the meeting that gets canceled when something urgent comes up. It's the meeting that determines whether your AI investment produces results or produces status updates.

One pipeline, compounding

The first initiative through your pipeline will feel slow. You're building the scoring model, establishing review processes, learning to evaluate architecture fit, and figuring out who owns what. That overhead is real and worth it.

The second initiative will be faster. The scoring criteria exist. The review rhythm is running. The team has experience defining success criteria and evaluating feasibility. The third faster still.

This is the compounding effect that scattered experiments never produce. Each initiative that ships to production generates data, builds capability, and surfaces opportunities for the next one. The feedback loops from production teach the organization what actually works in ways no pilot ever could.

Unisys CEO Mike Thomson described where the market is heading: "More functional deployments of AI, a focus on quality rather than cost-cutting, and the emergence of AI applications that will deliver repeatable, ROI-driven results." The pipeline is how you get there. Not through a single ambitious bet. Through a repeating cycle of finding the right problems, evaluating them honestly, shipping solutions, and learning from the results.

One initiative at a time. One review at a time. Compounding.