Our Process: How We Go From 'Where Do We Start?' to 'It's Working'

The question we hear most often in first conversations isn’t about AI technology. It’s simpler than that:

“How does this actually work?”

It’s a fair question. AI consulting can feel opaque. You might have had experiences with consultants who talked a lot and delivered little. Or you’ve heard stories from peers about projects that dragged on without clear progress.

So let me walk you through exactly what it looks like to work with us — from that first “we’re curious about AI” conversation to a solution that’s actually delivering value.

Phase 1: Discovery (Understanding Before Proposing)

This is where most AI projects fail, so it’s where we invest the most. (For more on this, see why AI projects fail.)

The first conversation

We start by listening. Not pitching — listening.

What are you trying to accomplish? What’s frustrating your team? Where are the bottlenecks? What have you tried before? What worked, what didn’t? What does success look like to you?

This first conversation is exploratory. We’re not trying to sell you anything yet. We’re trying to understand whether we can actually help — and if so, where the highest-value opportunities might be.

Sometimes, after this conversation, we tell people they don’t need us. Maybe the problem is simpler than they thought. Maybe it’s not an AI problem at all. Maybe they’re not ready yet. We’d rather be honest than take on a project that won’t deliver.

Deep discovery

If there’s potential fit, we go deeper. This is the phase where we really learn your business.

We interview stakeholders — not just the executives who sponsor the project, but the people who actually do the work. The knowledge that matters most often lives with frontline employees, not leadership.

We observe processes. Not the documented version, but the real one. How do things actually flow? Where are the workarounds? Where does the data get messy?

We examine the data. What exists? What’s reliable? What’s missing? Is the data in a state where AI can use it, or is there cleanup needed first?

We map the workflows. Where would AI fit? What would change? Who would be affected?

This phase typically takes 2-4 weeks depending on scope. Some clients find this slower than they expected. But this is exactly where rushed projects go wrong. The time invested here saves multiples later.

What you get from discovery

At the end of discovery, you have:

Clear opportunity identification. Specific use cases where AI can add value, ranked by impact and feasibility.
Realistic ROI projections. Not vendor math — honest estimates with documented assumptions.
Readiness assessment. What’s in place, what needs work before AI makes sense. (This is what happens in an AI readiness assessment in more detail.)
Recommended approach. What to start with, how to sequence, what might wait.
Risk identification. What could go wrong and how we’d mitigate it.

You own this deliverable. If you want to take it and implement with your own team or another partner, that’s yours to keep. We’d rather give you real value upfront than hold it hostage.

Phase 2: Pilot Design (Start Small, Prove Value)

We don’t believe in big-bang AI implementations. We believe in proving value quickly, then scaling what works.

Selecting the right pilot

Based on discovery, we identify the best starting point — not necessarily the biggest opportunity, but the one that balances:

Potential impact: Meaningful value if it works
Feasibility: Realistic to implement in weeks, not months
Visibility: Success will be observable and measurable
Risk: If it fails, the consequences are manageable

The goal is to get a win on the board quickly. That win builds credibility, creates internal champions, and generates the organizational energy needed for larger initiatives.

Defining success upfront

Before we build anything, we agree on what success looks like. Specific, measurable outcomes that we’ll track:

“Reduce processing time from X hours to Y hours”
“Decrease error rate from A% to B%”
“Free up Z hours per week of team time”

This isn’t just good practice — it’s how we hold ourselves accountable. If we don’t hit the targets, we haven’t succeeded. Period.

Setting scope and timeline

We define exactly what the pilot will include:

What functionality will be delivered
What integrations are needed
What the timeline looks like (typically 6-12 weeks for pilot)
What resources are required from your team
What investment is required

No ambiguity. No scope creep. Clear expectations on both sides.

Phase 3: Implementation (Building What Works)

This is where things get built. But even in implementation, our approach is different from typical consulting.

Iterative development

We don’t disappear for three months and come back with a finished product. We work in short cycles — typically two weeks — with regular check-ins.

At each checkpoint, you see progress. You provide feedback. We adjust. This means the solution evolves based on real input, not assumptions. And if something isn’t working, we catch it early — not after months of wasted effort.

Real data, real context

We build using your actual data, in your actual environment, for your actual workflows. No demos with fake data that won’t reflect reality. The solution we develop is the solution you’ll use.

Your team stays involved

We’re not building something in isolation and handing it off. Your team members participate throughout — providing context, testing features, flagging issues.

This involvement serves multiple purposes:

Better solutions (your people know things we don’t)
Smoother adoption (they’re bought in, not surprised)
Knowledge transfer (they understand how things work)

Documentation and training

As we build, we document. How things work. How to use them. How to troubleshoot common issues. By the time we’re done, your team has what they need to operate confidently.

Phase 4: Validation (Proving It Works)

Before we call anything done, we prove it works.

Testing in real conditions

The solution gets tested with real data, in real workflows, by real users. Not in a sandbox — in the actual environment where it will run.

We watch for edge cases. We identify gaps. We refine until it performs as expected.

Measuring against success criteria

Remember those success metrics we defined? Now we measure against them.

Did processing time actually drop? Did error rates improve? Did team capacity actually free up? We don’t rely on feelings or impressions. We look at the numbers.

Addressing gaps

If there are gaps between what we projected and what we achieved, we address them. Sometimes that means technical refinement. Sometimes it means training or process adjustment. Either way, we don’t move on until the solution is actually delivering value.

Phase 5: Handoff and Scale (Making It Sustainable)

The pilot worked. Now what?

Transition to operations

We transition the solution from “project” to “operations.” This means:

Your team is trained and confident
Documentation is complete and accessible
Monitoring and maintenance processes are established
Escalation paths are clear if issues arise

The goal is that your team can run this independently. We’re not trying to create dependency — we’re trying to build capability.

Scaling decisions

With a successful pilot, you have proof that AI works in your context. Now you can make informed decisions about scaling:

What other processes should use similar approaches?
What learnings from the pilot apply elsewhere?
What infrastructure investments make sense now?

We help you think through the scaling roadmap, but the decisions are yours. You have real evidence to base them on.

Ongoing partnership (if you want it)

Some clients want ongoing support. Others are ready to run independently. Both are fine.

If you want continued partnership, we can help with:

Monitoring and optimization
Expansion to new use cases
Handling changes as your business evolves
Training new team members

But this is optional. The pilot is designed to be a complete deliverable, not a hook for ongoing dependency.

What makes this different

If you’ve worked with consultants before, some of this might sound familiar. But there are key differences in how we execute:

We invest in understanding before we propose solutions. Discovery isn’t a formality — it’s where we do our most important work. We’d rather spend extra time upfront than build something that doesn’t fit.

We define success in measurable terms. Vague outcomes like “improved efficiency” don’t cut it. We commit to specific metrics and hold ourselves accountable to them.

We start small and prove value quickly. Big transformations sound impressive but often fail. Focused pilots that deliver real results build momentum for bigger things.

We build capability, not dependency. Our goal is to make you capable of running and extending what we build. The best outcome is when you don’t need us anymore.

We’re honest about what we see. If something isn’t working, we say so. If AI isn’t the right answer, we say so. If you’re not ready, we say so. Trust is built through honesty, not just positive spin.

The timeline, realistically

Every engagement is different, but here’s a realistic timeline for a typical first project:

Initial conversations: 1-2 weeks
Discovery: 2-4 weeks
Pilot design and approval: 1-2 weeks
Implementation: 6-12 weeks
Validation: 2-3 weeks
Handoff and transition: 1-2 weeks

Total: 3-6 months from first conversation to working, validated solution.

That might seem slow compared to some vendors’ promises. But it reflects reality. Projects that skip steps go faster initially — and then fail. (This is often why plug and play AI solutions don’t work.) We’d rather take the time to do it right.

Starting the conversation

If you’re curious about what AI could do for your business, the first step is simple: let’s talk.

No pitch. No pressure. Just a conversation about what you’re trying to accomplish and whether we can help.

You’ll know pretty quickly whether there’s a fit. And either way, you’ll walk away with a clearer sense of where the opportunities are.