FAQ — Impact Zone

GETTING STARTED

Getting Started

Do we actually need AI, or is this hype?

Half of what gets sold to you as "AI transformation" is hype dressed up as strategy, and you're right to be skeptical of it. Here's the test that actually matters. Not whether AI is trending, but whether you can answer one question honestly. What are you actually trying to change? Slow turnaround, a high error rate, a process that eats hours it shouldn't. If you can't name the bottleneck, you're not ready for a tool, you're ready for a conversation.

Where should we start?

Start small and specific, the way a trim tab moves a rudder before the rudder ever moves the whole ship. Pick one workflow with a clear beginning, a repeatable process, and a result you can actually measure, customer support triage, first-draft writing, scheduling. Get that one thing working and the rest of the building starts believing it's possible. Skip that step and you're just buying expensive doubt.

How long before we see results?

Weeks, if you scoped it narrow. Months, if it touches your existing systems or how your team actually works day to day. Anyone promising instant transformation across your whole company is selling you a story, not a plan.

You already know which bottleneck is costing you the most right now. Start there.

BUSINESS CASE & ROI

Business Case & ROI

How do we measure ROI?

This is merely arithmetic, and most companies skip it anyway. Name the metric before you build anything, hours saved, error rate, cost per transaction, whatever moves the needle for you. Then measure where you actually stand today. Without that number you have no way to prove the after, and neither does anyone selling you the tool.

What does this actually cost?

The consulting fee is the smallest number in the budget, not the biggest. Data cleanup, integration with what you already run, training your team, ongoing monitoring, these regularly cost more. A consultant who only quotes you the tool price isn't lying to you. They're just not telling you the whole truth.

88% → 6%

88% of organizations use AI somewhere. Only 6% capture over 5% of EBIT from it, that gap is the value strategy closes. (McKinsey State of AI, 2025)

What's the difference between AI "strategy" and AI "implementation"?

Let me name what's actually being sold to you here. Strategy tells you what to do. Implementation builds it and ships it. A lot of companies pay real money for a deck that confirms what they already suspected, and nothing ever gets built. Ask before you sign. Does this end in a working tool, or a PDF?

What are you actually trying to change? Answer that before you sign anything.

DATA, SECURITY & PRIVACY

Data, Security & Privacy

Is our data safe if we use AI tools?

Ask the vendor plainly how your data gets used. Is it training their model? Is it stored, and for how long? Is it actually anonymized, or just labeled that way? If you can't get a straight answer before you sign, you won't get one after.

Do we need to clean up our data first?

Probably, and most companies think they're more ready than they are. AI doesn't fix messy data, it amplifies it, missing fields, inconsistent formats, duplicates multiplied across every output. A short audit before you spend the budget saves you from spending it twice.

What about compliance and regulation?

The rules around your data don't change because AI is now handling it. If anything they get stricter. Privacy law, intellectual property, employee monitoring, all of it still applies, and the legislation is still catching up to the technology. Build your own standard for what data goes in and what stays out. Don't wait for the law to draw that line for you.

Write your data policy before the tool goes live, not after something leaks.

IMPLEMENTATION & INTEGRATION

Implementation & Integration

Will this work with our existing systems?

This is where the real cost hides. Not in the AI tool itself, in what it takes to connect it to everything you already run. Ask specifically how a provider plans to access, clean, and integrate with your systems. Don't assume, ask. Integration is where timelines and budgets actually slip.

Should we build custom AI or use existing tools?

The case for building custom is real. It's yours, it fits exactly, nobody else has it. But for most companies, existing tools wired into one specific workflow beat anything custom, at least at first. Build custom once you've proven the use case matters and you have someone who'll actually maintain it. Building for its own sake is just an expensive hobby.

What's a realistic rollout timeline?

Plan around your slow season, not your development calendar. Launch when your team has room to learn something new, not when your busiest quarter is already eating every hour they have. Pilot one workflow. Measure it. Then expand.

You already know which system is the tangled one. Start the integration conversation there.

PEOPLE & CHANGE MANAGEMENT

People & Change Management

Will AI replace our employees' jobs?

Ask yourself first. Are you trying to replace tasks, or are you trying to replace people? Your team can tell the difference even when you can't say it out loud. Be honest instead of vague. In most implementations AI takes over specific tasks, not entire roles, and frees people for the judgment work AI still can't do. Where a role genuinely will shrink, say so early and build a real reskilling plan. Let people find out sideways and you've lost their trust for good. Done well, AI will leverage the potential for all your employees, while boosting your organization's effectiveness.

How do we get employee buy-in?

Resistance is rarely about the technology. It's about feeling blindsided. Bring the people who'll actually use the tool into choosing it and testing it, tell them plainly what changes and what doesn't, and give them a real way to push back, not a one-way announcement dressed up as a town hall.

What skills do our people need?

What most of your team will need is the instinct to question AI output the way they'd question a junior employee's first draft, useful, often right, still worth checking before it goes out the door. Teach that to everyone before you teach deep technical skill to a few.

Your people already know when something is being done to them instead of with them. Which one is this?

ETHICS, RISK & GOVERNANCE

Ethics, Risk & Governance

How do we prevent AI mistakes from causing real damage?

Keep a human in the loop anywhere a mistake would actually cost you something, money, safety, someone's legal standing, someone's trust in you. AI can be confidently wrong, and confident is not the same as correct. Build the review step in before launch. Don't bolt it on after the first expensive mistake teaches you why you needed it.

Who's responsible when AI gets something wrong?

You are. The tool doesn't take the blame, and neither does the vendor who sold it to you. Assign the responsibility before launch, who reviews, who signs off, who owns the outcome. If you can't answer that plainly right now, STOP, because you're about to ship a decision nobody actually owns.

How do we avoid bias in AI-driven decisions?

AI reflects whatever it was trained on, including whatever bias was already baked into your own historical data, especially in hiring, lending, and how you treat customers. Audit the outputs against real fairness, not just technical accuracy. This costs you time you'd rather not spend. Spend it anyway, before the bias costs you something you can't take back.

Name who's accountable before you need to. That's the whole practice.

CHOOSING A CONSULTANT OR VENDOR

Choosing a Consultant or Vendor

Should we hire a consultant or figure this out ourselves?

If you can name your problem, your data situation, and the number you're trying to move, you may not need help for a first small pilot. Bring someone in once you're choosing between unfamiliar tools, wiring into a complicated system, or you've already tried this once and stalled. A good consultant's value isn't a broad view of AI, it's specific knowledge of your situation stacked on top of patterns they've already seen across many others.

How do we tell a real AI consultant from someone riding the hype?

Ask what they'd specifically recommend against for your business, not just what they'd recommend. Ask for something specific, recent, and actually in production, not a case study, not a concept, not a slide with a client logo on it. Someone who can point to what they shipped in the last few months is in a different category from someone who can only hand you a strategy deck and a vendor comparison chart. That difference is the whole ballgame.