Building an AI strategy that actually works for your team

Every business knows it needs to do something with AI. Where boardrooms and workstreams all over the country are coming to a standstill is deciding how to approach AI without it becoming just another expensive pilot that nobody uses.

The reality is that AI implementation success is subjective. What works for an up and coming SaaS solution might not fit a distribution warehouse operating on the other side of the country. And that's okay. You don't have to follow suit with what everyone else is doing - you just have to create what's best for your team, your processes, and ultimately, your business.

Start from Where the Needs Actually Are

One of the first and foremost missteps of AI strategies is starting with the technology instead of the challenge. Someone reads about a fantastic tool, gets inspired, and positions AI adoption without asking the simple question of "what are we trying to fix?"

Instead, to create an AI strategy that works best for your situation, consider where the pain points truly lie. Is your customer service department inundated with mundane questioning? Are your sales people spending far too much time inputting data into a CRM (and not enough time actual speaking with prospects)? Are your operations folks getting bogged down by routine tasks that take little thinking to complete but just have to be done manually over and over again?

If you've recognised these pain points, you're already ahead of the competition. You've substantiated why AI could actually work for you instead of trying to follow trends.

Get Team Buy-In (Because Without It, You're Fighting an UpHill Battle)

Now we get interesting. Because even if you create the most foolproof AI strategy, if your team believes you're going to replace them or make their lives more complicated, you have a hard road ahead.

Team resentment toward the implementation of AI comes from fear. Fear of losing their jobs. Fear of current conditions being changed - and people often don't like change regardless of how good change might be if things are not going well.

Companies do this right by involving teams early in the process; they're transparent with why they're doing what they're doing - and importantly, they've gotten feedback from those in the trenches who actually would use these tools.

Your customer service team knows which questions waste their time asking and which ones they can jot down for automated responses. Your analysts know which analytics take forever to compile. These insights are crucial before AI implementation for strategic planning.

When teams understand that AI will inherently help them complete mundane tasks so they can better focus on assignments requiring human nuance and creativity, team resistance is cut drastically.

Choose Technology That Fits Your Reality

The AI marketplace is vast, and sellers love to boast how their product is transformative for your business. Some of these products are fantastic; some are hogwash.

What matters more than finding the perfect technology is finding one that fits your reality. This means assessing whether it will integrate into the systems you already have or if it will be siloed on an island. Does it require a set team of data scientists to keep the wheels turning, or can your current workforce maintain it? Does it live comfortably within your budget with any purchase markers (subscriptions, etc.)?

Many companies benefit from utilising professional AI adoption consulting services that get through the marketing jargon to help align tools with genuine needs. This saves companies time and energy from trialing certain systems only to find them ill fitting.

The other consideration is whether or not it's scalable - chances are that after you start with one department or use case, you could eventually expand if things go well. You don't want to have to reload down the line.

Test the Thoughts First

Smarter companies don't gamble everything on one assumption. They run pilots. They test hypotheses. They track outcomes before everything goes company wide.

A pilot programme can be as simple as an entire team or one use case - or trialing an AI that automatically drafts email responses for customer service OR a tool that compiles insights from sales data as examples; the point is to learn in a controlled environment where missteps are manageable.

While you're testing during this phase, pay attention not just to whether or not technology works - but how teams work with it. Are they finding creative workarounds because it's difficult? Are they getting value out of it - or does it seem more like busy work? This feedback informs your ongoing strategy before bigger implementation.

Pilots also help provide proof of concept; when you reduce response time by 40% or confirm employees can save ten hours per week on their schedules, because of successful pilot results versus getting bogged down in day to day tasks, it's easier to get approval to expand once you share results with leadership and other teams.

Train Effectively

Finally, you can best prepare people to use and enjoy new tools by providing effective training.

Training isn't just a one-hour meeting where you tell people how to log into a system; it's positioning how AI fits into their day to day requirements and answering the question of "why would I use this?" before diving into technical how to elements.

The best training programmess are iterative - AI tools are updated frequently, and teams will realize certain aspects over time that work better for them than anticipated. Providing materials people can reference - quick tips sheet, videos, internal champions who know the ins and outs - continues momentum after launching.

Some companies also find value in outside professionals who've done this type of implementation before; sometimes another set of eyes can catch gaps that insiders won't see just because they're too close.

Measure What Matters

Without measurement, there's no realisation of increased value from implementation (or confirmation of a lack thereof).

The metrics you use should tie directly back into those initial pain points you created when deciding where, how, and why AI could be viable. If you bring in an AI programme to reduce customer service response times - track response times. If you determine taking mundane hours off employee plates would free ten hours per week for every employee - measure time allocation accordingly.

Certain metrics show up quickly; others take months or longer. Either way, baselining data before IIAs confirms change - anecdotally from the team matters as well; sometimes wins aren't quantified but recognized through frustration reduction and enhanced job satisfaction.

Adjust Accordingly

No AI strategy survives first contact without necessitating any adjustments. You discover things that work better than expected and things that will no longer be used due to surprise insights. This is normal.

Companies that successfully engage in AI are malleable - willing to shift their perspective based on what they've learned along the way - and willing to make changes when their initial plans don't pan out as well as they'd envisioned. Maybe this tool doesn't deliver as well as expected but instead works in a use case you would never have thought about? Great - now you've successfully adjusted!

Building feedback loops through check-ins about how other teams using AI tools are faring in weekly meetings, more extensive monthly reviews of your measurements and maintaining open door policies makes for a more beneficial ongoing strategy than falling victim through tunnel vision.

Feel Confident

It's clear to see that companies with the highest successes implementing an AI strategy don't get lucky - they take their time matching capabilities to real needs, include their teams along the way - and get educated about what's best instead of trying to run a million miles an hour without clearly knowing where they're heading.

When you centre an AI strategy around what's appropriate based on your team's reality instead of what's trendy to be successful as a business operation, things like AI become less stressful forces that demand implementation and instead become practical tools worthy of enhancing efficacy that's ultimately deserving of such meaning.