AI Implementation: A Step-by-Step Guide for Modernizing Your Business
AI has been a transformative tool for business over the past two years, and it shows no signs of slowing down. New features and tools are launched daily, making it hard to keep up with the pace of change. Senior management and company owners are mostly sold on the transformative power of AI, and have challenged their people to use it to improve productivity and automate simple processes.
Step 1: Craft a Strategy
Implementing any new technology requires careful consideration and a strategy. Meet with your senior management and ask questions like what they want AI to do, how they see it fulfilling key KPIs, and when it shouldn’t be used.
Once you’ve talked to upper management, it’s important to align on goals with each individual department. Don’t skip that step, as you’ll likely find discrepancies to address between what your people on the ground are looking for and what executives expect.
When you’ve crafted your strategy, you can look for solutions to put it into practice. Slow and steady wins the race for proper implementation, not just building up your tech stack to say that you’ve adopted AI. AI should enable your workflows rather than being a gimmick to “AI-wash” your company.
Step 2: Research and Test New Solutions
It’s important to remember that many AI features overlap. For example, ChatGPT, Gemini, and CoPilot are similar in practice, although each has its own standout features. What you’re looking for are AI capabilities that integrate well with your current tools and better enable you to meet goals and KPIs. You aren’t looking for what tech experts think is the hottest solution of the moment; you want what’s best for your business.
Once you’ve agreed on one or a combination of the above “Big 3,” you’ll want to look at AI add-ons to your current solutions, such as your CRM or project management software. Do they improve your workflow? Do they integrate well with other tools? Are they just copying features that already exist in the “Big 3?”
From there, look at niche tools requested by employees and see if their capabilities are already covered by what you’ve selected - chances are high they are.
Step 3: Adopt a Phased Approach
Once you’ve researched and tested your new solutions, introduce them to your business slowly, implementing only one significant change or two minor ones at a time. For example, if you’re changing your productivity suite from Google to Microsoft or vice versa, that’s a big lift and you only want to focus on that. If you’re introducing minor automation tools for your CRM, you can roll out a couple at a time.
This allows your staff time to train on new solutions without feeling overwhelmed. If you load them up with too many new tools, they will be more focused on the new tools than their work. They are also likely to feel overwhelmed by new information, which can be stressful.
The exception is if a department brings forward a niche tool that will help them accomplish a specific task. If it isn’t a company-wide rollout, and the solution is needed to meet a KPI, there’s nothing wrong with granting this kind of request. Just make sure you test the solution as you would any other first, since salespeople may promise features that don’t exist, which could lead your staff to think the tool does something it doesn’t.
You’ll also want to ensure that the niche tool’s capabilities aren’t already available in one of the “Big 3” solutions you chose. For example, if your marketing department wants AI image generation, CoPilot, Gemini, and ChatGPT can all handle it. Make sure a niche tool can do it better before adding it to your tech stack.
Step 4: Measure Implementation With Prodoscore
Before you release a company-wide AI solution to improve productivity and efficiency, you need to be confident that you can measure the impact.
Prodoscore is a workforce analytics solution that visualizes the adoption of various tech investments and highlights how employees interact with the tools. You will see how often your people use your AI tools, which tools they use, and how they improve productivity. It’s all done non-invasively, running in the background without screenshots, keystroke logging, or any “surveillance-style” monitoring measures.
Ideally, you would deploy Prodoscore before your AI solutions so that you can compare productivity and tool activity before and after their implementation. You can also use Prodoscore to flag who may need extra training in the tools if they are not using them, or where you may have unused licenses.
