The difference is never the tech. It's whether the people are ready. That's the lens Michael Wakefield brings to every recommendation SplashCandy makes for you.
Michael has been in sales and business leadership since the early '90s — inside multinationals, in small and medium businesses, and in companies of his own. In that time he watched the cell phone arrive, then the internet, then APIs, and now AI. Each wave landed the same way: some companies adopted it well and some failed, and the difference always came down to how management shifted and where the company kept its focus. That pattern is what SplashCandy screens for before recommending anything to you: not whether a tool is impressive, but whether your team and workflow are actually ready to use it.
He had a strong computer background in school and chose sales instead — back then programming was a real gate, and you were on one side of it or the other. AI removed that gate, which is why SplashCandy can pair sales-side, process-tree thinking (how decisions flow, how work actually moves through your business) with the imagination to see what a build could become. For you, that means recommendations that start from your workflow and your team's readiness, not from whatever AI product is newest.
AI feels big, so the decision gets postponed. Meetings happen, articles get forwarded, and the business keeps running exactly the way it always has.
A business grabs the nearest product with AI on the label instead of asking what it actually needs. The tool arrives, the workflow doesn't fit it, and a few months later everyone quietly goes back to the old way.
Both come from skipping the same question: what does this business actually need?
When the need is unclear, we can start with an optional AI Readiness Assessment: a written look at what is worth doing first and what your team needs to make the change work. If you already know what you need, we can scope training or a project directly.
Some clients need training. Some need a build. Some need a person holding the AI seat while the ground is still unfamiliar — and as your team learns, you should outgrow us and hire your own person. When that happens, the partnership becomes occasional and project-specific: we come back for the projects that need us.
See the AI Readiness Assessment