Solve problems using technology tools. As the quotation goes: "the pace of change has never been this fast, yet it will never be this slow again". Did you know that 82% of small businesses fail due to poor cash flow management, an example of a problem that technology tools can help solve? The problem doesn't even have to be that broad. Technology tools can even be used to help solve simpler problems such individual processes because the processes that may have worked before eventually get bogged down or slower due to multiple factors such as more data being added or more variables are needed. Whatever the reason, the task just needs to get done and the established methods become outpaced by the technology used.

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T3chtools—distinctively spelled with a "3." The three Ts represent Talent, Time, and Trust: three things no one can afford to waste. Technology is evolving too quickly, and stopping to learn how to use it to solve problems is becoming necessary. AI is no longer a future concept; it is current reality that we are all using in one form or another. You hear about it in news headlines, where it receives both praise and scrutiny. While we realize the immense benefits of AI in complex problem-solving, we also recognize the significant problems and risks it poses. As individuals and companies continue to build more AI, the agentic-types of AI in particular, the more access to your systems you need to enable and therein lies the problem, what are you willing to risk to use it? But just like any tool, users must first understand and learn the tool. The first step is to understand it. AI fluency framework begins with the 5 D's: 1) Domain-humans are the "real" intelligence behind the tool. Learn the tool, know why and what you are using it for. Artificial Intelligence (AI) is a powerful tool. If the Spider-man quotation, "with great power, comes great responsibility" applied to anything, it needs to be applied to AI. 2) Delegation—identifying which tasks are suitable for automation, which require a person, and which call for a mix of both, with the human making the most important decisions! 3) Description—providing precise, clear instructions, as output quality is directly tied to input quality. 4) Discernment—evaluating AI results with a critical lens rather than accepting them as absolute truth. Yes, AI makes mistakes, but the users need to remember they are ultimately responsible for the AI they generate. This takes us to 5) Diligence—retaining full accountability for the outcome, as responsibility never shifts simply because the labor did. The deployment of AI has consequences, both actual and potential. 

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To illustrate the effects of AI on a system, we developed AIRA, an Artificial Intelligence Risk Assessment tool. Designed to be a non-technical, easy to follow, user-friendly informational guide that asks questions about a company's data systems and gauges systemic AI risk on a scale of 1–100, with 100 being the highest risk. Scores of 1–33 indicate low risk, 34–66 moderate risk, and 67–100 high risk, each with clear next steps to reduce risk.

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The number one asset is obviously people—the talent. This is the "real" intelligence (domain knowledge) that can use technology tools to help them. Our goal is to help individuals and businesses solve problems by using technology tools.