The shift to problem-first thinking is straightforward. Before any major decision, simply ask: “What problem are we trying to solve?.
The explosion of technology over the last 25 years has been a double-edged sword. While offering incredible potential, it’s also created a more chaotic and complex landscape, further amplified by the recent emergence of AI.
We’re drowning in solutions. Automation offers the potential to revolutionise every facet of business, from marketing and sales to invoicing. But the sheer volume of options, now including a rapidly evolving field of AI tools, makes choosing the right fit incredibly challenging.
Avoid jumping to solutioning before fleshing out a problem statement
A quick Google search can be misleading, prioritising paid placement over genuine suitability. Asking your network is a common approach, but what works for one business may not work for another. And now, with AI solutions entering the mix, the variables have multiplied exponentially.
Even after selecting a solution and getting it up and running (often with excellent vendor support), the nagging question remains: Is it actually meeting our needs? And with AI, that question becomes even more complex, as the technology itself is constantly learning and changing.
Too often, the answer is no. Project after project fails to deliver the promised benefits, and the root cause remains surprisingly simple: We jump into solutions before defining the problem. This is especially true with AI, where the hype can easily overshadow practical application.
First step is identify your problem
Businesses invest time and resources researching and selecting tools, including AI-powered options, but they skip the crucial first step: Asking “What problem are we trying to solve?”
Problem-first decision-making offers a more strategic approach. It allows you to:
* Clearly define your requirements for a solution, including specific needs that AI might address.
* Objectively evaluate solutions, including AI tools, against those requirements.
* Confidently determine when your project is complete, even as the AI landscape evolves.
The shift to problem-first thinking is straightforward. Before any major decision, simply ask: “What problem are we trying to solve?”
This pause can prevent you from rushing into solution mode, especially with the allure of new AI technologies. You might discover alternative solutions that don’t involve new technology, or even realise the perceived problem wasn’t a problem at all.
And in the realm of AI, this pause is crucial for responsible and effective implementation.
About The Author
Anthony McMahon is a seasoned IT professional with over 20 years of experience in the industry. He specializes in IT service management, business analysis, and project management. Anthony is passionate about leveraging technology to drive business success and has a proven track record of implementing effective IT strategies via his NZ based business, Targetstate.
Target State is a leading New Zealand-based consulting firm dedicated to simplifying technology for businesses of all sizes. Founded in 2018, the company bridges the gap between enterprise-level expertise and the unique needs of small and medium-sized enterprises (SMEs). Target State offers a comprehensive range of services, including technology strategy development, IT management, cost optimization, and project management. Their mission is to empower businesses to make smarter technology decisions, driving innovation and growth.
