Stop Chasing Tech and Start Solving Problems
Focusing solely on discovering the "best" AI technology often leads to a counterproductive cycle of perpetual tool-hopping, while overlooking the fundamental purpose of tech: solving real business problems.
Stop Chasing Tech and Start Solving Problems
There has been a lot of chatter about AI being transformational, existential, profound, ethical or evil. A whirlwind of thoughts have been swirling around since Open AI dropped the bot at the end of 2022.
This piece is not about that, (though our opinions are wide, rich and deep).
This piece is simply about the practical and common sense adoption of helpful tools. For over a year, we have explored, tested and applied a range of apps, platforms and use cases for AI and automation. Here's what we learned.
Learning 1: You can either be busy trying to find the best AI tech or you can be busy trying to solve the right problem with AI tech.
Focusing solely on discovering the "best" AI technology often leads to a counterproductive cycle of perpetual tool-hopping, while overlooking the fundamental purpose of tech: solving real business problems and eliminating friction while trying to get things done. Reframing the use of tech as problem-solving rather than tool selection leads to more impact and scalability.
Successful teams first identify specific pain points in their workflows - whether it's customer service bottlenecks, data analysis inefficiencies, or repetitive administrative tasks - and then select AI solutions that directly address these challenges in an ongoing and sustainable way.
A problem-first approach ensures more tangible business value.
Learning 2: Step back from the technology, take a breath and see it for what it really is.
Technology is like an orchestra - individual instruments may be impressive on their own, but true magic happens when they play together in harmony, conducted by a clear purpose. Connecting specialised bots is analogous to putting together multidisciplinary teams to take on completing processes day in and day out with literally a click.
When the output is critical for decision making and action but the process is repetitive and tedious, these are the interesting 'problems' to address with well-designed intelligent automation.
Learning 3: Don't look at the tech as only costs reduction.
Look at it as something that can increase the speed to insights and info to help make smart business decisions. Today, AI and automation systems designed for pattern recognition can do analysis at a tiny fraction of the time it used to take.
Consider business driving outcomes as a goal and not go for a knee jerk reaction to cut, cut, cut.
Learning 4: Don't throw out the baby with the bathwater.
New tech with tested and trusted strategic frameworks are a pretty good combination. We use JOBS TO BE DONE and journey mapping:
- Understand the critical JTBD in an organisation
- Prioritise them based on IMPACT and SCALABILITY potential
- Select experiments for pilots or evolving systems
- Map the current task journey and optimise it with AI & automation
- Apply agile methodologies of experimentation, iteration and testing
Learning 5: Talk is cheap and so are experiments. In a good way.
Too often we see teams jumping in and eagerly wanting to get things done. It pays off to step back and think, discuss and plan.
Aim before you fire.
To recap:
- Don't look for use cases in the tech. Look for use cases in the business.
- Apps, API platforms and interoperability make things possible, not standalone bots.
- Don't delay too long planning and experimenting. There are opportunity costs.
- Use cases are nearly infinite. Find yours.