CTO of Pipedrive with expertise in scaling technology and organizations. Experienced as an innovator, founder and C-level manager.
As Abraham Maslow is largely attributed as saying, “To a man with a hammer, everything looks like a nail.” But in a world with generative AI, algorithmic trading and AI-assisted software development, there’s not as much need for a hammer. But has there ever been a greater need to learn to manage and analyze data, become an expert in engineering and design principles, or develop writing and linguistic skills—all useful across multiple domains?
Spoiler alert, there hasn’t. CTOs can create an army of value-adding staff within the tech team and beyond if they ensure that timeless skills like critical and design thinking are applied. These often sit in the tech team, coming from initial career training and further qualifications. As technology innovations compound, these are the foundational principles and skills that allow people to solve bigger problems with AI and automation. Without these skills, users won’t be able to reason out how best to use data, instruct AI or judge their outputs.
Such skills come from many areas but revolve around just a few core issues: Thinking clearly, optimizing decisions, solving problems and communicating effectively. Even the common communication skills I mentioned are being updated for new uses like AI prompt engineering in which more effective queries save time and money.
For a decade, only engineering has been promoted as a “winning skill.” Times are changing.
Psychology—human behavior and its causes—are vital skills. AI can offer all kinds of nonsense. Some of it is brilliant, but before building something from it, it must be evaluated in terms of human behavior. Is it likely that people have the problem that the AI aims to solve and that the solution proposed is how people want to solve it?
The different and powerful mental models that boost critical thinking skills are very popular with Silicon Valley technologists and investors. They inform software engineering best practices and help shape general business principles in multiple domains. If we’re to accelerate tech’s positive impact, then leaders can’t be shy about encouraging staff to apply these models in their interaction with tech, particularly generative AI, to engender greater understanding and success.
Any short article on this topic will be terribly incomplete, but this is a brief tour of a few approaches. These are for the tech team, data explorers and everyone across the wider business who wants to make more of their productivity and work better with new AI tools.
For Exploring Data And Using AI
The concept map model helps users visualize the layout of a system and the relationships of its elements. These maps promote a holistic understanding of how a solution works. Engineers and business users alike can show their processes and workflows and then analyze if they’re fit for purpose.
The five whys model encourages you to simply ask the question. “Why?” Why did this happen? Repeat the question with the answer you get and if the data and interpretation are correct, you should find the underlying cause of your issue after a few repetitions.
The six thinking hats technique encourages users to slow down and use different perspectives in turn to interrogate a problem. The creator (who also coined the term “lateral thinking”) suggests adopting these personas to review the issue at hand: the conductor (manage the process and the people involved with a set format), the creative (your license to ideate), the heart (your emotional responses), the optimist (consider the benefits and value to be gained), the judge (a concerned risk assessor) and the factual gatherer of information, both what’s known and what might be missing.
For Making Decisions
Many mental models aren’t necessarily sophisticated. The issue is that left to their own devices, our brains take shortcuts and don’t take the care that fully optimized solutions warrant. Consciously using such models ensures we bring the best algorithms to bear—much like AI.
The hard choice model is a four-by-four grid that allows the decision-maker to categorize their challenge under two types: whether the options are easy or hard to compare and whether the decision has high or low impact. There are many models you can string together to improve both decision-making quality and speed.
First principles thinking is very popular with tech innovators. It’s easy to say but can be hard to put into practice. It simply means questioning every assumption regarding the problem. This ensures it’s correctly understood before coming up with a great solution.
Second- and third-order effects are an excellent practice. If first-order effects are the immediate consequences of a decision, the second is longer-term effects, and the third is the significant and final states. Sometimes, we don’t have the knowledge to be able to accurately forecast these, and a lot of the time, it’s human nature to be blinded by any immediate benefits we want to see. But following the timeline further out in our minds and considering all the wider stakeholders potentially involved helps us minimize any possible negatives.
As a final suggestion, consider the power of inversion. As well as focusing on what you want to achieve with your project, plan to avoid what you don’t want. It’s a method of looking for and avoiding failure. As a technique, it might be one of the most powerful to employ in and beyond technology, with human interactions like difficult conversations.
Build The Mental Muscles
As mentioned, this article is no more than the briefest overview of only a few of hundreds of models and methods that can help all business users get more out of their technology and business processes. It’s well worth ensuring that your business has the mental toolkit to make better decisions with its data and its technology stack and apps—particularly those with automation and AI. Teach your employees that if they’re not in conscious control of their tools, they’re the ones who are being used!
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