5 Strategies to Steal to Survive AI

All the talk of Artificial Intelligence (AI) and smart robots poised to “eat your lunch” can be unsettling. According to Ray Kurzweil in his book The Singularity Is Near, we’re at “the knee of the curve” in terms of exponential technological growth. In Kurzweil’s words:

Exponential growth is seductive, starting out slowly and virtually unnoticeably, but beyond the knee of the curve it turns explosive and profoundly transformative.

As humans, we’re great at understanding linear growth (repeatedly adding the same number) - red squares in the above graph. We’re less good at estimating the impact of exponential growth (repeatedly multiplying by the same number) - yellow circles in the above graph.

The legend of the king and the chessboard demonstrates this well.

So, if we’re truly on the brink of the fourth industrial revolution, what are some of the options open to a knowledge worker to ensure you can adapt and thrive as technological change gathers pace?

Thomas H. Davenport in his recent book “Only Humans Need Apply” puts forward 5 strategies you can follow to help you stay relevant and in demand in an age of smart machines.

The general premise of the book is that augmentation (the human worker is able to create more value by having the machine's help) trumps pure automation. Here’s a roundup of the 5 paths:

1. Step Up

The “Step Up” strategy involves you finding ways to work your way up the value chain in your field. You’ll need to operate at a strategic level above the AI systems by forming decisions and insights to unstructured problems computers find hard to master. This will involve you being knowledgeable on the business implications of AI and transitioning from simply carrying out instructions to guiding others using your leadership skills. As automation strategies are rolled out across organisations, there will be a period when workers, AI systems and intelligent robots will work side by side in blended teams. Individuals who can lead these teams will be in demand.

2. Step Aside

The “Step Aside” approach involves you avoiding areas where AI excels. Instead you focus on a field that’s still hard for AI to dominate. Roles where there’s a strong element of convincing or coaching other people or showing human empathy are typical “Step Aside” roles. If your day job involves person to person selling, entertaining, educating or caring for others, estimates are you’re likely to be in demand in the #futureofwork.

3. Step Narrow

The “Step Narrow” strategy is where you develop very niche specialist skills in an area that’s not only high in demand but also hard to automate. High in demand is important here as there’s not much point in becoming a master underwater basket weaver. You could master it yes, but very few people will need your baskets. Entry level research work within a legal firm is already being replaced by AI capable of searching 1000s of prior cases and generating insights. However, the “Rockstar” criminal lawyer who uses these insights to argue and win a case will still have an important role to play for some time to come.

4. Step In

The “Step In” strategy is where you learn how to work alongside AI. You become an expert at setting up and configuring the AI systems and troubleshooting them when they break. You’ll be one of the top performing subject matter experts in your current role and will have intimate knowledge of the meaning of the underlying (and all important) data that feeds the AI. It will be some time before AI can handle every exception. So, in the meantime, this blended co-dependence will be the norm. Those who can demonstrate expertise in this role will be an important part of any organisation embracing AI.

5. Step forward

The “Step forward” strategy describes those at the forefront of technological change. These will be the entrepreneurs, data scientists and engineers who envision, design and build the systems that use the latest AI, machine learning and deep learning technologies to create new businesses, products and services that reduce costs and generate value for others.

The ideal path for many may well be a mix of some or all of the above. Perhaps the most important thing is to decide what works best for you and to start running your experiments today.

The author Brian Downer is founder of The Practice Grower - a digital marketing consultancy that helps businesses win clients online and get ready for an automated future. If what you've read above resonates but you just haven't had the time to make these changes in your business, The Practice Grower can help. You can find out more about Brian here or check out one of his previous posts below:

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#FutureOfWork #ArtificialIntelligence #AI #MachineLearning #DeepLearning

Image Credits: Graph, Chessboard, Rugby