It seems that Artificial Intelligence (AI) is everywhere. At least this is what you feel reading all the articles, blog posts and listening to the marketing hype. I don’t think this is the true picture. Most companies will not have an AI strategy – yet.

A McKinsey Survey of 3,000 Executives reveals that much of the AI adoption outside of the tech sector is at an early, experimental stage. Early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership.

Harvard Business Review summarises some of the findings of the survey very well:

  • Don’t believe the hype: Not every business is using AI… yet.
  • Believe the hype that AI can potentially boost your top and bottom line.
  • Without support from leadership, your AI transformation might not succeed.
  • You don’t have to go it alone on AI — partner for capability and capacity
  • Resist the temptation to put technology teams solely in charge of AI initiatives.
  • Machine learning is a powerful tool, but it’s not right for everything.
  • Digital capabilities come before AI
  • The biggest challenges are people and processes.

So what can businesses do to create a AI strategy? May be this is the wrong question.

In the O’Reilly article “Planning for AI” Mike Loukides argue that businesses should not build an AI strategy without first thinking about your business objectives. Better even: incorporate AI into your business strategies rather than building an AI strategy. Answer the question how can AI help to achieve your goals?
Enterprises need AI systems to work smart, to take advantage of their data, to learn about and improve on their past performance. NOT to just become an AI company or do something they don’t understand. He recommends to start building that strategy by asking what, precisely, you want to accomplish. Improve customer service? Internal processes like HR? product design or make AI part of your solution offering?

But one thing is clear. You need people who understand Artificial Intelligence and the technologies used to implement AI. And these experts are hard to find. And businesses do not only need technical experts and data scientists.

“How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist”
The average company faces many challenges in getting started with machine learning, including a shortage of data scientists argues HBR. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities.
Having an intuition for how machine learning algorithms work – even in the most general sense – is becoming an important business skill.

Will Knight says in the MIT Technology Review article  “You Could Become an AI Master Before You Know It. Here’s How”.
Automating machine learning will make the technology more accessible to non–AI experts. AI researchers and companies are now trying to address the AI skill gap by essentially turning the technology on itself, using machine learning to automate the trickier aspects of developing AI algorithms.

There is without doubt a war for talent going on in the AI space as you can read in the New York Times article “Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent”
Nearly all big tech companies have an artificial intelligence project, and they are willing to pay experts millions of dollars to help get it done.

Many companies will not be able to compete. Not talking of governments or universities. This is a risk not to be underestimated. More on this in a future blog post