In last couple of years it has become crystal clear that AI will be a major factor for business.
Strategy and AI are terms that are hard to pin down — they mean different things to different people. Combine AI and Strategy — 人工知能 战略 — and now you have a harder problem to tackle! The goal of this post is to bring in the best advice out there about AI strategy and add a practitioner’s point of view for a successfully crafting AI strategy.
This post is organized in three sections. Strategy and Planning, Building Blocks — Technology, People/Culture and Roadmap for launching and sustaining AI.
In last couple of years it has become crystal clear that AI will be a major factor for business. Gartner included AI in its top 10 strategic technology trends for 2019. Most companies are keenly aware that they should have at least have an AI strategy. If nothing else, the propensity to follow AI rivals is high .
The competitive intensity surrounding the technology suggests that a wait-and-see strategy could be a costly mistake. To get a share of the global profit pool of $1 trillion that AI will produce by 2030, the McKinsey Global Institute says companies should begin adopting it at scale within the next three years.
Enterprises, however, are struggling to get AI strategy right, very much like they had challenges with e-commerce adoption in late 90s. Many things can go wrong with nascent technology adoption — Choosing or spending on the wrong initiatives, not finding the right talent and choosing the wrong direction.
Fundamentals of Strategy and AI
First, let’s be crystal clear on the definition of strategy. I love the way Rumelt  defines strategy and will follow his no BS, practical approach. In “Good Strategy, Bad Strategy” Rumelt writes that there are three key parts to a strategy that make up the Strategy Kernel.
Second, let’s define AI: broadly speaking AI encompasses the techniques used to teach computers to learn, reason, perceive, infer, communicate and make decisions similar to or better than humans. AI areas includes intelligent automation, machine learning (classical machine learning and modern deep learning aka deep neural networks) and robotics. Self-driving cars combine a lot of what is symbolic of modern AI.
Here’s my one-liner AI for business equation.
AI = data+algorithms+compute → business problem → results
The hard problem is to craft an AI strategy that works for your specific business context — i.e it considers all the external factors as well as internal factors. Andrew Ng recommends “becoming a leading AI company in your industry sector, where developing unique AI capabilities will allow you to gain a competitive advantage.”
How AI affects your company’s strategy will be industry- and situation-specific, Andrew adds.
Many experts note that you can’t start with AI strategy as a first step because you need to develop understanding of AI and that requires experimentation under uncertainty. This has the benefit of learning through the so-called Virtuous Cycle of AI.
Just like the architecture of any strong building requires, you need to start with the ground conditions and ensure that you build a strong foundation. Otherwise whatever superstructure you build will suffer from costly problems in future.
The next sections talk more about how to build a digital foundation with data, algorithms and computing infrastructure that is appropriate for your situation. You need people with diverse backgrounds, education and skills — for instance you need people with data expertise and machine learning knowledge but also those who are good at bridging business and technical issues, also called “translators.” And you need strong processes.
Be realistic about benefits as well as limitations of AI. There are good use cases for AI and there are use cases that could get you on a rabbit trail with no clear pathway to value. There’s a gradual path that a company could take such as trying out Robotic Process Automation before engaging in more complex projects.
“AI will improve products and processes and make decisions better informed — important but largely invisible tasks.” — Thomas Davenport 
As Rumelt points out, it is helpful to look at examples of bad strategy so you can avoid it. Here are a few commonly found elements of bad strategy.
Remember, Strategy is all about taking action to overcome obstacle or seize opportunities . Any AI effort will rely on three main building blocks: data, infrastructure and talent.
Linking data across customer segments and channels, rather than allowing the data to languish in silos, is especially important to create value. -McKinsey 
Andrew Ng  advises that for a company to be great at AI, it must have:
Resources to systematically execute on multiple valuable AI projects — either outsourced and/or in-house technology and talent.
Sufficient understanding of AI: There should be general understanding of AI, with appropriate processes in place to systematically identify and select valuable AI projects to work on.
Strategic direction: Is the company’s strategy broadly aligned to succeed in an AI-powered future?
Read the full story on towards data science