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Ultimate AI Strategy Guide

Babar Bhatti, towards data science

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 [1].

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 [2] 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.

  1. Diagnosis — what is wrong, why? link tech with biz strategy
  2. Guiding Policy — foundation, governance, culture, ethics
  3. Coherent Actions — resource allocation, implementation, buy/build decisions, process orchestration, talent development/hiring/retention, culture and change management
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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.

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From Andrew Ng — AI Transformation Playbook [4] — Link

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 [10]

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.

  • Mistaking goals for strategy — don’t just state that you want AI leadership, create conditions to get you there
  • Fuzzy Strategic Objectives — a long list of things to do without a clear plan on how to get there, lack of focus of energy and resources
  • Lack of Coordinated Actions — duplication of efforts, politics of ownership, decisions that send mixed signals, lack of fundamental understanding of AI by senior executives
  • Inability to Choose and Prioritize— which projects are good candidates for AI? what management and organizational structure should we choose?

Understand the Building Blocks — Technology

Remember, Strategy is all about taking action to overcome obstacle or seize opportunities [2]. Any AI effort will rely on three main building blocks: data, infrastructure and talent.

  • Data is the essential element of today’s AI that drives insights — more than the algorithm itself in many cases. It requires significant effort and investment to get your data foundation right. Improving data quality and governing data is a complicated and long-term effort. Data ownership is a vexing problem for managers across all industries. Some data is proprietary, other data is fragmented across data sources, requiring consolidation and agreements with multiple other organizations in order to get more complete information for training AI systems.
Linking data across customer segments and channels, rather than allowing the data to languish in silos, is especially important to create value. -McKinsey [15]
  • Computing Infrastructure, including software and hardware, must be in place to run machine learning models effectively. ML needs specialized hardware (GPU, FPGA, or ASIC) — whether in the cloud or on-premise (due to regulation or other business reasons.)
  • AI Talent is vital in making effective use of machine learning. While not every company will seek to build an internal AI organization, having access to experienced data scientists, data engineers, data product managers and AI dev ops specialists is key to driving value from AI and scaling it to profitability.

Andrew Ng [4] 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?

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