The Building Blocks of an AI Strategy

Organizations need to transition from opportunistic and tactical AI decision-making to a more strategic orientation.

As the popularity of artificial intelligence waxes and wanes, it feels like we are at a peak. Hardly a day goes by without an organization announcing “a pivot toward AI” or an aspiration to “become AI-driven.” Banks and fintechs are using facial recognition to support know-your-customer guidelines; marketing companies are deploying unsupervised learning to capture new consumer insights; and retailers are experimenting with AI-fuelled sentiment analysis, natural language processing, and gamification.

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work.

Opportunities and Challenges

■Artificial intelligence (AI) isn't an end in itself, but rather a strategic capability that organizations can develop and mature to achieve specific, measurable and transformative business outcomes.

■Moving from pilots and short-term plans to large-scale implementations of AI is a daunting task that requires sound planning.

■Organizations will need new or updated strategies as vendors exploit AI capabilities within business suites, enterprise applications, platforms, infrastructure support services and the customer experience.

1.     Business strategy

Creating an AI strategy for the sake of it won’t produce great results. To get the most out of AI, it must be tied to your business strategy and your big-picture strategic goals. That’s why the first step in any AI strategy is to review your business strategy. (After all, you don’t want to go to all this trouble and apply AI to an outdated strategy or irrelevant business goals.)

In this step, ask yourself questions such as:

• Is our business strategy still right for us?
• Is our strategy still current in this world of smarter products and services?
• Have our business priorities changed?

2. Strategic AI priorities

Now that you’re absolutely clear on where your business is headed, you can begin to identify how AI can help you get there.

In other words:

• What are our top business priorities?
• What problems do we want or need to solve?
• How can AI help us deliver our strategic goals?

3. Short-term AI adoption priorities

Transforming products, services or processes is never going to be an overnight task. It may take some time to deliver the use cases you’ve identified. For that reason, I find it helps to also identify a few (as in, no more than three) AI quick wins – short-term AI priorities that will help you demonstrate value and gain buy-in for bigger AI projects.

Ask yourself:

• Are there any opportunities to optimise processes in a quick, relatively inexpensive way?
• What smaller steps and projects could help us gather information or lay the groundwork for our bigger AI priorities?

4. Data strategy

AI needs data to work. Lots and lots of data. Therefore, you need to review your data strategy in relation to each AI use case and pinpoint the key data issues.

This includes:

• Do we have the right sort of data to achieve our AI priorities?
• Do we have enough of that data?
• If we don’t have the right type or volume of data, how will we get the data we need?

5. Ethical and legal issues

Here, you’ll need to ask yourself questions like:

• How can we avoid invading people’s privacy?
• Are there any legal implications of using AI in this way?
• What sort of consent do we need from customers/users/employees?
• How can we ensure our AI is free of bias and discrimination?

6. Technology issues

Here you identify the technology and infrastructure implications of the decisions you’ve made so far.

Consider:

• What technology is required to achieve our AI priorities (for example, machine learning, deep learning, reinforcement learning, etc.)?
• Do we have the right technology in place already?
• If not, what systems do we need to put in place?

7. Skills and capacity

For those companies who aren’t Facebook or Google, accessing AI skills can be a real challenge. Therefore, this step is about reviewing your in-house AI skills and capabilities, and working out where you need a skills injection.

For example:

• Where are our skills gaps?
• To fill those gaps, do we need to hire new talent, train existing staff, work with an external AI provider or acquire a new business?
• Do we have awareness and buy-in for AI from leadership and at other levels in the business?
• What can we do to raise awareness and promote buy-in?

8. Implementation

Here you need to think about how you’ll turn your AI strategy into reality.

This might surface questions such as:

• How will we deliver our AI projects?
• What are the key next steps?
• Who is responsible for delivering each action?
• Which actions or projects will need to be outsourced?

9. Change management issues

Because people are so wary of AI, particularly what it might mean for their jobs, change management is a really important part of any AI project.

Example questions include:

• Which employees and teams will be impacted by this AI project?
• How can we communicate effectively with those people about the change?
• How should the change process be managed?
• How will AI change our company culture, and how will we manage that culture change?

 #management #technology #ai

References:

1.    https://www.forbes.com – Article by Bernard Marr : https://www.forbes.com/sites/bernardmarr/2019/03/19/how-to-develop-an-artificial-intelligence-strategy-9-things-every-business-must-include/?sh=24d5ba9d8360

2.    https://www.gartner.com – AI research paper : Craft an Artificial Intelligence Strategy:

3.    MIT Sloan Management Review