4 steps to develop successful artificial intelligence strategies



As with any new transformational technology, business leaders often rush to any new “shiny object” that promises to streamline their business. For artificial intelligence (AI), this was especially true in 2020, as a recent survey found that 43% of companies around the world were stepping up their AI initiatives in response to the pandemic.

Unfortunately, many of these companies have rushed to bring AI into their business without stopping to ask who, how, and why. As businesses look to take advantage of the business insights and other benefits AI can offer, it’s important that they don’t try to put square pegs in round holes.

AI may sound like magic, but it’s not magic. Bad algorithms give bad results. While investment and experimentation are extremely important, the biggest and most common strategic mistake companies make when exploring AI is not defining a clear use case and desired results with a clear and quantifiable metric for the technology in the first place.

To solve this problem in my workplace, we decided to turn to the principles of design thinking. A human-centered approach to AI begins with who will consume AI, how they will consume it, and why AI is even needed. It starts with critically thinking about the issues your business faces, defining those challenges in potentially AI-resolvable ways, and then identifying and refining the use cases that are critical to your business goals.

With a data-driven, human-centric approach, we as business leaders can design AI that successfully connects every strategic piece of AI data and initiative to a company’s defined business goals. If you are interested in finding out how AI could be of use to your own organization, I encourage you to take a similar approach.

1. Define the intention.

Many companies don’t really have a clear idea of ​​what they hope to gain from AI beyond a vague notion of “efficiency.” That’s why it’s important to refine your intentions by spending time uncovering targeted AI business opportunities that exist in your existing business strategy. Are you trying to keep workers safe? Make customers happy? Start with a clear intention that is grounded in your primary business goals.

2. Identify yourself.

Once you have determined your overall goal of implementing AI, you can then define the use cases and types of AI solutions that users need and that will eventually be integrated into your infrastructure. AI is advancing rapidly in many areas, from computer vision which determines the content of an image to natural language processing AI found in chatbots and virtual assistants. How can these apps advance the intentions you described?

3. Evaluate.

The evaluation step is to determine the data you need to make the use cases that you have identified effective. Different types of teams focus on different priorities and different sets of numbers, which means most industry data is siled to some extent. To implement successful use cases with AI, you need to ensure that your AI receives accurate and clean data from across your organization.

4. Plan.

The last step of the design thinking approach focuses on setting up concrete actions using statements of intent as a guide for technical implementation. The goal is to help customers operationalize AI across the enterprise by connecting each solution to the defined AI strategy.

Critically, an implementation strategy must take into account user trust: how will your customers react to your organization by using data in this way? How can consumers and the public know that your AI implementation is explainable and trustworthy?

Designing a successful AI strategy is also about determining who has a seat at the table. It’s important that businesses include diverse voices and the right stakeholders at every step of the way.

In my workplace approach, strategy definition sessions are attended by senior company executives who define intent, define types of information, develop business assumptions, identify cases of use and infuse corporate ethics into strategy. Technical sessions invite data scientists, designers, and developers to come together to translate the intentions defined in the strategy session into a detailed strategy, defining use cases, evaluating data, and planning execution. Throughout each exercise, visual narratives, pictures and graphics are used to ensure that, even though they come from different fields, everyone involved has the opportunity to speak the same language.

The most common takeaways? Often times when working with clients their “aha moment” comes during the “assess” phase. Too often, companies think they already have all the data they need to run the AI ​​models they want. This is rarely, if ever, the case.

For example, a client in the financial services industry wanted to develop an AI solution that would help accelerate the economic recovery of small businesses affected by the pandemic. But, when evaluating the data needed to create value for selected users, the team realized for the very first time that their data was disorganized, siled, or unusable. Before you start implementing a reliable model, you need to address the data collection, infrastructure, and platform issues that are hampering the development of trustworthy AI.

There is no doubt that AI is already transforming businesses today. From healthcare organizations using natural language processing to help process COVID-19 related queries to financial services companies using AI to analyze tedious compliance documents, early adopters of AI continue to develop new use cases by tens. But what these successful implementations all have in common is a clear intention and plans that tie the benefits of AI to the top priorities of a business.

The opinions expressed here by the columnists of Inc.com are theirs and not those of Inc.com.


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