How to Use AI to Deliver Results

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Artificial Intelligence (AI) is no longer a thing of the future. It’s the here and now. AI is a force in business with promise of increased speed, accuracy, integration, and predictive qualities of data. CEOs face increasing pressure to deliver on its promise. As with too many things, digital buzzwords become the order of the day. Despite its name, AI isn’t really about intelligence nor is it cognitive. It’s about using data for real-time, prescriptive analytics.

While CEOs do not need detailed knowledge of how AI works, they must understand what it means and how it can drive their business. AI needs to be integrated in a company’s corporate strategy—not handled in a one-off or separate strategy. Like many innovations, unless technology investments align to business strategies, success remains elusive.

It’s important to distinguish between two kinds of AI applications: (1.) those that replicate human tasks (e.g., converting handwritten texts to computer readable forms) and (2.) those that outperform humans because they draw upon greater volumes of data and computational power. The former provides business value because they can be cheaper and more accurate than human labor. However, CEOs should really focus on the latter category because these applications have potential to transform, or even disrupt, business models.

Ajay Agrawal, co-author of Prediction Machines: The Simple Economics of Artificial Intelligence, advises leaders to think about AI by asking: “If I could predict something better how would it change my business?” At SSA & Company, we’ve seen Agrawal’s points come to life in our work helping clients unlock the value of AI. Below we outlined a few principles to help CEOs win with AI.

1. Don’t forget the fundamentals
Align the goals and purpose of your AI projects to your business strategy as you would with any other initiative. Keep in mind the following question:

• What business problems can be solved by predictions? Your first AI uses will likely be in business areas already analytic in nature, the more transformative ones will come when you apply AI in new ways. For example, Amazon’s anticipatory shipping patent will enable Amazon to use data history/preference data to ship items before customers order them.

• What do humans do with predictions? True knowledge is about drawing conclusions, applying creativity, and creating innovation. AI might conclude that writing a particular class of insurance risks would be profitable, but only human judgment can assess the reputational risk in that offering.

• What actions will need to be taken based on the prediction and the judgment? Are you comfortable having AI execute autonomously or should humans stay in the loop applying judgement…at least for now?

Once you’ve identified the possible applications of AI to your business, drive execution with the tips below. Keep in mind that the journey often begins with incremental gains that lead to bigger wins over time.

2. Understand not all data are created equal
Much of the data needed to jumpstart your prediction machine lies within your current databases. However, critical data also resides in people’s heads, spreadsheets or other unstructured formats (e.g., process notes, gut instincts). You may need to look outside your organization for additional data that can provide predictive perspective. You will also need to plan for an active flow of new data to run and improve predictions, optimizing your predictive technologies and systems.

3. Ensure the right people with the right skills are in the right roles
A recent EY survey reports that leaders’ biggest obstacle to AI is talent. Finding properly trained employees—who can build new predictive systems, customize them to meet specific business needs, and effectively run them—remains challenging. As businesses integrate AI into the workplace, the roles, responsibilities, and skills of their talent pool evolves—often times faster than their recruiting processes and systems can handle. On the plus side, AI can tackle simple and repetitive tasks. Allowing your human workforce to focus on critical thinking, judgment, and reasoning required for guiding AI-driven decisions.

4. Focus on culture change early and often
In commenting on today’s AI-accelerated world, Microsoft services CIO Norm Judah says “executives need to understand that becoming an ‘AI company’ is as much about culture and change-management as it is about dazzling technology.” Leaders must help their businesses re-imagine themselves, their customers, and opportunities in an AI accelerated world which dramatically differs from the legacy analog world. To win with AI, you must build a culture that embraces change as opportunity. At SSA & Company, we have seen with clients that culture change impacts and often determines success.

5. Assess and adjust actions
Every prediction presents an opportunity for learning and improvement. Smart phones that predict the next word you will type provides an everyday example of this idea at work. Google’s Gboard now predicts which emoji or other graphical elements you’ll use next. Disney continuously improves its outstanding customer service by gathering hundreds of data points from visitor magic bands. Guests enjoy the convenience of having a single “key” to the Disney experience and Disney gets data to create a more customized experience.

AI offers CEOs an unprecedented opportunity to improve and re-imagine their businesses. To win, CEOs must navigate a landscape in which the deck constantly reshuffles. The marketplace has shown us that businesses that fail to use AI effectively fall behind. Those who use AI and integrate in their business strategy thrive and win.

We’d love to open a dialogue and hear your thoughts. What has been the most impactful way that you have used AI to fuel your transformation? We’d invite you to share your thoughts with #Run2Digital.

Nick Kramer serves as Vice President, Digital – Analytics at SSA & Company



John Blankenbaker serves as Principal Data Scientist at SSA & Company

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