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Artificial Intelligence (AI): 8 habits of successful teams

The adoption of artificial intelligence (AI) in the enterprise continues: More than half (58 percent) of respondents to McKinsey & Company’s recent global AI survey say their organizations have embedded at least one AI capability into a process or product in at least one function or business unit, up from 47 percent in 2018. Those increases were reported across all industries. What’s more, nearly a third (30 percent) are using AI in products or processes across multiple business units and functions, McKinsey’s data says.

But, as the McKinsey research and others point out, some organizations are much further along in scaling their AI initiatives.

[ Do you understand the main types of AI? Read also: 5 artificial intelligence (AI) types, defined. ]

8 things successful AI teams do

What are teams succeeding with AI doing that others can emulate to propel their efforts? Here are 8 habits to consider:

1. Have clear strategies

65 percent of AI high performers report having a clear data strategy, McKinsey data says.

The organizations that McKinsey identified as AI high performers were deliberate about their plans to scale AI and were more likely to have addressed key issues like business alignment and data. Nearly three quarters (72 percent) of respondents from AI high performers said their company’s AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies. Similarly, 65 percent from the high performers report having a clear data strategy that supports and enables AI, compared with only 20 percent from other companies.

[ Get our quick-scan primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

2. Take a multi-disciplinary approach

Being successful with AI programs requires that organizations create working teams with representation from multiple disciplines, says Seth Earley, CEO of Earley Information Science and author of The AI-Powered Enterprise.

The particular mix of skills required will vary based on the flavor of AI.

Vodafone, for example, tried to build their AI capability by looking for “cognitive engineers.” “The problem is that cognitive engineer is a new job role and there were none on the market,” says Earley. “Instead, they built their own by assembling a team consisting of data scientists and programmers (obviously), but also linguists, information architects, user experience experts, and subject matter experts from the business.”

The particular mix of skills required will vary based on the flavor of AI. “Predictive analytics would not likely require a linguist, for example,” Earley notes.

3. Cast a wide net

Think of as many business use cases for an AI solution as possible.

“Companies looking to implement AI-enabled solutions need to ensure they aren’t being limited by their own creativity,” says Dan Simion, vice president of AI and analytics at Capgemini. He advises AI teams to think of as many business use cases for a solution as possible. “While there may be examples of AI-enabled use cases that organizations have implemented previously, there are likely additional cases that have never been thought of. If aligned properly with unique business needs, they could immediately solve an organization’s burning issues,” Simion says.

Casting a wide net of use cases can determine how far the new AI-enabled solution might go – and help the organization identify which use cases are going to offer the quickest payback. “If sequenced correctly, the initial use cases can bring immediate ROI, helping to self-fund future use cases within the program as it progresses,” says Simion.

4. Get specific

“Successful AI projects model what users actually need and determine this through actual working sessions with users, observations, and process mapping,” Earley explains. “These need to be specific and testable.”

AI systems built based on generic use cases like “personalizing the customer experience” will not be testable unless they specify the details of the user, the scenario, and exactly what personalized content and a personalized experience looks like, says Early.

Let’s look at four more best practices:

5. Focus on proof of value rather than proof of concept

How much will  the AI solution help the organization save or earn?

Before implementing AI, successful AI teams work with business stakeholders to get clear about what KPIs the AI-enabled solution will impact, what problems it will solve, and how much it will help the organization save or earn, Simion says.

Smart AI teams validate any quantifiable outcomes with a trusted financial executive or function, Earley says. “Including the right people who can ride along and help quantify the benefits (or believe the ROI calculations) will ensure that the business impact measures are taken seriously,” he says. “The risk is that the benefits will not be achieved but it is better to know that sooner than later.”

For those projects where the work is more foundational (with no direct ROI), the focus should be on verifying the link between the initial investment and the eventual ROI-generating applications that will result, Early adds.

6. Secure executive sponsorship

Active participation from an executive sponsor who has credibility and influence in the organization is critical for strategic AI initiatives. “The message to the enterprise will be that the project is not that important if sponsors are not involved and holding people accountable for results,” Earley explains.

The key to getting high-level support is demonstrating its positive business impact. “The more thorough the plan (with risk mitigation), the greater the likelihood of getting a strong sponsor who will risk their political capital for such a project,” Earley says. “I have seen sponsors turn down funded projects because they did not want to take on the risk of failure even though many stakeholders wanted to move forward.”

7. Prioritize pragmatism

AI has tremendous transformational potential, but those who succeed with it focus on realistic, practical use cases that make sense based on where their organization is on its AI journey. “To ensure your AI-enabled solution has a successful deployment, particularly in the current business climate, where many are facing budget constraints, it’s important to earn executive buy-in from key stakeholders within the company,” Simion says.

“There may be ongoing initiatives to build and expand from, or new projects that are logical and reasonable to complete. By showcasing the business outcomes that can be achieved in real-time with a fast-paced deployment, you will not only earn the executive sponsorship you seek but empower the rest of your employees as they witness the benefits.”

8. Focus on user adoption and experience

“The goal is for everyone in the organization to be able to extract insights in real-time,” Simion says, “so it’s important to find ways to make the solution easy to use and available.” Successful AI teams invest in change management specialists and processes. “Much of AI success lies in getting buy-in and ensuring that users trust the system output. That cannot be assumed,” Earley says. “Socialization, education, and ongoing user engagement are critical.”

[ How can automation free up more staff time for innovation? Get the free eBook: Managing IT with Automation. ] 

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