Organizations seeing the most benefits from Artificial Intelligence (AI) work are more likely to be true believers in cognitive capabilities. Indeed, AI high performers, as identified by McKinsey, invested more of their digital budgets in AI than their counterparts, were more likely to increase their AI investments in the next three years, and employ more AI-related talent, such as data engineers, data architects, and translators, than their counterparts.
Winning over the end users of AI-enabled capabilities is just as – if not more – important to your success.
“Winning support for AI across the business is crucial for CIOs and other IT leaders hoping to scale their programs,” says Dan Simion, vice president of AI & Analytics at Capgemini North America.
How to make the case for AI and build support: 11 tips
To successfully earn buy-in and sponsorship from C-suite executives and line of business teammates alike, there are several moves IT leaders can make:
1. Create excitement within IT
The implementation of AI across the entire Lenovo organization is enabling greater efficiency and effectiveness. But, says Arthur Hu, Lenovo Group’s vice president and CIO, in the past, there has been a gap between strategy and execution. Keep your eye on the macro picture, he advises. “AI is constantly changing, so during strategic planning, I encourage my team to talk about the lifecycle of technology that is currently in use around the company,” says Hu, who asks his team to consider what’s ready for retirement and what’s headed for the mainstream. “By letting my team get creative, it naturally builds momentum and develops excitement.”
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
2. Get executives involved to match AI to digital transformation strategy
Success requires plenty of input from senior executives. “Executives need to work with their AI teams to ensure that the AI system’s input and output will be aligned with their overall digital transformation strategy,” says Justin Silver, AI strategist at AI platform provider PROS. “Collaborative strategy sessions that bring together executives and AI researchers can bring broader visibility to AI initiatives and keep AI research and development efforts tethered to key needs of the business.”
3. Assemble other advisory councils
“When making [changes] like this it is important to connect and host advisory councils with your organization’s partners, suppliers, and employees to gain their insight and opinions on implementation,” Hu says. “By doing so, you will see where it is wanted and needed.”
Understand that while IT can assist in AI-enabled innovation, IT cannot demand it of the business, says Hu. It must be co-created. “We have to remember to encourage applied curiosity, and then we can figure out our next steps for execution.”
[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
4. Integrate AI teams into the organization
“Don’t put an AI team in a small silo and tell them to transform the business by themselves,” says Peter Scott, AI consultant and founding director of Next Wave Institute. “That’s like running power to one only one room and telling them to electrify the enterprise. AI is leveraging the intersection of technology and cognition, and it impacts every part of your business where people are thinking.”
5. Personalize the benefits
To encourage user adoption of AI throughout an organization, IT leaders and managers need to demonstrate how the resulting changes will benefit employees. Present quality data that shows how business processes can be improved. “Presenting a successful use case with support from senior leadership and easily recognizable data can incentivize usage,” says Chris Fielding, CIO at Sungard Availability Services. “IT leaders and managers should clearly communicate to team members why the incorporation of AI is beneficial and the positive impact it will have on productivity and efficiency both day-to-day and in the long run.”
Staying people-focused will go a long way, says Silver of PROS. “Retention of employees who have the skills and experience working with AI systems can yield positive returns.”
6. Address job loss concerns
“One of the challenges in AI adoption is the fear that many functional leaders have about losing their jobs or becoming obsolete,” says Nancy A. Shenker, author of Embrace the Machine. “When it’s positioned primarily as a technology improvement or cost-saving advancement, line managers who don’t fully understand the power of AI and ML will start to freak out and get defensive about the human elements of their jobs.”
By emphasizing that AI allows teams to focus on the actions to take based on the insights generated by AI and ML solutions rather than spending time mining the data for patterns, it becomes clear that AI does not remove the need for human decision-making, but instead enables a more effective, efficient route to insightful outcomes, says Simion of Capgemini.
Let's explore five more tips: