Investment in AI is moving forward, according to a recent Gartner survey. Organizations that have initiated AI strategies reported that now they have four projects with plans to add six more apiece on average in the next 12 months. They expect to add another 15 projects within the next three years.
These businesses are highly motivated and anticipate a return on their investment. Gartner’s AI business value forecast predicts organizations will receive $1.9 trillion worth of benefit from AI this year alone. That number will grow to $2.6 trillion in 2020.
Top motivators driving AI adoption
The recent Gartner survey reveals two key areas for AI adoption: improving the customer experience (CX) and automating tasks.
Forty percent of organizations named CX their most important investment for AI tech. When used effectively, AI reduces the time it takes to respond to customers. AI-enabled technology can also generate a near-infinite number of personalized messages. Chatbots and virtual customer assistants serve external clients directly. Some organizations use AI to support internal processes, such as internal decision-making.
Twenty percent of respondents say that using AI to automate repetitive tasks is their first area of investment, but when the top three areas are counted together, task automation surpasses customer experience. Use cases vary, but in finance, for example, AI processing improved contract validation or invoice analysis. HR personnel take advantage of it to automate the screening process in recruiting or even to conduct automated first-level interviews.
But the path to AI isn’t necessarily straightforward.
Biggest challenges to overcome when adopting AI
Fifty-six percent of respondents to the Gartner survey said one of their three top hurdles is a lack of available skills.
Managers can do four things to help get past this hurdle.
- Without the skills in-house, you should consider buying a prepackaged AI solution. Assess how your prospective vendor prioritizes your needs and how well they handle critical issues. Demand vendor proofs of concepts or pilots.
- You can look locally to develop the needed skills the market demands. Gartner has talked to data-driven organizations who developed their own basic classes in AI skills.
- Don’t just provide learning content and self-service development options. Connect employees to cross-organizational and even extra-organizational skill-building opportunities.
- Develop the skills you require on an ongoing basis, using available local programs. Organizations can partner with schools and universities to upskill existing employees, or even create internships for grad students to augment employee skills. It’s also a great way to hire full-time employees.
It isn’t always easy to know where to start. Forty-two percent of Gartner survey respondents say one of their top three concerns is that they don’t fully understand the available AI use cases.
The temptation is to create an experimental AI laboratory, but these labs often have little connection with real-world business factors. Managers and IT staff should consider these four tactics to get clear on the best uses of AI for their organizations.
- Partner with the business to identify and prioritize the AI use cases that will have the biggest impact. Also, you can identify the best use cases by looking at how other organizations are using AI both in their industries and other industries.
- Focus on your most urgent, most important problems. Look at the key functional areas in your business. As you identify and clarify your problem areas, begin to scope your staffing and skills requirements.
- AI is fueled by information. Look for your high-quality data, available in high volumes, and invest in improving collection practices for future applications.
- Repetitive tasks and areas requiring complex decision-making are often good targets to address first. Workers hope AI can help in these categories.
The Gartner survey shows many organizations use efficiency as their measurement for success. This metric is more prevalent in organizations that say they are conservative or mainstream. Companies that claim to be more aggressive in their adoption strategies are likelier to seek improvements in customer engagement.
Regardless of the perspective, measurement is a common and executive-friendly way to answer the question “Is it working?” These strategies can help you stay accountable.
- Start with projects that can be measured easily.
- Early on, you are building momentum, not creating workplace complications. The early wins–or even losses with lessons–can gather executive support.
- Tie success (or failure) to metrics and key performance indicators. Promote these successes to showcase how AI is making an impact on internal stakeholders.
Misconceptions about the value of AI in business abound. To get through this hurdle, mangers should set realistic expectations and then identify suitable use cases.
Whit Andrews is a distinguished research vice president at Gartner, Inc. where he addresses use cases and business opportunities for AI and cognitive computing, and previously served as the agenda manager for artificial intelligence. Jim Hare is a research vice president for the Technology Product Management practice in Gartner’s Technology and Service Provider (TSP) Research and Advisory group.