Artificial Intelligence: From ‘Use Case’ to Mass Adoption

Four top female data and analytics executives discuss why AI is growing beyond individual ‘use cases’ and how AI will solve key business problems in 2021

A growing number of companies are using AI technology to solve business problems. In this special panel, which was broadcast live on LinkedIn, four leading female data and analytics experts outline how businesses can take their AI aspirations to the next level.

Research from consultancy PwC shows that more than three-quarters of companies that have fully embraced AI are now reaping the benefits to create better customer experiences, improve decision-making and innovate products and services.

“What we've seen now is that AI, machine learning, data science and analytics can be helpful in solving a wide variety of business problems," observes BitlyIQ CDO and GM Dr Amy Gershkoff Bolles.  "Whether that's optimizing your marketing spend, optimizing product or user experience, streamlining your operations, reducing costs, improving efficiency or adding top-line revenue.”

She adds: “And we're starting to see more of this, not only in traditional tech businesses but also in some nontraditional non-technology businesses as well.”

A Focus on AI Capabilities

AI is integral to the development of business capabilities for a growing number of companies. As AI strategies mature, data and analytics leaders are broadening the scope of their AI initiatives and looking for opportunities to innovate.

“Every company, whether it realizes it or not, is a data company and an AI-first company,” says Levi Strauss Chief Strategy and AI Officer Katia Walsh. “Therefore, it's upon us to make sure that we grow this capability from ‘use cases’ to a business central and integral holistic function.”

Nirali Patel, Director of Data and Analytics at BT Openreach adds: “We've moved on as organizations from AI projects or use cases and more into what I'd like to call AI capability. So, organizations are looking for talent for infrastructure, and for technology that has AI capabilities.”

As practitioners of data and analytics gain more experience with AI technologies, they are looking for new ways to implement the technology to maximize efficiency and growth.

“I also see it as a maturity thing,” says Finnair Data and Analytics Lead Minna Kärhä. “When companies start with AI, it's, of course, natural to pick one or couple of use cases to try out and to learn.”

“When moving forward it's important to take those lessons learned and move towards a more holistic vision and strategy to embed [AI] into the business processes and business units and spread that across the organization,” Kärhä concludes.

Paving the Way for AI Adoption

When working with a fast-developing technology like AI, there is always something new to learn. However, for organizations still at the beginning of the AI journey the panel recommends focusing on three crucial building blocks.

“We always start with people; people who understand what's required to have a passion for AI and who have the perseverance to pursue something that is not fully known,” says Walsh. “The people are really the most important ones to have the vision, the collaboration and the knowledge of business problems.”

“Number two is really a technology foundation, and this is critically important. And this is an area that I think sometimes some companies under-invest in,” says Dr Bolles. “It's really about setting up an architecture and an infrastructure environment that enables machine learning models to be utilized to their fullest potential and to be deployed in a way that matches your business model.”

“The third building block is of course the data,” says Walsh. “Every company is a data rich company. [Data] is constantly streaming because we live now in the time of data tsunamis: Not data lakes, but data oceans.”

Once companies have built the foundations for successful AI adoption, they should build strong monitoring capabilities to guide their evolution, says Dr Bolles. Customer behavior evolves over time and can be affected by disruption in the marketplace, as we've seen with COVID-19.

“It need not be a disruption as dramatic as COVID-19 to necessitate changes to machine learning and AI models as your customer evolves,” she concludes. “As the marketplace evolves, [it] can necessitate updating or improving certain aspects of the machine learning and AI models that might guide user experience, for example.”

Key Takeaways

• AI will move into the mainstream in 2021. From customer experience to optimizing products and growing revenue, AI-enabled companies are already reaping the rewards.
• Enterprises are moving away from individual AI use cases. As AI strategies mature, data and analytics leaders are scaling AI technologies across their businesses.
• Build a strong foundation for AI. Successful AI adoption relies on strong foundations of people, technology and data.