Across the global business world, enterprises are racing to determine how best to leverage and integrate AI technologies to improve business performance and outcomes. The opportunities are certainly compelling. AI can positively impact all aspects of the customer lifecycle, from product management to sales to customer service. In addition to enhancing operational performance, AI can be a force for transformational change. In a recent study, McKinsey discovered 45% of companies are piloting GenAI solutions, with several already attributing 10% of EBIT to their use of GenAI.
How, where, and when to implement and deploy an AI strategy continues to be a matter of pervasive debate, one that was tackled during a panel discussion at the most recent Aria Recur networking event in Italy.
By and large, the panelists agreed that there is no one way or common blueprint for implementing an AI strategy. Each business is unique and has its own motivations, requirements, and objectives. However, successfully introducing an AI initiative must serve a clearly defined business need, according to Aria’s Mike Judge: “Anyone can buy the technology and deploy it tomorrow, but if you don’t have a business objective it’s not going to accomplish anything. Without defined objectives, you’re making bad choices from the start.”
Enterprises have taken different approaches to implementing AI projects. Some are led by business teams, some by IT, while others have created AI committees or a Chief AI Officer position. Rinoy Varkey, Customer Success Officer at IdeaHelix, believes that in most cases, AI strategies should predominately originate from the business leaders in the organization: “Unless the business leaders are involved in the overall AI strategy, it is going to fail, especially if you are looking at AI as a way of transforming some aspect of the business.”
Aria customer Mindbody, a B2B company that provides scheduling and business management software for the wellness services industry, uses AI to enhance the product and offer clients new tools in their engagements with their customers. According to Nate Terrell, Head of Business Support Systems, product management teams are therefore more deeply involved in the AI strategy: “We are selling a high-technology product to our business customers and part of that is delivering AI capabilities. We’re using AI to support our customers better and give them enhanced capabilities to better manage and engage with their own customers. The product management team is driving the AI strategy from that perspective.”
When determining how and where within the organization to deploy AI, many start in the customer service function of their respective businesses, seeking to leverage generative AI to lower service costs, improve customer experience, and potentially solve problems before customers even become aware that a problem exists. Others have turned to predictive AI models to improve forecasting and projections.
Aygul Aytuglu of Amazon Web Services (AWS) believes that no matter the objective, area of the business, or whether the AI strategy is being led by business or technical teams, it’s important to start with a smaller use case. “Everyone is trying to find the best technology for everything, and it takes a while to progress and achieve outcomes. Start with a project that fixes a small problem and builds muscle. Fail fast and succeed faster.”
No matter how and where within the business AI is deployed, its success depends on a common denominator: data. According to IDC, up to 20% of enterprises run the risk of failing to meet their AI goals due to ineffective integration of data into AI models and corresponding business applications.
“AI is a huge transformational venture,” said Varkey. “Enterprises will fail in their efforts to deploy AI strategies if they haven’t effectively integrated data with AI and business applications.”
Mindbody’s Terrell says AI models and new technologies must have access to the metadata behind the scenes for them to be useful. “It’s not just about having the data in Snowflake or some data lake, it’s about having usable data that you can use to be effective in your AI and business strategies.”
The inextricable link between data and AI success was a key driver in the development of Aria Billie™, Aria Systems’ recently launched AI solution. Aria Billie uses predictive and generative AI alongside billing data to increase employee productivity, reduce the need for assisted customer care, and greatly improve the customer experience. Learn more about Aria Billie or schedule a demo.
