7 last-mile delivery problems in AI and how to solve them

The term last-mile problem comes from the telecom industry, which observed that it costs inordinately more to build and manage the last-mile of infrastructure to the home than to bring infrastructure to the hub city or residential perimeter.

Businesses are starting to discover a similar last-mile delivery problem in AI: It is much harder to weave AI technologies into business processes that actually run companies than it is to build or buy the AI and machine learning (ML) models that promise to improve those processes.

“The path to deploying ML is still expensive,” said Ian Xiao, engagement lead at Dessa, an AI consultancy. He estimates that most companies deploy only between 10% and 40% of their machine learning projects depending on their size and technology readiness.

In fact, the last-mile problemis a bit of a misnomer when applied to AI deployment in the enterprise. There is a collection of last-mile delivery problems. In addition to building the digital infrastructure required to integrate AI into business processes, companies are grappling with last-mile issues related to understanding what AI is, empowering users, updating models and even contract management.

The technical and cultural challenges associated with deploying AI in the enterprise are more diffuse and, for some companies, more intractable than the hurdles a vendor might face when integrating AI into its products and services. In the long run, AI’s ability to shine a light on business processes — where a process works well and where it can be improved — will eventually close the gap between the relative ease of buying and building AI and the challenge of deploying it successfully.

Here are seven last-mile delivery problems associated with deploying AI in the enterprise and how to solve them.

According to Xiao, the biggest AI deployment impediment for most companies is indeed the last-mile infrastructure for connecting AI into the business. Robotic process automation (RPA), integration platform as a service (iPaaS) and low-code platforms help close the gap, but these are relatively new technologies. Many companies have not yet adopted them or and may not have the requisite skills on hand to make them work. Once this kind of infrastructure is in place, however, Xiao expects to see data science teams doing less work on AI and machine learning in the name of innovation and more on integrating AI and machine learning into core operations. Ramesh Hariharan, head of innovation & technology at LatentView Analytics, an AI consultancy, also expects to see a convergence of complementary technologies to plug the digital gap. “In the long term, the development of machine learning models is expected to become more automated, thereby enabling much broader adoption of machine learning,” he said. RPA, for example, will be used more and more to pass on data to ML models in real-time, he said, as well as to generate data to build new ML models. iPaaS can make it easier to deploy AI models that work with applications using a microservices architecture. The adoption of no/low-code could extend the capabilities of citizen data scientists who are adopting tools like Tableau and Jupyter that remove barriers to analytics and machine learning. Hariharan said the biggest change enterprises will see from AI and the complementary tools that help make it work is in the types of decisions traditionally made by employees. More and more of these workplace decisions are likely to be automated, as machine learning generates more accurate predictions and becomes more trusted by business managers.

AI can often deliver better, cheaper and faster predictions than humans in a wide range of settings, but it is up to business managers to figure out what these settings are. Hariharan recommends that managers not look at AI predictions in isolation but instead focus on how to automate the decision-making process.