Many view artificial intelligence and associated Internet of Things technologies as the means by which manufacturers will find the most efficient ways to produce goods of the highest quality. Similarly, with its ability to provide a continuous feedback loop, machine learning (ML) opens the door to innovation and continuous improvement, using historical data to adjust and propel innovation. Nevertheless, many manufacturers hesitate to make a significant investment in AI for fear of not seeing a significant return on their investment.
In a study reported in Harvard Business ReviewOpens a new window , 90 percent of the companies that had engaged in AI spent more than half of their analytics budgets on activities that drove adoption, such as workflow redesign, communication, and training. The obstacles leave many manufacturers wondering if AI is worth the investment.
Most initiatives require careful analysis, budget considerations, and significant internal preparation, but AI carries some elements, as well. One critical aspect of AI adoption is ensuring endorsement from senior-level management. Leaders provide the necessary vision that communicates why AI is important now and how people’s jobs will fit in. The disruption AI brings may require handholding to reassure employees that although their roles may change, they will not be eliminated. Here are specific areas to consider before embarking on AI integration.
Define the Problem
First, the organization must decide which problem it’s trying to solve and what the goal of AI integration is. Every area of the company must be represented during the planning stages, ideally by cross-functional teams with a mix of skills and perspectives, to ensure that AI initiatives address goals rather than simply isolate problems. These teams must identify which processes are critical to the business. They must identify bottlenecks and opportunities to optimize and deploy new methods that can enhance the business’ current offerings or add features to existing products. The teams must determine how much time and money the organization could save by optimizing one workflow over another, how long it will take to incorporate an automatic workflow, and the training the new processes will require.
Stage Integration and Adoption
With the planning work done, the implementation team should optimize one workflow at a time. Gradually rolling out well-designed ML processes helps minimize disruption within the business. In addition, AI will steadily gain momentum, continuously gathering and maintaining the support of internal teams as AI expansion continues. An important component of AI rollouts is optimizing the feedback loop for continuous improvement.
Data vs Knowledge
The company has lots of data, but those data must be manipulated to match the specific AI model adopted. For example, will the data the company has be enough to support the model? Or, will leadership need to persuade competitors to share data to improve the industry?
Build a Model That Achieves the Ideal Outcome
What should success look like? The organization must have a clear vision of the solution, with identifiable metrics linked to the problem AI was brought in to solve. The success of the business’ ML model can be determined by staging metrics and key results throughout model implementation, with a clear path to assessment and refinement.
Like all endeavors to increase value, AI takes time to develop. Leadership endorsement is necessary, but employees must feel empowered to contribute their own knowledge, ideas, and perceptions as AI is implemented throughout the organization. It’s no guarantee, but these considerations could help AI deliver as promised.