If you worry that your organization is falling behind in its artificial intelligence initiatives, don’t feel so bad — just about everyone is still in the learning and piloting stages. And if it’s unclear whether there will be return on investment at the end of all this work, there is financial advantage as well, research out of MIT finds.
There are at least four logical stages in AI advancement, and most enterprises are still working through the experimental and pilot stages, an analysis of 721 companies by the MIT Center for Information System Research (CISR) concludes. As AI proceeds, there is now evidence that overall financial performance advances as well.
Most enterprises in the survey were in the first two stages of AI maturity and had financial performance below the industry average, according to the report’s authors, led by Peter Weill and Stephanie Woerner, both with MIT. Enterprises in stages three and four, on the other hand, had financial performance well above industry average — exceeding 10 percentage points.
Weill and Woerner identified and measured the following four stages of AI progress:
Stage 1: Experiment and prepare (28% of organizations). “In this stage enterprises focus on educating their workforce, formulating AI policies, becoming more evidence-based, and experimenting with AI technologies to grow more comfortable with automated decision-making,” the researchers explained. Company leaders start taking a look at how to address concerns such as ethics and skills to ensure a smooth path forward.
Companies in Stage 1 averaged 9.6 percentage points below the industry average, the study found.
Stage 2: Build pilots and capabilities (34%). At this stage of AI, proponents “define important metrics, begin to simplify and automate business processes, and develop the enterprise capabilities they’ve learned.” At this stage, use cases are piloted, with work on leveraging enterprise data and developing APIs. Work with large language models also commences at this stage.
Companies in Stage 2 averaged 2.2 percentage points below the industry average.
Stage 3: Develop AI-driven ways of working (31%). At this stage, AI essentially becomes industrialized, meaning it is available and replicable across the enterprise. This includes work on building a core platform for AI, ensuring transparency to decision-makers via dashboards, and ultimately transforming the organizational culture to encourages data-driven and innovative thinking. Foundation models and small language models are introduced and applied to enterprise opportunities.
Companies in Stage 3 averaged 8.7 percentage points above the industry average.
Stage 4: Become AI future ready (7%). At this achievement stage, “AI is embedded in all decision-making throughout the enterprise,” the researchers state. “They leverage proprietary AI internally, and many sell new business services based on this capability, the AI capability as a service, or both to other enterprises.”
Companies in Stage 4 averaged 10.4 percentage points above the industry average.
Successfully moving through these stages of AI growth requires a cross-enterprise collaborative effort, as the technology can recast and accelerate many parts of the enterprise. Weill and Woerner cite examples of well-known companies at various phases of their AI journeys, such as Kaiser Permanente in the process of identifying AI values and ethics, to DBS Bank committing to conducting one thousand AI experiments per year, which has led to 350 AI use cases. And here’s the clincher — DBS expects the economic impact of these to exceed $1 billion in 2025, they report.
One thing is clear; AI success is a journey, and the ability to rapidly leverage and adapt resources and technology is key — as new technologies and capabilities keep arising almost every day.
