Mapping AI Adoption by Industry: What’s Your Company’s Next Move?

To succeed with AI, companies must assess their industry’s AI maturity and adapt their strategy accordingly.

Almost every company today feels pressured to act quickly on AI. But not every industry faces the same challenges or opportunities. Some industries, such as financial services, IT, telecommunications and healthcare, have been integrating AI technologies for decades, using expert systems, machine learning and predictive analytics. Others, like construction and utilities, are still in the early stages of adoption.

Mapping AI Adoption by Industry: What's Your Company's Next Move?

Because industries are at different points in their AI journey, companies need a clear view of their industry's level of AI adoption to make informed decisions. Using data from HG Insight’s GenAI Readiness Report, we’ve mapped “AI maturity”across industries to help businesses assess their position and determine the best path forward (see figure below). Understanding where an industry stands in AI adoption also helps companies identify relevant competitive intelligence – whether by benchmarking against direct competitors or looking to other industries for inspiration. Maturity stages of AI adoption

Assessing the path ahead The first step is to compare AI deployment to competitors across three horizons: Close horizon: Direct competitors within the same geography or value chain segment should be closely monitored. A company’s market position may weaken if competitors adopt AI for differentiation or to reduce costs. For example, a European construction firm must watch the AI strategies of other European construction companies. Intermediate horizon: Companies should track firms within their industry that operate in different regions or at different points in the value chain. While these businesses may not pose immediate threats, their AI advancements can provide valuable insights. For instance, a European construction company should observe technology adoption by counterparts in China or Brazil, as well as its European suppliers. Distant horizon: It is also key to keep an eye on AI developments in unrelated industries. Although these companies pose no direct competition, their experiences can offer innovative perspectives. For example, a construction firm in Europe could gain insights from AI adoption trends in banking or transportation. Determine your strategic posture Once companies clearly understand their industry’s position in the AI lifecycle and have assessed the three horizons, they can determine the appropriate strategic posture that would enable them to effectively leverage AI opportunities. The first strategic posture is Monitor. It involves observation of what is happening across all three horizons, without taking immediate action. While prudent up to a few years ago in industries in which the adoption of AI was slower, this posture is no longer advisable for any player, except for firms without any resources to invest in AI. The second posture, Pilot, involves low-cost experimentation with AI to obtain quick learnings while mitigating risks and building up AI capability. Here, firms need to pay a lot of attention to Close and Intermediate horizons. The third posture, Industrialise, involves making sizeable investments to scale AI across functions, business units or the entire enterprise. This approach requires digital transformation with the aim of either outpacing competitors through AI-driven efficiencies or avoiding being outpaced by early adopters. At this stage, companies will also need to focus on what is happening in the Distant horizon, given that most of their competitors have already adopted some AI solutions. They will need to come up with some “out of the box” ideas to differentiate their company. Typically, these ideas would come from firms operating in different industries. For instance, an executive or board member working for a German construction company (at the lower end of the AI adoption lifecycle), may find opportunities for experimenting and low-risk AI deployments. They should monitor the sector along the value chain for relevant developments in Germany that may require a response. By contrast, an executive from a retail bank operating in Belgium, (an industry at the higher end of the lifecycle) should closely monitor direct competitors while scanning for new solutions from specialist vendors and suppliers. They should also be examining the moves of global financial services leaders, such as J.P. Morgan or Citibank in the US, which are at the forefront of AI deployment. By monitoring developments across Close, Intermediate, and Distant horizons, business leaders can gain insights into industry trends, emerging threats and cross-sector opportunities. This enables them to choose the right strategic posture for their organisation. Ultimately, AI is less about technological capability and more about making the right strategic choices at the right time. The companies that succeed will be the ones that continuously reassess their position and stay alert to industry shifts, taking inspiration from unexpected places.