Why every industry needs a tailored AI strategy

Across many industries, AI adoption has become a vital component of modern business strategy. In fact, 54% of IT and data decision-makers say that AI will be very important in helping their organisations achieve revenue growth goals in the next two years.

Despite this widespread adoption, most admit they aren’t close to realising its full value. Some industries are far along in experimenting with advanced use cases, while others are still in the early stages of scaling AI. Without tailoring strategies to industry context, AI initiatives risk falling short of their full potential.

The reality of AI adoption

AI adoption has become a central focus for many organisations across industries, especially those looking to boost efficiency, customer service, and innovation. Despite this, adoption is far from uniform.

Globally, AI is most commonly applied to three main business functions: IT and security (82%), customer service (53%), and research and development (53%). But it’s not limited to these areas. Organisations are experimenting more broadly, with AI being used across an average of five different functions, proving its versatility and the momentum behind its adoption.

The difference often comes down to maturity. While early adopters typically spread AI broadly across functions, more advanced organisations strategically focus their efforts to sharpen operations and generate measurable outcomes. As maturity grows, so does confidence in where AI makes the greatest difference, enabling the shift from experimentation to realising its full transformative value.

How industry realities reshape AI adoption

AI isn't a one-size-fits-all. Instead, applications reflect the priorities and pressures of each industry. For example, energy, insurance, and manufacturing organisations report using AI across six operational areas, above the global average of five. These industries manage complex processes and prioritise operational efficiency to stay competitive. In practice, this means manufacturers and energy suppliers turn to AI for supply chain optimisation and predictive maintenance, while insurance organisations choose to automate claims processing and enhance customer service.

Other sectors take a narrower approach. Banking, for instance, averages just four operational areas. Even within financial services, the contrast is striking: 61% of insurance leaders use AI for customer service compared to only 37% of their banking counterparts. These differences reflect distinct operational pressures and customer interaction models that AI is well-suited to address.

While industry priorities are the primary driver, national context also often plays a role. France, for example, shows a wider spread of AI use across functions than the UK, reflecting policy and regulatory environments that influence organisational priorities.

Measuring the metrics that really matter

AI’s versatility is both a strength and a challenge. Across industries, organisations leverage it to reduce costs, improve customer experience, and fuel innovation. But just as those priorities differ, so does the way success should be measured.

Many organisations still rely on traditional operational metrics such as efficiency gains and ROI to demonstrate value. While these are important, this can confine AI to a cost-saving tool rather than a driver of strategic change. That’s why more mature organisations are shifting towards metrics that capture long-term impact, such as competitive differentiation and new revenue streams.

This evolution in measurement proves that there is no single metric for AI success. Instead, the key is aligning the measurement of success with industry priorities and organisational maturity. Organisations that evolve their KPIs accordingly will be best positioned to move AI from one-off projects to lasting business transformation.

The data challenge behind AI outcomes

Just as success measures vary by industry, so do the data challenges shaping AI outcomes. Across industries, one of the biggest challenges is the quality of the data fueling their AI. Organisations have more unstructured data than ever, and it remains the hardest to prepare and govern.

The impact of this barrier varies by industry. In manufacturing, this may mean maintenance logs that aren’t standardised, while insurance companies may face handwritten claims notes or decades of policy documents. In both cases, valuable insights stay locked away until the data is cleaned, organised, and made accessible for AI.

How organisations respond depends largely on their level of maturity. The most advanced players have already integrated unstructured data into their critical business applications, recognising it as essential for reliable AI outputs. The organisations making the most progress are those that build the right foundation of preparation, governance, and infrastructure to turn unstructured data into AI-ready fuel.

Where industry priorities meet AI success

AI is no longer about just adoption—it’s about strategic alignment. With each industry holding its own priorities, pressures, and definitions of success, these distinctions should shape how AI strategies are built.

While AI strategies succeed when they’re grounded in industry context, they can only advance as far as the data foundation allows. Iron Mountain InSight^® Digital Experience Platform (DXP) provides this essential base, transforming unstructured data into AI-ready insights. With configurable workflows and scalable integration, InSight DXP ensures that your AI stays aligned to the specific demands of each industry.

The organisations that gain the greatest competitive advantage are those that elevate context-aligned AI strategies to a business priority. Learn how InSight DXP turns scattered information into industry-ready intelligence.