Artificial intelligence (AI) in the workplace: 6 use cases

The data backs up this idea that businesses that fail to integrate AI will lose a competitive edge. In fact, of the firms that are currently leveraging the technology, nine out of ten say that it’s helping them get one up over their competitors. As we move forward, the divide between businesses that use AI and those that don’t will only become more pronounced. 

6 use cases for AI in the workplace

Compared to just recent history, AI’s capabilities have quickly expanded. For organisations, it offers more than operational efficiency — it’s a catalyst for innovation, strategy, and long-term growth.

Here’s a look into six of AI’s key use cases that are shaping modern business.

1. Intelligent document processing and automation

Organisations rely on large volumes of data. Manual document handling can be time-consuming and costly, prompting many to explore automated solutions — it’s then no surprise why 41% of organisations already use generative AI to automate routine tasks. Intelligent document processing tools can read, categorise, and extract information from unstructured and structured files with fewer errors than manual methods. Plus, by digitising physical assets and applying advanced models, enterprises gain consistent workflows that reduce repetitive input and enhance data accuracy.

2. Enhanced knowledge management systems

Organisations often house valuable information in a mix of physical archives and digital folders, which can lead to overlooked insights. As expressed by 96% of study respondents, a unified asset strategy — one that addresses both physical and digital sources — boosts AI-driven initiatives. Through responsible AI integration that includes digitising older records and indexing them alongside digital documents, workplaces create a single, searchable platform for all types of files.

Generative AI models enhance these systems by analysing text for patterns and relationships, which helps teams uncover new connections between documents. Meanwhile, they also free up employees to spend more time on analysis and application instead of manual searches, increasing employee productivity. This method of discovery reduces the burden on IT and data teams, who can then focus on improving system performance rather than fielding repeated requests for lost or hidden files. With AI’s ability to ingest diverse data, a unified knowledge management system can evolve with an organisation’s changing needs — driving operational efficiency and enabling business leaders to make more informed, timely decisions.

3. Customer experience personalisation

AI-driven personalisation streamlines tailoring initiatives across marketing, sales, and support. In fact, 63% of retail study respondents say their organisations already use generative AI to interact — and improve interactions — with customers, whether through chatbots, recommendation engines, automated email campaigns, or other avenues. As AI analyses user data and behaviour patterns, it’s capable of delivering product or service recommendations that match individual needs — providing quicker problem resolution and a more relevant experience.

Across the board, we find organisations increasingly recognise personalisation as a competitive necessity rather than a luxury: Companies that effectively leverage AI for personalisation report higher customer satisfaction scores, increased customer lifetime value, and improved retention rates. The key differentiator appears to be not whether to implement AI-driven personalisation, but rather how to do so in a way that balances automation with human oversight, respects customer privacy preferences, and allows human skills to complement AI’s analytical power.

4. Historical analysis

Workplaces often hold large volumes of legacy data in physical archives that can reveal long-term trends or patterns. However, these documents are commonly underutilised. Through leveraging responsible AI solutions, these archives can be managed and analysed more effectively.

AI technology can extract and analyse this information, whether it comes from hand-written documents, audio recordings, or aging digital formats. By converting older materials into machine-readable text or structured datasets, teams can apply advanced AI algorithms to uncover correlations that might go unnoticed in day-to-day operations.

AI-driven historical analyses can help teams to spot recurring events, shifts in market preferences, patterns in customer behaviour, and more. It also supports audit trails for compliance, as historical records carry timestamps and original documentation that AI can summarise or flag for review. With the right level of human oversight, organisations can also ensure that these analyses align with responsible AI principles. And, when organisations integrate historical data with current metrics, they build timelines that reliably track performance and foster operational efficiency over time.

5. Asset lifecycle management

Organisations commonly manage both physical and digital assets that pass through multiple phases, from creation and storage to eventual disposal — impacting employees across the workplace. Yet, 38% of study respondents say sourcing, protecting, and preparing data from physical and digital assets is a challenge. AI systems help monitor each step of that lifecycle, from automating inventories and applying consistent labeling to suggesting archiving or recycling actions when assets are no longer needed — ultimately enhancing the employee experience.

When combined with a cohesive strategy, businesses benefit from AI as it can recognise redundant data and either recommend consolidation or flag outdated versions. This keeps storage costs predictable and lowers the risk of data leaks or misplacement. Some organisations also incorporate generative AI tools to interpret asset usage patterns, which supports AI-driven decisions about purchasing, redistribution, or retirement.

6. Environmental sustainability planning

Organisations use AI for sustainability initiatives in multiple ways, from evaluating resource consumption and optimising supply chains to identifying opportunities for waste reduction and responsible material usage. With appropriate human oversight, these solutions help business leaders ensure that environmental goals align with operational efficiency. Plus, by mapping energy usage and production outputs, AI models can estimate an operation’s environmental footprint over time. On the commercial front, 47% of businesses say they intend to use generative AI to add value to customers through improved services and products, which today includes designing more sustainable offerings. AI also surfaces insights on material choices, logistics routes, and packaging methods, so teams can decide where to reduce emissions or conserve resources.

Facilities equipped with sensors benefit from real-time monitoring systems that identify inefficient processes or equipment. When machine learning algorithms detect patterns of overuse, teams can schedule maintenance or adjust workflows before small issues grow into larger problems. Some workplaces merge environmental data with broader business metrics, demonstrating a successful AI integration that clarifies how sustainability measures intersect with cost savings and compliance objectives.

The Iron Mountain solution

Organisations face shifting standards as regulations around AI mature, and many leaders prioritise risk management to stay ahead of potential legal and ethical pitfalls. These concerns go beyond the basics of data security: they include ownership of AI-generated content, bias in algorithms, and the need for clear policies on how models are trained and used.

Some leaders view the Chief AI officer (CAIO) role as a way to address risk more systematically. Nearly all surveyed organisations without a CAIO plan to hire one, believing this position can bridge gaps between IT, data governance, and legal teams. A central figure who oversees AI initiatives can track changing regulations and refine policies accordingly. 

Meanwhile, unified asset strategies help define how physical and digital records intersect with AI workflows, giving businesses greater clarity around data usage and lifecycle management. This fosters consistency in how teams handle content, whether they produce it themselves or rely on large language models.

At Iron Mountain, we help organisations navigate these challenges through our comprehensive, unified asset strategy solutions. We enable businesses to seamlessly integrate their physical and digital assets while implementing AI capabilities across their operations. We understand that successful AI adoption requires both careful governance and strategic asset management. Our framework helps organisations automate document processing, enhance knowledge management, and drive sustainability initiatives while maintaining security and compliance.

Through our expertise in both physical and digital asset management, we provide the foundation organisations need to confidently explore AI's possibilities while minimising associated risks and maximising value-creation opportunities.

Optimise your digital future with a unified asset strategy.