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Adopting AI in the enterprise: key challenges and solutions

Implementing AI in the enterprise can be a daunting prospect; from resolving concerns about security, to ensuring the data is AI-ready, and understanding the risks of shadow AI. We’ve surveyed UK business leaders to understand their main AI goals and what’s getting in their way – in this video, Aiimi CEO Steve Salvin and CTO Paul Maker discuss the results. Let’s look at some common challenges to AI adoption and ways to overcome them in pursuit of fully operationalised enterprise AI.

Safety and security: AI implementation challenges

IT security and legal teams are charged with protecting the business from harm, and they’re justifiably concerned about data privacy issues with AI in the enterprise. It’s important these groups aren’t seen as ‘blockers’ but rather as supporters of safe and sustainable progress with AI. There’s nothing to be gained by rushing in – enterprise organisations must strive to properly understand how AI models work. Some of this is a skills gap, but it’s also about getting data quality and governance strategies AI-ready. More on this below...

Data Quality & Governance: essentials for enterprise AI adoption

We know AI is only as good as the data we give it; as the old saying goes, ‘garbage in, garbage out’. And in turn, Data Governance is key to data quality. Fortunately, AI itself can be part of the solution, used to help fix data quality issues and automate data governance. AI can bring structure to previously unstructured information (typically 90% of an organisation’s data) through consistent classification and labelling, and be used to spot processes and information that have flown under the radar of governance and compliance. When you know the data you’re feeding AI is good quality and well governed, you can build trust in the resulting AI outputs.

Information Retrieval in enterprise AI use cases

We need to be confident we’re handing the right information to an AI model, in order to have confidence in the resulting AI outputs. So, we need to be good at information retrieval – without this, success with generative AI in the enterprise will be limited. This means businesses need to consider data quality in the context of information retrieval; people naturally describe and label data in different ways and terms, but when all your data is managed and labelled consistently and automatically, it helps drive relevancy and improve RAG outputs.

The risks of shadow AI in the enterprise

With so many apps launching their own AI co-pilot or LLM integration, shadow AI is on the rise. The explosion of Generative AI means people are potentially disclosing sensitive corporate information to unregulated tools, outside of all the usual rules and safeguards. Instead of exposing the business to the risks of edge AI, the solution is to have all these different tools tapping into one universal AI platform that spans all your enterprise data and also respects all your existing governance and permissions.

Data silos: a barrier to enterprise AI solutions

AI co-pilots are great for boosting productivity for a specific app. But each of those AIs and the insights it can deliver are limited to the data within that single repository. It compounds the problem we see with data silos, where each team only has access to their own data. The solution is to adopt AI at an enterprise level, so everything is interconnected and making use of your organisation’s complete data picture – not just a partial view.

A strategy to operationalise AI in the enterprise

AI requires a new approach to strategy. New AI models are constantly emerging, and new risks and regulations will continue to emerge, too. Only an agile, evolving AI strategy will enable organisations to keep up and make the most of the latest tools in a safe way. This starts with creating an AI Lab within the enterprise and developing an iterative AI strategy that can keep pace with the rate of change.

Watch our video to see Aiimi CEO Steve Salvin and CTO Paul Maker discuss where the most common challenges lie for AI in the enterprise, and download the full AI in business survey report here.

Contact us for a free ‘Ask the expert’ session and let our AI strategy consulting team answer your urgent questions about adopting AI in the enterprise.