Why Most Enterprise AI Projects Fail And How to Succeed

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If you scan the business news, or the pages of LinkedIn, then there is really only one story at present – artificial intelligence (AI). Every corporate leader is exploring this technology and asking what can be done inside their business?

Can AI generate greater efficiency or lead to large gains in productivity? Can it drive a new era of innovation that can lead to new products and services?

These are all valid questions and ideas. AI is not just a single product. It is more like the introduction of wide-ranging tools such as email or social media – it is entirely changing how some businesses can operate.

But some AI researchers are noticing that many companies are creating AI pilot systems that never go into production. They have a lot of ideas for how AI might be used in their business, but once they start testing the technology there are many barriers that prevent the experiments becoming a reality. In one study they found that only 10% of pilots are becoming production projects.

Gartner research has suggested that 85% of AI projects fail. There are many reasons for this, but the most important causes of failure are:

• Data – if you remember the expression ‘Garbage In Garbage Out’ then you know the problem. Humans often create workarounds and fixes to improve the data they have to work with. Once you are handing tasks to AI then you need to give the system high quality information. If your data looks like garbage right now then this is your first priority
• Technology not strategy – this is where the CEO has been excited by a news report about AI and asks ‘why are we not using this?’ Or a business unit invests in a cloud-based AI solution without talking to anyone else in the organization. This is when managers are looking for a problem to be fixed with AI, rather than stating the business problem or opportunity they want to address and then figuring out what AI can do to help
• People – not just the skills, although you may need to boost the AI skills on your team. This is mostly around the problems of changing processes – creating a new way of managing your business with AI. This is a bigger challenge than many managers imagine and needs to be considered from the start. How do you manage the culture of changing everything that your business does today?

Pilots are a smart way to test new ideas. There is no harm in starting small and testing ideas before scaling up, but the problem that many companies have found is that they test the ideas and then there is no path to scaling because the people are not ready, the data is not ready, and often the ideas themselves were lead by the technology team rather than being specific and strategic business objectives.

The opportunities are out there. McKinsey has estimated that over $4 trillion of business value can be generated through the use of generative AI. The question is which would be the right solution, the right technology, and the right time for your business to manage a transition?

In China, around 83% of all companies across all industrial sectors have adopted the use of generative AI in their business. This adoption rate is lower in markets such as the US, but American companies are more advanced in focusing on the improvement of AI. They are focused on AGI.

This does mean that in regions such as Europe and the US, there is a window of opportunity where AI can present an opportunity to innovate, to outflank your competition, or to dramatically increase your productivity. Are you ready for this opportunity?

As you explore these ideas, consider why AI project failures are so common. The opportunity clearly exists, but it requires a strategy and careful planning to succeed.

IBA Group has an experienced practice focused on data management, data analytics, and AI. Check the website here for case studies and examples of projects that have been designed and delivered in addition to ideas, insights, and project suggestions.

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To learn how organizations turn data and AI into measurable business advantage, read the full article here