Enterprise AI Implementation Guide Why Businesses Should Adopt Practical AI Solutions Now

Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.

Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That's the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.

That's the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space 'ever since she was a corporate baby', so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.

"If I were to be very practical, the one thing I've noticed is companies jump into adopting AI before they're ready," says Sheth.

Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.

Even as recently as 2024, Sheth saw many organisations struggling because their data was 'nowhere where it needed to be.' "Not even close," she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.

Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation," Sheth adds.

With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model.

"They've moved from raw data to descriptive analytics, moving into predictive analytics, and now we're actually setting up an AI strategy for them," says Sheth.