Maximising agentic AI’s impact: the importance of a strong data foundation

This is a guest blogpost by Sumeet Arora, chief development officer at ThoughtSpot.

The new wave of AI has arrived in the form of agentic AI, and it is already making significant strides in the business world – despite its novelty. Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, a significant leap from 0% just last year.

The reason why shouldn’t come as a surprise – the technology’s ability to operate with near-autonomy, adapt to changing environments and quickly make business decisions makes it highly attractive to businesses looking to sustain an edge in the AI era.

Having seen the negative repercussions that lagging behind in the GenAI race has had on organisations, businesses across the globe are eager to avoid repeating the same mistake with AI agents. Companies across the globe are already investing heavily into agentic AI, with global spending projected to reach $47B by 2030. As a result, we can expect the technology to become integral in our daily workflows. However, as with GenAI, organisations need to be cautious in their rush to capitalise on this next wave of AI innovation. If they want to ensure they are reaping the full benefits of agentic AI, it must be implemented on a foundation of high-quality data – but this is easier said than done.

Data analysts – the key to AI success

One of the biggest hurdles organisations are facing when adopting AI is ensuring that their data is up to the task, with 35% of businesses identifying data challenges as their primary bottleneck.  If an organisation’s data isn’t ethically governed, minimally biased and accurate enough, it will not serve as a solid foundation for AI models.

The main issue here is not a lack of data, but whether the data is robust enough to support complex AI models. This is where data analysts are key. Their responsibility is to ensure that the data is suitably clean, well-structured, and relevant, laying the groundwork for safe and effective deployment of AI. Once the groundwork is in place, organisations can effectively implement AI for the best results.

The role of the data analysts guarantees the strength of data in a number of ways:

• Data preparation – Through centralising data sets into one singular database, analysts are able to make sure data is accessible from one hub. This means that workflows are then able to deliver AI-ready data that can be used alongside agentic models wherever they are deployed within an organization.
• Ensuring consistency and eliminating efficiencies – Complete analytics workflows are essential for maintaining clean and consistent data. Through eliminating inefficiencies, analysts are able to ensure that their organisation’s data is prepared to work effectively under the demands of agentic AI models.
• Strategic infrastructure investment – Data teams bring a strategic perspective to infrastructure investment. By understanding how and where data performs best, they’re able to identify where resources will have the greatest impact to guarantee better performance and enable more efficient, scalable operations.
• Data augmentation – Data analysts can also augment data with advanced analytics such as forecasting and clustering to enable business decisions with predictive and prescriptive capabilities.

Real-world advantages  

Organisations globally are already proving the value of building a strong data foundation in driving efficiencies and unlocking new revenue streams.  For example, Odido – a mobile network operator – has cleaned their ‘data zoo’ by moving as much data as possible to the cloud, allocating only the most relevant data to be available ‘on-demand’, and providing open access to data. This has enabled Odido’s data analysts to generate insights from their data in less than 15 minutes, significantly freeing up resources.

Matillion – a cloud platform for data integration – has removed siloes across data teams by integrating infrastructure for data analytics into one centralised platform in a phased approach, starting with an assessment, and then a gradual deployment across the organisation. This strategy resulted in a transformative company-wide adoption rate of 60% and created £75,000+ in annual cost savings for the business.

Looking to the future: agentic AI and data

It goes without saying that agentic AI poses a significant growth opportunity for organisations’ productivity. The technology substantially cuts down analysts’ workloads, speeds up question-to-value time, and massively reduces operational costs by quickly making insights actionable. However, these benefits can be quickly undone if the underlying data is weak or poorly managed, which would result in organisations being left back at square one.  An agent is only as good as the information and data it operates with. In fact, agents and data are essential components of the upcoming era of autonomous business.

Organisations that systematically prioritise preparing their data sets prior to implementing agentic AI have already seen tangible rewards – from cost savings to decreased reliance on dashboards. This proves that, while the urgency to implement agentic AI may seem pressing, a deliberate, data-first approach ensures a more effective, and profitable deployment.