Laying the foundations for successful GenAI adoption

The time for talk is over. After two years of exploring the potential use cases, growing numbers of organizations are beginning to adopt generative AI (GenAI) to drive tangible business value. Gartner reports that investment in these technologies will continue to rise in the coming months — driving global IT spend to almost USD 6 trillion in the next year.

CIOs are keen to progress beyond the proof-of-concept stage and start putting GenAI to work. Although exciting new capabilities and use cases are emerging on a daily basis, GenAI needs to be built on firm foundations to deliver results. The teams charged with coming up with ideas on how GenAI can be used – and the leaders signing off on their investments of time and money need a solid understanding of how it works. First and foremost, however, they need to focus on making sure they have the data required to fuel the successful adoption of Gen AI tools.

Covering the bases

From Microsoft leadership teams to US courtrooms, experts are sounding the alarm: with AI, ‘garbage in = garbage out’. If they fail to heed these warnings, organizations will not unlock the benefits they are expecting. Before investing time and money into adopting new use cases for GenAI, organizations need to get the data in place to enable it to succeed. Specifically, they need to cover four core main bases:

1) Modernize existing data

First, organizations need to transform the existing data sets that will be used to train models and drive insights. They need to map and analyze their current data to understand the existing landscape, then use a mix of data warehousing and data lakes to lay the foundations for a robust architecture. They also need to consider the data aggregation, storage, and retrieval requirements, to ensure they can conduct analytics in real time. Data modernization projects can take years to complete, but there is no time to waste – they must be completed in a matter of months.

2) Identify and ingest new sources of quality data

Next, they need to enrich existing data with external insights to add crucial holistic context to supercharge AI. To date, ingesting external data sets has been a time-consuming process, but cloud-based Extract, Transform, Load (ELT) solutions can automatically create pipelines. This enables organizations to quickly bring in reliable data sets that can put them on the path to unlocking deeper insights to fuel their AI use cases.

3) Proactively remove any bias

Next, organizations need to review the entire data landscape to ensure it is clean. They need to be certain their data can be trusted to inform their AI, driving it to make the right decisions. It’s crucial that they identify and remove any unintended biases that might emerge if they feed this data into their AI. By stepping back to consider the potential biases that could arise in their AI use cases before deploying them, they can head off the likelihood of these problems arising in advance.

4) Ensure visibility to underpin data quality and governance

Finally, organizations must eliminate silos, unifying data with end to end visibility to create a single source of truth. AI will not be reliable and accurate if fed with conflicting data - so they must be able to identify confusing conflicts, and remove them. Data evolves over time, which means it is important to maintain visibility over who has changed or added data, and why. This traceability will help identify and overcome potential mistakes, for example, if synthetic training data has been accidentally used for real-world decision-making.

Increasing AI literacy to capitalize on the opportunity

This data provides the raw materials, but it needs to be used in the right way to drive GenAI success. Building knowledge across the business will enable teams to identify use cases that can really generate value. Multiple departments could potentially benefit from GenAI in different ways, so it’s crucial to start with a clear vision and objective in mind. Organizations that invest budget and manhours in training will likely be rewarded with use cases that enable them to confidently deploy GenAI in ways that unlock the fastest ROI.

To enable this, leadership teams must also have a solid level of AI literacy and data literacy. Business leaders need understand how traditional and GenAI models work and how underlying data and training can influence the inferences presented by these models. This will give them a deeper appreciation of the recommendations coming out of an AI based solution in the context of the business use case and they will find themselves in a much better position to accept or decline such recommendations. This is the whole point of the “human in the loop” which is a key factor in the success and acceptance of AI based solutions.

Building on the foundations for successful adoption

By laying solid data foundations, empowering teams to uncover use cases and ensuring leaders can green-light the right projects, organizations will be on the path to successful GenAI adoption. The opportunity is very exciting, and evolving at a rapid pace, so there is no time to lose. CIOs just need to balance the need for speed with a firm focus on making sure none of the corners are cut. Taking time to lay solid foundations will put them on course for successful GenAI adoption that will unlock value and benefit many different teams across the business.

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