Your company has a team of people working on the best ways to incorporate Generative AI and other AI into your business processes. Ideally with game-changing results.

How do we know? Every Wavestone client that we speak with has at least one such committee – generally supported by a top-tier management consulting firm – and every conference one goes to nowadays has sessions dedicated to Generative AI.

The use cases that look to be tackled by AI and Generative AI are impressive:

  • Investment Management: Generating higher investment returns by selecting better investments through improved analysis results
  • Insurance: Improving loss ratios with better selection and pricing of policies during the underwriting process
  • Retail Investment: Enhancing customer loyalty and associated brand stickiness through hyper-personalized, empathetic, and relatable interactions for individual consumers
  • Life Sciences: Rapidly increasing drug time-to-market and improving drug-trial outcomes with superior internal pricing, better selection of patients for drug trials, and more accurate pre-drug-trial simulations
  • Sports Media: Deploying automated journalism to increase games and events coverage volume and accelerate game analysis time-to-market
  • Consumer Retail: Increasing average basket size and driving profitability with hyper-personalization, targeted offers, and streamlined in-person and online customer experiences

Data-Driven Needs for Generative AI

What do all of these have in common? The need for high-quality data in high volumes and often in real time. Unstructured data in many cases needs to be converted into structured data so that it can be used by the end use cases. Data that is used to generate business outcomes will need to have lineage to the source in order to understand why the models generated what they did. Data quality confidence scoring will need to be available so that end users understand and can make decisions on the appropriateness of data.

The Role of Data Leadership

Who will be expected to deliver the data needed for these use cases once the committees are ready to move on them? YOU will – despite the messy data environment that exists at your company, and that you are desperately working to improve. And on top of the data commitments you have already made to the business this year. According to the Wavestone Data and Analytics Leadership Annual Executive Survey 2023:

  • Only 40% of organizations are managing data as a business asset
  • Only 24% have created a data-driven organization (meaning one where there is a reasonable likelihood that upstream systems will provide quality and timely data for AI purposes)
  • Only 40% of firms have well established policies & practices in place around data responsibility and data ethics – which would seem like a pre-requisite for using data in AI

Be Prepared -> Get Prepared!

To get yourself prepared, you need to review your current approaches and policies to ensure they are still relevant for Generative AI application. This includes having revised deliverables and roadmaps around things like:

  • Practical approaches for making AI use cases real
  • Operating model to support increased data demand (people + process)
  • Prioritization frameworks, demand management processes, and tools to help evaluate business value/ROI
  • Right-sized data governance
  • Data security
  • Master Data Management & data quality expectations and approaches
  • Appropriate policies
  • Technology best practices and recommendations for achieving the above


Believe it or not, one of the ways to prepare for AI is by leveraging tools that provide ML-assisted processes for preparing, understanding, and cleaning your data. But the operating model around how the whole thing needs to work is crucial. Getting external help might be your path to faster success!

Have a question? Just ask.

As always, we remain at your disposal. Consult a Wavestone expert for bespoke guidance on incorporating AI into your business processes.