Episode 2: How to Become an
AI-Powered Organization

One of the biggest misconceptions is that generative AI will replace all preceding AI tools.


That would be a big mistake.

Now that AI has matured enough to be put into production and scaled, you can harness its potential to bridge the existing Gen AI maturity gap, especially in well-established use cases such as fraud analytics and churn analysis.

For those people who just want to get to an answer – and fast – without the time, desire, or skills to perform an analysis, we will see an increase in auto-generated visualizations and insights, enhanced with explanations in natural language.  

Consumers also tend to trust people more than data, so collaboration and data sharing will be key. There are bonus points if this can happen within the systems where they operate.

The majority (Forrester says 80%)¹ of the world’s data is unstructured.

Many have previously tried and failed to analyze unstructured data, but with new GenAI techniques, we can unlock its potential and value.

The opportunities for combining structured and unstructured data in a trusted way will be endless. You can re-use verified questions and answers to efficiently scan and analyze your entire data estate.

Increasingly, individuals might start their analytical journey in GenAI tools, using them for simple data visualization and business projections.

As a next step, they may want to tap into enterprise-grade tooling for deeper analysis, bringing the benefits of GenAI to their trusted tools.

If data quality and lineage were important before, they have become non-negotiable in a world with AI.

A new mechanism to clearly label and signpost data will emerge to create the equivalent of a DNA test for your data to ensure you know and can trust its origin.

With plain English (or any other language) now the new dominating programming language for GenAI tools, we are seeing an explosion of apps built by the ‘everyday developer’ – resulting in a flurry of innovation.

Just as data literacy has been crucial in the last few years, we now need to turn our attention to AI literacy to improve standards, avoid governance chaos and application glut.

As this process puts very strong powers into the hands of the many, organizations must take steps to educate their workforce in the benefits and pitfalls of GenAI.

Now is the time to facilitate the creation of rich, thoughtfully designed applications that 
enable exploration.

New platforms, combined with the evolution of data fabrics, will consumerize data engineering to a new breed of users — especially if augmented with powerful AI, automation and data science.

Making the hard stuff easier and merging the roles and capabilities of data engineering, data science and analysis will enable organizations to solve tougher problems with fewer resources. Eliminating barriers between previously siloed functions will also help businesses move data and outputs from big to better.

So far, GenAI tools have mainly been used to support reasoning and conduct analysis, rather than for action.

But with transformed data in near-real time and in the right place, we will start to see new ways of using GenAI with application automation.

GenAI, connected with automation, will mean less manual work for humans to connect and build workflows and instead take on the role of decision stewards.

Early applications of GenAI are massively scalable — but currently generic.

Over time, we’ll increasingly see AI customized to industry, and more specifically, business-to-business (B2B) use cases built on multi-cloud environments for efficiency and stability.

This will take the shape of applications where the foundation may be common, but with layers of customization that better serve the long tail. Furthering this trend, we will also see that with less effort and fewer consultancy hours, sophisticated applications can be built that address a specific industry or issue at hand.

Start to apply principles of product management to data, asking questions about what problem(s) we are solving, what it is going to be used for and by whom. This will emphasize the importance of data quality, governance and usability for end users.

The concept of treating data as a valuable asset or product means it can be surfaced in a catalog and used both indirectly (internally) to create huge value through improving internal systems and processes and directly (externally) to evolve into a tradable good.

This opportunity will encourage organizations to use their own data to further train Large Language Models (LLMs), like ChatGPT, which can then be monetized. The more the data product is used, the more valuable it will be.