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The explosion of Generative AI and opportunities for industries across FinServ, Telco, Federal/Public Sector, Healthcare, and Life Sciences, requires strategic approaches to optimize data utilization and maximize innovation.

A recent CMO Council Webinar in partnership with Cloudera, Talend and AWS, "High Opportunity And High Liability - Maximizing Data Utilization & Landscapes for Generative AI Opportunity in Regulated Industries," did a deep dive into the integration of Generative AI to move marketing innovation and mitigate risks, requiring strategic approaches to optimize data utilization and navigate the evolving landscapes of regulated industries in particular.

Host, CMO Council Senior Vice President, Business and Program Development, Bryan DeRose, set the stage, pointing out that organizations within regulated industries such as telecommunications, financial services, healthcare, life sciences and public sectors, are racing to harness the potential of Generative AI to unlock unparalleled opportunities and innovation.

The Webinar Panel harnessed the expertise of Morris St. Angelo, Global Cloud Partners, Cloudera; Ian Mercer, Data Governance and Data Management Category Lead, AWS; and Andy Smith, VP of Global Alliance, Talend. They unpacked data utilization strategies for exploring effective data collection, storage, and preprocessing methods; ethical considerations and regulatory compliance, including bias, privacy, and responsible AI practices; mitigating liabilities and risk, and future outlook; showcasing case studies and best practices of regulated organizations and brands leveraging Generative AI to drive business performance and growth.

As we know, “traditional” Artificial Intelligence analyses data and reports back on the findings, whereas Generative AI takes that data and creates something new, leading to innovation in a sector and providing organizations with creative insights. 

Opportunities and Requirements to Effectively Leverage Generative AI

Mercer explained how a key challenge centered on deficiencies in “data culture” within organizations. The issue is that this is less about Generative AI, and more about data at scale, with Generative AI being the lens through which organizational data is recognized. Most industries have specific cherry-picked data assets, but when you look at it holistically across the organization, data penetration is rarely embedded across the whole organization.

Opportunities have arisen in massive analysis of data and the “skill augmentation” to produce proprietary data assets, as well as supply chain optimization.

Data proliferation adds a whole new set of risks and data at scale is another capability that has been introduced. “The big ones for me are introducing the mechanisms, control and culture, to fully realize the Generative AI position and start to recognize the holistic data volume inside organizations -- and using Generative AI as the prism with which to recognize it,” said Mercer.

This is an opportunity to not only harvest data, but for enterprises to refine the models used for decision making for customers, said St. Angelo. “We are talking about open data lighthouses, data flow, and how the merger of different data sources represents an opportunity for customers utilizing Gen AI.”

Risks and Liabilities to Organizations

Privacy is a huge consideration, especially for regulated industries; as is “Gen AI hallucinations” where incorrect source data is harvested, presenting pernicious risk to organizations. Data must be organized and trusted to be used as a source for AI and trust is a significant part of data usage.

Corrosive challenges that organizations must be cognisant of, include inefficient data modelling and utilization; privacy, bias and ethical practices; ever changing regulations and policies; data governance, compliance and security. Every enterprise has a responsibility to protect customer data as there are serious consequences.

Big risks include “contextual limitations” when the Large Language Models (LLMs) like a ChatGPT produce the “Gen AI hallucinations” that use incorrect source data to produce a conclusion that is corrupted, creating catastrophic risk. Organizations need to ensure that data used is trusted, urged St. Angelo, as penalties will be punitive otherwise.

These challenges bring risk to organizations, and we all need to lean into this and solve these risks which we all have, said Smith, as Generative AI will be a huge driver for the evolution of data integration, quality, governance, and analytics – similar to the journey with migration to the Cloud.

Building a strong organizational foundation is essential to mitigate risks including who has access to the data, understanding the legal ramifications, and unpacking the complexity of integration.

Embracing Gen AI to Drive Business Performance

Gen AI will create fundamental change, and organizations which have embraced it as part of their AI journey, are in the mature thinking stage of evolving how it is embedded into their models. Opportunities exist in event monitoring, manufacturing spaces, chat commerce, and there is a huge amount of value in content search, and in the synthesis of information to enhance the human experience, said Smith.

We are in a “hype cycle” right now for Gen AI as it will be transformative for many industries. Mercer has identified four major areas of AI investment:

1. Customer Experience Enhancement – From customer 360 data analysis, personalization and utilization; to medical prognosis in healthcare, and portfolio management in financial services
2. Skills Augmentation – For research tools, leveling up technical skills, and proprietary language model creation
3. Operational Efficiencies – From front desk engagement to manufacturing maintenance
4. Development Efficiencies – In clinical trials, and automotive model development

Align Strategy on AI with Business Strategy

Start with a data strategy as AI needs a good foundation: identify areas where metric-based improvements are needed and see where Gen AI capability can be applied, such as chatbot investment, text creation and generation of marketing assets. While the capabilities of Gen AI are sophisticated, the rapid iteration of ideas and approaches for human asset augmentation to develop AI with massive data refinements, will  create innovation across industry.

Generation AI is a competitive business advantage to give brands the edge and improve customer experience --  and first movers will have the AI advantage.

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