June DLT Monthly Meeting Summary - "Burning Issues"

For the June 14th DLT Meeting, we had a series of burning issues discussion, which largely centered on the topic of AI in the following ways:

1) Building the case for AI and Governance

• Experimentation - One member shared the importance of rational experimentation and finding use cases.

• Existing platforms - Another spoke to their approach to AI integration, which leverages existing platforms and tools.

• Productivity - AI was being used to boost technician productivity and to minimize risks. Labor productivity was mentioned several times.

• Data & Governance - AI was also being used to manage unstructured data and enhance product cycles, while highlighting the importance of responsible AI and its integration into their governance structure.

• Balancing Innovation - Many mentioned the importance of balancing innovation with governance and risk management.

• Education and Lessons Learned - One member’s AI Council focuses on employee education and early-stage implementation of AI and lessons learned from early instances of Google, Copilot.

• Good IT Hygiene - Data classification and management, role-based access controls, database monitoring, and cloud migration all need to be in good shape.

• Customer Care and KM - Another member highlighted their focus on customer case and knowledge management; and that they were cautious about using sensitive data, initially limiting AI to internal data and associates.

• Business Development - AI is being incorporated into all business development discussions for one member company.

2) Vendor strategies

• The need for clear business cases - There is a challenge of building partner infrastructure without established business cases or ROI.

• Clarity of Priorities - One member highlighted the need to understand a company’s measurement and executive priorities to effectively implement AI and analytics.

• Ecosystem Development - Another member spoke to how they developed a partner ecosystem.

• Private Instances - One member spoke to how they are leveraging LLMs and private instances for their customers. It's critical to perform security checks and careful application reviews.

3) Skills required

• In-house team - In-house teams are key as they will have a strong understanding of the data and technology to ensure vendor accountability and project success.

• Solution architects - Include architects that can think systematically and understand both technology and business, would be most effective.

• Data scientists - Data scientists can be helpful in turning data into prescriptive analytics, but may be competing against big players.

• Bring AI into Existing Roles - Some discussion was around bringing AI expertise into existing roles.

So plenty of discussion, and we weren’t able to touch on all the submitted burning issues. We can leverage the Samudra private member forum on LinkedIn for additional conversations.