February DLT Monthly Meeting Summary - "AI Impacts on Enterprise"

It was so good to see many of you for the February 14th DLT meeting. During the call, we covered three major agenda items:

1) Framing How AI is Impacting Enterprises.

The impact of AI on organizations, jobs, and skills is the Spring Samudra research topic. Abby led us through some early findings:

• Don’t talk about AI; talk about business solutions or the problem you’re solving.
  Members are leading with business impact, not technology when discussing AI. Achieving operational efficiencies remains a top goal. Samudra's research framed this as Cause (Be clear on why you are deploying AI), Commitment (what resources and skills you will commit to the deployment), and Communicate (as in, communicate your intentions to employees and leadership in multiple modalities, many times.)

• Two Paths for Deploying AI: Autonomous (agentic) vs. Augmentative (co-worker)
  To date, almost all AI deployments follow a co-worker or augmented model. The emerging Autonomous or agentic AI promises a very different impact on productivity, skills, and org structures. While we expect a blend of both models to exist, we recommend comparing these models when determining how you want to deploy.

• AI as a Co-Worker: AI assists employees (e.g., copilots in decision-making). This model accelerates the upskilling of junior employees. Members are already hiring more junior-skilled employees with AI skills and quickly finding them to be more productive than longer-tenured staff.

• AI as an Autonomous Agent: AI operates independently, reducing the need for junior roles but retaining skilled human oversight. This is an emerging model but yields enormous productivity gains, many fewer employees, and advantages highly skilled problem solvers over workers.

• What Can We Learn from AI-First Organizations

• Start by optimizing the customer or user experience, then work backward to build workflow and process.

• “Service-as-Software”

• Lean into proprietary data to build verticalized AI expertise

• Practical Use Cases:
  Members shared concrete AI applications:

• Leveraging AI for customer and product lifecycle management.

• Using AI during organizational transitions, especially for merging operations and enhancing efficiencies.

• Members shared concerns about regulatory fragmentation, data privacy, and educating teams and leadership.

• We also discussed use cases in the press:

• Klarna reports AI impact of:

• Customer Service: One chatbot = 700 agents; answers queries, on average, 9 minutes faster.

• Marketing: $10M saved using AI-generated imaging.

• Operational Efficiency: in-house counsel generates contracts in minutes versus hours

• Jerry Insurance uses AI for customer service automation, insurance comparison & quotes, personalized use engagement, vehicle maintenance insights, data analysis, and operational efficiencies.

2) Additional Member approaches and responses. Some key takeaways:

Workforce & Talent Strategies

• Junior Talent vs. Senior Expertise:

• As noted above, in the AI as co-worker model, some companies are hiring more junior staff due to faster onboarding and upskilling through AI.

• Others are reducing entry-level roles and focusing on retaining senior engineers to manage AI-driven processes.

• Members raised concerns about the pipeline for future experts, as fewer junior hires may limit long-term growth.

• Upskilling & Role Evolution:
  Members highlighted the need for continuous learning and adapting existing employees to work alongside AI.

• One member described a five-year journey to upskill or transition legacy employees.

• Companies are investing in prompt engineering and other AI-specific skills to stay competitive.

AI Governance & Risk Management

• Shadow AI Management:
  According to a McKinsey survey last month, three times more employees are using AI for more than a third of their work than management expects. This trend is reminiscent of the early days of "Shadow IT."

• Practice: Implement clear governance policies to monitor AI use and ensure compliance. Also, embrace and harness employee use to fully harness the power of AI.

• Data Governance Integration:

• Companies are extending data governance frameworks to track AI-generated outputs and ensure audit traceability.

• One member uses AI monitoring tools to detect data leakage and nefarious activities in real-time.

• Legal & Compliance Considerations:
  Members raised concerns about AI regulations differing across global regions, especially regarding privacy and data sovereignty.

• Some companies are consulting AI ethics experts to navigate evolving legal landscapes.

Change Management Practices

• Scaling AI Education & Adoption:

• Companies are establishing internal learning programs to educate staff on using AI tools responsibly.

• A member created a digital twin of herself to deliver AI-related messages, improving communication efficiency.

• Governance at Scale:
  Members discussed scaling governance frameworks to match AI’s rapid adoption. This involves:

• Building federated models for managing AI across business units.

• Creating playbooks to standardize AI implementation and minimize risks.

• Partnerships for AI Security:

Companies are increasingly relying on external partners to manage AI security and critical vulnerabilities.

3) “DLT topics” survey results and future DLT topics

• The March meeting will focus on “storytelling and improving strategic communications.”

• Additional topics (and other slides) are included at the end of the meeting deck, which can be found here.