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Discussion board 9.5: Think & share 💡Leading Organisational Change for Agentic AI Adoption

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  1.  Module 9: Introduction and Instruction (Completed)
  2.  Video 1 [03:50]: Module Introduction (Completed)
  3.  Video 2 [08:02]: Agentic AI Landscape (Completed)
  4.  Video 3 [04:40]: AI in Operations (Completed)
  5.  Video 4 [06:46]: Trust & Compliance (Completed)
  6.  Self-Study Quiz 9.1: Think & Apply💡 Evaluating Agentic AI Opportunities in Operations ((Must submit the Quiz))
  7.  Video 5 [08:43]: Account Management Shift (Completed)
  8.  Video 6 [06:05]: Pipeline Transformation (Completed)
  9.  Video 7 [05:56]: Future & Integration (Completed)
  10.  Video 8 [07:50]: Agentic AI: Implementation & Pitfalls (Completed)
  11.  Self-Study Quiz 9.2: Think & Apply💡 Leading Agentic AI Transformation Across Customer Operations and Sales ((Must submit the Quiz))
  12.  Video 9 [07:32}: Strategy & Operating Model (Completed)
  13.  Video 10 [03:16]: Final Thoughts-2 (Completed)
  14.  Self-Study Assignment 9.3: Think & Act💡Leading in an AI-Enabled Workplace (Completed)
  15.  Reading: The Executive Decision Room (Completed)
  16.  Self-Study Assignment 9.4: Think & Act💡 Leadership Lessons for Organisational Transformation (Completed)
  17.  Discussion board 9.5: Think & share 💡Leading Organisational Change for Agentic AI Adoption ((Not yet completed))
  18.  Summary and Video Transcripts: Module 9⭐ ((Must view in order to complete this module item))
  19.  Professor Ian's Recommendation for Further Learning: Module 9 ((Must view in order to complete this module item))
  20.  Module 9: Q&A Discussion Board ((Not yet completed))
  21.  Module 9: Feedback Survey ((Must submit the Quiz))

Module Outcomes.pngLearning Outcomes Addressed: 

  • Evaluate readiness gaps in workflows, data and governance required for effective agentic AI adoption.
  • Apply five strategic principles to design and scale agentic AI initiatives within their own organisational context.

Scenario

Imagine your organisation has decided to introduce agentic AI within the next 12 to 18 months.

As a leader or manager, identify one operations or sales workflow that you believe offers the greatest opportunity for agentic AI adoption.

In your discussion post, address the following:

Discussion Questions

  1. Describe the workflow you selected and explain why it would be a suitable starting point.
  2. Based on the concepts covered in this module, identify the greatest readiness challenge your organisation is likely to face. Consider workflow maturity, data quality, governance, system integration or people readiness.
  3. Explain how you would apply one or more of the strategic principles discussed in the module to improve the likelihood of a successful deployment.
  4. Describe one implementation risk that could prevent the initiative from succeeding and explain how you would mitigate it as a leader.
  5. Finally, explain how you would determine whether the initiative has been successful. Consider both operational outcomes and measures of adoption, quality, or business value.

Support your viewpoints with insights from the module and, where appropriate, draw upon your own professional experience.

Peer Engagement

After posting your response:

  • Review at least one peer's contribution.
  • Identify one governance or operational risk they may have overlooked.
  • Suggest one practical action that could improve scalability.

We also encourage you to go through your peers' responses and share your views.

Estimated Duration: 30 minutes

NOTE: This is a practice activity and will not be graded or impact your programme completion. However, we strongly encourage participation for a more holistic learning experience.

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    • Collapse Subdiscussion Edoardo Bertolani

      Edoardo Bertolani

      I would select the corporate client relationship-management workflow, from identifying opportunities to preparing meetings and following up. Agentic AI could collect information, prepare client briefs, identify opportunities and recommend next actions, allowing relationship managers to spend more time with clients. The module shows that AI can significantly reduce account preparation time and increase client coverage. The biggest challenge would be data quality and integration, as client information is often fragmented across different systems. I would therefore follow the principles of “redesign, don’t automate” and “data quality is a binding constraint.” The main risk is low employee trust and adoption. I would start with a small group of enthusiastic users who could later become internal champions. Success should be measured through reduced preparation time, more clients covered, better opportunities identified, adoption and ultimately increased business generated.
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      I would select the corporate client relationship-management workflow, from identifying opportunities to preparing meetings and following up. Agentic AI could collect information, prepare client briefs, identify opportunities and recommend next actions, allowing relationship managers to spend more time with clients. The module shows that AI can significantly reduce account preparation time and increase client coverage.

      The biggest challenge would be data quality and integration, as client information is often fragmented across different systems. I would therefore follow the principles of “redesign, don’t automate” and “data quality is a binding constraint.”

      The main risk is low employee trust and adoption. I would start with a small group of enthusiastic users who could later become internal champions.

      Success should be measured through reduced preparation time, more clients covered, better opportunities identified, adoption and ultimately increased business generated.

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        • Collapse Subdiscussion Davide Silva

          Davide Silva

          In a preminent international bank I previously led an AI/ML Lab generating leads for product-specific investment campaigns. At that time ... AI Agents were not availalble yet. Nowadays I would adopt them with focus on sales lead generation and campaigns orchestration: an agent could identify high-potential clients, prioritise opportunities, recommend next-best actions and coordinate campaign execution on my behalf.  The greatest readiness challenges would be data quality and governance, particularly ensuring reliable client data and explainable recommendations ("black-box" phenomenon should be absolutely avoided this time). I would apply a human-in-the-loop, value-first approach, starting with a narrow workflow and measurable use case before scaling. The key risk is low user trust; I would mitigate this through transparent recommendations, training and early user involvement. Success would combine conversion uplift, productivity gains, recommendation quality and sustained adoption.
          Manage Discussion Entry

          In a preminent international bank I previously led an AI/ML Lab generating leads for product-specific investment campaigns. At that time ... AI Agents were not availalble yet.

          Nowadays I would adopt them with focus on sales lead generation and campaigns orchestration: an agent could identify high-potential clients, prioritise opportunities, recommend next-best actions and coordinate campaign execution on my behalf. 

          The greatest readiness challenges would be data quality and governance, particularly ensuring reliable client data and explainable recommendations ("black-box" phenomenon should be absolutely avoided this time). I would apply a human-in-the-loop, value-first approach, starting with a narrow workflow and measurable use case before scaling.

          The key risk is low user trust; I would mitigate this through transparent recommendations, training and early user involvement. Success would combine conversion uplift, productivity gains, recommendation quality and sustained adoption.

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