The following is just one of the slides from the Gartner AI webinar this morning that caught my attention.
Here are Gartner’s definitions and interpretations of the key terms from the above slide:
1. Develop AI Ambition
This involves determining where and how your organization will utilize AI. Gartner recommends using the AI Opportunity Radar to map out these ambitions, helping to focus efforts on areas with the highest potential impact.
2. Create Initial Use Case Portfolio
This step entails identifying and prioritizing specific AI applications that align with your organization’s strategic goals. By selecting a diverse set of use cases—ranging from quick wins to long-term projects—you can demonstrate AI’s value and build momentum for broader adoption.
3. Assess AI Maturity
Assessing AI maturity involves evaluating your organization’s readiness and capability to implement AI initiatives. Gartner’s AI Maturity Model outlines stages from initial awareness to full transformation, helping organizations understand their current position and plan for advancement.
4. Develop AI Strategy
Developing an AI strategy means creating a comprehensive plan that encompasses vision, value realization, risk management, and adoption pathways. Gartner emphasizes the importance of aligning AI initiatives with business objectives and ensuring stakeholder buy-in for successful implementation.
5. Define AI Roadmap
An AI roadmap outlines the sequence of initiatives and investments required to achieve your AI strategy. It includes timelines, resource allocation, and key milestones, ensuring a structured approach to AI adoption and scaling.
6. Improve Capabilities with AI Tools
Enhancing capabilities with AI tools involves integrating AI technologies into existing processes to augment decision-making, automate tasks, and drive innovation. This step focuses on selecting the right tools and ensuring they are effectively utilized across the organization.
By systematically addressing these areas, organizations can effectively harness AI’s potential to drive innovation and achieve strategic objectives.
MY THOUGHTS AND QUESTIONS
- How do we know this strategy of “everything fitting together” works?
- What is a possible, better alternative strategy?
- Why isn’t assessing AI maturity the first step before developing an AI ambition and creating an initial use case portfolio?
My Main Question:
How do you know that the AI Ambition is correctly aligned with all stakeholders, both within and external to the enterprise?
For example, referring to the following video—https://youtu.be/49BS-MkGoak—would the ambition of automating the procurement process for the DND have been successful without knowing how all stakeholders throughout the enterprise work? As a side note, my self-learning algorithm solution for the DND operated within an early AI platform.
Why I Chose To Lead With AI Maturity:
To ensure AI ambition aligns with all stakeholders (internal and external), organizations must adopt a structured, inclusive approach that prioritizes deep stakeholder understanding, continuous feedback, and ethical governance. The DND procurement case study exemplifies this, where success hinged on mapping hidden stakeholder dynamics. Below is a framework for alignment, supported by industry research and lessons from the video:
1. Stakeholder Mapping & Discovery
- Identify all agents in the system:
- Internal: Procurement teams, finance, IT, end-users (e.g., DND’s service department technicians).
- External: Suppliers, logistics partners (e.g., UPS), regulators (e.g., customs), SMEs.
- Tools:
- AI-powered stakeholder analysis to uncover hidden relationships and incentives (e.g., technicians “sandbagging” orders to hit targets, suppliers’ geographic constraints).
- Influence vs. interest grids to prioritize stakeholders based on power and engagement needs.
Example: In the DND project, stakeholders included customs officials, small suppliers, and service teams. Without understanding their workflows (e.g., order timing impacting customs delays), the AI solution would have failed 1.
2. Early, Transparent Engagement
- Conduct pre-implementation workshops to:
- Surface stakeholder pain points (e.g., service teams’ incentive structures).
- Address ethical concerns (e.g., bias in supplier selection algorithms).
- Use AI-driven sentiment analysis to track stakeholder sentiment in real time (e.g., supplier feedback on bidding processes).
Why it matters: The DND team engaged UPS and customs early to design a unified PO/shipping system, avoiding delays.
3. Iterative Feedback Loops
- Embed stakeholders in AI development:
- Co-create solutions with “AI Cells” (cross-functional teams of stakeholders and technologists).
- Use dynamic stakeholder maps to adjust engagement as priorities shift (e.g., suppliers’ pricing volatility).
- Metrics: Track alignment via stakeholder satisfaction scores, adoption rates, and conflict resolution speed.
Example: The DND system allowed buyers to adjust supplier ranking weights (price vs. delivery reliability), reflecting evolving stakeholder needs 1.
4. Ethical Governance & Accountability
- Assign accountability: Designate stakeholders (e.g., procurement directors, suppliers) to oversee AI outcomes.
- Implement Responsible AI frameworks:
- Bias audits for AI models (e.g., ensuring SME suppliers aren’t excluded).
- Explainable AI (XAI) dashboards for stakeholders to audit decisions.
Risk Mitigation: In the DND case, customs compliance was automated, but human oversight ensured alignment with regulatory changes 1.
5. Balance AI with Human Expertise
- Hybrid decision-making: Use AI for data analysis (e.g., predicting part price fluctuations) but retain human judgment for nuanced decisions (e.g., supplier relationship management).
- Upskilling: Train stakeholders to interpret AI insights (e.g., procurement teams analyzing dynamic flux trends).
Outcome: The DND system reduced costs by 23% over seven years while maintaining supplier diversity-a balance of automation and human oversight 1.
Why Traditional Approaches Fail
- 80% of AI projects fail due to misaligned stakeholder expectations and opaque decision-making 6.
- Key gaps:
- Assuming stakeholder needs are static (e.g., ignoring customs’ evolving documentation requirements).
- Over-reliance on centralized data without contextual stakeholder input (e.g., ERP systems missing SME supplier constraints).
Conclusion
Aligning AI ambition with stakeholders requires proactive discovery of hidden agents, continuous feedback integration, and ethical guardrails. The DND case succeeded because it treated stakeholders (technicians, suppliers, customs) as co-designers, not bystanders. As McKinsey notes, procurement transformations demand “vision, ambition, and sustained commitment”-all rooted in stakeholder collaboration.
Best Practices:
- Map stakeholders with AI tools to uncover hidden influencers.
- Embed stakeholders in AI development cycles.
- Use hybrid human-AI governance to balance efficiency with ethics.
- Measure alignment through satisfaction metrics, not just ROI.
By adopting this approach, organizations can avoid the “over-promise, underdeliver” trap that plagues 80% of AI projects.
ONE FINAL THOUGHT
Never use a technology-led, equation-based model because it becomes an exercise in cost justification of a technology product investment. Instead, use a human-led, agent-based development and implementation model.
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Procurement Insights
According To Gartner When It Comes To AI This Is “How Everything Fits Together”
Posted on May 16, 2025
0
The following is just one of the slides from the Gartner AI webinar this morning that caught my attention.
Here are Gartner’s definitions and interpretations of the key terms from the above slide:
1. Develop AI Ambition
This involves determining where and how your organization will utilize AI. Gartner recommends using the AI Opportunity Radar to map out these ambitions, helping to focus efforts on areas with the highest potential impact.
2. Create Initial Use Case Portfolio
This step entails identifying and prioritizing specific AI applications that align with your organization’s strategic goals. By selecting a diverse set of use cases—ranging from quick wins to long-term projects—you can demonstrate AI’s value and build momentum for broader adoption.
3. Assess AI Maturity
Assessing AI maturity involves evaluating your organization’s readiness and capability to implement AI initiatives. Gartner’s AI Maturity Model outlines stages from initial awareness to full transformation, helping organizations understand their current position and plan for advancement.
4. Develop AI Strategy
Developing an AI strategy means creating a comprehensive plan that encompasses vision, value realization, risk management, and adoption pathways. Gartner emphasizes the importance of aligning AI initiatives with business objectives and ensuring stakeholder buy-in for successful implementation.
5. Define AI Roadmap
An AI roadmap outlines the sequence of initiatives and investments required to achieve your AI strategy. It includes timelines, resource allocation, and key milestones, ensuring a structured approach to AI adoption and scaling.
6. Improve Capabilities with AI Tools
Enhancing capabilities with AI tools involves integrating AI technologies into existing processes to augment decision-making, automate tasks, and drive innovation. This step focuses on selecting the right tools and ensuring they are effectively utilized across the organization.
By systematically addressing these areas, organizations can effectively harness AI’s potential to drive innovation and achieve strategic objectives.
MY THOUGHTS AND QUESTIONS
My Main Question:
How do you know that the AI Ambition is correctly aligned with all stakeholders, both within and external to the enterprise?
For example, referring to the following video—https://youtu.be/49BS-MkGoak—would the ambition of automating the procurement process for the DND have been successful without knowing how all stakeholders throughout the enterprise work? As a side note, my self-learning algorithm solution for the DND operated within an early AI platform.
Why I Chose To Lead With AI Maturity:
To ensure AI ambition aligns with all stakeholders (internal and external), organizations must adopt a structured, inclusive approach that prioritizes deep stakeholder understanding, continuous feedback, and ethical governance. The DND procurement case study exemplifies this, where success hinged on mapping hidden stakeholder dynamics. Below is a framework for alignment, supported by industry research and lessons from the video:
1. Stakeholder Mapping & Discovery
Example: In the DND project, stakeholders included customs officials, small suppliers, and service teams. Without understanding their workflows (e.g., order timing impacting customs delays), the AI solution would have failed 1.
2. Early, Transparent Engagement
Why it matters: The DND team engaged UPS and customs early to design a unified PO/shipping system, avoiding delays.
3. Iterative Feedback Loops
Example: The DND system allowed buyers to adjust supplier ranking weights (price vs. delivery reliability), reflecting evolving stakeholder needs 1.
4. Ethical Governance & Accountability
Risk Mitigation: In the DND case, customs compliance was automated, but human oversight ensured alignment with regulatory changes 1.
5. Balance AI with Human Expertise
Outcome: The DND system reduced costs by 23% over seven years while maintaining supplier diversity-a balance of automation and human oversight 1.
Why Traditional Approaches Fail
Conclusion
Aligning AI ambition with stakeholders requires proactive discovery of hidden agents, continuous feedback integration, and ethical guardrails. The DND case succeeded because it treated stakeholders (technicians, suppliers, customs) as co-designers, not bystanders. As McKinsey notes, procurement transformations demand “vision, ambition, and sustained commitment”-all rooted in stakeholder collaboration.
Best Practices:
By adopting this approach, organizations can avoid the “over-promise, underdeliver” trap that plagues 80% of AI projects.
ONE FINAL THOUGHT
Never use a technology-led, equation-based model because it becomes an exercise in cost justification of a technology product investment. Instead, use a human-led, agent-based development and implementation model.
30
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