20% AI Time Best Spent:

1. Determine an AI strategy at the organizational level
a. First, ensure any AI vision is derived from the business vision. Later, during strategy execution, AI-driven opportunities may also positively reinforce and inspire strategic thinking.
b. As part of the overall business strategy.
c. And autonomously, regarding AI.
d. Determine a maximum of five critical AI themes that should drive strategy execution.

 Example: Microsoft aligned AI goals with cloud strategy, fueling Azure growth through AI-enhanced services aligned with corporate vision.

2. Invest in AI knowledge and skills
a. For everyone.
b. To understand — not to pass any exam.

Example: Amazon trained all employees on AI basics, empowering teams to identify automation opportunities across logistics and customer service.

3. Embed AI in the yearly change management portfolio

a. Integrate AI within the critical change initiatives of the annual change portfolio.
b. Break down the AI themes into a maximum of nine AI execution initiatives, ranging from “sea containers” to “shoeboxes.”
c. Balance AI across the three types of change: improvement (Type 1), renewal (Type 2), and innovation (Type 3).
d. Choose one AI-change “must-win battle” per year within the annual change portfolio.

Example: Unilever integrated AI projects into its annual transformation roadmap, focusing on demand forecasting and sustainable supply chain optimization.

80% Best AI Time Best Spent:

4. Enforce business objectives within sharply chosen AI-change initiatives (“shoeboxes”)

• Reason from the business towards AI.
• Think outside-in: from customers and processes toward AI technology.
• Acknowledge common mistakes when using new technologies. Technological impulses have historically led to wasteful expenditures due to a lack of outside-in and process-oriented approaches.
• Strategy = AI; AI = Strategy. Iterate between business objectives and AI opportunities. AI thinking can inspire business thinking.

Example: Airbnb used AI to improve search results, directly increasing booking conversion rates and customer satisfaction.

5. Apply AI potential integrally across all elements of the business model
a. Renew processes, structure & governance, HR & behaviors (i.e., culture), technology, and knowledge.

Example: Netflix applied AI across content recommendation, production, marketing, and customer support—transforming its entire digital business model.

6. Leverage human potential and ethics
a. AI should enable human capital to reach its full potential and create more value — especially in the context of labor scarcity. Invest in human-AI collaboration.
b. Make ethics a virtue and embed it. The core elements of AI ethics — accountability, transparency, and trust — should be integrally designed, just as behaviors (culture) only develop when they are consciously addressed.

Example: Salesforce embedded ethical AI principles in product development, enhancing trust and human-centered innovation in its CRM tools.

7. Real AI value rests mainly on data management

a. AI value = data value. AI excels at identifying patterns and unlocking business potential. Refer to the Turner series “The 10 of Data Management.”
b. Without a strong data strategy, governance, and management, AI will result in costly guesswork and trivial spending.

Example: Starbucks built strong data infrastructure before AI personalization, boosting customer retention through data-driven product recommendations.

8. Adopt a value chain and ecosystem mindset
a. Especially when tackling radical business model renewal and innovation. These initiatives only succeed when they cross silos, organizational, and even sector boundaries.

Example: BMW collaborated with suppliers and tech startups to co-develop AI systems across its manufacturing and logistics ecosystem.

9. Business case > Use case
a. AI should drive measurable benefits. Ensure every initiative adds value — and measure it.
b. Once use cases are defined, simultaneously define the business case before proceeding to AI execution.
c. Install rigorous execution and benefit management. Avoid letting AI become another hype where practical, grounded implementation comes five years too late. Demand a business case for every use case — and measure both output and outcome (e.g., client satisfaction, margin improvement, costs, productivity) meticulously.

Example: HSBC linked each AI use case to ROI metrics before implementation, avoiding tech vanity projects and ensuring measurable gains.

10. Lead and reward AI execution over conceptual ‘bursts’
a. It’s human nature to focus on the conceptual (“sexy”) parts of any hype, trend, or new concept — and AI is no exception. Leaders must often say “no” 80% of the time when new ideas arise. This is true for all strategy execution. The scarcity isn’t in ideas but in sheer, relentless execution.
b. Install a second-order AI execution learning process. Developments continue to accelerate. Today’s breakthrough is tomorrow’s commodity.
c. Adopt the fundamentals of the Strategy = Execution methodology. For example, use short analysis and design cycles (4–6 weeks), execute in waves to learn and scale, and maintain an optimal balance between persistence and agility. Obvious but essential: balance the hard (objectives, content design), the soft (ownership, execution strategy), and the (Agile) project/program management elements.

Example: Procter & Gamble prioritized factory AI deployment over pilot hype, driving productivity gains through focused execution.

Important user guide
a. Focus and smart combinations.
Successful strategy execution requires focus and domain expertise in change initiatives. That’s why this specialized series “The 10 of…” exists. At the same time, modern strategy execution demands that change initiatives (projects and programs) aimed at reducing costs and increasing productivity do not come at the expense of profitability, growth, and innovation—and vice versa. This calls for: both a specialized approach to specific challenges (as offered in this “10 of” series) and a smart, integrated deployment of multiple change initiatives. See also the other editions in the “10 of” series.

b. Tailor the core elements of the Strategy = Execution method to your context:
(1) brief analysis and design phases (4–6 weeks),
(2) execution and scaling in waves (not pilots, but self-improving waves),
(3) including benefits management and embedding results.
Naturally, the hard side (goals, content), the soft side (ownership, execution strategy), and working in a project- and/or programmatic way must be balanced.