You’ve set strategic goals for AI in your organization, but how do you turn a strategy into a successful implementation plan? In this article, we’ll explore the key approaches identified by reputable sources and provide insights on building an effective AI plan.
Integrating AI into Business Strategy
The Boston Consulting Group and Accenture recently published studies analyzing successful AI adoption in companies. These studies highlight the importance of choosing the right set of AI-enabled applications for long-term success. According to the research, here are six key approaches:
- Integrate AI into your business strategy.
- Prioritize revenue growth over cost reduction.
- Take calculated risks to achieve greater impact.
- Align AI development with its usage.
- Treat AI as a business transformation.
- Invest in AI talent, governance, and process change.
Factors Driving Strategic Scalable Adoption of AI
Accenture’s report, based on insights from over 1,500 C-Suite executives globally, identifies several factors driving the strategic scalable adoption of AI:
- CEO focus with advanced analytics and data teams solving significant challenges.
- Multi-disciplinary teams championed by a Chief AI, Data, or Analytics Officer.
- Ability to filter out data noise and focus on essentials.
- Intelligent automation and predictive reporting.
- Addressing digital/AI/data asset debt.
- Adopting an experimental mindset to achieve scale and returns.
Setting the Strategic Levers of AI Strategy
It’s crucial to focus on business and key applications when setting the strategic levers of your AI strategy. Understanding your organization’s culture and capability is equally important. While technology enables value, it doesn’t create value itself. Recognizing this is essential for success.
However, many AI strategies fail when transitioning from strategy to plan. Often, companies lack the capability to understand the possibilities AI offers, leading to missed opportunities or misjudged implementation challenges. Sometimes, the focus is solely on feasible technology, neglecting the capabilities and company structures required.
Components of an AI Plan
To build a successful AI implementation plan, consider these four components:
- Business applications: Create a catalog of AI-driven use cases with a clear description of the value or cost impact.
- Available data: Identify and evaluate internal and external data sources for activating AI use cases, considering quality, coverage, relevance, and bias.
- Capability evaluation: Assess the available capability required for activating AI use cases, addressing the five capabilities for effective and safe AI and ML usage.
- Feasibility assessment: Determine if the business use case can be solved with the available data and capabilities using AI technology, ensuring expertise in a wide range of AI and machine learning technologies.
With these components, you can prioritize and execute your AI strategy effectively by focusing on growth opportunities, quick wins, and the right technology platforms. Additionally, consider acquiring necessary data assets, establishing appropriate controls and safeguards, and finding the optimal mix of insourcing and outsourcing.
Planning Tools
When planning AI business use cases, it’s essential to treat them as products rather than projects due to their continuous iteration and evaluation. Modern agile planning tools, such as the Machine Learning Canvas, can help manage AI use cases effectively. It condenses the ML business value proposition onto a single page, aligning all stakeholders.
The ML Canvas outlines the value propositions, predictions, learning, and evaluation aspects of an AI product. It helps keep everyone involved on the same page, including data scientists, software engineers, product designers, and business managers.
The ML Canvas is a valuable tool, licensed under the Creative Commons. You can access it at machinelearningcanvas.com.
In Conclusion
By mapping your business problems into AI products, assessing feasibility, and aligning requirements with capabilities and data, you can develop a de-risked plan for adopting AI in your business. Taking a structured approach like this aligns with the success quadrants identified by both BCG and Accenture in their surveys of thousands of companies.
Remember, the key to success lies in integrating AI into your business strategy, prioritizing revenue growth, aligning with usage, treating it as a transformation, and investing in talent, governance, and process change. With a well-crafted AI plan, you can turn your AI strategy into a reality and drive long-term success in your organization.
