3 Principles for Investing in AI

 

Artificial Intelligence (AI) is transforming the business landscape, offering unprecedented opportunities for optimisation and value generation. However, effective AI adoption requires more than just technology; it requires strategic investment and a clear focus on specific business processes.

 

AI is making processes more efficient and decisions more data-driven. Yet, without a clear strategy, AI can become an expensive experiment with little return on investment, as it points out **Barbara Wixom (**principal research scientist at the MIT Center for Information Systems Research), CISR research fellow Cynthia M. Beath and Brian Eastwood (MIT Sloan Business School) in his article “Artificial intelligence is now everyone’s business”

The main challenge lies in understanding how to invest in AI effectively.

By following three guiding principles—investing in AI capabilities, a holistic process approach, and focusing on value realization—companies can ensure that their AI investments lead to meaningful and sustainable improvements.

Principle 1: Invest in Building your own AI Capabilities

While it may be tempting to rely on off-the-shelf AI tools, these solutions often lack the customization and deep integration needed for long-term success. As Wixom points out, “AI is advanced data science, and you need to have the right capabilities to work with it and manage it properly.” Therefore, organizations should focus on developing internal capabilities that allow for greater control and understanding of AI technologies.

Principle 2: A holistic end-2-end business process approach

AI should not be siloed; it must be integrated across the full end-2-end business processes. This requires involving stakeholders from different parts of the business process cope, ensuring that everyone understands the potential and limitations of AI.

Engaging a broad range of stakeholders helps bridge the gap between technical and non-technical teams.

Principle 3: Focus on value and the money (even within big corporations)

More than ever, AI bridges the gap between digitalization and business.

The Value Creation Cycle

  1. Data Collection: Gather comprehensive data from relevant sources.
  2. Insight Generation: Use AI to analyze this data and generate actionable insights.
  3. Action: Implement changes based on these insights to improve processes.
  4. Value Creation: Achieve tangible improvements, such as increased efficiency and customer satisfaction.
  5. Value Monetization: Translate these improvements into financial benefits.

Monetization is often the most challenging step. Many organizations achieve value creation but struggle to realize financial returns. Without a clear focus on monetization, AI risks becoming an underutilized tool. As Wixom emphasizes, “AI will end up being a tool that sits on a shelf unless you link it to specific initiatives and outcomes that are compelling for your organization.”

By investing in AI capabilities, involving stakeholders, and focusing on value realization, enterprises can optimize their business processes and achieve significant returns on their AI investments. These principles are interlinked, each reinforcing the other to create a robust AI strategy. As AI continues to evolve, adhering to these principles will be key for successful integration, ensuring that AI becomes a valuable asset rather than an expensive toy.