AI ROI vs AI R&D: Knowing Which Game To Play

🔗 https://shre.ink/TobiasZ-ROI-vs-RD

In this blog post, Tobias Zwingmann explains how to differentiate and balance two key approaches in the implementation of artificial intelligence (AI): ROI-focused projects and R&D-driven initiatives.

1. Two Different Games

There are two primary strategies for AI implementation:

1.1 Game 1: ROI-First (Quick Wins)

This approach targets immediate, tangible benefits by leveraging established AI methodologies. Characteristics include:

• Clear Financial Returns: Achieving measurable gains within 3-6 months.

• Focused Projects: Addressing specific challenges with well-defined solutions.

• Proven Techniques: Utilizing AI applications that have demonstrated success in similar contexts.

Example: Implementing AI to automate invoice processing, thereby reducing manual labor costs by 30% in a quarter.

1.2 Game 2: R&D (Long-term Transformation)

This strategy is centered on pioneering advancements that may redefine business operations over time. Key aspects include:

• Technical Innovation: Pursuing breakthroughs that could revolutionize industry practices.

• Capability Building: Developing new competencies that offer a competitive edge.

• Insight Generation: Gaining knowledge that may lead to future business improvements.

Example: An industrial manufacturer creating AI systems capable of autonomously operating new machinery with minimal human instruction.

2. When Companies Mix Them Up

Problems arise when organizations conflate these two strategies:

• Overpromising: Presenting R&D initiatives as quick ROI projects to secure funding, leading to unrealistic expectations.

• Overcomplicating: Imbuing straightforward ROI projects with unwarranted complexity under the guise of transformation.

Consequences include project failures, loss of credibility, and increased skepticism toward future AI endeavors.

2.1 Warning Signs For When Games Get Mixed Up

Zwingmann identifies indicators of strategic misalignment:

2.1.1 High ROI in Short Time Promise

• Viewing AI as a quick fix for deep-rooted organizational issues.

• Spending excessive time in discussions without actionable progress.

• Frequent shifts in project goals without clear justification.

2.1.2 Buzzword & Complexity Land

• Labeling simple automation efforts as "digital transformation."

• Over-engineering solutions to appear innovative.

• Rejecting quick wins for not being groundbreaking enough.

• Experiencing analysis paralysis by attempting to address all problems simultaneously.

3. A Rough-Cut Decision Framework

To assist in strategy selection, Zwingmann proposes a decision framework:

• Define the Problem: Is it a specific issue or a broad challenge?

• Assess the Solution: Is there an existing AI solution, or does it require innovation?

• Evaluate the Timeline: Is the goal short-term impact or long-term transformation?

This framework aids in determining whether to pursue an ROI-focused project or an R&D initiative.

4. Balancing Both Games

Zwingmann advises organizations to maintain a balanced AI portfolio:

• Allocate Resources: Dedicate efforts to both quick wins and transformative projects.

• Set Appropriate Expectations: Align project goals with their respective strategies.

• Foster a Culture of Honesty: Clearly communicate the nature and objectives of each project.

4.1 Communicating Your Game

Effective communication is crucial:

4.1.1 ROI Projects

• Emphasize immediate benefits and specific outcomes.

• Highlight the use of proven AI techniques.

4.1.2 R&D Projects

• Focus on potential for innovation and long-term value.

• Acknowledge inherent uncertainties and risks.

5. Connecting ROI And R&D With Your AI Roadmap

Integrate both strategies into a cohesive AI roadmap:

• Short-Term Wins: Implement ROI projects to build momentum and demonstrate value.

• Long-Term Vision: Pursue R&D initiatives to drive future growth and transformation.

• Continuous Evaluation: Regularly assess and adjust the balance between ROI and R&D efforts.

6. Conclusion

Zwingmann concludes by reiterating the importance of distinguishing between ROI and R&D strategies in AI implementation. Clear differentiation and appropriate alignment of expectations are essential for successful outcomes and sustained organizational credibility.