Not all AI projects are the same: Tailor your AI project value proposition

Leadership in the Loop Volume 7

Amir Hartman | Managing Director,?Dasteel Consulting?| Director AI Strategy Research Experience Alliance, Fidere.ai, Praxis-AI

Venkataraman Lakshminarayanan |?Board Member, CRON AI, Former ServiceNow Value Leader

Having worked with senior business leaders for over 20 years, we’ve witnessed firsthand the soaring enthusiasm for artificial intelligence (AI) among business leaders. Companies across industries are eagerly exploring AI's potential, with a flurry of pilot projects and proof-of-concepts (POCs) underway.

However, our research reveals a concerning trend: albeit over 80% of companies are experimenting with AI, most of these experiments fail to translate into enterprise-wide deployments. We pointed this out in the first article in this series several weeks ago, and it still holds true.

The issue we see isn't a lack of use cases; in fact, the challenge lies in having too many possibilities and struggling to prioritize and execute effectively. Companies are drowning in a sea of AI opportunities, unable to focus their efforts and drive real, scalable impact. Part the problem is that companies don’t have a structured governance approach to AI initiatives, which makes roles, responsibilities, and accountabilities for outcomes difficult. Our work with the Experience Alliance highlights this issue.

It's time to break free from this cycle of experimentation and embrace a strategic, focused approach to AI implementation. We must move beyond the hype and address the critical barriers that hinder enterprise-wide adoption.

In this article, we'll explore practical strategies for overcoming POC paralysis and unlocking the true potential of AI across your organization. We'll delve into one of the key success factors, portfolio management, and aligning AI initiatives with core business priorities, where AI deployment leaders excel.

By adopting a disciplined, execution-oriented mindset, we can transform AI from a collection of isolated experiments into a powerful, enterprise-wide catalyst for growth and competitive advantage.

If you are a business leader overseeing substantial business units, you have felt the need for a structured approach to justify and fund AI projects. Here is a strategic guide that will help you tailor your approach based on the business objective the AI project targets, whether it is innovation, growth, operational efficiency, or risk mitigation.

Any AI project fits into one of these four quadrants of the framework:

The framework we present here is anchored by two critical dimensions:

Innovation / newness assesses the potential of an AI initiative to deliver new value to the market and, by extension, to the company.

Business Criticality measures the extent to which the enablement of a particular business process with AI is pivotal to the company's strategic objectives and competitive differentiation.

In the ROI of AI article in the Leadership in the Loop series, we defined several types of technology projects which help us understand the purpose of the project. Let’s now turn our focus to how we might tailor the value proposition for each type of project highlighting its strategic, financial and operational value, as applicable.

1.???? Innovating the Business

Focus on pioneering AI initiatives and new market entries. Traditional ROI metrics are not applicable; instead, innovation metrics and customer feedback are used.

• Strategic Value: Explore how the project will drive market disruption and create new revenue streams
• Financial Value: Quantify with ROI or IRR, though with a cautionary note to expect higher risk and longer gestation period
• Operational Value: Showcase how the project will overhaul existing processes or create new frameworks
• Success Measures to use: Adoption rates, new patents filed, first-to-market instances.
• Success Measures not to use: ROI (traditional 3-year discounted cash flow model)

2.???? Growing the Business

Make Investments in new AI-driven products and services to drive top-line growth. Success measured by revenue growth and market share.

• Strategic Value: Explain how the project will strengthen market positioning and enhance customer engagement / experience
• Financial Value: Point out any direct correlation with revenue growth
• Operational Value: Describe how the project will improve sales and marketing efficiency. Quantify in terms of efficiency metrics
• Success Measures to use: Customer acquisition rates, ROI from marketing campaigns.
• Success Measures not to use: Cost-saving metrics.

3.???? Running / Improving the Business

Undertake projects aimed at process automation and efficiency gains. Measured by cost savings and increased operational efficiency.

Improve the Business: Targeted at improving margins and optimizing processes. KPIs include margin improvement and error rate reduction.

• Strategic Value: Showcase the ability to streamline operations to achieve lean management. Quantify through operational metrics such as labor and cycle-time.
• Financial Value: Quantify cost savings from improved efficiency, and ROI with a medium-term time scale.
• Operational Value: Show how the project will automate routine tasks and optimize supply chains. Quantify using efficiency metrics
• Success measures: Operational cost reductions, process cycle times, Margin improvement
• Success measures not to use: New customer metrics.

4.???? Regulate / Protect the business

Focus on compliance and adherence to legal standards. ROI is not the primary measure; “cost of poor quality” metrics such as penalty reductions and audit results are more relevant.

Protect the Business: Emphasize securing data and maintaining trust. “Cost of poor quality” metrics such as reduction in incidents and improved response times are key.

• Strategic Value: Show how the project can deliver enhanced security and compliance with evolving regulations.
• Financial Value: Quantify cost and effort required for risk mitigation.
• Operational Value: Showcase advanced fraud detection and cybersecurity measures.
• Success Measures to use: Number of prevented security breaches, compliance audit successes, cost of poor quality
• Success measures not to use: Short-term profit metrics.

Here is an example of how you might apply it to your portfolio of projects.

Takeaways:

• Treat your AI investments like a portfolio, just like your financial investments.
• Be intentional with your AI investments.
• Don't expect the same returns from every initiative. Some will boost operations quickly. Others take time for big, disruptive innovation.
• Fund and resource them differently.

This investment framework, or one that you like better, can guide your AI journey. It will help you navigate the complexities and maximize value from AI.

As always, we also want to learn from you. Please share your experiences - what's working, what's not. Tell us your successes and challenges. Together, let’s unlock AI's full potential.

Please check out the other articles from the Leadership in the Loop series:

#1: AI: It's not Just About Technology

#2: Strategies for Overcoming AI Deployment Challenges

#3: Dual Dynamics: Harnessing and Adapting to Agentic AI for Tomorrow's World

#4: ROI of AI Projects: a Slam Dunk?

#5: The AI Literacy Imperative

#6: Embracing the Hippocratic Oath for AI

#AI #GenAI #AIGovernance #AIinvestments #AIportfolio #AIoutcomes #AIROI #valuemanagement