AI Investments: How to Stop Wasting Money and Start Driving Real Impact

Stephanie Gradwell Managing Partner

7 min read .

AI is not plug-and-play. Yet, businesses continue to roll out AI solutions, expecting seamless efficiency gains and revenue uplifts, only to find themselves disappointed. Despite the hype, only 24% of companies see meaningful returns from their AI investments. (BCG,2024)

Why does this happen?

  • They don’t see the whole picture – AI is often seen as a tool rather than a strategic enabler, leading to disjointed projects that fail to align with business objectives.
  • They underestimate the ripple effects – AI initiatives don’t operate in isolation. A poorly thought-out project can disrupt workflows, create resistance, or introduce hidden costs.
  • They don’t prepare for what’s next – AI evolves rapidly. A project that seems cutting-edge today might be obsolete tomorrow.

To avoid these pitfalls, organisations need a structured, forward-thinking approach that ensures AI initiatives not only fit within the current strategy but also shape its future direction.

This is where the Compass Framework comes in—a practical method for evaluating AI initiatives through the lenses of business value, feasibility, risk, ethics, and long-term resilience.

The Compass Framework: A Smarter Way to Assess AI Initiatives

Think of AI investments like navigating uncharted waters. Without a clear framework, it’s easy to get lost—wasting resources, misallocating budgets, and failing to see measurable impact.

The Compass Framework ensures AI investments stay on course, helping organisations assess initiatives objectively and strategically.

How It Works

  1. Map the Criteria – Define the key areas that matter most, such as business value, technical feasibility, and ethical considerations.
  2. Set Priorities – Assign weightings based on your organisation’s goals, risk appetite, and long-term vision.
  3. Evaluate and Score Initiatives – Rank AI projects based on their potential impact and alignment with strategic objectives.
  4. Test Against Future Scenarios – Before committing, stress-test the initiative against potential disruptions, such as technological advances or regulatory changes.
  5. Refine and Adjust – Use the insights to course-correct before making significant investments.

Applying the Compass Framework: AI in Logistics

Let’s put this into practice. Imagine a logistics company exploring AI-powered route optimisation to reduce fuel costs and improve delivery times. At first glance, it seems like an obvious win. But a structured assessment can help uncover hidden risks and opportunities.

True North: Business Value & Strategic Alignment

💡 Does this initiative contribute to our business strategy today—and help shape it for tomorrow?

  • AI investments should generate measurable impact—whether through cost savings, revenue growth, or improved customer experience.
  • But beyond immediate gains, is this initiative moving the business in the right strategic direction?
  • Could it become a foundation for broader innovation, such as autonomous vehicles or net-zero emissions logistics?

📌 Short-term: AI optimises delivery routes, reducing fuel costs and improving delivery times. 📌 Long-term: If successful, the AI system becomes the foundation for fully autonomous logistics operations, positioning the company as an industry leader in sustainable and AI-driven freight solutions.

📉 What failure looks like: A short-lived cost-saving exercise with no long-term strategic benefit. The AI system improves routing but is disconnected from the broader company vision, limiting future scalability.

East: Feasibility

💡 Do we have the infrastructure and expertise to make this work?

  • Data readiness – Is real-time GPS, traffic, and fleet data available and high-quality?
  • Technical capability – Can our existing systems integrate with an AI-powered optimisation engine?
  • Skills gap – Do we have the right in-house AI expertise, or will we need external support?

📉 What failure looks like: The AI model generates optimised routes, but drivers ignore them due to real-world constraints, such as road conditions or vehicle limitations. The lack of integration with existing logistics platforms renders the system ineffective.

South: Operational Costs & Sustainability

💡 What is the full cost of ownership—not just the upfront investment?

  • Ongoing model updates – AI models need continuous recalibration as conditions change.
  • Maintenance and support – AI systems require technical oversight to ensure smooth operation.
  • Cloud processing and data costs – AI at scale means increased data storage, API calls, and cloud compute expenses.

📉 What failure looks like: The initial investment delivers expected cost savings, but rising operational expenses quickly erode the financial benefits, making it harder to justify continued investment.

West: Risk, Ethics & Explainability

💡 Are we making responsible AI decisions?

  • Bias in route optimisation – Are we inadvertently prioritising cost-cutting over driver well-being?
  • Explainable AI (XAI) – Can we justify why certain routes are selected?
  • Regulatory and reputational risk – Could new laws or public backlash affect this initiative?

📉 What failure looks like: The AI system optimises for speed and cost at the expense of driver safety, leading to pushback from employees and potential legal challenges. Without explainability, stakeholders don’t trust the system’s recommendations.

Checkpoint: Scenario Planning for Future Resilience

💡 Will this investment still be relevant in five years?

  • Quantum computing disruption – If quantum-powered AI dramatically improves optimisation speeds, will this system still be competitive?
  • Regulatory shifts – What if governments mandate stricter emissions controls, forcing changes in AI-driven route planning?

📉 What failure looks like: The AI system becomes outdated faster than expected because leaders failed to anticipate disruptive shifts in the industry.

Why the Compass Framework Works

✔ It provides a structured way to evaluate AI initiatives – No more knee-jerk decisions based on trends or hype.

✔ It balances short-term wins with long-term strategy – AI investments should be scalable and future-proof.

✔ It prevents costly missteps – By identifying risks and challenges before investing, businesses avoid wasted effort and expense.

✔ It aligns AI with business growth – The best AI projects don’t just support strategy—they become the drivers of future innovation.

And that last point is critical.

Too often, AI is viewed as just another tool to optimise existing processes. The reality? AI should be a catalyst for transformation. In the logistics example, route optimisation isn’t just about reducing costs—it’s about building the foundation for AI-driven, autonomous logistics operations. The more AI aligns with long-term strategy, the greater its business impact and return on investment.

From Potential to Progress: What’s Next?

AI has the potential to reshape industries, but only if businesses take a structured approach to evaluating, investing, and scaling it. The Compass Framework ensures AI initiatives aren’t just about solving problems today but shaping the future of the business.

Next Steps:

✅ Run your current AI initiatives through the Compass Framework – What ranks highest? What might need rethinking?

✅ Scenario test your investments – Will they hold up in 5 years, or are they too narrow in scope?

✅ Start small, think big – Prioritise AI projects that can scale and drive strategic advantage over time.

💡 AI isn’t just a technology investment—it’s a commitment to smarter decision-making and long-term transformation. The Compass Framework is your guide. Put it into practice and let me know what you think?