The Secret to AI ROI: 7 Step Framework for Success

Assessing AI project ROI requires a holistic approach that goes beyond simple financial calculations. So to thanks for the next 5 minutes of your valuable time and let's cut to  the chase and pose,  the questions nobody  seems to  be able to  answer:

1 What criteria should an enterprise use to assess AI project ROI?

• Clearly Defined Objectives: Start with a crystal-clear understanding of the project's goals. What specific problem are you trying to solve or opportunity are you trying to seize with AI? How will success be measured?

• Data Quality and Availability: AI thrives on data. Assess the availability, quality, and volume of data needed for training and ongoing operation of the AI system

• Technical Feasibility: Can the problem actually be solved with current AI capabilities? Do you have the necessary technical expertise in-house or access to it through partners?

• Implementation Costs: Factor in all costs, including, infrastructure (hardware, software, cloud computing), data acquisition and preparation, development and deployment, ongoing maintenance and monitoring

• Expected Benefits: Identify and quantify potential benefits, both tangible (e.g., cost savings, increased efficiency, revenue growth) and intangible (e.g., improved customer satisfaction, better decision-making, enhanced brand reputation).

• Risk Assessment: Consider potential risks, such as model bias, data security issues, regulatory compliance, and the need for ongoing model retraining.

Time Horizon: Establish a realistic timeframe for achieving ROI. AI projects often have longer development cycles and may require ongoing investment before full benefits are realized.

2 What makes this approach effective?

This approach is effective because it:

• Provides a Comprehensive View: It considers all relevant factors, not just immediate financial returns.

• Sets Realistic Expectations: By acknowledging potential challenges and risks, it helps avoid overhyping AI and setting unrealistic expectations.

• Facilitates Better Decision-Making: It provides a framework for evaluating AI projects and prioritizing those with the highest potential for success.

Encourages Long-Term Thinking: It emphasizes the importance of ongoing investment and continuous improvement to maximize AI's value over time.

3. Which metrics can be used to measure potential AI project ROI?

• Estimated Cost Savings: Calculate potential savings from automation, reduced errors, or improved efficiency.

• Projected Revenue Increase: Estimate the potential for AI to drive new sales, improve customer retention, or enable new product/service offerings.

• Productivity Gains: Quantify how AI can free up employee time for higher-value tasks.

• Improved Customer Satisfaction: Use metrics like Net Promoter Score (NPS) or customer churn rate to assess the impact of AI on customer experience.

• Risk Reduction: Estimate the potential for AI to reduce errors, fraud, or other risks.

4. Which metrics can be used to measure current AI project ROI?

• Actual Cost Savings: Track actual reductions in expenses after AI implementation.- Revenue Growth: Measure the direct impact of AI on sales and revenue.

• Efficiency Improvements: Monitor key performance indicators (KPIs) related to speed, accuracy, and productivity.

• Customer Satisfaction Metrics: Track changes in customer satisfaction scores and feedback- Risk Reduction: Measure the decrease in errors, fraud incidents, or other risks.

5. What can an IT leader do to get a sub-par AI project up to speed?

• Revisit the Objectives: Ensure the project goals are still relevant and achievable.

• Assess the Data: Is the data sufficient, high-quality, and properly labeled?

• Evaluate the Model: Is the AI model appropriate for the task Does it need retraining or refinement?

• Optimize Infrastructure: Is the infrastructure adequate to support the AI system's performance requirements?

• Address Skill Gaps: Does the team have the necessary skills and expertise? Provide training or bring in external consultants if needed.

• Improve Communication: Ensure clear communication and collaboration among stakeholders

6. What's the best way to terminate an AI project, which despite best efforts, has failed to match expectations?

• Conduct a Thorough Post-Mortem: Analyze the reasons for failure. What went wrong? What lessons can be learned?

• Document the Findings: Capture the key insights from the post-mortem analysis.

• Communicate Transparently: Explain the decision to terminate the project to stakeholders.

• Salvage Valuable Components: Identify any reusable assets, such as data, code, or models.

• Properly Archive Project Data: Ensure compliance with data retention policies.

7. Additional food for thought

Embrace an Agile Approach: Iterate and adapt as you learn. AI projects are often experimental in nature, so be prepared to adjust your approach based on feedback and results.

Focus on User Experience: AI should be designed to enhance human capabilities and improve user experiences.

Prioritize Ethical Considerations: Ensure fairness, transparency, and accountability in AI development and deployment.

By following these guidelines, enterprises can increase their chances of achieving a positive ROI on their AI investments. Perhaps start with Small, Well-Defined Projects preferably using SLMs (small or specialised Language models that are scalable like #SCOTi from smartr.ai . These models are private, use your existing infrastructure, costing less in both financial and recourses terms. These smaller projects will have a high probability of success and can demonstrate the value of AI. Take a look at the 5 teaser videos here.

Thank you to Neil Gentlemen-Hobbs with SmartR.AI for sharing his knowledge and expertise in our knowledge base.

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