How do you measure Gen AI Deployment & pilot success: Key Performance Indicators and Metrics
Measuring the performance of your gen AI experiments and pilots is crucial for a) verifying their effectiveness and b) refine subsequent iterations of the project c) assessing the impact and value they deliver to the organisation goals.
MIT Sloan Management Review and Boston Consulting Group (BCG) Report (2017): This report, titled “Artificial Intelligence and the New Era of Productivity,” found that companies with well-defined KPIs for AI initiatives were 1.5 times more likely to report exceeding their business goals.
Without clear metrics, it’s difficult to determine if your AI is actually working. KPIs provide quantifiable measures to evaluate the effectiveness of your AI in achieving its intended goals. In terms of Alignment with Business Objectives, to bring data-driven adjustments and improvements to your AI model, quantify the return on investment (ROI). Business landscape is constantly evolving, KPIs insights refine your AI strategy and ensure it remains relevant over time. Stakeholders can understand the value proposition of AI clear and concisely.
By setting the right KPIs, tracking them diligently, and using the insights to make adjustments, organizations can maximize and optimize the potential of AI and generative AI technologies for better results.
Here are some key questions to consider:
The purpose of the Gen AI deployment: What do you want the Gen AI to achieve?
Are you aiming to improve customer satisfaction or automate tasks?
The target audience: Who will be using the Gen AI powered chatbot? (Support Agents, Marketing team, End Customers, etc.)
The budget: How much are you willing to spend on the AI execution?
End user’s expectation: what are their experience preferences for a Gen AI tech?
The available resources: Do you have the resources to develop and maintain the Gen AI chatbot?
Enough data: do you have enough data to tailor Gen AI/LLM model?
Quantitative and Qualitative success
Evaluating the effectiveness of generative AI in requires a blend of quantitative and qualitative metrics. Here’s a breakdown of key areas to consider:
A study published in the Journal of Information Technology Research found that companies focusing on measuring the business value of AI projects achieved a 3x higher return on investment (ROI) compared to those without a clear measurement strategy.
Quantitative Metrics:
- Resolution Rates: Track the percentage of issues resolved by the generative AI without needing human intervention. This reflects the AI’s ability to handle customer inquiries effectively.
- Self-Service Adoption: Monitor how often customers (for external facing support) or employees (for internal facing assisatnce) utilize the generative AI. High adoption rates suggest the AI is user-friendly and fulfills customer needs.
- Average Resolution Time: Measure the time it takes for the AI to resolve an issue. Faster resolution times indicate efficiency and a positive customer experience.
- First Contact Resolution (FCR): Track the percentage of issues addressed during the initial interaction with the AI. High FCR indicates the AI’s competency in handling inquiries without escalation.
- Customer Satisfaction Surveys: Embed surveys after interactions with the AI to gauge customer sentiment. Tools like Net Promoter Score (NPS) can measure customer loyalty and satisfaction with the AI’s support.
Qualitative Metrics:
- Effort Score: Surveys can assess the level of effort required from end user to resolve their issues using the AI. Lower effort scores indicate a smooth and efficient experience.
- User Feedback Analysis: Analyze qualitative feedback from customer user and conversations to identify areas for improvement in the AI’s responses and functionalities.
- Human Agent Efficiency: Measure how generative AI impacts employee workload. If the AI effectively resolves simpler issues, it frees up employees for more complex inquiries.
- Cost Savings: Evaluate if generative AI reduces costs associated with traditional workflows, by automating mundane tasks.
- Agent Productivity: Measure the time saved by employees due to the AI deflecting routine inquiries. This can free them up for complex issues improving efficiency.
Additional Considerations:
- Natural Language Processing (NLP) Performance: Evaluate how well the AI understands and responds to natural language queries. This ensures a seamless and intuitive user experience.
- Human-in-the-Loop: Assess the effectiveness of integrating humans with the AI for more complex issues. A seamless handoff process is crucial for maintaining customer satisfaction.
- Fine-tuning Requirements: Measure the amount of effort needed to train the LLM model and maintain the AI for optimal performance. According to McKinsey research, establishing KPIs allows organizations to prioritize data collection efforts, ensuring they gather the information most critical for AI success.
Measuring the success of early Generative AI programs and pilots
Evaluating the success of early generative AI programs and pilots requires a nuanced approach. Here’s a framework that blends quantitative and qualitative measures:
Early-stage Considerations:
Focus on Learning: Early generative AI programs are often about exploration and learning. Embrace experimentation and prioritize gathering insights over achieving perfect results.
Data Collection: Set up mechanisms to capture data on user interactions and AI performance during the pilot. This data will be invaluable for refining the model in future iterations.
Iterative Improvement: Don’t expect a perfect solution right away. Use the learnings from the pilot to iterate on the AI and gradually improve its capabilities.
Incremental vs. Exponential Pilots: Early programs can be designed to test specific functionalities (incremental) or explore broader business model opportunities (exponential). Choose the approach that aligns with your goals.
Incremental Pilots, KPI metric:
Accuracy and Reliability: How well does the generated output match the desired format. This could involve measuring the factual correctness of creative text formats, the coherence of generated code, or the effectiveness of automated responses in interactions.
Completion Time: Measure the time it takes for the AI to generate the desired output. Faster generation is generally better, but prioritize quality over speed for complex tasks. This identifies areas for improvement in the AI’s capabilities.
Time Efficiency: Measure the time saved by using the generative AI compared to the traditional method. This is crucial for repetitive tasks the AI automates.
User Satisfaction: Gather feedback through to understand user perception of the AI’s usefulness and ease of use with the specific functionality being tested for any task.
Exponential Pilots, KPI metric:
User Adoption: Monitor how many users interact & how often a user interact with the generative AI, how many queries Gen AI able to solve, within the pilot program
Engagement Metrics: Analyze session length, user input complexity, and the number of tasks attempted using the AI. This gauges user engagement and the range of use cases explored.
Cost Savings Potential: Estimate the potential cost reductions achievable if the AI were fully implemented across relevant business areas. While cost might not be the primary goal, this helps assess potential return on investment (ROI).
Alignment with Business Goals: Evaluate how the pilot impacts broader business objectives. Did it uncover new opportunities? Did it validate the potential of generative AI to solve a critical business challenge? A Deloitte report highlights that clear KPIs create a common language around AI success, fostering collaboration between technical teams and business stakeholders.
To Wrap up
Make sure your KPIs are SMART — Specific, Measurable, Achievable, Relevant, and Time-bound.
The KPIs for a pilot program will differ from those for a fully deployed generative AI solution. For early pilots, focus on core functionalities and user engagement. Later, track business impact and ROI.
Don’t rely solely on hard numbers. User feedback, surveys, and focus groups can provide valuable insights into user experience and satisfaction with the generative AI.
Gradually move beyond basic functionality metrics. Track how generative AI creates value for your organization. This could be cost savings, improved efficiency, increased revenue, or enhanced customer satisfaction.
Balance Leading and Lagging Indicators. Leading indicators that reflect the effectiveness of your GenAI implementation (e.g., AI-powered self-service resolution rate in customer support). Additionally, monitor Lagging indicators that measure the ultimate business impact (e.g., customer satisfaction score).
At Fluid AI, we stand at the forefront of this AI revolution, helping organizations kickstart their AI journey. If you’re seeking a solution for your organization, look no further. We’re committed to making your organization future-ready, just like we’ve done for many others.
Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together, unlocking the full potential of AI for your organization and starting with your Pilot or Production journey. Remember, the future belongs to those who prepare for it today.
