AI Product Selection and Deployment – An Accelerated Approach Leveraging Evaluations

Selecting AI Products requires a slightly accelerated approach. This can be done when AI Strategy, Governance and Principles are in place, making it to easier to proceed with relatively short Evaluations to confirm expected results. Trying to deploy AI products without these processes thought through beforehand, means your teams may need to ‘rewire the airplane in flight’. It can be very difficult to agree on AI principles mid-deployment, if, for example, transparency, security or bias haven’t been defined or agreed. Teams that have already invested in a specific product or deployment may be resistant to changing products mid-stream when a failure to meet AI trust worthiness that cannot be mitigated is revealed. For example, many organizations have limited the use of LLMs such as Chat GPT as their policies restrict the flow of company data into a public model. If a product vendor cannot establish upfront that company data is secure and not used to train public LLMs, and is a fundamental part of their product architecture, a deployment may need to be abandoned mid-stream.  

The phases of an AI deployment can be broken down into several key stages. While the specific names and number of stages may vary slightly depending on the source, here's a common breakdown.  

Assuming you have your AI Strategy, AI Governance structures and processes in place, and AI Principles defined, each deployment can follow an agile approach as follows, and depicted in the Figure below.  

1. Prioritize AI Use Cases by Value

• Identify high-value AI Use Cases, the business problem(s) that AI will solve, and the expected overall benefit. It’s important to understand, prioritize use cases and map the processes that they relate to one another. AI-powered products can collapse a process that took weeks, to minutes. How will that affect the end-to-end processes?  
• Avoid creating a ‘Hurry-up and Wait’ scenario where the AI-powered process hits a wall downstream, and the overall benefit is not realized. Has it become more, or less, streamlined with AI?
• Set up Success Criteria and expected results for an AI-powered use case.  

2. Set Up and Evaluation in a Way that Reflects Reality  

• Chose a product to evaluate in a compressed timeframe, with the right team members and data to maximize learnings and fit.  
• Ensure the product is provisioned in a way for IT to plan out what a full deployment would entail, with checklists and administrative procedures.  
• If required, use data scientists to gather and understand the relevant data that will be used to train the AI model.
• Use real data and ensure the evaluation team members are properly trained to avoid common pitfalls.

3. Validate the Use Case and Compliance with AI Principles before Commencing the Evaluation

• Simulate the Use Case, to understand the As-Is process and measure components like time to process, number of people involved, audit trails and reporting.
• Estimate the impacts to people, processes and technologies; both AI-powered and legacy.  
• Ensure compliance with your Responsible AI and Trustworthy AI principles and policies.  
• Model Development and Training: In this stage, data scientists select or develop the appropriate AI model architecture and train it on the prepared data. This training process may involve fine-tuning the model to optimize its performance for the specific task.

4. Run the Evaluation and Refine the Model

• As you run the evaluation, measure the effectiveness of the product, and validate expected value for use in an ROI model.
• Identify where Human in the Loop pauses need to occur for collaboration, comments, discussion or enhancement.
• Ensure that opportunities for job/role enrichment are understood as the process is transformed.
• Note deployment considerations around training, rollout and IT services.
• Understand requirements around Model Evaluation and Refinement: Once trained, the AI model should be rigorously evaluated to assess its effectiveness and accuracy. Metrics appropriate to the task are used to identify any biases or areas for improvement. The model may be refined or retrained based on the evaluation results.

5. Complete the Evaluation and Prepare for Full Deployment

• Your evaluation results should include Expected vs Actual results, ROI confirmation, Key learnings, potential new use cases and HR involvement for organizational change management.  
• Deployment : If the product and model(s) meet performance benchmarks, it can be deployed into a production environment. This may involve integrating the model with existing upstream or downstream systems or developing a user interface for interaction.  
• Ensure resilience, business continuity and failover processes are considered fully accommodated.  
• Monitoring and Maintenance: After deployment, the AI system is continuously monitored to track its performance and identify any issues. Real-world data may expose biases or performance degradation requiring further refinement or retraining. This ongoing process ensures the AI system remains functional and delivers value.

Remember: AI requires oversight from multiple groups to ensure good governance so make sure you’ve got the right stakeholders involved upfront and committed.

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