The Value of AI Pilot Programs
In the rapidly evolving world of artificial intelligence (AI), running a successful pilot program can seem daunting. The challenges are numerous, ranging from selecting the right use case to ensuring the technology effectively meets business needs. At ScottMadden, we leverage our deep automation expertise, from RPA to AI, to help organizations navigate these complexities, significantly enhancing the probability of your pilot’s success. This article outlines key considerations and strategies derived from our experience, aiming to guide corporate executives through the intricacies of implementing effective AI pilot programs.
Selecting the Right Use Case
A critical step in any AI pilot is choosing appropriate use cases. It is essential to:
- Identify high-value use cases that have the potential to deliver “needle-moving” results. These are impactful opportunities that can significantly advance business objectives, thereby capturing the attention and support of executives and decision-makers. Prioritizing such use cases can demonstrate the tangible benefits of AI and secure broader organizational buy-in.
- Focus on a manageable number of use cases relative to the size of your testing team. Overextension can dilute focus and resources, undermining the potential success of the pilot.
- Set clear, measurable goals for each use case. Define what success looks like upfront by setting hypotheses and striving to prove or disprove them during testing. This clarity helps maintain alignment throughout the project.
- Ensure that success metrics are actionable and realistic, given your available resources, time constraints, and the current state of technology. This balance is crucial for setting achievable targets without stifling ambition.
Assembling Your Team
The composition of your team can significantly influence the outcomes of your AI pilot:
- Select team members who are skilled in prompt engineering and aware of AI limitations and are also genuinely interested in AI technology. Enthusiastic participants are more likely to persist and experiment when outcomes are not as expected, driving better results.
- Utilize pairs or small groups for each use case to encourage diverse perspectives and approaches. This collaboration can lead to more comprehensive and creative solutions than individual efforts.
- Provide explicit guidance on crafting effective prompts. Do not assume uniform knowledge or skill levels across the team, even among experienced members.
- Ensure subject matter experts are involved in testing so that model output accuracy can be evaluated efficiently and effectively.
- Engage key stakeholders, such as Legal, IT, Controls, and HR, early in the pilot. Their early involvement ensures they understand the AI’s capabilities and limitations, reducing the risk of future objections that could hinder transitioning a successful pilot to production. This proactive approach fosters collaboration and paves the way for smoother integration and compliance with organizational policies and standards.
Model Configuration
Configuring your models effectively is a cornerstone of a successful AI pilot. Here are key aspects to consider:
- Choose a model that aligns with the specific needs of your business case. The model’s capabilities should match the complexity and type of data it will handle.
- Optimize model performance by adjusting parameters such as learning rate, temperature, batch size, and the number of epochs. This tuning is crucial for enhancing the model’s accuracy and efficiency.
- Develop precise and effective prompts that guide the AI to generate relevant and useful outputs. This skill is essential in leveraging the full potential of large language models (LLMs), ensuring that the outputs are aligned with specific business goals.
Managing Data Effectively
Proper data management is pivotal for the success of AI applications:
- Define and provide the necessary support documents for each use case. Consider aspects such as document ownership, confidentiality, and format. Remember that the utility of your AI tool is contingent upon the quality and relevance of the data it receives.
- Source documentation format critically influences pilot outcomes. Our experience indicates that the format of unstructured documents (including size, the inclusion of graphics and tables, etc.) used to configure a model significantly affects pilot performance, often being a primary factor in success or failure. Documents may require reformatting, standardization, file type conversion, or other modifications to ensure they are optimally configured for processing by the LLM.
- Maintain meticulous records of the data used during testing. Changes in the data input, whether by modification or substitution, can significantly impact the outcomes of your pilot.
Embracing Iteration
AI technology, especially LLMs, demands a willingness to iterate:
- Adopt an iterative process in your pilot program. The learnings from each phase should inform subsequent efforts, continuously refining the approach and technology used.
- If initial results aren’t as expected, consider further investments in prompt engineering, model fine-tuning, or additional model training. See our LLM framework article for considerations as you go deeper in working with this emerging technology.
Monitoring Progress and Making Adjustments
Continuous monitoring and flexibility are key to navigating the complexities of AI pilots:
- Establish regular intervals for interim updates to assess progress against goals and adjust strategies as necessary. This periodic review helps maintain the pilot’s alignment with its objectives.
- Stay adaptive to changes in source material and advancements in AI tools. If new, more suitable tools emerge, or if source materials are updated, be ready to pivot your strategies to incorporate these changes.
Your Partner in AI Innovation
ScottMadden is dedicated to helping your organization not only execute an AI pilot but also excel at it. By understanding and implementing the strategies discussed, corporate executives can significantly enhance the success rate of their AI initiatives. Whether you are starting your first AI pilot, seeking to establish an AI strategy, or ready to start putting in place the governance models to ensure a successful and scalable AI program, ScottMadden offers the expertise and tools necessary to achieve outstanding results. Let us help you unlock the full potential of AI in your business operations.
* Additional contributors include Jessica Schrepple and Mark Ladisch
