Performance Benchmarking
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Organizations are constantly looking for innovative ways to enhance efficiency, reduce operational costs, and boost employee satisfaction. Generative AI (Gen AI) is increasingly used to pursue applications to drive these efforts. By launching pilot programs, companies are actively exploring how Gen AI can improve their operations.
While pilot programs offer valuable insights and proof of concept, transitioning from a successful pilot to a full-scale, enterprise-wide deployment presents its own challenges. A pilot’s limited scope and controlled environment don’t always translate directly to the complexities of organization-wide implementation. Factors such as scalability, integration with existing systems, user adoption, and long-term maintenance come into play when moving beyond the pilot phase.
Given the potential benefits of Gen AI applications and the success of initial pilot programs, how can organizations expand their pilot experiences into robust, enterprise-wide applications that deliver sustained value?
We’ve drawn upon our direct experience in advancing Gen AI applications from the pilot phase to full-scale deployment to address this critical question. The following sections outline key factors to consider as you navigate this transition, ensuring that your organization can fully leverage the power of Gen AI across the enterprise regardless of whether you’re implementing a from-scratch solution or an add-on from an existing service provider.
Redefine your goals:
After a successful pilot, it’s crucial to reassess and refine your objectives for the deployment. This step ensures your Gen AI application aligns with organizational needs and lessons learned from the pilot phase.
For example, in an HR policy chatbot deployment, we limited the bot’s scope compared to the pilot’s scope to get more reliable answers.
- Evaluate pilot outcomes and adjust expectations accordingly
- Identify areas where the application exceeded or fell short of initial goals
- Consider any new opportunities or challenges discovered during the pilot
By redefining goals, you create a roadmap for the deployment, ensuring the Gen AI application delivers maximum value to your organization.
Adjust your application:
Based on pilot results and redefined goals, your Gen AI application may need fine-tuning. These adjustments are critical to improving performance and addressing issues identified during the pilot phase.
For example, some pilot chatbot responses needed to be more accurate. This was addressed by improving the bot training with additional FAQ content. Some adjustments that can be made to meet your goals include:
- Modify AI model temperature settings to balance creativity and factual accuracy
- Ensure underlying documentation is accurate, up-to-date, and consistent
- Implement document prioritization instructions to the AI model, where necessary
These adjustments help bridge the gap between pilot performance and production requirements, enhancing the application’s effectiveness and reliability.
Identify risks:
Deploying a Gen AI application into production introduces new risks that must be identified and mitigated. Proactive risk management is essential for maintaining the integrity and effectiveness of your application.
- Assess the potential for, and consequences of, providing or unintentionally selecting incorrect or outdated information
- Evaluate the application’s ability to handle sensitive or confidential data
- Consider legal and compliance risks associated with AI-generated responses
- Implement monitoring systems and scheduled performance reviews to track application performance and identify issues
Identifying and addressing risks early can ensure a smoother deployment and minimize potential negative impacts on your organization. Solutions as simple as a reminder to validate results can be effective in mitigating risks
Continue stakeholder engagement:
Ongoing stakeholder involvement is necessary for the successful transition from pilot to production. Their input and support can significantly influence the application’s adoption and effectiveness.
- Maintain engagement with stakeholders involved in the pilot phase
- Build stakeholder confidence through regular demonstrations and updates
- Conduct repeated testing with stakeholders and Subject Matter Experts (SMEs)
- Incorporate stakeholder feedback into application refinements and updates
Continuous stakeholder engagement ensures the application remains aligned with organizational needs and increases the likelihood of acceptance and adoption. Stakeholders should be chosen to represent a robust cross-section of the business, including HR, IT, Legal, Controls, etc.
Enable change:
Effective change management is essential for successfully integrating the Gen AI application into daily operations (as well as many other applications). It helps ensure user adoption and maximizes the application’s impact.
- Develop basic instructions for end-users to promote effective use
- Create awareness and/or internal marketing campaigns to highlight the application’s benefits and features
- Provide ongoing support and resources for users during and after deployment
A well-executed change management strategy smooths the transition from pilot to production, promoting user acceptance and maximizing the application’s value to the organization.
Develop operational governance and assign maintenance responsibilities:
Once your application is up and running, it’s crucial to develop a detailed governance plan for how it will be maintained over time and to identify who will be responsible for supporting it.
- Create a schedule for regularly reviewing and updating the application’s knowledge base.
- Designate functional professionals to ensure the application’s knowledge base remains accurate and up-to-date
- Establish a dedicated resource or team (e.g., AI or Advanced Analytics Center of Expertise (COE)) to oversee and periodically assess performance and make improvements as needed
By developing a comprehensive maintenance plan and clearly defining maintenance responsibilities upfront, you can ensure that your application remains a reliable and valuable resource for your organization over the long term.
Test, re-test, then test again
Rigorous testing ensures the Gen AI application’s reliability, performance, and security as it transitions from pilot to production. Comprehensive testing in test and deployment environments helps identify and resolve issues before they impact end-users.
- Develop a strategy for monitoring and testing performance and identifying areas for optimization
- Perform user acceptance testing (UAT) with a diverse group of end-users to validate functionality and usability
- Perform final testing in the actual deployment environment to catch any unforeseen issues
Continuous and iterative testing throughout the deployment process helps minimize risks, ensures optimal performance, and builds confidence in the application’s readiness for full-scale production use.
Your Partner in AI Innovation
ScottMadden is dedicated to helping your organization not only implement Gen AI solutions but to excel at it. By understanding and implementing the strategies discussed, your organization can significantly enhance the success rate of your AI initiatives.
Whether you are starting your first AI pilot, seeking to establish an AI strategy, or ready to begin establishing the governance models and frameworks to ensure a successful and scalable AI program, ScottMadden offers the expertise and a range of services to develop the proper management practices necessary to achieve outstanding results. Let us help you unlock the full potential of AI in your business operations.
