From Theory to Practice: Vynamic’s Hands-On AI Journey

Similar to our clients across the health industry, Vynamic has been buzzing about the potential implications of Artificial Intelligence (AI), particularly Generative AI (Gen AI). We’re excited by its promise to transform elements of the health industry value chain and the opportunity to help our clients navigate this unfamiliar, rapidly evolving terrain.

Today, we want to share a glimpse into our own experience of learning and experimenting with AI for our business, and some of the key lessons we’ve learned along the way. We hope this will serve as a guidepost for those just getting started, or a reference point for those evaluating their own progress. We look forward to sparking new conversations with you.

Our Experience with Gen AI for our Business

Vynamic began to focus on AI and what it might mean for our business and clients over two years ago, in early 2022. Soon after, Gen AI stormed onto the scene and key questions began to emerge. Was it an existential threat to management consulting? Was it the key to solving some of the biggest challenges facing the health industry? Somewhere in between? We were eager to get started.

Hands-On Experience

Recognizing that Gen AI is a domain where academic or theoretical knowledge can only get you so far, we knew we had to get hands-on experience. Our first effort was to demystify Gen AI by providing our team with training on what it was, how it could be used, and provide some illustrative examples that brought it to life. Recognizing that some may be inclined to start exploring the application of Gen AI to their Vynamic work, we put some guardrails in place to ensure its appropriate use, such as restricting use of client information and ensuring team accountability for the accuracy of any outputs.

Microsoft Copilot

When Microsoft Copilot became available, we rushed at the opportunity to start using it and stood up a pilot consisting of roughly 25% of our organization. At the time, in Q1 2024, we found that Copilot’s ability to generate notes from meeting transcripts was potentially game-changing, but the bulk of its capabilities across the MS Office Suite were not yet fit-for-purpose to add real value. We decided to scale back our pilot to avoid distraction while continuing to track its latest developments.

ChatGPT-Enterprise and an Internal Center of Excellence

Our next foray was with OpenAI’s ChatGPT-Enterprise (ChatGPT-E), which provides the full-featured capabilities of their latest publicly available models with the added benefit of a secure environment into which we can safely upload confidential business information.

To lead this initiative, we launched an internal Gen AI Center of Excellence (COE) with a team of 10 volunteers who were passionate about AI and dedicated to learning its nuances. Each member of the COE was paired with a leader from the business, who ensures we are focused on priority areas where AI can alleviate challenges or enhance efficiency in their area of responsibility. Using ChatGPT-E, the team has focused on building advanced prompts and custom GPTs for target use cases, testing real-world scenarios, and expanding our organizational knowledge.

A Horizontal Capability Across Our Business

Based on our experience to date, Gen AI at Vynamic is increasingly viewed not only as a discrete initiative, but as a horizontal capability woven throughout all aspects of our business. Some of the most promising use cases have emerged in areas core to our operations.

For instance, in Business Development, Gen AI helps us streamline proposal generation – a previously very time intensive process. Other applications include onboarding new team members, training and skill development, and general knowledge support.

Further, we’re developing solutions to enhance and accelerate the services we deliver directly to clients. Each client organization has their own policies with respect to the use of Gen AI, and the security of their data more generally, which may limit some applications of Gen AI, but we are confident we can help our internal and/or client counterparts appreciate the benefits of better, faster delivery for certain project phases or deliverables.

Lessons Learned on our AI Journey

Like many organizations, we’re navigating the evolving AI landscape while learning valuable lessons along the way. Here are five insights we’ve gained that could benefit companies embarking on similar AI journeys:

1. Focus on Core Jobs

Identifying who you want to empower with AI and what use cases to prioritize are the first steps in transforming Gen AI from a novel toy into a powerful tool. However, as Gen AI capabilities are evolving rapidly, it can be hard to know where to start. A useful framework we’ve used to focus our efforts is innovation icon Clayton Christenson’s Jobs-to-be-Done theory.

A core principle of the theory is that while technologies come and go, a “job” remains stable over time. How we achieve things may change dramatically, but the job – the user need – persists. As a result, we have found that business owners closest to the team and customer needs are best positioned to identify and prioritize Gen AI use cases, instead Enterprise IT or a Corporate Strategy function.

2. Find and Support Your Champions

Identifying Gen AI advocates within your organization is crucial. These champions are not only early adopters but also evangelists who help others see its potential. By generating early wins and demonstrating the value of Gen AI, your champions can inspire broader buy-in while generating institutional learnings. They also tend to be those most willing to wade through ambiguity and create clarity for the rest of the organization.

3. Data is King

AI’s power lies in data, and proprietary, high-quality data is critical to generating meaningful, differentiated insights. However, as good as Gen AI is at analyzing unstructured data, pointing it at a disorganized mass of SharePoint folders is not a recipe for success.

Much of our Gen AI effort requires curating and integrating our data sources and setting up new data governance processes to capture and store data more effectively. Doing so strategically requires a thoughtful understanding of what data sets are needed to power your priority use cases, where that data exists today, and how it may need to be transformed to be effectively utilized by your AI toolset.

4. Embrace the Learning Curve

Meaningfully adopting AI into your organization is not a linear process. We have approached it with an experimentation mindset, recognizing that we will need to test and learn along the way. At Vynamic, we embrace the value of “failing forward”, and our focus on AI has allowed us to lean into this.

5. Take One Bite at a Time

The ultimate Gen AI vision for many use cases includes an end-to-end workflow of harmonized data elements and user interactions to deliver transformative results. Building out those comprehensive solutions will take time and investment. Rather than trying to “eat the elephant all at once”, make the problem smaller by shifting your mindset toward proving out individual hypotheses along the way. These will give you and your leadership confidence that once you have all of the pipes connected and the workflows in place, your AI-powered solutions will really hum.

 Vynamic’s Road Ahead

Our experimentation has shown us that Gen AI can empower teams, streamline operations, and provide valuable insights—but only if approached thoughtfully and with a clear vision. At Vynamic, our journey continues, and we’re here to help organizations navigate theirs.

Whether you’re taking your first steps into Gen AI or looking to enhance your existing capabilities, our experience can help you avoid common pitfalls, prioritize impactful applications, and create a roadmap that’s aligned with your business’s unique needs.

For more on how we can help mobilize AI within your organization, learn more here on our AI approach and offerings.