How many times have you seen leaders request investments for generative AI platforms (or any digital software platform for that matter) from their leadership without providing a solid business case to support their request? This is a common occurrence, especially as the potential of generative AI in life science communications has grown exponentially over the last few years.

To make the best possible recommendation for a generative AI platform, you need to develop a solid business case to support it. So, what’s a business case? It’s a document that provides justification for undertaking a project, program, or even the purchase of a platform. It evaluates the benefit, cost, and risk of the preferred solution. It also provides the strategic context, economic analysis, commercial approach, and financial case for the solution.

In order to build a successful business case, and secure an investment, you have to start by asking—and answering—a few key questions.

What are my goals and objectives?
First, start with a simple question: Is my goal internal or external?

An external goal could be to establish a new product and service offering to increase revenue and fulfill an unmet market need. An example of this is Doximity GPT— a platform that improves workflow for healthcare professionals’ practices by generating prior authorization letters, insurance appeals, or letters of patient support. An internal goal, on the other hand, might be to improve your company’s own efficiency and decrease the amount of labor required to complete specific tasks, like editing medical content to conform to the AMA Manual of Style.


What are my use cases? 


You may have one use case, or you may have several. The important thing is to be very specific about each one. If you notice an "and" while developing your use case, you likely have two separate cases. For example, "writing medical content" and "editing content to conform to the AMA Manual of Style" should be treated as two distinct use cases.


Is my solution scalable? 


If you work in an organization with multiple business units, it is most advantageous if others have the same use cases as yours. This allows you to amortize the cost of the platform across the organization, while simultaneously increasing both the revenue generation potential and ability to scale.

Do I need to build a custom solution, or can I buy one off the shelf and configure it to meet my needs?


There are hundreds of off-the-shelf generative AI platforms that can be licensed and configured to meet virtually all the use cases for pharma/biotech marketing and advertising, as well as medical communications. Some of these options include:

• Consensus: Provides the ability to search references, get simple answers to scientific questions, and write articles and content backed by academic papers.
• MediWrite: Interprets statistical results based on the statistical output you need help with, including tables, figures, or descriptions of the results. MediWrite can generate Kaplan-Meier graphs in seconds, draft medical papers, and even generate medical imagery using DALL-E.
• Jasper: Offers AI-generated content tailored for marketing, including blog posts, social media content, and more, with specific tools for regulated industries, including biopharma.
• Concured: An AI content platform that helps in planning, creating, and optimizing content strategies, particularly useful for highly regulated industries, like life sciences.
• Writesonic: Offers a range of AI writing tools for creating marketing copy, ads, and social media posts, with customizable options suitable for life science and medical communications.
• BioRender: A specialized design tool that creates professional-grade scientific and medical illustrations, BioRender provides an extensive library of pre-made icons and templates tailored for biomedical communication. It can also be utilized in creating scientific diagrams, medical illustrations, and visual aids for research papers and presentations.
• SmartDraw: A diagramming tool that includes templates for creating medical diagrams, flowcharts, procedural guides, and schematics, suitable for both educational purposes and professional medical presentations.

Many preexisting generative AI platforms can be used without any additional programming, development, or training. Others, however, may require additional configuration to generate the high-quality outputs needed to satisfy your use cases.

Conversely, building a custom platform is extremely costly, requires continual updating, and needs a product roadmap to ensure the feature sets expand and continue to deliver value over time. It also requires a full-time development and IT team to manage the solution.



What are the guardrails for use of the generative AI solution?


You must think about many, many aspects regarding the use of your proposed platform. You need to know if it will generate outputs that infringe on another company’s intellectual property (IP). If there is a risk that it will, you will need to have a mechanism in place that ensures no IP owned by another party is incorporated into your outputs.

Similarly, you want to make sure no outputs are copyrighted by someone else, and that it can’t be considered plagiarism. The earlier versions of so-called image generators were sophisticated search-and-retrieve programs. They would take in a user’s description of what they wanted, process it, scour the internet, and return a series of existing images. They did not generate uniquely new imagery from scratch and could have potentially violated another user’s copyright.

You’ll also need to determine whether you will upload your own propriety data or client confidential materials and ensure that none of it becomes the property of the platform provider. While there are many other factors to consider, this should be enough to get you started. 


What are the financial upsides?


Calculating the return on investment (ROI) is critical. Your business case should clearly outline the financial benefits of the investment. For example, for agencies charging clients using a fixed fee model, generative AI can significantly boost gross margin and profit, since the fee remains the same whether generative AI is used. You’ll also want to forecast the positive impact on your earnings before interest, taxes, depreciation, and amortization (EBITDA). In the scenario outlined above, your EBITDA can improve through your existing staff’s increased capacity and reduce the need for additional hires.

You’ll also want to estimate the number of outputs required to break even on your initial investment—any revenue after covering this is pure profit. And you’ll need to determine if you will be licensing the platform or charging a fee for its configuration and use in delivering the outputs. In either case, you can develop a business model that includes license or fee renewals that result in passive annual recurring revenue (ARR).


What are the risks?


In the above example, utilizing generative AI boosts your financials. However, if you charge using a retainer model, you could actually lose money. Greater efficiency equals fewer billable hours. The result? A reduction in your revenue.

Because you won’t need as many hours to complete projects, you’ll need to take on more projects to maintain your present revenue. Delivering growth is even harder. And you should analyze your staffing model, as you might now be overstaffed. There's also a risk that clients might bring work in-house or that a competitor could launch a superior generative AI platform, delivering higher-quality outputs faster and taking away your business in the process.

Is the security of the platform sufficient? 


Security is huge. Some impressive generative AI platforms have been found to have terrible security. Issues include the absence of single sign-on (SSO) and multifactor authentication (MFA), lack of documentation on the programming behind the solution, no security measures for staff or new hires, and no backups performed. A word to the wise—do your due diligence.

Conclusion
Your company or investors will likely require more details. But answering these initial questions will give you a good start in building your business case.

Utilizing generative AI platforms for life science marketing and communications holds true promise and growth potential, but it requires careful consideration of a range of factors. From determining ROI and assessing business models to ensuring consistent security measures, finding the correct model demands thorough due diligence.

By addressing critical concerns upfront and continually adapting to emerging challenges and opportunities, businesses can leverage generative AI to drive innovation, enhance productivity, and stay ahead of the curve.

Looking to understand the potential of generative AI in digital and omnichannel life science marketing? Contact us today to learn more about working with Kurt and the team of Bracken’s expert consultants.