Editorial – 5 ways to build an AI business pitch that will attract investors
AI offers the opportunity to create more sustainable healthcare systems, but this opportunity can only be harnessed with sufficient investment in the technology. Obtaining investment can seem a daunting task, with several challenges faced by AI developers. Here, Paloma Cabello, a venture capital and alternative investment specialist and first European Member of the Global Advisory Board of the MIT Enterprise Forum, outlines some of the key steps that start-ups, small- and medium-sized enterprises (SMEs) and innovators can take to turn their AI solution into an attractive business prospect for investors.
The global market for AI in healthcare is growing and predicted to reach $61.6 billion by 2027.1 This is big business, and there are many players looking to secure investment for their particular AI solutions. Key to success will be an ability to turn an innovative idea into a viable business model – something that may be difficult for some AI developers who are experts in their field but less familiar with the world of business.
Indeed, in my experience, one of the first mistakes people can make when seeking investment is to confuse their technology with their business model. Developers often focus too much on whether or not an algorithm is good at solving a particular problem and fail to give enough attention to whether it is a good business prospect. While a solution that works is of course essential, it won´t transfer to the market at an investable scale if it cannot be incorporated into a viable business model. Developers should be conscious of this from the early design phases to avoid the need to make changes further into the development process, which will unnecessarily consume precious resources.
This stage is incredibly important because developing and scaling AI can be expensive for many reasons – not least because it currently relies on a uniquely qualified taskforce, with specific skillsets that are quite scarce. But even once the initial development is financed, it must be remembered that commercialisation can also be costly. This is a stage that entrepreneurs often underestimate in terms of how much it will cost them but also how much time it will take.
So, what steps can start-ups, SMEs and innovators take to build a business model that increases their chances of attracting investors? Every project will of course be different, and the business model will reflect the specific problem, solution and environment that the product has been created for. However, there are some considerations that broadly apply.
1. Know what funding is available
There has been a 22-fold increase in venture capital investments in AI between 2015 and 2019 in Europe.2 Both governments and private investors are showing an interest in AI for healthcare.2 Each has different motivations, and this should inform both where you apply for funding and how you pitch your solution; for example, governments will be looking to invest in projects that align with their policy priorities, private investors may be more agile to respond to new opportunities. Regardless of what type of investor you end up applying to, the first step is to do your research and educate yourself about what they are looking for, the funds available, and when you can apply.2
When deciding on where to apply first, many people find it advantageous to begin with free money, that is, non-refundable public grants or similar. Money from clients (development partnerships, etc.) might be a good second-best option as this may not require equity involvement either and also provides the opportunity to dynamically test the products or services, and quickly pivot to achieve a final design with stronger prospects for success. Smart money from professional investors who know the industry is a good next option. Smart money may be prioritised by some ambitious teams looking to maximise on the expertise such investors may have with rapidly scaling sales of technological solutions, which can attract more participants in further investment rounds. Finally, financial providers such as banks and funds could be approached as soon as the company development delivers predictable future cashflows.
When it comes to choosing investors to pitch to, my expertise is primarily in private investment and my advice is to first look for investors whose investment strategy (ticket size, company development stage, industry, etc.) fits your company, without worrying about geographical location. These investors can then be approached with a short marketing item – either a one pager or an elevator pitch. If an investor shows interest, they can be sent a longer deck that provides more information. Remember, the process takes time, and this should not be underestimated. But even with knockbacks, it is time well spent – feedback from uninterested investors should be asked for and can be learned from to inform the next steps.
2. Identify your hook
Like any business pitch – you need to engage your audience’s attention first.3 Whether it’s the particular problem you’re tackling or the novel approach, investors want to know why they should invest in your company over others. Whatever opportunity you’ve identified in the market, it’s likely there are competitors doing something similar. Communicating to investors about why they should choose you over others is essential.
What investors are looking for is a clear, packaged product that can solve a specific problem and that requires little human intervention to scale. Many AI products offer bespoke solutions, which investors will see more as a consultancy service than a scalable business prospect. While it is possible to make a living providing consultancy in this way, it doesn’t work as a business. Investors will be much more interested in products that solve particular problems well and can be packaged up for many different customers. Importantly, such solutions can be scaled without the need for a large human workforce and also do not require long commercialisation cycles. The latter is one of the factors that is most underestimated by development teams. AI start-ups with “consultancy” models (i.e., those relying on salaries to deliver bespoke products and/or services to their clients), often struggle with cashflow because they must first finance their own sales cycle. This can lead to a fatal spiral for start-ups, as more sales means increased treasury pressure. Investors are well aware of this and so tend to shy away from this kind of business model.
Successful examples illustrate these points. For example, Exscientia are using AI to precision engineer new medicines. They have raised $379 million4 so far for their simple solution, using AI to learn about candidate molecules in the drug discovery process and more quickly identify compounds for clinical trials. The clear benefit here is that return on investment could be huge given the potential to drastically reduce time and cost needed for developing new drugs. There are also examples from across the EIT Health network. iLoF is a pre-product company supporting patient stratification for clinical trials, making the process faster, cheaper and less invasive for the patient. Their solution addresses a notoriously expensive and resource intensive element of drug development and is scalable, something that is therefore attractive to investors. Both of these show how AI algorithms can be translated into a successful business.
3. Demonstrate the value of your product
Currently, solutions that help manage the healthcare workforce may be more likely to attract investors as they have a clearer return on investment.2 But that does not mean investors won’t be interested in other beneficial outcomes. Communicating how your solution supports more sustainable healthcare either through improving patient reported outcomes or delivering operational efficiencies (freeing more time for patient care) could be attractive to certain investors. Moreover, as healthcare moves towards value-based care, cost-efficiency is key, and demonstrating the value you can offer should be a consideration for any AI solution.5,6
I believe AI has huge potential for big returns. While some challenges remain (such as the retraining of workforces needed to enable widespread deployment, a lack of experience creating uncertainties around regulation etc.), investors are still expecting high returns, not least due to some of the hype around the technology. But remember, as with any investment, they will primarily want to know how much investment is needed and how long before return on investment can be expected. Focusing on how long it will take before you can scale and the time taken to create value is really important as it can be a point of difference from other healthcare investments (such as drug development, which is a lengthy and costly process).
4. When to open the “black box”
The “black box” nature of AI (i.e., being unable to see or fully understand its inner workings), might make investors uneasy and increase perceived risk.2 It is rational therefore to assume that any pitch should focus on creating a full understanding of how your AI solution works.7-9 However, while it might seem incredibly important to make sure funders really understand your technology, they actually don’t need to know and won’t be interested in the finer details. They will hire experts or seek professional advice for this purpose once they are seriously considering investing. What’s most important is that you explain the use of your solution and show some validation that proves your technology is robust. In my opinion, what works best are validating quotes and/or letters from experts that relate directly to the technology you are developing or using.
While it may not be necessary to open the “black box” for investors, regulation may make it difficult to attract investment for non-explainable AI solutions anyway. In democratic countries or regions, such as the EU, regulation is already being introduced to reduce the use of non-explainable algorithms. Therefore, a certain degree of transparency will be a necessary prerequisite for regulatory approvals and investors will want the reassurance that your solution will meet those approvals. In non-democratic countries, things will likely be different, and it will be interesting to see how these different approaches will interact globally.
5. Create a clear business model
Finally, you need to pull all of that information into your business model. As ultimately, to attract investment, you need a strong business model. What goes into that model depends on the kind of pitch it is for. In a short pitch, it is enough to provide answers to the basic questions regarding the opportunity and why someone should invest in you specifically to take advantage of this opportunity. In a longer pitch, there’s the opportunity to go into greater depth on both the market opportunity and the competition, as well as your value proposition. But most importantly, when you’re planning to pitch to investors, don’t fall into the trap of focussing on your technology to the neglect of your business model – the main focus should be on how you plan to create a business from your good idea.
Both government and private investment in AI healthcare innovation is increasing, with the 50 largest companies receiving $8.5 billion in cumulative investment in 2019.2 While the process of securing investment can be long and difficult, these tactics can help increase the chances of your success, enabling you to develop your AI solution and play a role in transforming European healthcare.
References
1. Reports and Data. (2021). Artificial Intelligence In Healthcare Market By Offering (Hardware, Software), By Technology (Machine Learning, Context-Aware Computing, Natural Language Processing, Computer Vision), By End-Use (Hospitals & Healthcare Providers), And Region Forecast To 2027 [Online] Reportsanddata.com. Available from:https://www.reportsanddata.com/report-detail/artificial-intelligence-in-healthcare-market (Accessed August 2021).
2. EIT Health and McKinsey & Company. (2020). Transforming healthcare with AI: The impact on the workforce and organisations. [Online] EITHealth.eu. Available from: https://eithealth.eu/wp-content/uploads/2020/03/EIT-Health-and-McKinsey_Transforming-Healthcare-with-AI.pdf (Accessed August 2021).
3. Open Data Science. (2019). Funding Your AI Startup — 6 Pitch Writing Guidelines to Consider. [Online]. Available from: https://medium.com/@ODSC/funding-your-ai-startup-6-pitch-writing-guidelines-to-consider-6492d9c8f33 (Accessed August 2021).
4. Crunchbase (2021). Exscientia – Crunchbase Company Profile & Funding. [Online]. Available from: https://www.crunchbase.com/organization/exscientia (Accessed August 2021).
5. Esposito F, Banfi G. (2020). Fighting healthcare rocketing costs with value-based medicine: the case of stroke management. BMC Health Serv Research; 20: 75. DOI: 10.1186/s12913-020-4925-0.
6. NEJM Catalyst. (2017). What is value-based healthcare? [Online]. NEJM Catalyst. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.17.0558 (Accessed August 2021).
7. Matheny M, Thadaney Israni S, Ahmed M, Whicher D (editors). (2019). Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine.
8. Kuan R. (2019).Adopting AI in Health Care Will Be Slow and Difficult. [Online]. Harvard Business Review. Available from: https://hbr.org/2019/10/adopting-ai-in-health-care-will-be-slow-and-difficult (Accessed August 2021).
9. Cohen IG, Evgeniou T, Gerke S, Minssen T. (2020). The European artificial intelligence strategy: implications and challenges for digital health. Lancet Digital Health, 2(7), pp.e376–e379.