Can ChatGPT Develop Startup Financing and GTM Strategies?

Since the general release of ChatGPT 4.0 from OpenAI, I have used it daily along with several other AI tools like Anthropic's Claude, Google's Bard – now Gemini, Microsoft Co-Pilot, and domain-specific models, however briefly, such as BloombergGPT (see my blog post here) for financial analysis.

My initial interactions focused on content creation use cases rather than strategy formulation. I had three reasons for avoiding strategy generation: (1.) I wanted to build expertise in using generative AIs for content, (2.) I doubted large language models' had the ability to produce reliable startup strategies, and (3) my personal library of startup and public company strategies and business plans provided segment-specific insights, data, and other great stuff to leverage.

However, I tested ChatGPT's startup strategy capabilities using fictional but sector-relevant examples. I analyzed financing and go-to-market (GTM) plans for three scenarios: (1.) an open-source software tool pursuing commercialization, (2.) an AI-powered cloud optimization platform, and (3.) a direct-to-consumer cosmetic product.

Through these experiments, I discovered ChatGPT can generate informed, trustworthy startup strategies as an outline or first draft, though it falls short of creative ideation and dynamic real-world thinking. I see value in using ChatGPT's strategy outputs as starting points requiring additional enhancement and customization.

Here are my findings:

Financing Strategies

All three financing strategies start with initial funding from personal or small-scale sources like bootstrapping, angel investors, or friends and family. As the businesses grow, they seek additional funding from venture capitalists and corporate VC for Series A rounds. Each strategy involves forming strategic partnerships in their respective fields. Additionally, there is a strong emphasis placed on networking and community engagement to find potential investors and partners.

The three financing strategies diverged when it came to the open-source software and AI-driven cloud storage ventures targeting investors specific to technology. At the same time, the D2C product focuses on investors in consumer goods. The tech products leverage accelerators and incubators for market entry, contrasting with the D2C's use of crowdfunding and influencer partnerships. In revenue strategies, the tech ventures reinvest commercial revenue and consider revenue-based financing, whereas the D2C product focuses on direct online sales and retail partnerships.

GTM Strategies

When comparing the GTM strategies for the three cases, ChatGPT developed basic strategies for a general market analysis and segmentation, highlighted the need for competitive analysis and unique value propositions (UVP); pricing based on a value-aligned pricing model; sales channels that included one- and two-step distribution options; trade shows, content and digital marketing choices, and a regulatory compliance outline. These strategies and approaches will not induce existential trepidation in marketing communications firms based on these strategy recommendations: They were very rudimentary.

The AI-based cloud platform (#2) strategy shows a more nuanced approach to sales strategy, customer support, strategic alliances, and staying ahead of technological trends.

Interestingly, there were differences registered in the three cases to regarding target market specificity, AI-based versus non-AI-based product sales and complex software solutions in a sales channel, marketing plan, customer support, strategic alliances, and product evolution. The strategy for the AI-based products was a more focused approach and simply better informed.

There were some missing elements: All three did not mention Ideal Customer Profiles (ICP), a product-market fit feedback loop, community involvement, or a Product Led Group (PLG) approach. Perhaps ChatGPT did not consider this part of the GTM; perhaps it considered these, operationally, part of product management and engineering, or operations.

Conclusion

ChatGPT created the impression that the strategies and tactics its developed were based on a framework or template that applied to all three rather than a unique, dynamic, and original strategy and tactics for each case. The strategies gave the impression that ChatGPT was drawing from a standard framework or template for all three cases rather than developing unique, tailored approaches for each scenario. It appeared that the approach was, "This request calls for strategies for three startup cases, so I will plug the specifics into formulaic models that I have for financing and GTM." The result was boilerplate strategic components with slight customizations in certain areas.

Using ChatGPT as a tool for developing financing and go-to-market strategies is akin to creating a foundational draft or an outline. While it offers a valuable starting point, the initial suggestions require extensive further development. This involves adding specific details – especially data, contextualizing to the unique aspects of the business, and customizing based on the customer(s), target market(s), and industry trends. Additionally, the draft should be enhanced with data, innovative ideas, practical insights, and strategic considerations to ensure it aligns with the business's objectives and the dynamic nature of the market. Through this process, the basic outline provided by ChatGPT becomes a practical, detailed strategy ready for real-world application. It is tailored to meet specific business needs and is adaptable to the ever-changing market environment.

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