In the fast-paced world of software product development, the decision on how much to invest in AI can be challenging. For software businesses, being "all in" on AI is essential to offer users the maximum benefits available today and staying competitive. If you don't make the investment to go all in, your competitors certainly will. However, this isn't a straightforward decision, as today's leading AI technologies can quickly become outdated with each new announcement from OpenAI, AWS, and Google, which often provide new functionalities for free.
The Intuit Case Study
Take Intuit, for example. They made a significant investment in developing their own GenOS platform, a proprietary system for integrating generative AI across their product teams. At the time, AWS didn't offer similar tooling as part of their platform. Intuit went ahead and built custom LLMs, tooling, instrumentation, test frameworks, and observability infrastructure. Shortly after, similar capabilities became available for free from major AI providers like OpenAI, Google, and AWS.
Without this investment, Intuit risked falling behind competitors like Xero, who were also heavily investing in AI. This underscores the importance of being proactive with AI investments, even when the technology is rapidly evolving.
The Propel Ventures Approach
At Propel Ventures, we faced a similar challenge. Our clients requested a brand voice agent to help their staff write all communications in the appropriate brand voice. We knew that whatever we built now might seem outdated within months. To address this, we ensured the architecture of our solution was flexible and adaptable.
We built the agent to allow for easy upgrades as LLMs improved, for example, enabling us to switch from GPT-4 to Gemini as new models surpassed each other in capabilities. This approach of building lightly and quickly ensures that we remain up-to-date with the rapid pace of model improvements by swapping out the fastest-changing pieces of functionality.
Key Considerations for AI Investment
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Future-Proofing: Invest in a flexible architecture that can adapt to new advancements. This means designing systems that can easily integrate new models and functionalities as they become available.
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Strategic Planning: Be aware of the AI landscape and anticipate upcoming releases from major providers. This helps in planning your investments wisely without overcommitting to technologies that might soon be outdated.
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Incremental Development: Build and deploy AI solutions incrementally. This allows for continuous improvement and adaptation without the risk of massive overhauls.
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Competitive Edge: Recognise that timely investment in AI can provide a competitive edge. Even if the technology evolves, being an early adopter can help you stay ahead of competitors who might lag in implementation.
Conclusion
Investing in AI is essential for staying competitive in the software product market. While the rapid pace of AI advancements can make investment decisions challenging, adopting a flexible, incremental approach can help mitigate risks. By planning strategically and future-proofing your architecture, you can ensure that your AI investments continue to provide value, even as the technology landscape evolves.
