{"id":14447,"date":"2024-02-20T08:30:20","date_gmt":"2024-02-20T08:30:20","guid":{"rendered":"http:\/\/TheNextWeb=1404021"},"modified":"2024-02-20T08:30:20","modified_gmt":"2024-02-20T08:30:20","slug":"turning-ai-frenzy-into-fundraising-strategy-how-startups-can-win-the-2024-investment-race","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=14447","title":{"rendered":"Turning AI frenzy into fundraising strategy: How startups can win the 2024 investment race"},"content":{"rendered":"\n
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Until now, generative AI has been akin to fairy dust \u2014 just sprinkle it in your pitch, and investors are all too ready to open their wallets wide.<\/span><\/p>\n

This is changing. <\/span><\/p>\n

Q4 of 2023 saw a 55.3% decline in AI<\/a> funding compared to Q1, mirrored by fewer deals. Experts like Matthew Marwick from the Intelligence Unit at CB Insights warn<\/span> that<\/span> while the AI funding will likely pick up steam again in the following quarters, the bloated investment rounds typical for 2021 and 2022 are now off the table.<\/span><\/p>\n

The dust after the initial AI boom has somewhat settled, exposing the first challenges. Many AI companies that raised easy rounds during the funding boom didn\u2019t achieve the commercial success investors expected.<\/span><\/p>\n

A mere mention of generative AI is no longer enough to raise massive rounds. Investors now seek strategic defensibility and differentiation in your stack against incumbents and new entrants flooding the market. They want long-term<\/span> market value<\/span><\/i>. <\/span><\/p>\n

The question is, in a world where everyone pitches AI, how do you cut through the crowd and attract investment<\/a>?<\/p>\n

The differentiation and competitive moat key to AI funding success<\/strong><\/h2>\n

Foundational models have been instrumental to the progress of the space. The problem arises when startups begin to pitch the same generic underlying models as their core value and competitive moat.<\/span><\/p>\n

This approach is deeply flawed. The investor view is that AI and LLMs possess no inherent moat or competitive advantage \u2014 just like anything that is available to essentially everyone. If your company isn\u2019t explicitly centred on researching, developing, and distributing foundational models, your moat and product differentiation must lie elsewhere.<\/span><\/p>\n

AI should act as an amplifier of a pre-existing product or strategy differentiator.<\/em> It is a supporting infrastructure for AI-first and AI-enabled startups that magnifies the initial value you deliver to the user \u2014 making the process cheaper, faster, more precise, and enhancing quality. But it doesn\u2019t <\/span>create<\/span><\/i> value out of thin air. <\/span><\/p>\n

Cool AI features mean nothing if they don\u2019t help to meet the needs of a specific persona, elevate user experience, or address specific use cases. This \u201cAI for the sake of AI\u2019s\u201d approach most companies have mistakenly adopted has spawned a phenomenon termed \u201cAI tourism,\u201d where users, not witnessing substantial value addition to their lives or workflows, don\u2019t stick with the product. <\/span><\/p>\n

Lesson learned: presenting an interesting case for investors will mean diving deep into the tangible use cases and value your AI technology will create. <\/span><\/p>\n

Companies with vertical data moats will lead the next wave of funding<\/strong><\/h2>\n

Vertical AI \u2014 software customised for a specific industry and its problems \u2014 is gaining momentum among investors and end-users. Index Ventures<\/span> partner Paris Heymann called vertical AI \u201cthe next logical iteration of vertical SaaS\u201d that might act as an industry\u2019s game-changer.<\/span><\/p>\n

By delivering high-quality results on specific use cases without generating irrelevant BS, such tailored models increase time-to-value and user retention. This ability will make vertically integrated apps key in driving differentiation.<\/span><\/p>\n

Unique data sets<\/span><\/i>, <\/span>customised go-to-market plans<\/span><\/i>, and the <\/span>capacity to deeply integrate into user day-to-day processes<\/span><\/i> make them hard to replicate and promise immense potential. <\/span><\/p>\n

Looking at the investment landscape, we already see this trend playing out massively in data-heavy industries like legal and healthcare \u2014 but it will surely play out in other sectors as well. <\/span><\/p>\n

The core mistakes of pitching AI \u2014 and how to avoid them<\/strong> <\/span><\/h2>\n

So, what does it mean for founders looking to raise money for AI solutions today? And how do you make sure your pitch lands in just the right spot? In 2023, we at <\/span>Waveup<\/span><\/a> worked on over 20 successful (and not so much) AI fundraisers, witnessing firsthand the difference between AI winners and losers. <\/span><\/p>\n

Here are the mistakes we see companies make when pitching AI and how to fix them:<\/span><\/p>\n