1. AI success is challenging

Getting an acceptable return on investment (ROI) from AI is challenging, especially for a smaller operation. More than 80% of AI projects fail to achieve desired outcomes – double the rate of non-AI IT initiatives. The reasons behind these failures are often complex, too.

Many leaders misunderstand or miscommunicate what problems AI can solve. Others lack enough data for AI to produce reliable insights. Mismanagement, a lack of relevant goals and insufficient IT infrastructure also frequently cause issues. Startups must recognise these obstacles and temper expectations accordingly, as small businesses are less able to absorb economic hits on the journey to long-term benefits.

2. Not all AI use cases are created equal

While getting an impactful ROI from AI can be hard, some use cases can yield impressive results. However, not every AI application will. It’s more than a matter of careful investment and model training. Even when you manage all other factors perfectly, some AI uses are more beneficial than others.

Generally speaking, AI is best at automating data-heavy tasks with high levels of repetition and predictability. While AI can technically perform more nuanced work like generating marketing copy, its benefits over human experts start to wane in these creative or variable roles. Recognising AI’s strengths and weaknesses is key to learning where you can safely invest in it.

3. AI benefits different businesses differently

Similarly, AI’s usefulness varies between organisations, even within the same use case. Consider how 51% of technology and media companies believe AI will heavily impact productivity in customer success roles, but just 31% of those in life sciences believe the same. AI’s efficacy hinges partially on how your unique workflows operate.

A customer service process involving more data and repetitive reporting tasks will benefit more from AI than one relying more heavily on face-to-face interactions. You’ll likely gain more from AI in some applications than competitors and less in other areas. So, go beyond looking at what AI is generally good at and identify which of your specific workflows have the optimal conditions for AI to succeed.

4. Processes and people matter more than technology

Startups must also realise that AI success is about more than matching the right technology to the right application. While that’s crucial, studies have shown that the top-performing AI projects invest more in adapting their processes and workforce than optimising AI models themselves.

A BCG study comparing AI leaders and those failing to drive value from it found that 70% of AI challenges stem from people and processes. Another 20% came from technology-related issues, and the remaining 10% involved actual AI algorithms. Consequently, getting a positive ROI requires a mirrored investment – put roughly 70% of your resources into ensuring your workers and workflows are AI-ready and less into the model itself.

5. AI cannot replace human talent

Relatedly, you should avoid the temptation to view AI as a replacement for human workers. Automating positions into redundancy may seem promising, especially for startups with tighter operating margins, but it’s become increasingly clear that AI works best when it complements employees.

Many companies have replaced human roles with AI, but 55% of those businesses later admitted that making these jobs redundant was a mistake. Organisations that were too bullish on AI often ran into skills barriers. They lacked the human talent necessary to fully capitalise on the technology that replaced workers. The solution is to invest in upskilling to foster an AI-ready workforce and view AI as a way to maximise employee productivity instead of an alternative to these workers.

Startups must approach AI carefully

AI can produce substantial improvements at a startup if you deploy it effectively. However, there are many mistakes along that journey that are all too easy to make. Given the impact of these choices, you must recognise these five facts about AI before investing in it.