Can small companies compete on the AI stage?

Gero Keil

For most people, the terms "artificial intelligence" and "machine learning" invoke grand visions of IBM’s Watson or Google’s DeepMind being run by busy teams of data scientists and engineers. For smaller companies, a question is whether those terms will remain buzzwords or become key to daily business operations.

An inconvenient truth

Research firm MarketsandMarkets has predicted that the machine learning market will grow to $8.81 billion by 2022, scaling exponentially while reshaping the global economy. However, most companies – regardless of their size actually! – spend way more time talking and thinking about AI rather than doing something valuable with it. And who could blame them? It’s new, it’s mysterious and it’s desirable.

In many areas, algorithms and models that minimize human intervention are the best way forward to improve profitability and/or withstand competition.

However, even as the revolutionary potential of AI materializes, decision makers at small and medium-sized businesses are still struggling to fit it in their vision for value creation. In most cases, slow adoption is falsely attributed to the technical side alone – something that indeed is exclusive to companies that can afford expensive teams of data scientists and engineers.

The aspect which is discussed less frequently is that (re-)designing processes with and around AI does not come natural to most most people: We are not used to looking at problems through the eyes of a machine – even though we often perform tasks in a machine-like manner. But this is good news because the solution is not tied to company size.

Let's look at both issues one by one.

Democratization of AI technology: Accessibility through affordability and efficiency – and self-service

According to a report by MMC, AI now plays a key role in the products or services of one in 12 European startups. Many of these companies are working towards making AI accessible to a larger audience, leading to a democratization of the technology surrounding it. Hence, highly intelligent process automation is becoming a reality across a wide range of use cases.

We believe that the most fundamental shift comes through a further aspect: Decoupling AI from the necessity to write programs, commonly referred to as the no-code movement. This allows people outside of engineering to run experiments and realize ideas. We have seen a similar development with the rise of Zapier, a product that brought the power of tool integration into the hands of the masses.

Thanks to the emergence of affordable, ready-made ML solutions to optimize business processes, this belief must be gradually adjusted. With those barriers out of the way, the emphasis therefore must lay on the creative use of those tools. The practical question is therefore: How can you safely build a bridge between speculation and actualization of the AI/ML revolution?

Mental adoption of AI: Not if, but how

In working with our customers, we are seeing that our most successful ones are following a similar path to adopting AI tools in their products and processes:

  1. View problems through the lens of AI. Machines have become smart but we are still years away from human-like intelligence. However, until then it is possible for us to understand how machines can learn from data. Depending on the use case, data may be classified, segment, interpreted, extracted and you can run regressions on it. Looking at your business processes (and data) in this manner is the entry ticket to the promised land of AI.
  2. Start with something small. Regardless how intuitive a software is, new tools and technology can be overwhelming. Not only because of the newness itself but also the change aspect around it. Therefore, it has proven worthwhile to set up a first experiment – or pilot – with a high probability of demonstrating a positive return on investment.
  3. Scale. AI benefits from what is called a "network effect". However, not in the traditional sense as a social network or chat app. AI applications are becoming more valuable as the amount of data grows that is being processed. So once the use case has been proven, it is in the best interest of the business to integrate as much data of the same kind as possible – the reason being that accuracy grows with each new instance the model receives, leading to a higher overall performance of the company.

Conclusion

If you made it this far, I hope that you agree with us that the future looks bright for SMEs when it comes to the technical side of AI. We believe that the answer to this article's title is a firm yes. There are wonderful tools taking care of everything that requires valuable developer time and it is up to you to put them to creative use. This may not lead to a breakthrough in AI research – but that is probably not what your business is about.

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colabel is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.

If you liked this blog post, you'll probably love colabel.

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