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 – 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 into 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 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
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?
How AI can help small businesses
Artificial Intelligence has been helping big companies for years. Only recently has AI become available to small businesses through AIaaS (AI as a service). The benefits for small businesses are similar to those of big companies—let’s take a look:
- Reduce time spent on mundane tasks: using AI to automate repetitive tasks and analyze large amounts of data helps small businesses free up time to focus on other tasks.
- Lower costs: implementing an AI solution reduces costs in the long run by improving efficiency and freeing up time and resources to develop the business.
- Improve service: using AI to understand your consumers and how they feel about your product can help you proactively improve your offerings.
- Improve security: AI solutions can be implemented to help small businesses detect any suspicious cyber activity before it becomes an issue.
These are some of the general benefits of implementing an AI solution in your small business. It can help increase efficiency, productivity, and quality of insights—but, how?
Let’s take a look at some of the use cases for small businesses.
AI can help improve customer service
AI solutions can help businesses improve customer service in a number of ways, from analyzing customer sentiment to categorizing support requests.
Having insights into customer behavior and sentiment is key for improving your support and product—often before customers themselves request it.
AI can simplify operations
AI can help businesses by improving efficiency and productivity when it comes to business operations. Whether that’s by categorizing incoming emails based on their contents, or identifying promising leads and unpromising leads.
AI can help with both simple and complex issues and can help you and your team free up a lot of time for other important tasks.
AI can help you improve your product
AI enables businesses to continuously improve their product and offerings with insights into how users interact with them. Simply use AI to monitor what users are talking about and the sentiment behind their words, and make changes accordingly.
AI can help highlight product issues before they’re vocalized by users—meaning you can solve problems before they even arise.
Gone are the days when only the biggest organizations leveraged AI—small businesses can now use AI to optimize their processes, too. AI solutions make harnessing the power of Machine Learning easier than ever before—let’s see how to implement AI in your small business.
How to implement ai in small businesses
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:
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, segmented, interpreted, or 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.
Start with something small
Regardless of how intuitive 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.
Scale
AI benefits from what is called a "network effect". However, not in the traditional sense of 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.
