AI in micro-businesses: a revolution similar to 1980s computing?

How to implement AI in a micro-business: a 4-step strategy for a successful transition, based on the experience of adopting computing in the 1980s.

Artificial intelligence (AI) is often perceived as a technology reserved for large companies, but it can bring considerable benefits to very small businesses (micro-businesses) as well. The key to successful adoption lies in a gradual and well-structured approach. Here is a 4-step strategy for integrating AI into a micro-business, together with examples of concrete applications and suggested tools.

The adoption of computing in the 1980s and the implementation of AI today share many similarities in terms of progression and the reluctance shown by companies. Here is how a parallel can be drawn between these two technological revolutions through the four-step AI adoption strategy.

1. Introduction: one-off computing vs. one-off AI

1980s - One-off computing

In the 1980s, computing was first introduced into companies on a one-off basis, often for specific tasks. For example, the first computers were used to replace typewriters in secretarial departments or for precise financial calculations in accounting departments.

Today - One-off AI

Similarly, AI is today used for specific, one-off tasks. For example, companies begin by using AI to draft content, create automatic replies, or generate marketing ideas. This first approach makes it possible to understand how AI can ease simple tasks without disrupting the organisation.

2. Regular use: systematic computing vs. systematic AI

1980s - Systematic computing

Once computing had proven its usefulness, it became more systematic. Management software such as spreadsheets (e.g. Lotus 1-2-3) and rudimentary databases began to be integrated into the daily routines of companies, automating processes such as inventory management or accounting.

Today - Systematic AI

For AI, this stage is equivalent to creating routines in which teams use AI on a regular basis. For example, chatbots automate interactions with customers, or text-analysis tools are used to extract key information from reports. Companies recognise the usefulness of AI and begin to give it a more central role.

3. Optimising secondary processes: No-Code in the 1980s vs. No-Code AI today

1980s - No-Code computing

In the 1980s and then the 1990s, more accessible interfaces and “No-Code” software (even if the term did not yet exist) began to emerge, allowing non-programmers to create simple applications. Software such as dBase, then Microsoft Access (1992), made it possible to build databases without coding, and users were able to automate tasks without going through the IT departments.

Today - No-Code AI

Today, the same logic applies with AI. Tools such as Zapier, Make, or Airtable make it possible to automate secondary processes without writing any code. These tools embed AI to handle tasks such as creating workflows or analysing customer data, and even teams without technical skills can benefit from the advantages of AI.

4. Total transformation: complete integration of computing vs. complete integration of AI

1980s-90s - Complete integration of computing

Over time, computing became integrated into the key processes of every company, revolutionising entire sectors. ERP (Enterprise Resource Planning) software automated the management of company resources, while the internet transformed communication and business in the following years.

Today - Complete integration of AI

We are entering an era in which AI is becoming integrated into the core processes of companies. This can include optimising the supply chain, forecasting sales, or personalising customer experiences through predictive-analytics systems. AI is becoming a genuine strategic asset, just as computing was in the past, making companies more competitive and efficient.

Conclusion

Just as computing transformed the business world in several stages, AI is following a similar path. The key for companies is not to rush, but to adopt these new technologies in a gradual and considered way. The analogy clearly shows that, just as it was essential to invest in computing in the 1980s to remain competitive, integrating AI today is becoming a necessity for companies of all sizes.

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Paul-Antoine TUAL · AI Transformation Leader · Croissance & Transitions