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When AI was first introduced, it was seen as a technology that could revolutionize business models by creating a thorough understanding of the customer's profile and needs. It would be an indispensable technology for large companies to improve their balance and a magical tool for small business owners to break down barriers between their product or service and the end user – using the right data, of course.
At first, AI seemed to be something that could almost “magically” make perfect predictions about the success or failure of a product, just by analyzing the profiles of different consumers and their buying behavior. Startups envisioned a world where market traction could be achieved simply by running a Python script rather than optimizing a product with minimal viability.
Financial investors have hailed AI as the start of a new financial cycle that is pouring huge sums of money into every stock labeled as AI-based. The “dotcom era” that began in the 1990s – and with it the stock market crash due to excessive speculation – seemed to be replaced by the “AI era”. Images of people resting while robots worked for them were the collective image of what this new era would look like. FOMO (fear of missing out) did the rest, and large companies jumped into this technology without fully understanding it, or in many cases without knowing what to do with it. Python developers have quickly become one of the top hiring needs of many businesses.
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Since its inception, adopting AI has proven to be very challenging for large businesses and creating huge barriers for small businesses. from the embarrassing moments of Amazon Alexa and Facebook to the Chinese police's admission to shaming the wrong person (who turned out to be a billionaire) caused by trusting their facial recognition system. Machines have turned out to be imperfect and behind people in many cases, and to this day, their glitches have shown that they are simply not quite there yet. Hence the question: if large companies haven't mastered AI yet, how can small companies accomplish this task? And a more general question beforehand: Can small companies benefit from AI, or do only large companies – with large amounts of data, programmers and data analysts – benefit from it?
1. Automate your boring tasks
Before you are told that small businesses simply don't have enough data to get into AI and / or that not hiring a data scientist is seriously putting your budget at risk, clarification is needed. AI is often confused with machine learning. AI is a broad umbrella term that encompasses all applications from text analytics to robotics, and machine learning is only a subset of AI. While AI is meant to make human tasks faster and more precise (eliminating the factor of human error), machine learning helps predict outcomes and make estimates.
Although machine learning has become much more accessible with platforms like Amazon's pre-trained AI services or Google Colab – which allow you to build a base model in minutes without hiring data scientists, you may not have the skills or knowledge more likely is that the data is running a trustworthy model. However, you still have the option to use AI to automate your daily routines. Any repetitive task you can imagine can be fully automated thanks to AI. For example, you can record a range of actions on your computer and use AI tools with on-screen image and text recognition to activate them automatically. Or you may want to use expanded snippets of text organized by the AI and type in autocomplete to avoid writing the same things over and over.
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2. Use the AI in your parking spaces
Let's face it, AI is a magical buzzword that is grabbing people's attention. Labeling your product or service as "using AI" increases credibility and the likelihood that people will hear what you say. Every company can find a way to implement AI in its processes, regardless of whether images or texts are recognized, algorithms are compared, bots are communicated, smarter classifications, natural processing language, etc. are used.
My company tested this hypothesis. I own a PR agency and as such we match brands with journalists, or at least we tell stories, hoping to find journalists who are interested in what we tell them. We created an automatic scraper to collect the latest articles published by target journalists, store them in a database, and classify the data using AI. The aim was to get a clear idea of what any journalist is more likely to want to write about. In all honesty, the results weren't much better than when we did all of this manually. In some cases the results were worse, but that allowed us to reposition ourselves. We have transformed from a traditional PR agency into an AI platform. We just had to create a landing page that sums up our "new" approach. Adding "using AI" did the rest. The result of that? Our conversions – as measured by the number of people who would sign up for our program – have basically doubled.
3. Use AI to interact more with your customers
One of the greatest benefits of AI for customer service is that it allows many processes to run at the same time. Imagine having a person running a customer service counter. They are expected to have one customer at a time, maybe two. AI gives you the ability to serve more people at the same time. AI does not take on our work, but helps us to perform our roles more efficiently.
Take the messaging pop-up boxes on websites. Previously they were operated manually, which means someone was typing for you. Nowadays, AI can do and do some of the work for you, e.g. B. Pre-screen your customers or give them some general answers to their questions. Often times, the chatbot can resolve the client query, or if it can't find a solution, a human operator can intervene.
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This has the advantage that several queries can be processed at the same time, whereas previously only one query could be processed. It is never good for a company's image to save companies from long lines at customer service desks or long waits on the phone. The AI process gives departments time to focus on the customers who need it most.
AI intelligently classifies the information so that in complex customer service environments (e.g. highly specialized IT departments) it can route the customer request to the correct customer service representative. This means that specialists can address complex problems immediately without the customer being redirected from one department to another.