Business Growth with AI, Part 1: Trajectory and Applications
The AI and ML hype hit a few months ago seeing rapid advances in the capabilities of large language models (LLM) such as ChatGPT, Llama and PaLM. What many might not realise is that these groundbreaking techniques have been shaping our world for over 70 years.
Picture this: 17 years ago, I moved to the UK and my visa verification took an agonising 4 hours. Armed with a South American passport, my airport escapades were nothing short of nightmares. Fast forward a decade and the same airport used facial recognition, turning a security marathon into a swift sprint. Goodbye awkward small talk with stern-faced border control officers (well, almost). For me and countless global citizens, these advancements were nothing short of a business travel revolution.
Behind the scenes, automated passport control, biometric recognition, eye tracking analysis, face recognition and border surveillance are all fuelled by the magic of AI, ML, deep learning, computer vision and NLP (Natural Language Processing). The timeline below provides a quick trip through AI’s major milestones.
With this article, my aim is to demystify the use of these AI and ML techniques whilst helping business leaders easily understand the opportunities at hand.
Simply explained, think AI for automating tasks such as customer segmentation, anomaly detection, customer data analysis, research and or the initial activities carried out by a friendly call centre representative or intern now substituted by chatbots. Think ML for prediction jobs such as pattern recognition, user behaviour analysis, churn prediction, segmentation, A/B testing and any general decision making process previously done based on the HIPPO’s (Highest Paid Person’s Opinion) gut feeling or instinct.
AI orchestrates automation, while ML dances with prediction.
A place that supports me staying ahead of the curve is Kaggle, a data science competition platform acquired by Google in 2017 which hosts a variety of competitions, ranging from predicting medical outcomes to classifying images or identifying fraudulent transactions. It also broadened my horizons to understand where the world is heading and how these breakthroughs can be applied to different fields.
AI and ML everywhere, all at once
Let’s explore a classification table showcasing AI and ML use cases tailored to each business unit, offering a glimpse into the realm of possibilities. Alongside, I’ve outlined the most likely optimisation algorithms employed to tackle these cases.
As we delve into specific applications, it is important to note that my focus centres around solutions I’ve actively engaged with — whether through work, development, usage, or partnership collaboration. Keep in mind that this is not an exhaustive list nor an endorsement; rather, it serves as a curated perspective on the diverse landscape of AI applications.
According to a Github survey involving 500 programmers, 92% reported using AI tools to enhance their work processes. In a separate study assessing the productivity impact of GitHub Copilot, developers with Copilot access finished their projects 55% faster than those without, hinting at the potential for AI coding tools like Copilot to contribute to a substantial $1.5 trillion increase in global GDP.
Conclusion
Rapidly reshaping the business landscape, AI and ML have become transformative forces. Major players like Google (PaLM), Meta (LLaMa) and OpenAI (ChatGPT and GPT4) have been instrumental in advancing text Generative AI. Meta’s introduction of LLaMa has paved the way for a wave of remarkable open-source alternatives to ChatGPT.
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Chatbots powered by AI can streamline tasks, offer customer support and engage users across diverse domains like art, education and business. They’re instrumental in enhancing efficiency, productivity and overall business performance.
CEOs who are able to leverage these technologies effectively will be well-positioned for success. It is important to emphasise problem-solving over technology adoption and striking a delicate balance between innovation and responsibility to manage potential risks.
These are just a few examples of the many ways businesses are using AI and ML to grow. As technologies continue to develop, we can expect to see even more innovative and effective ways to use these technologies for business advancement.
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Stay blessed,
Katherine
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