How Artificial Intelligence is Changing the Way We Work

Artificial Intelligence (AI) has rapidly transformed from a futuristic concept into a fundamental part of our daily lives. AI is ubiquitous, from virtual assistants like Siri and Alexa to recommendation algorithms on streaming platforms like Netflix.

However, its impact extends far beyond our leisure activities. In this blog post, we will explore the origins of AI, its historical development, and how it is revolutionising the way we work today.

The Birth of AI: A Brief History

The idea of artificial intelligence dates back to ancient civilisations, with myths and stories featuring mechanical beings brought to life. However, the formal birth of AI as an academic discipline can be traced to the mid-20th century.

1. 1837 Mechanical Computer: In 1837, Charles Babbage proposed the first general mechanical computer. Due to funding issues, it wasn't built until 1910, when Henry Babbage, Charles Babbage's youngest son, completed a portion of this machine and performed basic calculations.
2. 1873 Theory of Neural Networks: The preliminary theoretical base for contemporary neural networks was independently proposed by Alexander Bain in 1873 and William James (1890). Over the years, researchers have built on these theories for artificial neural networks.
3. 1938 Electromechanical Computer: Konrad Zuse built the first programmable electromechanical computer between 1936 and 1938.
4. 1943 Electrical Computer: The Colossus was the first electric programmable computer developed by Tommy Flowers and was first demonstrated in December 1943. The Colossus was created to help the British code breakers read encrypted German messages.
5. 1950 Alan Turing's Turing Test: Alan Turing, the renowned British mathematician and computer scientist, proposed the Turing Test to evaluate a machine's ability to exhibit intelligent behaviour indistinguishable from a human's.
6. 1950 Publication of I Robot: Isaac Asmimov's collection of nine short stories exploring theoretical interactions between humans, robots, and the potential unintended consequences of hard-coded morality in intelligent autonomous robots.
7. 1956 Official Birth of "AI": In the summer of 1956, the Dartmouth Workshop marked the official birth of AI as a field of study. This workshop, led by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, brought together computer scientists and mathematicians to explore the potential of creating intelligent machines.
8. 1957 First Learning Machine: Frank Rosenblatt developed The Perceptron, a machine learning algorithm. The Perceptron was one of the first algorithms to use artificial neural networks, widely used in machine learning today.
9. 1997 Machine Beats Chess Champion: IBM supercomputer Deep Blue defeated chess grandmaster Garry Kasparov in a match.

While everybody talks about AI today, AI has a long history. The biggest historical obstacles to AI were:

• Learning models need vast quantities of data to learn from.
• Learning models also require enormous computational power.

The Rise of Modern AI

The turn of the 21st century marked a resurgence in AI research and development. Several key factors contributed to this resurgence:

• Big Data: The explosion of digital data, coupled with advances in data storage and processing, provided the fuel AI needed to learn and improve.
• Computational Power: More powerful computers and GPUs have enabled the training of complex AI models.
• Machine Learning: With the data and computing power, researchers could fine-tune machine learning algorithms, particularly deep learning. They've proven highly effective in tasks such as image and speech recognition.
• Ethics and Regulation: Now that AI is a more practical concern than just theoretical, discussions around ethics, fairness, and accountability in AI have emerged.

How AI is Changing the Way We Work

AI is not always the solution, but it is a critical tool for automating some manual activities. In other words, you must have AI in your enterprise automation toolbox.

There are many decisions in a business environment that, because of compliance, third-party supplier constraints, or policy, are driven by thresholds and rules. These "fuzzy logic" use cases are better solved by decision engines - another tool in your automation toolbox.

Nevertheless, AI provides opportunities to automate many business activities that were previously impervious to automation.

In the diagram below, I've briefly summarised an automation toolset and highlighted the areas where AI has made recent strides.

Before everyone jumps on the AI bandwagon, I recommend you examine your business capabilities, business architecture, or value chain methodically and identify where automation adds the most value. Some variables that might help you identify these areas might be:

• Sales funnel underperforming competitors
• # of FTE
• Longest cycle time
• Lowest CSAT or NPS
• # of handoffs between team members along the entire end-to-end journey
• Highest error rates or complaints

Note: The diagram and list above are not exhaustive.

Assign a team to automate the work in the high-opportunity areas and ask them to simplify, streamline and automate (in that order) the processes.

Ask them to prioritise cost and highest implementation speed when it comes to automation tool selection. You want to avoid overly complex solutions when a simpler-cheaper solution will capture 80% of the value. You also want to avoid building from scratch if you can plug in a commercially available and competitively priced automation product.

Next Steps

The revolutionary part of modern AI is that it significantly lowers the cost of using AI tools and makes them available to middle-market and small business competitors – not just Fortune 500 companies with huge budgets.

That means the focus should move beyond using AI everywhere toward using it as effectively as possible to improve the performance of high-value or high-cost activities.

The journey of Next Gen automation is still in its early days, and it has the potential to reshape the way we work and live by making processes more efficient, decisions better informed, and customer experiences more personalised.

As AI continues to evolve, monitoring developments to maintain a lead over your competition is critical.

From an ethical standpoint, remember that AI is adept at adapting to and exploiting human behavioural biases.

• Imagine your AI tool is too effective at selling your product, you could risk being accused of manipulating or exploiting customers.
• Or perhaps your AI performance management tool finds a way to gamify work in a way that leads to excessive "voluntary" overtime to your employees' health detriment, which could lead to employee backlash.

 Anticipate and monitor the reputational risks of having an AI that is too effective.