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The Future of Work: Embracing AI Technology to Stay Ahead

In October 1981, during a magazine interview, Steve Jobs envisioned a future with (personal) computers on every worker's desk, a progressive culture where technology creates more opportunities for employees. At the time, and during the 80s and 90s, many did not share the vision of Steve Jobs. Similar to today, there were concerns and fears around technology replacing workers. Today, we have the emergence of artificial intelligence. The fear of AI replacing workers as the advancement of virtual assistants powered by OpenAI's GPT-3 and GPT-4 are becoming increasingly more capable.

Similarly, in 2011, Marc Andreessen envisioned that "Software is eating the world." Over the past decade, software has moved from being exclusive to the technology sectors to becoming the centre of many industries. Today's top companies in most sectors have embraced technology over that time period—changing strategies, ways of working, and becoming more productive. During this process, several of the largest companies in the world have become software companies: Amazon, Apple, and even Starbucks, through their development efforts into mobile apps and artificial technology with Deep Brew.

Similarly, after we acquired QGen Group(now rebranded as ArriTech) in 2022, we’ve worked on improving efficiency, primarily in our technology stack, by incorporating artificial intelligence and machine learning (ML). Our AI strategy relates to data processing, where we expect improvements through AI and ML, which in turn can allow our personnel to focus on other tasks, often relating to the mounting complexity of regulatory requirements within digital identities and verification across the business spectrum and our daily lives. Additionally, with Arringo, AI can help us improve the business while allowing employees to work with personalised solutions to improve our B2B customer’s operations.

There is no doubt that AI will replace some jobs. Personal computers and automation have done the same across various industries since the 1980s. But overall, the net effect of technological advancements has been positive for a growing workforce. The same will likely be true for large language models (LLMs) like ChatGPT. Some job positions will change, and some will disappear entirely, with an overall net positive effect with employees working smarter. Another example from the past is when software made calculators obsolete in the workplace; with computer software, the worker could recalculate rows and columns of data at the push of a button. Technology made many existing tools obsolete while enhancing the worker.

The technological advancements in AI over the last ten years have set the stage for artificial intelligence, enabling the recent rapid progress, including:

● Computing power for specialised AI chipsets has vastly increased. ● The amount of training data for AI algorithms is exploding with the advent of data lakes and a fully connected IoT (Internet-of-Things) world. ● Decreased cloud storage and computing costs, plus the facilitation of distributed collaborative working, made combining highly specialised knowledge easier.

We are now witnessing how AI is eating software. However, predicting seismic shifts like Amazon burying Barnes & Noble or what Netflix did to Blockbuster is difficult.

For now, it is helpful to think of AI as an extension of software, improving it and ultimately giving workers more capabilities and greater efficiency. Some software will be replaced by AI, and in other cases, AI will grant new capabilities. What we are seeing with ChatGPT's conversational interface is that the AI can take routine tasks and enable people to improve.

Instead of worrying, business leaders and workers should not feel threatened by AI but instead ask themselves how to implement AI to improve.

In writing for different business areas, creative writers need to become experts at writing prompts for AI. Leverage LLMs for an initial draft, gather ideas and assist with research. The writer's skills and knowledge are then applied to editing and finalising the output, proofreading, and fact-checking.

At ArriTech, we are working on AI image recognition software and optical character recognition (OCR) technology to automate data extraction from identification documents to validate and run authenticity checks.

A software developer can leverage LLMs for debugging and fixing errors. Programmers often use RegEx to locate or validate specific strings and patterns of text in a sentence, document, or any other character input. Working with APIs and databases requires using RegEx, which can be time-consuming and error-prone. Asking AI for a solution to display specific patterns based on criteria can improve the productivity of software developers, allowing them to focus on other areas.

Data engineers, data scientists, and data analysts in software development use SQL to communicate with databases. ChatGPT can generate SQL server tables with data and generate SQL code to translate the table, column names, and data to other languages. The code can then be used to create new tables in SQL using appropriate SQL INSERT statements. While reading this, AI models paint portraits, respond to customer queries, prepare tax returns, create new music, and provide health advice. AI is affecting our lives, and the arms race for AI has started.

The key takeaways about the future of work and the process of embracing AI are that:

Microsoft, Apple, Google, and Meta invest heavily in artificial intelligence. Elon Musk, who co-founded OpenAI and later left the organisation, recently announced his own AI company, a rival to Microsoft's OpenAI and ChatGPT. Artificial intelligence is being adopted on a mainstream scale. With readily available AI solutions on a commercial basis, we have updated our digital strategies in ArriTech and across our group of companies to help us get ahead of the curve by embracing new technologies. Something we believe all businesses can benefit from—keeping an open mind to new ideas and creating new processes:

● Have an AI playbook ready.

● Educate and encourage staff to experiment with LLMs in daily workflow.

● Consider building a personal AI trained for specific business needs rather than relying on public general-purpose models.

● Distribution of knowledge generated from such AI models.

● Distributed collaboration structure to allow organisations to leverage AI.