Next-Gen Automation: How AI will Impact Business

It was only ever a matter of 'when' AI entered the public consciousness. Too many novels and science-fiction films have been made about the dangers of Artificial Intelligence. The prospect of thinking machines has long captured the public imagination, as has the dangers presented by such a technology. We're still a long way from the doomsday scenario envisioned by the likes of Terminator or System Shock, but 2023 has given the first indications of how AI will impact society over the coming years. Already, there have been signs that the technology will disrupt business and society in new and unexpected ways.

No one foresaw that the first casualties of AI would be copywriters, but that is precisely what we saw at the start of last year. There are still plenty of copywriters in business, but far fewer than twelve months ago. Many small businesses now use ChatGPT for their marketing copy instead of paying a freelancer. Expect translation to be the next industry to be totally disrupted by Generative AI.

Both copywriters and translators suffer from the same problem. Many marketers would rather do it themselves. When someone else does either task, there are always vocal complaints about the quality and speed of output. That reflects the fundamental problem that will affect large swathes of the services sector. Why outsource tasks to another company when AI can do it for you instead?

AI Outsourcing

The last couple of decades have seen a wave of outsourcing to low cost locations. Consumers have become used to dealing with overseas call centres for customer service, while businesses have moved routine back office processes to offshore contractors and outsourcing companies based in countries with low wages. Such outsourcing contracts are still expensive once mark-up and account management fees are taken into consideration, while the quality of the output rarely meets customer expectations.

As soon as the technology is ready, companies will look to replace long term offshoring contracts with AI. That should allow companies to reduce costs while also increasing flexibility. When business needs change, it will be far easier to retrain an AI script than to retrain a team of human workers based in a different country.

Front-line AI

At highest risk are jobs that businesses have tried to offshore in the past without success. These are the people that companies such as BT are looking to replace by 2030. Call centres will be a top target, as will the aforementioned copywriters and translators. Replacing onshore workers carrying out routine tasks in high cost locations is how the biggest cost savings will be made, and make no mistake: cost-cutting will be a significant driver for AI adoption in enterprise. Anyone who uses a computer to follow a script or carry out routine tasks will be impacted.

As always, the brunt of cuts will fall on more junior workers. These people won't struggle for alternative employment. Employment levels are at record highs across the western world. Despite this, there are widespread shortages of teachers, nurses and social workers. We will see a wave of career changes as people made redundant through automation retrain for front-line careers in public services.

Meanwhile, many office workers will see a substantial change in their roles as AI begins to impact their day-to-day responsibilities. Entry level job descriptions will be need to be redefined, becoming more focused around AI oversight and internal collaboration. This will provide new recruits with the ability to ascend the skill ladder. I'm not forecasting drastic changes or widespread redundancies at middle management level though.

AI Management

Generative AI may be fast becoming a multi-purpose tool. However, it does not have the inter-personal skills or human experience needed to engage in management tasks. It cannot mentor junior team members, manage the changing business environment or engage in office politics. Ultimately, AI lacks accountability and the decision-making power to do those things. Human nature demands someone to blame when things go wrong. That will be the manager responsible for the relevant business function.

Businesses will still need their existing management layers to keep reporting lines flowing as well as to decide strategy. AI changes how managers approach their responsibilities, but it doesn't change the need for management tasks to happen. Each department will need an AI expert responsible for training and managing the AI models used within the team. That will likely be the relevant operations team already in place.

AI Operations

Managing AI is about more than just prompt engineering, although this will be an important skill in the short and medium term. It's also about maintaining the data flows and technology integrations necessary for the AI model to deliver the expected output. The proliferation of code-free integration tools is already making this significantly easier for the modern enterprise.

Mapping data flows and designing integration use cases requires a level of technical expertise beyond the skill of the typical manager. AI can offer advice and suggested field mappings, but it cannot validate all the necessary use cases and downstream impacts on other systems. System architecture is a specialist field for this reason. The need to comply with data protection and information security legislation adds an extra set of considerations, requiring human input from a specialist.

It will never be possible for a business user to configure a new AI model just from a single prompt unless that model is a standalone AI that doesn't use any data or applications available across the wider business. Compliance standards such as ISO 27001 will demand a design and governance process in order to prevent data leaks and ensure the model is non-discriminatory. We may also see copyright and brand compliance concerns leading to additional legal reviews for AI generated content in many situations.

AI Investment

As with any new technology investment, business cases will need to be written and budget secured before any new AI models can be deployed. That won't change as the technology matures, but gaining approval for new AI implementations will become easier over time. Once businesses understand the costs and benefits of AI, it will make scoping new use cases for the technology easier.

No business will adopt AI just for the sake of it. The technology will need to provide a return on investment. Much of the benefit from AI comes in the form of efficiency gains, which is generally the most challenging type of improvement to measure. The work hours saved through AI usage will need to be quantified in dollar terms, as this will offset the technology or server costs of using AI - which is currently very expensive for your typical Generative AI model. It is far easier to prove the financial benefits of AI if it is being used to slash agency budgets and contractor fees. That is another reason why translators and copywriters are especially vulnerable to being replaced by AI.

AI Productivity

Economists have been talking for years about stagnant corporate productivity. AI has long been touted as the source for any future efficiency gains, but older machine learning technologies were often expensive and didn't always deliver sufficient output to justify the high price tag. It's unclear as to whether this same problem will affect generative AI as well.

We are still in the very early days of the generative AI product cycle, and the most exciting use cases for the technology haven't made it into production yet. That shouldn't stop businesses from exploring use cases for AI. They can stop worrying about the impact of AI on their own jobs though. The technology does pose a significant threat to a highly limited set of careers. Everyone else should see substantial benefits, provided they have the operational and technical skills to make the most of this new and exciting technology.