It would be fun to be contrarian and make predictions about 2024 without mentioning artificial intelligence. Yet the truth is that AI will continue to dominate public discourse this year. The difference is this: If the disruptive potential of AI became crystal clear in 2023, then 2024 will see tangible business impact.
We will see new algorithms that allow AI to evaluate its own effectiveness and apply self-correction and self-improvement. Smart organizations will use these leaps in technology to empower employees to not just iterate more effectively, but to execute projects that were unimaginable before. We will also see leading companies create, not eliminate, jobs—in part thanks to a burgeoning need for experts to help safely implement and govern AI. For others, their job responsibilities may not change, but how they do their jobs will.
In the following three key sectors, organizations at the forefront of generative AI (GenAI) adoption will have the opportunity to accelerate their digital transformations and gain a competitive advantage over their rivals. Here’s what I think is within the realm of possibility this year—and the pitfalls business leaders should avoid.
Thanks to GenAI, loan applicants may no longer need to scramble to gather and submit paperwork to prove their creditworthiness, only to wait days or weeks for a decision. Banks are unlocking the ability to automatically collect and analyze reams of information to assist loan officers (with the applicant’s consent, of course). And this is where we have to proceed with caution.
Financial institutions control vast amounts of data. That can be a boon when it comes to training GenAI—but it also presents challenges. It’s essential that banks take care not to allow bias to infect their models. Being colorblind isn’t enough. For instance, even when information regarding race is excluded, AI can perpetuate historical discrimination if it’s trained on previous patterns of discrimination.
This year, we’ll see fintech companies and traditional banks use AI to make accessing financial products easier for everyone, allowing people to evaluate their financial health and take a more responsible approach to everything from investments to reducing barriers to homeownership.
The global disruptions of the last few years have revealed how fragile our supply chains are. Companies that use GenAI to proactively mitigate risks will gain a crucial advantage over their competition in 2024.
Digital twins of machine components, facilities, or even entire organizations allow manufacturers to troubleshoot problems, redesign work processes, and pave the path toward the smart factory.
Soon AI will go even further, enabling self-correcting machines to detect their own worn-out parts, replace them, and optimize their performance. In the factory of the future, like the one BMW plans to open in 2025, advanced sensors will help train an AI-powered digital twin that can predict maintenance needs before things break, provide precise quality control, and offer next-level analysis of current processes to improve sustainability and reduce costs.
These manufacturing use cases won’t be successful running on large language models (LLMs) trained on publicly available data. Instead, successful enterprises will devote resources to generating and organizing relevant, proprietary data. In the coming year, I expect digitally mature companies to curate data to improve their manufacturing processes and start reaping rewards.
All government agencies can benefit from AI providing better services to constituents, but adoption will vary widely from country to country, town to town, and even department to department.
The public sector has a legal and moral obligation to ensure services and processes are equitable, safe, and secure. An “ask forgiveness, not permission” approach obviously won’t work for governments or public agencies. This is not to say they will shy away from AI. For instance, by analyzing data and making projections, AI can make national defense and healthcare delivery more agile and responsive to macro trends. Where the public sector will lead at the cutting edge, I think, is in creating robust AI guardrails.
As AI is deployed more widely, business leaders will learn the hard way that humans need to be intimately involved in every use case to ensure effective safeguards and oversight.
This will be familiar to companies that went through cloud migration, as opposed to those native to it. Just a few years ago, many companies that migrated to cloud-based infrastructure with little to no strategy saw server sprawl become virtual machine sprawl, which then became cloud sprawl. They lost control over essential components of their business and learned the necessity for developing rigorous oversight.
Another past lesson that can be applied to AI concerns data lakes, or centralized repositories for structured and unstructured data. As data grows ever more important, organizations will need to refine their approach to data integrity, purpose, and access. I predict that we will see a flood of new tech companies that specialize in all the unglamorous uses of data—such as labeling, validating, and structuring—that make AI possible.
For all this, I’m very optimistic. I believe we’ll look back on 2024 as the year we deployed AI not just to help do our current work better, but to pioneer entirely new and transformative ways of working. That’s a fundamental change that can’t come soon enough.
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