ChatGPT one year on: six paradigms that AI and LLMs have introduced for brands

It’s a year since the launch of ChatGPT sparked frenzied conversations about AI and the seismic impact it will have on business and society. And the recent chaos at OpenAI, the $86 billion nonprofit behind the tech, only magnifies the disruption seen more widely across the field of generative AI.

With McKinsey hailing 2023 as the “breakout year” for AI, the launch of OpenAI’s revolutionary tool has triggered multi-billion dollar AI investments from Google, Microsoft, Amazon and Meta. And, at a time when the global economy is on the ropes, venture capitalists have still managed to splash out $15 billion in backing for generative AI-based businesses.

While AI has grabbed the headlines, the media spotlight has also honed in on Large Language Models (LLMs), the technology that powers services such as ChatGPT, Bing, Bard and Claude. While LLMs have been around for years,  their mainstream debut unlocks intriguing potential for brands around productivity, speed to market and the ability to monetise their domain expertise and intellectual property. 

For many companies, the prospect of hurtling headlong into AI/LLM investment might seem daunting – particularly after all the froth associated with previous tech innovations like VR, blockchain, NFTs and the metaverse. But unlike these hyped technologies, AI/LLMs have real-life applications and companies from all sectors are busy experimenting with them.  

Here are six paradigms that AI and LLMs have introduced this year which underline why brands should embrace this technology to proactively pursue first-mover advantage:

  1. LLMs can supercharge organisational intelligence

Until now, legacy brands have struggled to monetise and scale domain expertise and IP cost-effectively. But with LLMs, brands can feed their collective knowledge into AI models and turn it into tools with a conversational interface, which gives employees instant access to a company’s entire knowledge archive at scale and with human-like nuance. 

Morgan Stanley moved quickly here, unveiling its AI @ Morgan Stanley Assistant this autumn. The platform gives financial advisors at the bank instant access to information from 100,000 internal research reports, used to feed on-demand answers to queries around markets, recommendations and processes.

  1. AI co-pilots free workers to be more creative

Imagine having a highly capable AI-enabled tool that helps with the dull, predictable elements of your job, freeing you to be more creative. A new generation of AI copilot tools will move beyond conversational interfaces, to radically rethink how we work. These copilot tools will weave intelligence into tasks to help boost productivity and performance, replacing the enterprise tools we are familiar with. Early examples include GitHub Copilot, which provides developers with troubleshooting in real-time, including fixing bugs and demystifying complex aspects of code. As a result, users can “spend less time searching and more time learning.” 

  1. The rise of AI native businesses 

Beyond copilots, brands can also develop autonomous agents – basically software that can make contextually smart decisions without the need for constant prompts.  Autonomous agents can chain multiple tasks together to achieve specific goals. They can be used to dramatically improve performance,  reduce processes from days to minutes, and to free up  time that can be spent more creatively.  Early examples of autonomous agents include  Auto-GPT and BabyAGI. 

Looking ahead, autonomous agents could be deployed to redesign businesses’ entire operational models, moving away from rigid, linear models that were designed around the limits of human productivity and availability constraints.

New AI native operational models could allow brands to create products tailored to customers’ individual circumstances and brought to market with previously unimaginable scale and speed. 

  1. Customisation at scale

AI allows brands to create artificial personas for testing bespoke products and services, a development that has the potential to revolutionise sectors such as banking, insurance and retail. Time-consuming and costly focus group testing will be unnecessary which means that new products and services can be launched much faster. 

As a result, we will shift from a “one size fits all” era of mass commoditisation, to one where brands will use AI /LLMs to cater at scale to each customers’ particular needs and wants. 

  1. Technology has become highly open-ended

Until a year ago, brands were used to a relatively consistent rate of incremental change around prevailing social web and mobile technologies.  ChatGPT’s launch has completely redrawn the horizon – kickstarting a massive AI innovations race with a language all of its own. Google’s Bing, Microsoft’s Bard, Anthropic’s Claude and OpenAI Enterprise are some of the critical touch points in nearly 12 months of extraordinary transformation. 

Despite the unprecedented pace of change, brands need to get going with AI. Pioneers like Microsoft and OpenAI have invested heavily in ensuring businesses can easily use AI services. For example,  OpenAI Enterprise addresses concerns over data privacy, which has previously prevented companies from adopting the consumer version of ChatGPT. Looking ahead to next year,  sector-specific LLMs will create a new paradigm for software architecture, allowing the creation of more affordable, effective and sophisticated tools for business.  

6.   Increased conflict between commercialising AI benefits and mitigating AI risks

If the recent tussle over the firing and re-hiring of OpenAI CEO Sam Altman has taught us anything, it’s that there will always be an inherent tension between what AI promises –  its inherent value – and the risks of a technology potentially “too disruptive for its own good”.  Over the next year we can expect more OpenAI style fall-outs involving native AI companies,  regulators, governments and other stakeholders as society grapples with the implications of what is essentially a live experiment.  Companies engaging at any level with generative AI should expect and even encourage debate around how this dichotomy can be handled. 

None of these are reasons to stay static, however. The window for first-mover advantage is tiny. Soon, legions of different companies will become fluent in the new lexicon of LLMs and AI, using emerging platforms to create efficiencies, transform team performance, boost revenue and grow market share. Brands that fail to seize the initiative will invariably fall behind. At the very least, the willingness to experiment is a powerful first step.

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Leon Gauhman is co-founder and chief product & strategy officer at digital product consultancy Elsewhen. Elsewhen’s clients include companies like Spotify, Google, Microsoft and Mastercard. The company’s mission is to empower leaders to harness a cutting edge approach to design and technology to deliver positive impact for their organisations. Leon writes for publications including Sifted, Venturebeat, City AM and Fintech Futures. He loves using his experience in engineering and product to invest in promising early stage founders.

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