The AI effect: A business and professional new frontier

In less than two years, artificial intelligence has changed the way we work. As an AI Partner at EY, I've witnessed firsthand how these advancements have and continue to reshape organisations. We've seen AI grow quickly, becoming an essential part of our professional toolkit. Here are some of my key takeaways from the past year and how it’s setting the stage for an even more innovative future.

1. Technology is advancing in leaps and bounds:

Reflecting on my early days with AI, it was a niche field, a hard-to-explain technology that often revolved around ‘market basket’ analysis or other common applications. AI was a technology regarded by many as promising but by others as largely unproven. Its potential was locked behind the gates of complex algorithms and computing limitations. Even user-friendly machine learning interfaces couldn't shake its image as a technical enigma, often feared by executives.

Fast forward to today, we're in the midst of an AI revolution, with Generative AI at the forefront. Transformer models like GPT have changed the game, understanding and generating human-like text with a nuance and coherence that was once the stuff of science fiction.? Entire start up offerings have been superseded, seemingly overnight.

The AI industry is now fiercely competitive, with constant breakthroughs like Meta's Llama 3.1 model, offering GPT-4 level capabilities at low costs and on mobile devices.

Less than a year ago the models were too large to be anywhere but powerful computer servers – now they’re on our phones and their responses are instantaneous.

In just several months, we’ve gone from organisations considering the viability of finetuning and selling proprietary large language models (LLMs) as a business model to now questioning that it may not have ever been sustainable – and learning that many required applications can be completed without these resource-intensive and time-consuming approaches.

2. Personal learnings: AI for everyone

The past year has underscored the importance of a growth mindset in personal development, particularly in relation to AI. As technology races ahead, the need to continually upskill has become a universal imperative, extending well beyond the realm of technical specialists to encompass every corner of the organisation.

I’ve learned this past year that while many turn their minds to increasing their rate of AI literacy, many are confused on where to start in such a broad domain with such diverse applications.? I find this to be an incredible opportunity for all – from business professionals to tech teams and leadership - to explore and leverage AI in ways that are meaningful to them.

I’ve enjoyed watching our tech teams eagerly build AI Agents when they were an emerging concept and share technical accelerators that enhance data, security, and delivery processes. Our analyst teams are using AI to rapidly accelerate user stories and functional requirements with carefully curated cascading prompts and our internal teams turning around our newsletters in multiple languages with sharp, succinct text and images enhancing our experience.

There’s a renewed energy as we adopt an ‘AI in all we do’ mindset - professionals from all disciplines recognise that understanding AI and its applications is no longer optional but essential to staying relevant in an increasingly automated world and many of them are inspired to embrace new ways of working.

3. Client learnings: From enthusiasm and scepticism to top-down urgency

From a client perspective, the past year has highlighted a spectrum of responses to AI's rapid development. Some clients have eagerly embraced AI, integrating it into their business models to drive innovation and competitive advantage. These early adopters have seen tangible benefits with increases in efficiency, cost savings, and improved customer experiences.

Conversely, there remains a segment of clients who are either lagging in adoption or harbor disbelief in AI's practicality or value. This disparity often stems from concerns about the reliability, security, and ethical implications of AI systems and in some regards a low understanding of AI or taking a use case-based approach rather than a value-driven approach to organisational AI adoption.

To contrast the two (and there are more nuanced approaches between these). The organisations taking a top down, organisational wide and committed investment approach to AI see beyond the concern of small use cases failing. They generally hold a view that AI is here, it’s not going to slow down and it’s something they need to be on board with to remain competitive. ?AI is treated more like a key lever to enable the paradigm shift they will need into the future.

Many of those organisations are thinking about AI in the context of industry and competitor disruption. They are appointing Chief AI Officers and not over analysing technology platform decisions in favour of having first mover advantage.

For those with ambitions to lead in the coming era, the message is clear: Embrace AI or risk obsolescence.

4.???? Looking Forward: The AI crystal ball

The future is bright and brimming with AI potential and it has staked its claim as a permanent fixture in the landscape of innovation. However, I wonder what the future might look like? History may repeat itself – in some regards the patterns of old will continue to repeat, albeit with new nuances – AI technology will evolve to become faster, cheaper, multi-threaded and more transportable while also being easier to use.

The LLM race boils down to three levers: The speed of innovation and customisation; accessibility and cost effectiveness of models; and finally – data security.? Mass open sourcing could eventually create a tipping point to see it emerge as a preferred option - remember Linux anyone?

Consumers will expect AI to be there, just like our phones, it will become more integrated, pervasive, and invisible all at the same time. Interfaces will consolidate and integrations will become native. I am curious on what new challenges it will present, and what new legacy or new possible technical debt it will create.

The types, sizes and brands of models will continue to grow – and at the same time there will be new orchestration layers created to bridge the specialist models and domains in a way to connect them.

Concepts like having an LLM agent to complete one task (book me a company trip), while another agent completes another task (file my taxes) are already here.? Concepts like one agent that builds a new agent who can bridge the gaps (create an agent that can book me a trip and in real time update my taxes) are in experimentation.

The future isn’t just about the tech of course – As AI's role in decision-making grows, the ethical framework guiding AI development will become a cornerstone of corporate governance and there will need to be a consolidation of some of the rules before – simplifying for a new world and phasing out the old.

For the consumer and the employee, AI integration into everyday applications will become commonplace, with assistants, so intuitive that we will change how we do tasks and our jobs.? Some roles will chance to much they won’t need to exist – and new exciting roles will evolve alongside.

What have you learnt?

As we witness the pace of rapid AI evolution, I'm curious to hear your thoughts, observations, or insights on what real-world applications have you seen making an impact? How are you approaching strategy or skilling up teams for AI? ?How are you addressing the ethical implications associated with AI adoption? I invite you to consider these questions and reflect on the trajectory of AI's future in your own teams.

The views expressed in this article are the views of the author, not Ernst & Young. This article provides general information, does not constitute advice and should not be relied on as such. Professional advice should be sought prior to any action being taken in reliance on any of the information. Liability limited by a scheme approved under Professional Standards Legislation.?