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AI is already transforming the way we work, boosting productivity and efficiency. Just as we moved from horses to motorcars or jobs done by hand to computers, AI represents the next generational shift in productivity. During my recent meetings in California with the senior management of over a dozen companies, one term consistently emerged: “AI Agents.”
While being a great buzzword for the next generation of AI productivity and support, it is important to understand what an AI Agent really is and who the winners and losers will be. A good starting point is to de-bunk some of the misconceptions between Co-Pilots and AI Agents.
Co-Pilot versus AI Agents: What is What!
With the convergence of large language models (LLMs) and chat interfaces, the creation of Co-Pilots – AI assistants designed to aid productivity and creativity – have seen a rapid rise in usage. Co-Pilots leverage Natural Language Processing to understand and generate text. This process involves breaking down input text into tokens, known as tokenisation, which models then process to generate responses.
Initially, Co-Pilots operated through text, but have since evolved to support multimodal inputs and outputs such as images and videos. Although still in its infancy, scepticism persists due to the currently limited real-world applications for Co-Pilots in an enterprise setting. One notable limitation has been the tendency for Co-Pilots to produce erroneous facts or famously fabricate cases in legal research which have been submitted to court. Grounding is now an option for supported models to reduce model hallucinations and to anchor model responses to verifiable sources of information. Models can be grounded to publicly available data or tethered to specific private datasets, significantly improving accuracy and reliability.
Technically speaking, a Co-Pilot is a type of agent. However, the distinction between Co-Pilots and AI Agents lies in their interaction and autonomy. While Co-Pilots work alongside users, requiring input and interaction, AI Agents can operate autonomously, adapting and learning independently. This autonomy is what makes AI Agents so exciting.
As AI Agents operate totally independently, they are capable of managing complex tasks without continuous human intervention. For example, an AI Agent might manage your calendar, scheduling meetings but also being able to adjust them based on the availability of participants and other priorities, all the time without needing any input except of course where it has learned that you would like to be consulted! It is this adaptability that sets AI Agents aside, and they are designed to learn and adapt. They improve and evolve their behaviour with every interaction, providing better outcomes and dynamically changing with the environment. They will make informed choices by utilising vast amounts of data, streamlining workflows and ensuring the best possible outcomes.
There are limited applications of AI Agents in action today. Intuit, however, the online accounting software provider, has showcased Agent AI-powered workflows to assist in automating cash flow management, such as managing business postings and using specialised AI Agents to orchestrate invoice processing or bill creation. While ServiceNow, the market leading cloud-based software company that manages and optimizes enterprise workflows, envisions agent-to-agent collaboration that could address multi-departmental issues across workflows without the need for human intervention.
We are some way from multifarious use cases. The focus is currently on customer relationship management, think better and more autonomous chatbots for resolving customer interactions. There is also increasing use within various B2B applications where the agents operate in a more walled garden environment that is easier to manage.
Confusion remains around where Co-Pilots end and AI Agents begin, most notably in some industry verticals or consumer facing applications. The table below is an extract from a larger data set published under the heading “The Agent Economy” (Felicis). We highlight in the final column our view on the true classification of these disrupters.
Source: www.felicis.com/insight/the-agent-economy and Liontrust, October 2024. RPA = Robotic Process Automation. All use of company logos, images or trademarks in this document are for reference purposes only.
While some of the new companies in the right hand column are Agent AIs, especially in the horizontal provider category, those in industry verticals and consumer are largely Co-Pilots or RPA companies (Robotic Process Automation). Some even overclaim their ability! Recently, DoNotPay ageed to a $193,000 settlement with the Federal Trade Commission (FTC), after initially claiming to “generate perfectly valid legal documents in no time.” DoNotPay stated they would “replace the $200-billion-dollar legal industry with artificial intelligence,” however they did not conduct any testing to compare outputs to a human lawyer and did not have any attorneys. The company is now prohibited from making claims about its ability to substitute professional services without any evidence to back it up.
The confusion is such that, for example, reAlpha, an agent in the property investment field, came to the stock exchange in late 2023 (six months after the Nvidia Eureka moment) with disastrous consequences. Hailed as an AI agent with great potential, it conducted a direct listing at $8 per share as the reference price. It opened at $23 and rallied on day one to close at $407!! This left the founder, CEO Giri Devanur, with a paper fortune of $11.2 billion. Days later, the shares had crashed and today the shares trade at $1 and with a market value of just $54 million. This should act as a stark reminder of hype versus reality. We don’t doubt the potential but as with all new technologies, care is needed.
RPA is also often confused with Agent AI. This industry sub-sector came to prominence in 2012, and the first high-profile company was Blue Prism listed on the UK Stock Exchange. US companies followed, with the most high profile of these being UI Path that listed in 2022 at a price of $56. It quickly rose to $90 on AI hype but has since fallen back to $12.5, a fall of 86% from the peak. RPA has gone from AI boom to little more than a rules-based assistant.
It is estimated that over time, AI Agent’s will resolve 80% of inquiries autonomously, allowing human employees to focus on more complex projects1, while Gartner predicts that, by 2028, one third of interactions with GenAI services will invoke action models and autonomous agents for task completion.
1. Zendesk: www.zendesk.co.uk/blog/ai-agents/#georedirect
Will AI Agents drive a change in pricing strategies?
With AI’s growing impact, the question arises of how AI Agents can monetise the value they create. In the early days of software, perpetual licenses with optional maintenance and support fees were common. However, this model has largely been phased out in favour of a seat-based or consumption-based model or a blend of these two. Seat based offers lower initial costs and greater flexibility for users, whereas usage-based models allow customers to pay based on their actual usage and throughput. With AI Agents, given the cost of compute, a number of companies are experimenting with work-based pricing models. ServiceNow is considering the use of tokens for additional compute resources on top of their existing model. Salesforce recently announced a charge of $2 per conversation handled by its AI agents – helping to mitigate the possible impact of AI agents cannibalising their current model with labour efficiency.
When transformative technologies emerge, they typically attract vast amounts of capital and intense competition. However, caution is necessary. Marc Benioff, CEO and Co-Founder of Salesforce, recently criticised the hype as selling science projects to companies and comparing Microsoft Co-Pilot to the infamous Microsoft Clippy. The opportunity is clear but the speed of adoption is not.
Company valuations can often stray from fundamentals, with an overestimation of return on invested capital by the first movers. As data quality improves and models gain access to private company data, the integration of AI agents into existing workflows will significantly increase productivity and efficiency. This opportunity excites us; we are early in the innings for AI and the real enterprise use cases are just starting to emerge.
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