AI agents will soon be everywhere, automating complex business processes and taking care of mundane tasks for workers — at least that’s the claim of various software vendors that are quickly adding intelligent bots to a wide range of work apps.
Many companies appear to be listening. Over the next three years, at least 40% of Global 2000 businesses will use AI agents and agentic workflows to automate knowledge work, according to an IDC report, doubling productivity in the process — where the technology is successfully implemented, at least.
Should the technology live up to expectations, businesses will be faced with a significant shift in how work is done in their organization.
At a basic level, AI agents can be viewed as the next stage in the evolution of the AI tools that are already embedded in many workplace software applications — the assistants or copilots that generate content or retrieve information as directed by a worker, such as summarizing documents and drafting emails, for instance.
Autonomous AI agents, on the other hand, can complete complex, multi-step tasks with little or no input from human workers, combining large language models (LLMs) with workflow automation triggers and actions. The goal is to create intelligent and highly capable assistants that can plan, reason, and execute work tasks independently, or with minimal human oversight.
“For so long we’ve talked about the work about the work — the busy work, the stuff that gets in the way of you actually doing work,” said Chris Marsh, research director, Workforce Productivity & Collaboration at S&P Global Market Intelligence. “And I think there is a genuine opportunity now to really expedite high-value work by having agents automate all that crud.”
Defining exactly what an agent is can be tricky, however: LLM-based agents are an emerging technology, and there’s a level of variance in the sophistication of tools labelled as “agents,” as well as how related terms are applied by vendors and media.
And as with the first wave of generative AI (genAI) tools, there are question marks around how businesses will use the technology. IDC analysts pointed to business scepticism around AI agent performance, alongside privacy concerns, a lack of clarity around pricing, and a skills gap in terms of understanding how knowledge work is performed “outside of traditional-documented business processes.”
Achieving the “substantial gains” promised by agentic AI will require “significant overhead in terms of teams and companies adjusting to what this world looks like,” said Marsh. “And there are bigger questions … do you have the right data architecture? Do you have the right integration strategy to actually make the next phase of agentic AI a reality?”
The range of options for building and managing AI agents has quickly grown in the past year.
More recently, enterprise software vendors have added no-code platforms into their apps, too.
Salesforce’s Agentforce was the centerpiece of its recent Dreamforce event with the launch of its Agent Builder, a low-code tool for building AI agents, while Microsoft made its “autonomous agent” builder within Copilot Studio available in a public preview this week.
Agents are also coming to digital work apps that a wide variety of office workers interact with on a regular basis. Asana, Atlassian, Box, and Slack have been among the first to announce such features in recent months. “In some ways, it’s going to be just baked into the productivity apps that you’re using, like [Microsoft 365] Copilot,” said IDC’s Loomis.
While automation holds great potential to transform how work is carried out in future, the near-term reality for businesses is a different story.
The first generation of generative AI assistants and copilots have now descended into what Gartner terms the “trough of disillusionment,” with many projects remaining at a pilot stage due to a combination of factors: change management, a lack of clarity on ROI, and various security considerations, for example. And then there’s the propensity for language models to “hallucinate” answers. Many of the same challenges will be faced when deploying AI agents.
Businesses are understandably cautious about letting LLM-based agents act autonomously and access business systems, for example, even if they are subject to limitations in terms of the actions they are programmed to carry.
For the time being, most businesses will want some sort of human oversight. “There are no circumstances, at least right now, in which you would deploy this without some ‘human in the loop,’” said Kropp. This means that human workers have visibility into the agent’s actions and are consulted before taking riskier actions. That said, Kropp is confident that the problem of AI hallucinations will fade in significance as the technology matures, with agent reasoning capabilities also improving quickly.
While there are likely to be plenty of challenges along the way, Marsh expects that the combination of AI and automation will ultimately have a “profound” impact on how works gets done — even more so than other recent workplace shifts prompted by new technologies.
“I think the productivity gains are there. I think they’re real … If I think of all those changes that have happened over the past five years, this will be easily the biggest,” he said.
SOURCE: Finnegan, Matthew. ''AI agents are coming to work — here’s what businesses need to know'' 21/11/2024. Computerworld.com. (https://www.computerworld.com/article/3609764/ai-agents-are-coming-to-work-heres-what-businesses-need-to-know.html?utm_date=20241126152008&utm_campaign=US%20AI%20in%20the%20Enterprise&utm_content=slotno-2-brandposttitle-AI%20agents%20are%20coming%20to%20work%20%E2%80%94%20here%E2%80%99s%20what%20businesses%20need%20to%20%20know&utm_term=Editorial%20-%20IDG%27s%20Top%20Enterprise%20Stories&utm_medium=email&utm_source=Adestra&huid=).