Generative AI is starting to deliver promising but limited results. However, the IT industry is pushing full speed ahead to the next level of automation, agentic AI. Since AI can’t yet design, build, and deploy agents, it’s up to humans to learn to create and target these agents productively. But this development is going to take some time.
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That’s the conclusion from recent research from consultant Accenture, which suggested scaling AI-based services to achieve sustainable business value is a challenge, with only 13% of projects delivering significant results in this area. The Accenture study covered 3,400 executives and more than 2,000 client projects.
The rapid evolution to agentic AI calls for a new type of talent trained in AI and model development, as well as business acumen. “Our work with leading clients on talent and skills substantiates this,” said the report’s authors, led by Jack Azagury, group chief executive for consulting at Accenture.
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I asked Azagury for specific information about what this new type of skill means for technology professionals and managers. “Talent readiness is one of the biggest barriers to scaling and unlocking value for companies, so a proper skills architecture is needed to win in the age of Gen AI,” he told ZDNET. “Currently, organizations are spending three times more on technology rather than on people — this must change.”
Azagury said most companies are not training people for the age of AI. While Accenture found 94% of workers want to learn about Gen AI, only 5% of companies provide training in this area. “That gap must be closed,” he said. “One can invest in all the available Gen AI tools, but if your employees don’t know how or why to use them or put trust in them, the value will simply not be realized.”
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Azagury said three types of AI agents require companies’ attention at this time:
- Utility agents: “Perform routine, high-frequency tasks that enhance operational efficiency.” Examples include a function in an autonomous vehicle or a dynamic pricing system.
- Super agents: “Combine multiple functions, synthesizing data to drive strategic workflows.” An example is a marketing agent that assembles data from relevant sources and determines the sequence of steps to execute a campaign or report.
- Orchestrator agents: “Oversee end-to-end processes, breaking down silos and enabling seamless collaboration.” An example is an agent that brings together multiple agents across different services, such as a production system that coordinates individual agents handling specific tasks, such as order supply, inventory, and scheduling.
Azagury said building and deploying agentic architecture requires a special breed of teamwork. This approach means assuming a “dual role of driving a step change in the software development lifecycle,” he said. Technology professionals also need to ensure their organizations and employees “thrive using Gen AI overall.”
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With the rise of Gen AI – and now agentic AI – “there is a significant uptick in demand for data science and data engineering-skilled professionals who deeply grasp the use of models, know how to deploy an architecture where you can switch LLMs, and how to enable a flexible digital core with the right AI architecture,” said Azagury. “These significant, new responsibilities will require investing in upskilling the IT workforce with a methodical, skills-based, and structured talent strategy.”
He said there is a significant payback for organizations that deploy this strategy: “Companies that are creating enterprise-level value from Gen AI are 2.9x more likely to have a talent roadmap for their workforces in place. And 2.8 times more likely to have tailored Gen AI learning paths for both technical and non-technical roles.”
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Azagury said even CEOs need to be brought up to speed on the implications of AI agents.
The impact of AI on workflows – and even corporate culture – “requires a level of education in AI and technology acumen that is much more significant than what we’ve seen with other technologies,” he said. “This is because it is highly democratized, and with the coming advent of agents, this will require an even deeper partnership between humans and machines.”
