Artificial intelligence has already proven its ability to generate content and automate routine tasks, but today’s systems still rely heavily on human direction. As I see it, the next leap forward for AI is evolving from a tool that executes instructions to an agent that delivers outcomes.
Agentic AI goes beyond just generating content or providing recommendations. It can be made to understand user intent, make decisions autonomously within set guidelines and execute complex tasks with minimal human intervention.
For enterprises, this shift represents a profound transformation in how we work, moving the focus of employees from “how to do something” to “what needs to be achieved.”
And the change is happening fast. Agentic AI is top of mind for leadership, with companies working in overdrive to rethink workforce structures and develop new products that better support their customers. With so much promise, there’s a rush to unlock real business value, and for good reason. The companies that get it right will be the ones that win. The challenge now is to translate the promise of Agentic AI into practice and identify where it creates tangible value.
The shift toward intent-based systems
Agentic AI represents a fundamental change in how knowledge work gets done. It frees employees from repetitive tasks, empowering them to focus on higher-level strategy, creativity and decision-making.
I like to use examples to show how far we’ve come. In 1975, hiring a buyer in Italy was a months-long ordeal. You’d spend days researching employment agencies, paging through phone books and mailing letters overseas. Writing a job ad meant typing and retyping on an electric typewriter, then finding someone to translate it into Italian before sending it abroad. Four months later, if you were lucky, a candidate might respond.
By 2018, the process was much easier with digital tools like Google, LinkedIn and job boards, condensing weeks of work into hours. But while automation simplified each step, the work itself hadn’t changed: you were still responsible for research, production and distribution. Today, agentic AI changes that completely. You simply define your intent—what role you need, where and under what conditions—and the AI takes care of the rest: researching, producing, distributing and even identifying candidates, turning a complex process into a seamless experience that delivers results.
Agentic AI is freeing knowledge workers from mundane task execution. In essence, it acts as a well-trained digital assistant. It enables knowledge workers to specify what they need, provide the relevant data and constraints and have their agentic assistant handle the legwork and deliver results.
From promise to practice
Enterprises everywhere are racing to harness the potential of agentic AI. But turning promise into practice requires more than implementing new tools. It demands reimagining workflows and aligning technology with clear business goals.
One of the most common missteps I see companies making is searching for a problem to solve with AI. Many companies rush to integrate features simply because it’s trendy or because RFPs and investors now expect AI to be included in every presentation. This checkbox mentality results in shallow applications and poor returns. We are already seeing this play out with Gartner predicting that more than 40% of Agentic AI projects will be canceled by the end of 2027.
The real opportunity and success lies in reimagining workflows for an intent-driven world. Instead of layering AI onto existing steps, companies should analyze the execution work users currently do and shift that burden to AI agents. Users then focus on setting direction and defining outcomes, while the system manages the details.
The goal isn’t to make tasks faster — it’s to eliminate them.
Four principles for builders and buyers
To ensure agentic AI delivers lasting value, both technology providers and enterprise leaders should focus on four key principles:
1. Outcomes, not features
When evaluating AI, it’s essential to avoid the trap of implementing it for the sake of it. It should not be viewed as individual features, as their real value lies in executing tasks and freeing workers to focus on results and essential work. The question isn’t “does this product use AI?” but rather “does this AI help me achieve my desired outcome faster and with better results?”
In supply chain operations, for example, agentic AI can manage repetitive processes, such as tracking shipments or processing orders, freeing leaders to focus on strategic exceptions and optimization. Beyond automation, AI can deliver insights into why decisions were made and how they improved performance, helping teams continuously refine their strategies.
When applied correctly, AI transforms lagging indicators into real-time, forward-looking insights. It turns data into action, predicting demand shifts, optimizing production schedules and preventing disruptions before they happen.
2. Rethink workflows, don’t just automate them
Layering AI into existing processes can improve efficiency, but it rarely changes outcomes. Too often, companies boast about adding tools like chatbots and auto-summarizers, and while they may produce some results, they won’t create transformative value. True transformation comes from rethinking workflows and how work is getting done.
Imagine replenishment orders being generated automatically when inventory levels dip below optimal levels, or logistics systems that dynamically reroute trucks based on fuel prices, weather conditions and delivery windows. When AI takes on execution, leaders gain time to focus on planning, forecasting and innovation.
3. Redefine roles and skills
Agentic AI doesn’t just change what we do, it changes how we work. Employees shift from executing routine tasks to orchestrating technology that helps them achieve better outcomes.
Managing an AI agent is like managing a team member: it requires clear goals, structured feedback and trust. For example, supply chain planners might instruct an AI to “optimize next quarter’s replenishment plan for cost and service,” while defining constraints such as supplier lead times or sustainability targets. The system executes, but people remain responsible for reviewing the results and refining the inputs.
While exciting, this new model requires training and a cultural shift. Organizations that invest in upskilling and foster trust between humans and AI systems will unlock the most significant benefits.
4. Measure what matters
Outcomes should be what judges AI’s impact. The right metrics depend on the business, whether that’s improved forecast accuracy and reduced cycle times to fewer disruptions and lower emissions.
In supply chains, the effectiveness of agentic AI can be measured by its impact on efficiency, resilience and sustainability. The question is simple: Did AI remove time, cost or risk from the operation? If not, it’s not delivering value.
A new era for AI
Agentic AI empowers users to achieve their desired outcomes by simply expressing their goals, while AI handles execution. However, to realize the promise of this technology, it requires more than just adoption. It demands thoughtful integration, reimagined user experiences and support to help employees adapt.
The companies that succeed won’t be those that add AI for the sake of it. They’ll be the ones who look for real value in removing execution steps, focusing on intent and aligning software with what users are genuinely trying to achieve. Agentic AI is not just an upgrade. It’s a fundamental shift in how we work and how software is built to support modern businesses.
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