How agentic AI is changing work, strategy and competitiveness

From task-based work to goal-oriented orchestration

IT systems have long been viewed as tools people use. With agentic AI, the script is flipped: humans become orchestrators rather than doers.

For example, an agentic AI system can break down a general objective, such as finding the best way to optimize a company's logistics budget, into a series of individual and actionable activities. This process might include data extraction, negotiating with vendors and drafting contracts.

Automation isn't new in the modern workplace. But now, agentic AI systems are taking it a step further. Agents build on existing automated systems, performing tasks that connect different applications. For instance, the process of transferring data between spreadsheets and CRM applications was automated long ago. Agentic AI systems can perform this task, but they also review the data to identify customer churn potential and trigger retention campaigns through various platforms.

The following are some of the ways agents assist their human coworkers:

• Workflow autonomy. The agent connects various software systems, linking legacy systems and standalone applications to cloud-based platforms.
• Task-based focus. Agents perform the manual, routine and often monotonous work and decisions that people previously handled.
• Business continuity. Agents don't tire and can monitor and execute business processes 24/7. This makes them good at tasks such as cyberthreat detection and global supply chain management.

With agentic AI, the script is flipped: humans become orchestrators rather than doers.

For their part, the human professionals involved in the transition to agentic AI must learn to manage agents by defining tasks, guardrails, success metrics and how to monitor and audit the steps AI takes. They need to understand the logic and process design behind these AI systems versus simply performing the technical aspects of an action.

Clarity is essential for the technical and operational functionality of agentic systems. When guiding AI agents, the use of present tense and active voice results in more direct and less ambiguous actions. For example, the sentence The agent verifies the invoice will result in a higher level of effectiveness than The invoice will be verified. Specificity in language keeps the agent from becoming passive and having to wait for additional guidance before it acts. 

Strategic inflection in the autonomous enterprise

The vendor-neutral operational excellence of agentic AI technology brings a new era of leadership opportunities. An organization might aim to create an independent business system that distributes agents throughout its operational network.

According to Gartner, with AI, organizations will rely more on outcome-based metrics that connect AI investment to business results for strategic planning instead of input-based performance indicators such as productivity gains and time saved. Companies can grow faster because agents expand their business activities without the need to hire additional staff.

However, there are risks involved with agentic adoption. The greater the level of AI autonomous operation, the higher the risk of hallucinations, other AI errors and unauthorized disclosure of personal information. As a result, organizations at the forefront of the agentic AI transition rank AI governance as a fundamental business goal.

To do this, businesses are taking the following steps:

• Ethical standards. The business establishes an ethical framework to guide autonomous implementations that adhere to company values and comply with applicable laws and regulations.
• Active auditing. Second-level agents audit the first-level decision-making agents for both compliance and accuracy.
• Human oversight. The decision-making process of all autonomous operations is tracked with a reasoning log. This provides human oversight and forensic analysis of system errors.

Global competitiveness and innovation

The deployment of agentic AI technology worldwide will likely exacerbate existing economic disparities. Countries with rapid economic growth will adopt AI faster and benefit from it quicker than those with sluggish economic development. The ability of a nation's industries to integrate autonomous systems will determine its share of the global market in the coming decade.

Highly competitive industries, such as pharmaceuticals, aerospace and semiconductor design, are using agent-based AI to shorten R&D project timelines. Agents can perform thousands of autonomous simulations and then review those results to provide the best possible course of action for human researchers. The accelerated pace of innovation enhances efficiency and lets companies be the first to deliver potentially game-changing products and technology.

Digital sovereignty and national competitiveness are becoming more closely linked. This has governments aiming to build secure sovereign AI infrastructure to safeguard the business data of their citizens and businesses, ensuring it remains within their respective nations' legal jurisdiction and territorial borders.

The following are two important strategies in the drive for international competitiveness:

• Regional agents. Governments are supporting the creation of intelligent agents developed using regional and industry-specific data sets to have a competitive advantage in local markets.
• International communication standards for AI. Whoever wins the international AI competition will also have established the communication standard for agents owned by different organizations and countries.

Agentic AI implementation and its future

Agentic AI is … driving enterprises to constantly innovate, transforming global competition into a race to develop the most intelligent and reliable autonomous systems.

Agentic AI systems require ongoing, close observation to be transformed into operational assets. An agent's success depends entirely on the quality of data it processes. To ensure data quality, organizations should do the following:

• Continuously verify facts. The agent requires access to internal knowledge bases that must be kept up to date and accurate. Otherwise, agents will execute tasks based on outdated or incorrect information.
• Cite sources. Agents must be configured to cite the sources of the data used when making decisions to provide the transparency needed to continually monitor their accuracy and performance.

Agentic AI is transforming work from a series of manual tasks to a sophisticated combination of digital elements. It's transforming business strategy from a focus on cost reduction to one centered around creating value through autonomy. It's also driving enterprises to constantly innovate, transforming global competition into a race to develop the most intelligent and reliable autonomous systems.

The future will belong to organizations that effectively manage their AI agents versus simply using tools. The path will be difficult; however, the benefits in terms of increased productivity, faster innovation and global competitiveness make this a mandatory transformation for organizations with vision.

Kishan Kumar is a supply chain professional with more than nine years of experience. He's currently an MBA candidate in the STEM-designated Management and Leadership program at Southern Connecticut State University.