Menu

Global Scans · AI & Automation · Signal Scanner

The Rise of Agentic AI: A Weak Signal with Potential to Disrupt Business Decision-Making

Agentic artificial intelligence (AI) is emerging as a weak signal of disruptive change poised to transform how organizations make decisions by 2028 and beyond. Unlike narrowly focused automation tools, agentic AI systems may soon operate with greater autonomy, taking responsibility for complex day-to-day business choices. This shift suggests not only incremental automation improvements but a fundamental realignment of workflows, accountability, and operational risk across industries.

What’s Changing?

Current industry forecasts indicate a rapid increase in AI’s autonomy and its deeper integration into organizational decision-making processes. Gartner projects that by 2028, approximately 15% of day-to-day business decisions will be made autonomously by AI agents (VamsiTalksTech). This projection signals a shift from AI as mere support technology to AI as an active decision-maker. The implication is that AI systems could independently analyze data, evaluate risks, and execute decisions without constant human oversight.

This development coincides with a significant scaling of AI infrastructure investments, with major hyperscalers like Alphabet, Amazon, Meta, Microsoft, and Oracle expected to spend $520 billion to expand their AI capabilities in 2026—a 30% increase over 2025 levels (ThePlanAdvocate). This financial commitment could accelerate the availability and sophistication of agentic AI tools across various sectors.

Parallel to this growth in agentic AI, automation rates are projected to surge broadly. Gartner forecasts a fivefold increase in the automation of agent interactions—such as customer service—raising the rate from 1.8% in 2022 to around 10% by 2026 (Invoca). Moreover, studies have estimated that up to 30% of all workplace tasks may be automated within the next decade, with some industries facing automation levels as high as 40% (Robotics247).

These trends together suggest not just incremental automation, but a qualitative leap toward AI agents operating with substantial independence. Microsoft’s introduction of Windows 365 for Agents exemplifies this by enabling organizations to adopt AI agents with built-in compliance and governance frameworks, which may help accelerate responsible deployment at scale (Microsoft 365 Blog).

Compounding these technological shifts is the evolving regulatory and risk landscape. As AI assumes decision-making roles, new liabilities are emerging related to cyberattacks, geopolitical tensions, and AI-specific risks. Directors and officers may face increasing exposure to liability claims arising from AI-driven decisions or failures (Business Insurance).

Why is this Important?

The rise of agentic AI represents a potential inflection point. Traditionally, decision-making remains a human prerogative supported by data-driven tools. A migration to autonomous AI decision-makers introduces complexity across management, legal, operational, and ethical domains. These systems may improve speed and efficiency but also raise questions about accountability, trustworthiness, and unforeseen systemic risks.

Industries such as finance, healthcare, customer service, supply chain, and manufacturing could see deep transformation. For instance, agentic AI might optimize complex financial operations or pharmaceutical manufacturing processes regulated under new frameworks like the European Medicines Agency’s Annex 22 on AI governance (Forbes). However, firms may also confront operational risks if these autonomous decisions produce unintended outcomes or reinforce bias.

Moreover, the widespread automation trends threaten labor markets by potentially eliminating millions of low-skilled jobs over the next decade, heightening societal and economic impact considerations (ContentGrip).

The scale of investment in AI infrastructure by tech giants both enables and escalates competitive pressure, meaning organizations that do not prepare for or integrate agentic AI might fall behind. The operational benefits—improved efficiency, faster response times, reduced human error—may become prerequisites in sectors ranging from customer support to cybersecurity incident management (ITWire).

Implications

The transition toward agentic AI decision-making entails several implications for organizations, governments, and society at large:

  • Governance and Accountability: New frameworks will be required to govern autonomous AI decisions, clarifying liability and ethical use. As organizations deploy AI agents, they must ensure transparency, auditability, and compliance with emerging regulations such as Annex 22 for pharmaceuticals and potential sector-specific statutes.
  • Workforce Transformation: Automation of decision-making tasks may displace certain roles, urging companies and policymakers to invest in reskilling and upskilling programs. Professions reliant on routine decision-making could diminish, replaced by jobs requiring oversight of AI agents and strategy formulation.
  • Operational Risk Management: Increased reliance on AI agents means organizations will need to enhance risk detection and mitigation capabilities related to AI biases, errors, or vulnerabilities to cyber threats. Investment in AI-driven cyber incident recovery could improve resilience but also requires caution against overdependence.
  • Competitive Advantage: Early adopters of agentic AI may unlock faster, more accurate decision cycles in critical functions such as supply chain, customer interactions, and product development. The $520 billion hyperscaler investment signals an arms race in AI capabilities that could widen gaps between industry leaders and laggards.
  • Ethical and Social Considerations: Autonomous AI decision-making raises concerns about loss of human oversight, opacity in AI logic (black-box effects), and societal impacts such as job displacement. Transparent AI design and inclusive stakeholder engagement will be essential to balance innovation with societal interests.

Given these dynamics, organizations should begin pilot testing AI agents in controlled environments while simultaneously developing governance models that encompass ethical, operational, and regulatory compliance dimensions.

Questions

  • How can organizations establish clear accountability frameworks for decisions made by autonomous AI agents?
  • What training and workforce transition strategies are necessary to prepare employees for collaboration with agentic AI?
  • Which business functions stand to benefit most from shifting decision-making authority to AI systems, and where might human judgment remain critical?
  • How should regulators balance innovation and risk mitigation to ensure safe deployment of agentic AI across sectors?
  • What safeguards can prevent or minimize biases and unintended consequences in AI-driven autonomous decisions?
  • How might geopolitical and cybersecurity risks evolve in a future where AI agents operate with substantial autonomy?
  • What investment priorities should organizations adopt to build AI infrastructure that supports responsible agentic AI adoption?

Keywords

Agentic AI; Autonomous AI; AI Governance; AI Automation; AI Infrastructure; AI Risk; Workforce Reskilling

Bibliography

Briefing Created: 06/12/2025
See Evidence

Login

REMIND ME