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The Emergence of Agentic AI: A Weak Signal Set to Redefine Automation and Industry Dynamics

Artificial intelligence continues to evolve beyond traditional models of automation, with agentic AI—a form of AI systems capable of autonomous decision-making and goal-oriented behavior—surfacing as a weak but significant signal of change. Its potential influence on enterprise software, labor markets, and governance could disrupt how industries operate over the next decade. This article explores recent developments hinting at agentic AI’s rise, the implications for business and society, and the strategic questions stakeholders should consider to navigate this unfolding landscape.

What’s Changing?

Agentic AI refers to autonomous software agents that possess the ability to act independently to achieve specified goals, often by interacting dynamically with their environment. Unlike reactive AI, which responds solely to inputs, agentic AI can plan, learn, and adapt its behaviors over time without continuous human supervision.

Recent data indicate a rapid expansion of agentic AI integration into enterprise applications. For instance, recent research forecasts that by 2028, approximately 33% of enterprise software applications will include agentic AI components, signaling a fundamental shift toward intelligent automation systems that can initiate complex, multistep processes without human intervention (Kodexolabs).

Technology vendors are developing and embedding agentic AI capabilities within key business infrastructure. For example, Google’s Gemini multimodal intelligence, integrated into the Salesforce ecosystem, is designed to support hybrid reasoning and automate multistep processes across enterprise sales and IT service operations (Verdict).

Several geographic regions are advancing AI research that may enhance agentic AI capabilities. Norway’s recent €100 million EU-supported initiative for applied AI research aims to keep maritime actors and industrial stakeholders at the international innovation frontier, providing access to the latest AI and agentic technologies that could extend to autonomous navigation and logistics (Science Business).

Large-scale shifts in labor use are already underway. Estimates suggest that by 2026, 37-41% of companies intend to replace human workers with AI agents, implying that agentic AI systems might drive next wave of automation, affecting employment models across diverse sectors (Optimize Smart).

Alongside business, governments and regulators are starting to tighten oversight related to AI deployment. The Financial Stability Board and others focus on systemic risks as AI technologies grow more opaque and powerful, particularly where agentic AI could exacerbate concentration risks or introduce new vulnerabilities in financial and critical infrastructure systems (Churchill Mortgage).

Why is this Important?

Agentic AI stands apart from conventional automated tools because it can operate with autonomy and strategic intent rather than executing pre-scripted functions. This alters fundamental assumptions about control, predictability, and risk. The anticipated surge in agentic AI usage may prompt these outcomes:

  • Industrial productivity enhancement: Autonomous AI agents might manage complex processes faster and more efficiently, lowering costs and speeding innovation cycles in manufacturing, logistics, and services.
  • Workforce transformation: Mass adoption could catalyze job displacement at scale, while simultaneously creating demand for AI oversight, ethics, and integration specialists.
  • Increased systemic risk: Autonomous agents operating across interconnected systems without human-in-the-loop controls may intensify financial, cybersecurity, and operational vulnerabilities.
  • Regulatory and governance challenges: Policymakers will need to reconcile the economic benefits of agentic AI with transparency and accountability demands, including mitigation of bias and unintended consequences.

These factors emphasize the importance of anticipatory governance and strategic agility, especially in industries where precision, resilience, and regulatory compliance are paramount.

Implications

Enterprise leaders and policymakers may need to proactively consider several aspects as agentic AI advances from research labs to deployment:

  • Strategic investment: Organizations could assess readiness to adopt agentic AI-powered applications. Early investment in talent and infrastructure may enable competitive advantage, while late adoption may risk obsolescence.
  • Risk management and oversight: Developing robust frameworks to monitor, audit, and control agentic AI behavior will be crucial to prevent errors, bias propagation, or unchecked autonomous decisions with negative consequences.
  • Workforce transition planning: Retraining, redeploying, or restructuring labor forces will be key to mitigating social disruption and maximizing human-AI collaboration potential.
  • Cross-sector collaboration: Sharing insights between industries adopting agentic AI can build resilient standards, accelerate best practices, and guide responsible innovation.
  • Ethical and regulatory engagement: Governments and corporations must co-develop normative standards that balance innovation with societal safeguards.

Many related technologies, such as semiconductor innovation powering more sophisticated AI chips, digital city initiatives employing AI for governance, and emerging AI research hubs, all contribute to an infrastructure landscape uniquely enabling agentic AI's growth. For example, partnerships between manufacturing giants and AI chip designers illustrate how the agentic AI wave is supported by underlying hardware advances (Economic Times).

This convergence suggests disruption will not be isolated but instead ripple throughout digital transformation, supply chains, public administration, and even geopolitical dynamics related to AI leadership and control (DW).

Questions

Strategic planners and decision-makers might consider:

  • How will agentic AI shift the balance of control between human operators and automated systems within your organization or sector?
  • What governance frameworks must be pre-established to transparently manage autonomous AI behaviors and ensure alignment with organizational or societal priorities?
  • How prepared are current workforces for an accelerated adoption of agentic AI, and what retraining or upskilling initiatives are needed?
  • Which industries or processes stand to be most disrupted or transformed by agentic AI in the next 5-10 years?
  • What collaborative mechanisms could companies, regulators, and researchers deploy to share lessons, converge on standards, and manage systemic AI risks?

These questions will help surface blind spots and inform resilient strategies to thrive under increasing AI autonomy.

Keywords

agentic AI; intelligent automation; AI governance; workforce transformation; enterprise software; AI risk management

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Briefing Created: 25/10/2025
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