Agentic AI and the Next Corporate Operating Model
- Bridge Connect
- 2 days ago
- 4 min read
Part 1 of Bridge Connect series: Technology Futures
Introduction: Why Agentic AI Matters Now
Artificial Intelligence has long been framed as a tool — automating tasks, enhancing workflows, and supporting decisions. But a new paradigm is emerging: Agentic AI.
Unlike generative AI or predictive models, Agentic AI is designed to act independently, pursue goals, adapt in real time, and interact with other systems or humans with minimal oversight.
For corporations, this represents a fundamental redefinition of the operating model. It is no longer about how humans can use AI; it is about how AI agents fit into the structure of decision-making, governance, and accountability.
The shift is as profound as the move from manual to industrial production, or from analogue to digital.
Boards that fail to understand this transition risk being disrupted by competitors who build organisations where humans and AI agents co-create value at scale.
Section 1: From Tools to Agents – A Historical Perspective
First wave: Rule-based automation (1990s–2000s). Businesses codified processes into IT systems, automating clerical and repetitive work.
Second wave: Predictive analytics & machine learning (2010s). Companies gained insights from data, but humans still made final calls.
Third wave: Generative AI (2020s). Systems could create content, code, and designs — still framed as assistants.
Fourth wave: Agentic AI (mid-2020s). These systems act as autonomous agents, capable of multi-step planning, negotiation, and execution, sometimes in collaboration with other AIs.
“Agentic AI transforms AI from being a tool into being a colleague.”
Section 2: What Defines Agentic AI?
Agentic AI has four defining characteristics:
Autonomy – Able to take actions without explicit instructions, within defined constraints.
Goal-Oriented Behaviour – Works towards objectives, adjusting strategies in real time.
Interactive Capabilities – Negotiates with humans and other AI systems, often across functions.
Learning in Context – Adapts policies, supply chains, or investment strategies as new data emerges.
Where ChatGPT writes an email draft, an agentic AI could negotiate contract terms, trigger procurement actions, or rebalance a portfolio — and do so continuously.
Section 3: Implications for the Corporate Operating Model
Traditional operating models rely on clear chains of command and defined decision rights. Agentic AI challenges this in three key ways:
a) Decision Distribution
AI agents begin to hold decision rights in defined domains (e.g., pricing adjustments, risk flagging, logistics rerouting).
Humans move towards oversight and exception management rather than micro-decisions.
b) Governance and Accountability
Boards must set boundaries for autonomy: What decisions can an AI make alone? What requires escalation?
New corporate policies must define liability when AI actions lead to loss, bias, or regulatory breach.
c) Structure and Talent
A hybrid workforce emerges: human executives + agentic AI colleagues.
Roles shift towards strategic oversight, creativity, and values-based leadership.
“Boards that treat Agentic AI as just another IT upgrade risk sleepwalking into a governance crisis.”
Section 4: Sector-Level Transformations
1. Financial Services
Portfolio management by AI agents running 24/7 stress tests.
Autonomous negotiation of trade execution, removing intermediaries.
Risk: regulatory exposure if agents front-run or misprice risk.
2. Telecommunications & Infrastructure
AI agents managing spectrum auctions, network routing, and fault prediction.
Infrastructure resilience could be optimised dynamically, with AIs trading capacity in real time.
Risk: over-automation of critical infrastructure creates new attack surfaces.
3. Healthcare & Biotech
Autonomous drug discovery and trial optimisation.
Patient triage via goal-seeking agents across hospital networks.
Risk: ethical accountability for clinical decisions made by non-human actors.
4. Defence & Security
Autonomous drones and cyber agents coordinate in contested environments.
Decision cycles shorten from days to seconds — outpacing human oversight.
Risk: escalation spirals if human vetoes are bypassed.
Section 5: Risks Boards Must Address
Regulatory Gaps – Law lags technology; liability frameworks are unclear.
Bias and Ethics – Agentic systems may optimise for outcomes that conflict with values.
Security – Malicious actors could hijack or impersonate corporate AI agents.
Reputation – Public trust will falter if AIs are seen as making “cold” or unfair decisions.
Over-Reliance – Operational fragility emerges if human expertise atrophies.
Boards must implement AI governance charters, define red lines, and ensure auditability of decisions.
Section 6: Opportunities for Competitive Advantage
Speed: Faster cycle times for R&D, operations, and market response.
Resilience: AI agents can simulate shocks and propose mitigations in real time.
Productivity: Human talent is freed to focus on strategy and creativity.
Investment Insight: Agentic AI uncovers correlations invisible to analysts.
Companies that design AI-native operating models — not just bolt-ons — will win.
Section 7: The Boardroom Agenda
Boards should structure their response around five pillars:
Vision – Define what role AI agents will play in value creation.
Governance – Establish accountability frameworks and escalation protocols.
Risk – Integrate AI risk into enterprise risk management.
Capability – Train executives to work with agentic AI, not against it.
Investment – Back infrastructure that allows secure and scalable AI deployment.
“In the age of Agentic AI, governance becomes a competitive advantage.”
Section 8: Looking Ahead – 2025 to 2030
2025–2026: Early adopters embed AI agents in discrete functions (procurement, pricing, customer support).
2027–2028: Cross-functional orchestration; AIs interact across finance, HR, and supply chain.
2029–2030: AI-native enterprises emerge — organisations where AI agents sit in every layer of the value chain, co-determining strategy with human executives.
Conclusion: What Boards Must Do Now
The emergence of Agentic AI is not a distant scenario — it is happening today in startups, financial trading systems, and defence trials.
For boards, the task is not to debate if AI will become agentic, but how to harness it responsibly, competitively, and ethically.
The corporate operating model of the 2030s will look very different: flatter, faster, and co-created with AI agents.
The winners will be those who adapt governance, culture, and investment strategies early — turning Agentic AI into a partner, not a risk.
