The Agentic Enterprise: Predicting the Rise of the Chief Agent Officer

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Executive Summary

The evolution from generative artificial intelligence to autonomous, or “agentic,” AI represents an ongoing transition that is fundamentally re-architecting the enterprise. Agentic AI systems, capable of reasoning, planning, and executing complex, multi-step tasks, constitute a new form of digital labor.1 Managing this emerging autonomous workforce—with its unique potential for value creation and unprecedented operational risks—cannot be an ancillary duty of an existing executive. This new paradigm demands a dedicated C-suite leader with a unique mandate focused on operationalization, governance, and value realization. This report predicts the emergence of this role: the Chief Agent Officer (CAgO).

The CAgO’s mandate is to orchestrate the hybrid human-agent workforce, establish and enforce a new class of governance for autonomous systems, ensure ethical and secure deployment, and drive measurable return on investment (ROI) from automated operations.3 This role is distinct from the Chief AI Officer (CAIO), whose focus is on enterprise-wide AI strategy. The CAgO, in contrast, is the operational executive responsible for the day-to-day management, performance, and risk profile of the deployed digital workforce.

Key findings indicate that the governance of agentic systems requires a paradigm shift from static, policy-based compliance to dynamic, real-time automated oversight. Human supervision alone will not scale to manage a workforce that operates at machine speed. Consequently, the CAgO will need to deploy advanced technological frameworks, such as “Guardian Agents”—AI systems designed to monitor other AI—to ensure control and alignment.4 This report provides a detailed blueprint for the CAgO role, its responsibilities, and its organizational structure, concluding with a set of actionable recommendations for boards and CEOs to begin preparing for the agentic era.

 

The Dawn of the Autonomous Workforce

The technological and business context that necessitates the CAgO role is rooted in a profound shift in the nature of artificial intelligence. The enterprise is moving beyond AI as a simple tool for analysis and content generation toward AI as an active, autonomous participant in business operations. This transition from AI-assisted human work to human-supervised AI work creates a new category of labor within the organization, a digital workforce that requires a new management discipline. Historically, the emergence of new forms of labor, from factory workers to global service centers, has always precipitated the creation of new executive functions to manage them. The rise of the autonomous AI workforce is the 21st century’s catalyst for a similar evolution in the C-suite.

 

From Generative Tools to Agentic Collaborators

 

The recent technological leap has been from prompt-and-response generative AI (GenAI) to proactive, goal-driven agentic AI. While GenAI augments human tasks by generating content or providing analysis, agentic AI automates and executes entire business processes from end to end.2 These “digital workers” are defined by a set of core capabilities that distinguish them from their predecessors: autonomous reasoning, contextual understanding, dynamic workflow design, and the ability to interface with multiple systems to execute complex sequences of tasks.1

This capability is magnified through the deployment of multi-agent systems. In this model, collaborative “crews” of specialized agents are assembled to tackle sophisticated challenges that a single agent could not, such as managing a supply chain, executing a marketing campaign, or performing complex financial analysis. These systems can interact and collaborate to revolutionize process automation, leading to faster and more efficient outcomes.1

 

Solving the GenAI Productivity Paradox

 

The emergence of agentic AI directly addresses the “gen AI paradox,” a phenomenon where widespread corporate adoption of general-purpose AI tools like copilots and chatbots has often failed to translate into significant, measurable bottom-line impact.2 This disconnect arises because these “horizontal” tools scale quickly but deliver diffuse benefits that are difficult to quantify.

Agentic AI offers a solution by enabling a focus on high-impact, “vertical” (function-specific) use cases. By automating complex, core business processes such as adaptive supply chain orchestration, end-to-end customer issue resolution, or autonomous financial reconciliation, AI agents can deliver the tangible and substantial ROI that has so far proven elusive.2 Investing in an agentic workforce, and the leadership required to manage it, is therefore a direct strategy to unlock the true economic potential of artificial intelligence.

 

Industry Adoption and Early Performance Indicators

 

The shift toward an agentic workforce is not a distant future but a present-day reality, with adoption accelerating across industries. Projections indicate that 70% of firms aim to adopt agentic AI by 2025, driven by compelling early performance indicators.3 Concrete examples of these gains demonstrate that agentic AI is already a powerful competitive differentiator:

  • Technology: The use of code generator and support bot crews has led to a 2-3x increase in software release speed.3
  • Manufacturing: Deploying autonomous factory robots and inspection drones has resulted in a 30% reduction in operational downtime.3
  • Logistics: Automated route planners and delivery drones have achieved a 15% reduction in logistics costs.3
  • Professional Services: The use of research and modeling “swarms” has enabled 40% faster analysis and insight generation.3

These metrics underscore the urgency for organizations to develop a strategic approach to building, managing, and governing their nascent digital workforces.

 

A New Seat at the Table: Defining the Chief Agent Officer (CAgO)

 

The creation of a new C-suite role is a direct response to a new, complex, and strategically vital business domain that requires dedicated executive ownership. The Chief Agent Officer is the logical next step in the C-suite’s evolution, designed specifically to master the challenges and opportunities of an autonomous AI workforce.

 

Historical Precedent: The Evolving C-Suite

 

The C-suite is not a static entity; it adapts to master the disruptive forces of its time. A clear historical pattern shows how technological and market shifts have consistently created the need for new executive archetypes:

  • The Chief Information Officer (CIO) and Chief Technology Officer (CTO) emerged with the rise of enterprise IT infrastructure, tasked with managing the systems that became the backbone of modern business.6
  • The Chief Experience Officer (CXO) and Chief Customer Officer (CCO) arose from the strategic imperative of customer-centricity in the digital age. These roles were created to champion the customer’s voice in the boardroom, build specialized teams, and align the entire organization around delivering superior customer experiences.7
  • The Chief AI Officer (CAIO) is the most recent addition, driven by the need for a unified, enterprise-wide AI strategy and a coherent governance framework to manage investments and risks across all forms of AI.10

This history demonstrates that when a new capability becomes both powerful enough to transform the enterprise and complex enough to require specialized oversight, a new leadership role is born. The CAgO follows this established pattern.

Table 3.1: The Evolution of the Technology-Driven C-Suite
Disruptive Force Emerging Role Core Mandate Key Metrics
Mainframe/PC Chief Information Officer (CIO) Manage IT infrastructure, ensure system uptime and security. System Availability, IT Budget Adherence
Internet/Digital Chief Digital Officer (CDO) Drive digital transformation, create new digital business models. Digital Revenue Growth, Online Engagement
Customer Data Chief Experience Officer (CXO) Champion the customer, design and manage the end-to-end customer journey. Net Promoter Score (NPS), Customer Satisfaction (CSAT)
Big Data/ML Chief AI Officer (CAIO) Set enterprise AI strategy, govern all AI initiatives, foster AI literacy. AI Project ROI, Model Accuracy, Compliance
Autonomous Agents Chief Agent Officer (CAgO) Manage the autonomous AI workforce, ensure operational performance and safety. Agent Productivity, Process Automation Rate, Risk Mitigation

 

The CAgO Mandate: Core Responsibilities and Scope

 

The Chief Agent Officer is the executive accountable for turning an autonomous AI workforce into a reliable, secure, and value-generating asset. The role’s mandate is operational, spanning strategy, governance, security, and performance management.3

  • Enterprise Integration: Lead the systematic identification, mapping, piloting, and scaling of agentic use-cases across all business functions, from finance to customer service.3
  • Governance & Ethics: Chair the AI Oversight Committee, establishing the operational guardrails for agent autonomy. This includes ensuring alignment with emerging regulations like the EU AI Act and codifying “human-in-the-loop” protocols for critical decisions.3
  • Human-AI Collaboration: Serve as the architect of the future of work. This involves redesigning business processes to integrate human and AI labor, leading upskilling programs for the human workforce, and fostering a culture of effective collaboration.1
  • Security & Risk: Partner directly with the CISO and CIO to address the unique security threats posed by autonomous agents. This includes leading API threat modeling for agentic systems, commissioning penetration tests, and establishing robust fail-safe and rollback procedures for all automated workflows.3
  • Vendor & Technology Stack Management: Own the strategic selection of agentic AI platforms and technologies. The CAgO will also lead the “Agentic Centre of Excellence” (ACoE) as the central hub for talent, tools, and best practices.3
  • Continuous Improvement & Performance Management: Establish and track key performance indicators (KPIs) for the agentic workforce. This includes measuring ROI, monitoring operational efficiency, and making data-driven decisions to retire, retrain, or re-task under-performing agents.3

 

Distinct from the CAIO: A Necessary Specialization

 

As enterprise AI matures, a single CAIO overseeing all aspects of AI becomes a strategic bottleneck. A specialization of duties is required to ensure both high-level strategy and operational excellence. The CAgO is this necessary specialization.

  • The Chief AI Officer (CAIO) is the enterprise strategist for the entire AI landscape, including predictive analytics, generative models, and research and development. The CAIO’s role is to shape the overarching AI vision, prioritize broad investments, and establish high-level governance principles.10 The CAIO answers the question, “What is our enterprise strategy for all forms of AI?”
  • The Chief Agent Officer (CAgO) is the enterprise operator for the autonomous, action-oriented subset of AI. The CAgO’s role is to build, manage, govern, and secure the digital workforce that executes parts of the CAIO’s strategy. This executive is accountable for the operational performance, risk profile, and ROI of deployed agents.3 The CAgO answers the question, “How do we manage our autonomous workforce to deliver value safely and efficiently?”

This division of labor mirrors the relationship between a Chief Strategy Officer (CSO), who formulates corporate strategy, and a Chief Operating Officer (COO), who ensures its effective execution.14 The CAIO is the CSO of AI; the CAgO is the COO of the AI workforce.

Table 3.2: CAgO vs. CAIO – A Comparative Analysis
Dimension Chief AI Officer (CAIO) Chief Agent Officer (CAgO)
Primary Focus Insight & Strategy Action & Operation
Scope All AI Initiatives (Analytics, GenAI, Agents) Autonomous Agent Workforce
Key Verb Govern Manage
Core Skills Strategic Vision, Enterprise Architecture Operational Excellence, Risk Management
Primary Deliverable Enterprise AI Roadmap & Governance Framework Deployed & Governed Digital Workforce
Key Metrics Enterprise AI Value, Compliance Readiness Agent ROI, Process Automation Rate, Security Incidents

 

Organizational Placement and Reporting Structure

 

To be effective, the CAgO must possess the authority to drive transformative change across the entire organization. This necessitates a direct reporting line to the Chief Executive Officer.3 This placement ensures the role has the enterprise-wide reach and influence required to redesign core business processes.

The CAgO will operate as a peer to other C-suite leaders, fostering critical partnerships with the:

  • CIO/CTO for infrastructure, data access, and core systems integration.3
  • COO for redesigning operational workflows and business processes.3
  • CISO for managing the unique security vulnerabilities of autonomous agents.3
  • CHRO for leading workforce transformation, upskilling, and change management.1
  • CAIO for ensuring the operational deployment of agents aligns with the overarching enterprise AI strategy.

Furthermore, the CAgO will have a direct reporting obligation to the Board of Directors, providing regular updates on the ROI, risk profile, and ethical posture of the enterprise’s agentic workforce.3

 

The Governance Imperative: Managing a Digital Workforce

 

The most critical function of the Chief Agent Officer is to establish a novel governance framework for a workforce that operates at machine speed and scale. The governance of agentic AI requires a paradigm shift from static, policy-based compliance to dynamic, real-time, automated oversight. The governance function itself must become agentic to keep pace with the technology it oversees.

 

A New Paradigm of Risk: Beyond Traditional AI Governance

 

Governance for agentic AI is fundamentally different from traditional AI model risk management, which primarily focuses on static issues like bias in training data or the explainability of a single predictive model. The autonomy and interconnectedness of AI agents introduce a new class of dynamic, systemic risks:

  • Uncontrolled Autonomy & Emergent Behaviors: The risk that agents, in pursuit of a defined goal, may develop unintended and potentially harmful strategies or sub-goals that were not explicitly programmed.2
  • Cascading Failures: The danger that a single agent’s error or malfunction could propagate rapidly across a network of interconnected systems, causing widespread disruption before human intervention is possible.16
  • Expanded Attack Surface: Each agent with credentials to access multiple enterprise systems becomes a high-value target. A compromised agent could be hijacked to exfiltrate data, disrupt operations, or move laterally across the network.3
  • Lack of Observability: The “black box” problem is amplified in multi-agent systems, making it exceedingly difficult to trace and understand the complex chain of reasoning behind a specific outcome in real-time.2
  • Agent Sprawl: The uncontrolled proliferation of unmanaged, duplicate, or rogue agents across the enterprise, leading to resource waste, conflicting actions, and operational chaos.2

 

Architecting the Agentic GRC Framework

 

The CAgO’s primary responsibility is to architect and implement a robust Governance, Risk, and Compliance (GRC) framework specifically designed to mitigate these novel risks. This framework will synthesize principles from leading global standards, tailored for the challenges of autonomy.

The core pillars of this framework include:

  • Formal Risk Management System: Adopting principles from the EU AI Act to create formal processes for identifying, evaluating, and mitigating risks throughout the entire agent lifecycle, from design to retirement.18
  • Human Oversight & Intervention: Defining clear “human-in-the-loop” rules for critical decisions (e.g., those with financial, legal, or safety implications) and engineering robust mechanisms for human operators to override or shut down agents when necessary.3
  • Data Quality & Governance: Enforcing rigorous standards for the data that agents use to perceive their environment and make decisions, ensuring data accuracy, lineage, and fitness-for-purpose to prevent flawed actions.10
  • Transparency & Explainability: Mandating transparent, human-readable audit trails for every significant agent decision and action. This moves beyond technical logs to provide business-relevant explanations for accountability purposes.3
  • Robustness & Security: Implementing a Zero Trust architecture for the agentic workforce. Every agent must have a unique, verifiable identity and operate under the principle of least privilege, with dynamically adjusted permissions based on context and risk.16
  • Clear Accountability Framework: Establishing unambiguous ownership and liability for actions taken by autonomous systems, ensuring there is always a designated human accountable for an agent’s performance and impact.10

 

AI to Govern AI: Operationalizing Governance with Guardian Agents

 

Recognizing that human oversight alone is insufficient and will not scale to manage thousands of autonomous agents interacting at machine speed, the CAgO’s GRC framework must be operationalized through technology.4 The most forward-looking solution is the concept of “Guardian Agents”—AI systems designed specifically to monitor, control, and govern other AI agents.4 These guardians will be the primary operational tool of the CAgO’s governance function.

The deployment of Guardian Agents will likely follow a three-phase evolutionary path:

  1. Phase 1: Quality Control: In the initial stage, guardian agents will act as automated auditors, ensuring that other AI systems are producing the expected outputs with the required level of accuracy.4
  2. Phase 2: Observation: As they mature, guardians will monitor processes in real-time, explain the behavior of the AI they oversee, and provide a first line of defense by alerting human operators to unexpected or anomalous outputs.4
  3. Phase 3: Protection: In the most advanced phase, guardian agents will be empowered to act autonomously. They will proactively detect and shut down rogue or malfunctioning AI agents to prevent adverse outcomes before they occur, transforming governance from a reactive to a preventative discipline.4

The CAgO’s role is thus transformed from a traditional compliance officer into the architect and commander of this sophisticated, automated oversight system.

Table 4.1: The Agentic AI Governance, Risk, and Compliance (GRC) Framework
Risk Category Key Risks of Autonomous Agents Governance Controls & Mitigation Primary Oversight Tool
Operational Cascading failures; agent sprawl; un-auditable decisions; process failures propagating across systems. Mandatory human-readable audit trails; “human-in-the-loop” for critical financial/safety decisions; fail-safe/rollback procedures. Phase 2/3 Guardian Agents for real-time process monitoring and shutdown.
Ethical Algorithmic bias in autonomous decision-making; lack of transparency leading to unfair outcomes. AI impact assessments; continuous bias and fairness drift monitoring; explainability requirements for agent decisions. Phase 2 Guardian Agents for observability and fairness drift detection.
Security Credential hijacking; data poisoning; agents interacting with malicious sources; expanded attack surface. Zero Trust architecture with unique agent identities; principle of least privilege access; API threat modeling and penetration tests. Phase 3 Guardian Agents to detect and block malicious agent interactions.
Compliance Violating regulations (e.g., EU AI Act, GDPR); failure to meet documentation and oversight requirements. Unified regulatory mapping; automated evidence collection and storage; transparent audit trails for every agent action. Phase 1/2 Guardian Agents for automated compliance checks and logging.

 

The Ideal Candidate: Architect of the Human-Agent Enterprise

 

The unique challenges of the Chief Agent Officer role demand a new kind of executive. The ideal candidate is not a senior technologist promoted to a leadership position, but rather a seasoned business leader who is deeply AI-literate and possesses a rare combination of strategic vision, operational discipline, and transformational leadership skills.

 

The Strategist-Leader Profile: Beyond the Technologist

 

The most effective CAgO will be a cross-functional business leader, not a senior data scientist or machine learning engineer.20 The core of the role is not to write algorithms but to orchestrate the deployment of an autonomous workforce to achieve strategic business outcomes. The position is more analogous to a Head of Digital Transformation, who must manage through influence, communicate a compelling vision, and drive complex change across departments they do not directly control.20 The primary focus is on translating technical capabilities into business value and operational risk into business strategy, requiring strong business acumen above all else.21

 

Essential Skill Set and Experience

 

The CAgO must possess a unique blend of skills that bridge the gap between technology, business operations, and corporate governance.

  • Strategic Roadmap Delivery: A minimum of 10+ years of experience leading large-scale, complex technology or business transformation initiatives, with a proven track record of delivering measurable business value and ROI.21
  • Governance, Risk, and Ethics Expertise: Deep, practical knowledge of GRC frameworks, data privacy regulations, and the ethical considerations of AI. Experience operating in a highly regulated industry such as finance or healthcare is a significant advantage.21
  • Cross-Functional Communication & Influence: Exceptional ability to act as a “bridge-builder” between technical and business teams. The candidate must be able to translate complex technical concepts for non-technical audiences, including the board and executive peers, and possess a demonstrated ability to influence stakeholders across the organization without direct authority.20
  • Change Management Leadership: Proven experience as a change agent who has successfully guided an organization through significant technological and cultural shifts. This includes fostering a culture that is open to human-agent collaboration and addressing workforce concerns proactively.22
  • Strong AI Literacy: While hands-on coding is not a prerequisite, a deep conceptual understanding of AI technologies—including machine learning, natural language processing, and especially agentic architectures—is essential for credibility and sound strategic decision-making.3

 

Identifying the CAgO Talent Pool

 

Sourcing this rare talent profile requires looking beyond traditional technology leadership roles. The most promising candidates may be found in several areas:

  • Internal Candidates: Look for General Managers who have P&L responsibility and a deep understanding of operations, product, and finance. Leaders from existing digital transformation offices or senior product executives who have managed complex, data-driven products are also strong contenders.20
  • External Hires: Target executives from technology-forward companies who have already led early-stage agentic AI initiatives. Senior partners from top-tier consulting firms specializing in AI strategy and implementation can also bring the necessary strategic and cross-functional experience.
  • The “Elder Statesperson”: Consider a recently retired senior executive (e.g., CEO, COO, or divisional President) who has deeply immersed themselves in AI. Such a candidate can bring immense strategic credibility, a vast internal network, and the cross-functional authority needed to drive such a profound transformation.20

 

Implementation Roadmap: Building the Agentic Function

 

Introducing the Chief Agent Officer role and its supporting structures requires a pragmatic, phased approach. This roadmap allows an organization to build momentum through early wins, establish the necessary governance and talent structures, and scale the agentic workforce in a controlled and strategic manner.

 

Phase 1: Assess and Pilot (The First 90 Days)

 

The initial phase is focused on identifying opportunities and demonstrating value quickly to build organizational buy-in.

  • Step 1: Agentic Opportunity Scan: Conduct a rapid, two-week, cross-functional assessment to identify and prioritize high-potential use cases for automation by AI agents.3 This scan should focus on areas with high-volume, repeatable processes, significant customer or employee friction points, and high data readiness.13
  • Step 2: Select a “Lighthouse” Pilot: Choose one to three high-impact, low-risk use cases for an initial pilot project. The key to success is a tightly defined scope, clear boundaries for agent autonomy, and measurable baseline metrics against which to track performance.3 The goal is to achieve an early, visible win that builds momentum and justifies further investment.

 

Phase 2: Appoint and Structure (Months 3-6)

 

With a successful pilot underway, the next phase involves formalizing the leadership and organizational structure.

  • Step 3: Appoint the CAgO: Based on the pilot’s initial success and learnings, formally create the Chief Agent Officer role. Hire or promote a leader who meets the strategist-leader profile to own the CAgO mandate and drive the agentic agenda forward.3
  • Step 4: Establish the Agentic Centre of Excellence (ACoE): The CAgO’s first major act is to build their core operational team. The ACoE will report directly to the CAgO and serve as the central hub of expertise for the entire organization, consolidating talent, tools, and best practices for building and managing the agentic workforce.3

 

The Agentic Centre of Excellence (ACoE): Structure and Function

 

The ACoE is the operational arm of the CAgO, providing the technical and governance capabilities needed to scale agentic AI across the enterprise.

  • Core Team Roles: An effective ACoE requires a multidisciplinary team that includes AI Governance Experts, Machine Learning Engineers, Data Engineers, AI Security Specialists, Human-AI Workflow Designers, Business Analysts, and Domain Experts from key business units.24
  • Evolving Operating Models: The structure of the ACoE should adapt as the organization’s AI maturity grows:
  • Centralized Model (Early Stage): Initially, the ACoE operates as a centralized team that directly builds and deploys the first wave of agents. This approach ensures consistency, enforces standards, and consolidates scarce expertise when it is most needed.24
  • Advisory/Federated Model (Mature Stage): As AI capabilities mature and diffuse throughout the organization, the ACoE transitions to an advisory or “gatekeeper” role. It sets the guardrails, provides reusable assets and platforms, and embeds experts into business units, while frontline product and operational teams take ownership of delivery.24 In this stage, a matrix structure is common, where engineers report functionally to the ACoE for technical standards and to a project or product manager for day-to-day priorities.27

 

Phase 3: Scale and Transform (Months 6+)

 

This final phase focuses on expanding the agentic workforce and fundamentally redesigning the organization’s operating model.

  • Step 5: Scale and Integrate: The CAgO oversees the expansion of successful pilots to additional use cases across the enterprise. This involves deeply integrating agents with core business systems, data lakes, and existing employee workflows to unlock network effects and greater value.13
  • Step 6: Redesign and Reskill: This is the most challenging and transformative phase, led by the CAgO in close partnership with the Chief Human Resources Officer. It involves a fundamental re-architecture of how work gets done, shifting the management paradigm from overseeing people to managing the performance of hybrid human-agent systems.1 A critical component is the development of comprehensive training programs that teach employees how to collaborate with, manage, and escalate issues to their new digital colleagues, ensuring a smooth transition to the future of work.13

 

Strategic Outlook and Recommendations

 

The proactive establishment of the Chief Agent Officer role is not merely an organizational adjustment but a profound strategic decision. Companies that master the management and governance of an autonomous workforce will build a significant and durable competitive advantage, fundamentally outperforming their peers in agility, efficiency, and innovation.

 

The Competitive Moat of Agentic Leadership

 

Organizations that successfully integrate a CAgO and an agentic workforce will create a competitive moat built on three pillars:

  • Superior Operational Agility: The ability to rapidly experiment with, deploy, and scale new business processes and models at a speed that competitors cannot match. This allows for faster innovation and quicker adaptation to market changes.3
  • Built-in Compliance and Trust: Having a robust, automated governance framework in place will become a prerequisite for operating in regulated industries. It will build deep trust with customers, partners, and regulators, turning a potential liability into a strategic asset.3
  • Unlocking Human Potential: By automating repetitive, transactional, and analytical work, the agentic workforce will free up human employees to focus on the tasks that require uniquely human skills: high-level strategy, creativity, complex problem-solving, and empathetic customer engagement.1

 

Actionable Recommendations for the Board and CEO

 

To prepare for the agentic era, the organization’s leadership must take decisive action. The following recommendations are structured for immediate, near-term, and future implementation.

Immediate (Next Quarter):

  • Commission an “Agentic Readiness Assessment” co-led by the CIO and COO to identify the top three to five high-value, low-risk pilot opportunities for autonomous agents.
  • Task the Chief Strategy Officer with modeling the potential ROI and systemic risks of deploying an autonomous workforce in a core business area.
  • Add “Agentic AI Governance and Risk” as a standing agenda item for the board’s risk committee to begin building board-level literacy and oversight.

Next (6-12 Months):

  • Allocate a dedicated, ring-fenced budget for a lighthouse pilot project with clearly defined success metrics focused on both efficiency gains and strategic value.
  • Begin drafting a formal charter for a Chief Agent Officer role, defining its scope, mandate, and reporting relationships with the CAIO, CIO, and other key executives.
  • Initiate a mandatory AI literacy program for the senior leadership team and the board, with a specific module focused on the unique capabilities, risks, and governance requirements of autonomous systems.

Future (12-24 Months):

  • Based on the pilot project’s results, make a formal decision on appointing a Chief Agent Officer and provide the necessary funding to establish a foundational Agentic Centre of Excellence.
  • Develop a multi-year workforce transformation plan in partnership with the CHRO. This plan should outline the strategy for reskilling and upskilling the human workforce to thrive in a collaborative human-agent environment.