Rethinking Organizational Operating Models in Agentic AI and Robotics Age

The rise of generative and agentic AI alongside robotics will certainly shake up the way businesses will operate. Not only are these technologies disrupting traditional strategies for delivering products and services, but they will also be forcing a complete rethink of organizational operating models. During the coming decade AI—whether as advanced digital agents or physical robots—is set to gradually replace many blue-collar and even some white-collar roles, leaving a few key human experts to work side-by-side with AI. Strategic planners and enterprise architects must now factor these disruptive trends into their model designs.

But First the Reality Check

Before we delve deeper into the discussions about future organizational operating model changes due to agentic/robotics AI adoption we should take a quick reality check first.

Although Gen AI/ Agentic AI is advancing rapidly, its adoption within businesses is still in the early stages. Transitioning business operations to effectively integrate an AI workforce takes time—likely around 3 to 5 years for an early adopter, an average-sized organization. Given this, a phased approach makes the most sense: starting small and gradually increasing AI’s role in operations—crawling before walking, running, and eventually flying!

Organizations should view their AI adoption journey as a staged process—moving from simple experiments to full-scale agentic AI integration. In the early stages, companies can start by implementing AI as an assistant, automating routine tasks and providing data-driven insights that enhance decision-making without completely overhauling existing processes. This initial "crawl" phase will allow businesses to learn, experiment, and build internal expertise in AI technologies.

Once the benefits and challenges are better understood, the next step—or "walk" phase—is to integrate AI more deeply into workflows, where it acts as a co-pilot alongside human workers. Here, AI systems can support more complex operations such as customer service, data analysis, and process optimization, allowing employees to focus on higher-level strategic tasks while AI handles repetitive or data-intensive functions.

As organizations mature and become comfortable with these integrated systems, they can "run" by adopting more complex automation solutions. In this phase, AI not only supports but begins to drive entire processes, leading to significant improvements in efficiency, consistency, and speed. At this stage, companies are also likely to see the need for specialized roles and new governance structures to manage AI’s growing impact on operations.

Finally, when an organization has fully acclimatized to AI automation, it can "fly" by embracing agentic AI and robotics architectures—treating AI as a digital workforce that operates alongside humans. This mature stage involves deploying advanced generative models and AI-based physical robots, managed through dedicated Centers of Excellence. Here, AI becomes an integral part of strategic and operational decision-making, necessitating new frameworks for ethics, capacity management, and continuous training. The complete integration of agentic AI will represent not just an operational shift but a transformative leap in how business is conducted, likely unfolding over a 3- to 5-year timeline for an average-sized business.

Plotting the AI adoption journey as a gradual progression—from AI as an assistant, to a co-pilot, to full automation, and ultimately to agentic AI—will enable organizations to build confidence, adapt their operations incrementally, and ensure that each stage lays a solid foundation for the next. This measured approach is more prudent and will help manage risks and maximize the benefits of emerging AI technologies, positioning the business for sustainable success in an increasingly AI-driven future.

With that disclaimer out of the way, let’s discuss the potential operating model changes required when adopting AI/Agentic AI based workforce. ?But first here is a quick overview of the operating model Design.

Traditional Organizational Operating Model Design

The following diagram summarizes the composition of an organizational level operating model. This generic framework applies to design of all strategic, core, and support organizations within any business with specifics flushed out based on the nature of supported business function.

Historically, an effective organizational operating model has considered several key elements:

• Organizational Administration and Governance: Leaders establish clear structures and processes to ensure daily operations align with the company's mission and strategic vision. This involves setting policies, decision frameworks, and standards that guide all staff, making sure that every part of the business follows the same rules and values.
• Ways of Working: Organizations have relied on well-established value streams and processes for developing and delivering products and services. These processes, interlinked across the business, must be designed for efficiency and effective internal interactions.
• Organizational Structure: The structure is built around people with the necessary skills to deliver products and services. Ensuring the right team size and optimal configuration is critical for effective collaboration and efficiency.
• Information & Technology: Having access to the right information and technology is essential for operational efficiency and competitiveness in supporting the organizational business function. Identifying and integrating the best tools is a key part of the model.
• Physical Location: The geographical location has traditionally played a role in delivering products and services efficiently by leveraging local benefits such as talent, logistics, and market preferences.
• External Sources/Alliances: Partnerships with external organizations, vendors, or suppliers have been vital for gaining a competitive edge and ensuring effective service delivery.
• Physical Resources and Assets: In addition to non-physical resources like cash and talent, tangible assets such as raw materials, machinery, and facilities are needed to support operations.

These elements also define broader business capabilities by outlining the necessary people, processes, and technologies to deliver services to internal and external stakeholders.

Rethinking Operating Model Design for AI Adoption

Today AI technologies are being incorporated in all aspects of running business operations. Perhaps now considered to be ‘legacy’ AI, Machine Learning and deep learning models have been deployed by businesses in last decade to automate and personalize services. Many Robotics Process Automation (RPA) technologies have already reached the peak of the hype cycle and are reaching maturity.

Lately however, enterprises across industries have increasingly begun experimenting with and leveraging GPT and similar advanced generative AI models powered by natural language interfaces (i.e., that produce outputs in text, speech, audio, and video). These models are enabling rapid content generation, enhanced customer interactions, and automating routine tasks, forming the backbone of agentic AI-based operations where AI agents will soon become digital employees working side-by-side with their human counterparts.

By integrating these technologies into workflows, companies can offer more personalized services, streamline decision-making, and reduce operational costs—all critical factors for staying competitive in today’s fast-paced market.

So, how will the tomorrow’s operating models be designed, what key elements the designers and enterprise architects will take into considerations? Let’s take a closer look at each key element of the operating model.

Organizational Administration and Governance

With AI adoption, additional factors must be considered during establishing the administration and governance frameworks that continue to be aligned with existing business governance frameworks.

New governance structures will be needed to oversee AI operations, ensuring that decisions made or supported by AI align with ethical standards and regulatory requirements. AI can support decision-making by providing data-driven insights. However, governance must ensure transparency, accountability, and fairness in AI recommendations. With emerging regulations around AI usage, data privacy, and algorithmic accountability, governance bodies must be equipped with expertise in these areas, integrating AI risk management into traditional oversight processes.

A new layer of oversight is needed to manage AI agents. AI agents making autonomous decisions must be subject to rigorous oversight. Governance frameworks need to include audit trails, transparency measures, and explainability protocols to ensure that decisions align with the organization’s standards. Organizations must develop policies that address AI training, re-training, capacity management, and labor cost assessments. These policies will also cover risk management, including the mitigation of security threats and potential biases in decision-making. Develop reporting systems, and feedback loops for continuous monitoring of AI decision-making.

Governance structures will also be redefined to accommodate a hybrid workforce that includes both human employees and AI agents—digital as well as physical. This new paradigm calls for dual oversight mechanisms: one set to ensure that AI systems and digital agents operate ethically and transparently, and another to manage the deployment, maintenance, and security of physical robots. New policies will be required to address the “employment” lifecycle of AI agents and robots, including continuous training, model re-certification, labor cost monitoring (covering both digital and physical systems), and risk management that addresses cybersecurity threats and physical safety concerns.

Ways of Working

At least initially, workflows will incorporate human-AI collaboration, where mainly AI agents perform routine or data-intensive tasks while humans focus on oversight, approvals, strategy, creativity, and complex problem-solving. The rapid evolution of AI technologies requires more adaptive, agile methodologies that can iterate quickly based on AI-generated insights.

Processes must be redesigned to integrate AI agents as full participants. This involves assigning tasks based on AI capabilities, where routine or data-intensive work is automated, and humans focus on oversight. Just as human employees require ongoing training, AI agents need regular updates, fine-tuning, and re-training to adapt to changing business needs and environments. The operating model must support agile methodologies that allow for rapid deployment and scaling of AI capabilities. This includes systems for real-time monitoring of AI performance and capacity planning based on fluctuating demand. New metrics will be required to assess the performance of AI agents, including accuracy, efficiency, and ethical compliance.

AI-based physical robots will automate manufacturing, logistics, and even certain office functions, reducing the need for traditional blue-collar roles and, in some cases, automating aspects of white-collar work such as data analysis and administrative tasks.

Organizational Structure

With the AI adoption, especially incorporating digital employees will require complete rethinking of the organizations structure design. The organization structure will become dynamic and agile depending on the demand for the workforce capacity that can be fulfilled by both human and digital employees as necessary.

The integration of AI requires new team configurations where humans work alongside AI systems. This may lead to flatter structures with specialized roles (e.g., AI trainers, explainability experts, data ethicists). As AI systems take over repetitive tasks, there will be a growing need for talent that can interpret AI insights, manage AI-human interactions (e.g., prompt engineering), and oversee algorithmic performance.

Just as teams are optimized based on human skill sets, the organization must evaluate the “skills” of AI models/robots—selecting the right models/robots for specific tasks, managing training cycles, and planning for scalability. Teams may become more fluid and project-based, combining domain experts with technical specialists who oversee AI operations. This integration ensures that both AI and human contributions are effectively harnessed.

Organizations must also realign their talent strategies, investing in reskilling programs that prepare human employees to collaborate effectively with AI systems and robots.

Information & Technology

Reliable access to information and robust technology systems have always been central to operational efficiency. AI thrives on data, so the quality, quantity, and diversity of data become even more critical. Organizations need to invest in advanced data collection, storage, and processing infrastructures. New technology ecosystems will integrate traditional IT systems with AI platforms, requiring updated ontologies, seamless data flow and interoperability. High-quality, well-governed data becomes even more critical.

Organizations will need to invest even more in data collection, curation, and real-time analytics to power their AI agents. The technology strategy must include tools that allow for the interpretability of AI decisions. Stakeholders need to trust that AI agents act within the defined parameters of organizational policies and ethical standards.

As reliance on digital and AI systems grows, so does the need for advanced cybersecurity measures and comprehensive data governance policies to protect sensitive information and ensure compliance. New security protocols are essential for protecting AI systems from cyber threats. This includes advanced threat modeling, encryption of data in transit and at rest, and robust access controls.

Additionally consideration should be given to tailor security frameworks to protect AI systems from emerging cyber threats and adversarial attacks, create contingency plans for AI system failures, ensuring that tasks can be swiftly transferred to backup systems or human teams, continually explore new AI applications and refine existing models to maintain a competitive edge, and establish systems for collecting performance feedback from both AI outputs and human oversight to drive iterative improvements.

Seamless integration of traditional IT systems with AI platforms is critical. Robust data collection and analytics now will underpin both digital and robotic operations. Cybersecurity protocols must extend beyond protecting digital information to ensuring that physical robots operate safely and are shielded from adversarial attacks. Additionally, explainability and transparency tools are required to audit AI decisions—whether those decisions originate from a digital agent or a physical robot.

Organizational Physical Location

With AI and robots handling many operational tasks, physical proximity of human workers may become less critical. Organizations may adopt distributed or hybrid models, where remote work is encouraged by AI-enabled collaboration platforms. While certain functions might still require a physical presence (e.g., research labs or high-security environments), many human roles managing various AI functions can be decentralized, broadening the talent pool globally.

AI agents do not require traditional office space. Instead, investment shifts toward robust data centers, cloud computing platforms, and secure server farms that function as virtual “offices” for AI. Investments will shift from traditional office spaces to digital infrastructure. AI’s heavy computational needs encourage partnerships with cloud providers or building in-house AI data centers.

While essential human teams may remain tied to specific locations, the physical infrastructure supporting AI (e.g., specialized data centers) could be strategically placed to optimize latency, energy costs, and compliance with local data regulations. A mix of human and AI “workforces” will lead to distributed models where digital resources are as critical as physical offices, requiring new strategies for managing this blended environment.

Simultaneously, AI-based physical robots, which are increasingly deployed in manufacturing, logistics, and even office settings, require specialized physical installations (e.g., automated warehouses, smart factories) that integrate with these digital infrastructures to enhance productivity and operational efficiency.

External Sources/Alliances

Organizations will increasingly partner with AI startups, research institutions, and specialized technology vendors. This may include alliances for shared data pools, AI model co-development, or ethical AI auditing. The speed of AI innovation means alliances may be more fluid and temporary. Organizations might engage in agile partnerships that can rapidly pivot as technology evolves. Collaboration might also extend to shared AI platforms, where insights from one organization can benefit an entire industry or ecosystem, provided privacy and security measures are in place.

Organizations will forge dynamic alliances with AI model developers, data providers, and cloud services that support the continuous development and updating of AI agents. These partnerships can extend to shared AI governance and ethical oversight. AI vendors might be considered strategic partners whose products effectively become part of the organization’s workforce. This demands clear contracts and service-level agreements that address performance, training updates, and ethical guidelines. Alliances with research institutions and other organizations can facilitate knowledge sharing, ensuring that AI agents remain state-of-the-art and aligned with industry best practices

The ecosystem supporting these technologies is expanding beyond conventional suppliers to include AI developers, cloud providers, robotics manufacturers, and research institutions. Strategic alliances now encompass not only partnerships for advanced natural language processing and digital automation but also collaborations with robotics firms that supply AI-driven machines.

Other Physical Resources and Assets

In addition to traditional assets, significant investments will be directed toward AI-specific hardware (e.g., GPUs, specialized AI chips) and infrastructure that support high-speed data processing. Physical assets like manufacturing plants may be transformed into smart, sensor-driven environments that leverage AI for predictive maintenance, quality control, and supply chain optimization. AI can optimize the usage and maintenance schedules of physical assets, reducing downtime and operational costs while increasing overall efficiency.

The “workforce” now includes AI agents that require specialized hardware such as GPUs, TPUs, and dedicated AI chips. The maintenance and scaling of these assets become critical. Traditional assets (factories, warehouses) will be retrofitted with IoT devices and AI sensors, enabling real-time monitoring and predictive maintenance. This integration supports both human operators and AI agents in achieving operational excellence. Just as human staffing is managed based on demand, AI resource capacity must be dynamically allocated. Organizations will need to monitor computational loads, model performance, and energy consumption to optimize costs and efficiency.

Traditional resources such as warehouses and manufacturing equipment will further be augmented or replaced by AI-enabled robots that perform tasks autonomously. Organizations will be investing heavily in the robotics systems that handle physical operations. This investment necessitates new approaches to capacity management, where robotic deployments are dynamically scaled based on real-time demand, model performance, and operational efficiency.

Special Note About IT Operating Models of The Future

The new IT operating model in the age of agentic AI will be fundamentally restructured to support a range of new business capabilities—strategic, core, and support—with a special focus on creating an “AI Center of Excellence.” This center is envisioned as a dedicated unit that, much like the Cloud or Data Centers of Excellence of recent years, will drive the adoption, development, and operationalization of AI-driven processes across the organization.

At its core, the AI Center of Excellence must adopt new operational and development value streams. These value streams encompass stages from ideation and planning through design, build, test, and run, culminating in continuous monitoring and eventual replacement or upgrading of the agentic AI /robotics ecosystem. Each stage is critical. In the ideation phase, teams need to explore how AI can add value to various business functions, whether in marketing, product management, or operations. The planning and design stages involve laying out clear architectural blueprints and governance frameworks that align AI initiatives with business strategy.

As development progresses through build and test, new roles—such as AI developers, AI testers, and AI-focused enterprise, solution, and technical architects—become essential to ensure that AI systems are robust, ethical, and effective. During the run and monitor stages, continuous oversight is required to manage performance, address risk, and ensure that AI systems adapt to evolving business needs.

Crucially, the AI Center of Excellence must integrate seamlessly with the existing IT ecosystem. It will not operate in isolation but will work in tandem with established centers such as security management, risk management, change management, PMO, data and analytics management, application and integration, DevOps, reliability engineering, IT service management, and the broader enterprise architecture functions. This integration is necessary to ensure that AI initiatives align with overall IT strategies and maintain the high standards required in areas such as cybersecurity, compliance, and system reliability.

Furthermore, the evolution of AI is gradually replacing traditional workforce functions, which means IT may also need to assume some HR responsibilities—specifically in managing the lifecycle of AI agents. This includes recruiting the right AI models/robots, onboarding them into operational workflows, providing continuous training and re-certification, and eventually retiring outdated models. In essence, IT will be required to develop HR-like capabilities to manage AI resources as if they were human employees, ensuring that the AI workforce has the right skills and is optimally deployed across business functions.

From a strategic standpoint, the IT operating model must be forward-looking, taking into account future AI trends. Technology advancements will continue to drive more efficient AI models/robots and more sophisticated integration capabilities. Process changes will emerge as organizations refine agile methodologies to better incorporate AI insights in real time. And the evolving skill sets required will mean that both technical and domain expertise must blend to support a hybrid workforce where human intuition complements AI-driven data analysis.

Reliable access to information and robust technology systems have always been central to operational efficiency. This is one of the primary functions under enterprise architecture practice. With AI adoption many enterprise architecture capabilities must be reevaluated from design standpoint to ensure the right AI technology, Robotics, AI-OPS and data integration pipelines are properly designed keeping in mind all key domain and cross cutting concerns - security, integration, stakeholder experience, monitoring, maintenance, scalability, availability, performance and resilience of the IT ecosystem, ensuring its cost effectiveness.

The design of the new IT operating model, with its dedicated AI Center of Excellence, is about balancing innovation with integration. It requires building a specialized hub that drives AI value streams from ideation to operational excellence while ensuring tight interoperability with existing IT functions. Organizations that successfully implement this hybrid model will be well-equipped to leverage agentic AI and robotics, ensuring agile, secure, and scalable operations that support the next generation of business capabilities.

Final thoughts

The infusion of generative AI, agentic AI and robotics into business operations calls for a fresh look at traditional operating models’ design. Companies need to keep the core elements that made them successful while adding new AI-focused considerations.

Treating agentic AI and robots like a true “employee” means rethinking everything—from physical location and external partnerships to established processes. This shift requires new governance frameworks, revamped human-AI collaboration models, investment in digital and computing infrastructure, and flexible training and capacity management systems.

Agentic AI will be transforming operating models by combining advanced generative models with AI-based physical robots. Businesses that blend these digital and physical capabilities will be best positioned to handle a fast-changing market. They will benefit from better ethical oversight, more agile resource management, and a hybrid workforce that draws on the strengths of both humans and AI.

If you are excited about the future, have questions about agentic AI and wish to discuss your organization's readiness for AI adoption journey, then do not hesitate to contact.

Author: Sunil Rananavare, IT Strategy Planning and Architecture (CIO Advisory)

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