AI Agents: Build, Deploy, Scale

The landscape of artificial intelligence is rapidly evolving from systems that handle specific tasks to agentic AI capable of making decisions, acting on complex objectives, and continuously learning. This shift presents extraordinary opportunities for organisations seeking to enhance efficiency and drive innovation – yet a striking gap exists between aspiration and execution.

TLDR:

• Establishing robust governance frameworks with strategic oversight, operational management, and technical implementation layers
• Creating clear accountability systems with executive sponsors, program managers, technical leads, and ethics committees
• Implementing transparent documentation practices and audit trails to build trust
• Developing ethical AI guidelines that ensure fairness, non-discrimination, and privacy protection
• Aligning AI transformation with business goals through comprehensive strategic planning
• Reimagining workflows rather than simply adding AI to existing processes
• Upskilling employees and creating new roles like AI operations specialists and ethics officers

While 97% of organisations feel increased urgency to adopt AI, only 14% are fully prepared according to the Cisco AI Readiness Index. This disconnect stems from uncertainties around governance, organisational readiness, and scaling complexities. Successful implementation requires a comprehensive framework addressing these challenges head-on.

At the foundation lies robust governance with clear accountability systems. Executive sponsors align AI with business goals, program managers oversee implementation, technical leads refine algorithms, and ethics committees guard against bias. Consider UPS's Orion system, which optimised delivery routes while supporting sustainability objectives, or Tesla's continuous Autopilot updates using real-time data from millions of vehicles.

Transformation extends beyond technology to people and processes. DHL reimagined warehouse operations around AI capabilities, increasing fulfillment rates by 180%. The NHS overcame clinician skepticism when deploying AI for cancer detection through transparent communication and celebrating quick wins. New roles are emerging – from AI operations specialists to ethics officers – reflecting the growing need for specialised expertise.

Scaling requires strategic alignment with measurable outcomes, robust infrastructure, continuous monitoring, and leadership commitment. Amazon's fulfillment centres demonstrate how scalable infrastructure handles global operations, while General Electric's struggles with its Predix platform serve as a cautionary tale about insufficient alignment with business goals.

Ready to transform your organisation with agentic AI? Start with governance, reimagine processes, prepare your people, and build scalable systems. The future belongs to those who act decisively and responsibly today.

Chapters

• 0:01 Introduction to Agentic AI Framework
• 3:32 Governance: Building Trust and Accountability
• 8:53 Ethics and Compliance in AI
• 12:22 Strategic Planning and Process Reimagination
• 16:12 Change Management and Infrastructure
• 20:04 Scaling AI: Critical Success Factors
• 25:00 Conclusion and Call to Action

Transcript

Speaker 1: 0:01

Chapter 7. Implementing and Scaling Agentic AI A Comprehensive Framework for Organizational Success. Introduction A Framework for Agentic AI.

Speaker 1: 0:12

The emergence of agentic AI brings along with it the potential to radically change how organizations operate, innovate and create value. Ai systems to date have been great at handling predefined specific tasks. However, agentic AI is equipped to make decisions, act upon complex objectives, continually learn and improve. For organizations, this presents opportunities to enhance efficiency, drive innovation and gain competitive advantages. Important to note, however, this may also introduce new challenges that demand deeper deliberation and new strategies. Many organizations are excited about Agentech AI's transformative potential, but few have done the groundwork of implementing frameworks for AI deployment and governance. The Cisco AI Readiness Index highlights that, while 97% of organizations feel increased urgency to adopt AI, only 14% are fully prepared labeled as pacesetters. Why this gap between aspiration and execution? Perhaps this calls attention to uncertainties in governance, organizational readiness and scaling complexities.

Speaker 1: 1:29

Organizations face many challenges in their agentic AI journey. First, they must establish robust governance frameworks that ensure responsible AI deployment while maintaining operational efficiency. Second, they need to transform their existing processes and workforce to integrate AI agents into operations. Finally, they must develop strategies for scaling these initiatives across their enterprise while maintaining performance, security and ethical standards to deliver AI at scale Governance, building a foundation of trust. An effective governance framework has three primary layers Strategic oversight, operational management and technical implementation. The strategic layer, typically involving board members and executives, sets the overall direction and risk appetite for AI initiatives. The operational layer translates these strategic objectives into practical policies and procedures, while the technical layer ensures proper implementation and monitoring of AI systems. Integration with existing corporate governance requires scrutiny of current processes and policies. Organizations must assess how AI governance aligns with other governance frameworks like IT governance, data governance and risk management. This alignment ensures consistency in decision-making and prevents potential conflicts or redundancies. Sound leadership and board oversight are crucial for successful AI governance. Boards must be mindful of the opportunities and risks associated with agentic AI, making informed decisions about resource allocation, risk tolerance and strategic direction. This often requires upskilling of board members and establishing dedicated AI oversight committees.

Speaker 1: 3:32

Accountability Systems. Accountability is not a mere buzzword when it comes to agentic AI systems. You need systems that spell out who is responsible for every aspect of your AI's lifecycle. That spell out who is responsible for every aspect of your AI's lifecycle, from high-level strategy to the nitty-gritty of day-to-day operations. Without this, you are driving a self-driving car without a map. What does accountability look like?

Speaker 1: 3:57

Imagine a logistics company implementing AI to optimize delivery routes. The AI system might inadvertently create unfair delivery patterns. For example, if the algorithm optimizes purely for profit, it could end up providing faster service to wealthy neighborhoods while reducing deliveries to lower-income areas. This raises important questions about who bears responsibility. Is it the developers who created the algorithm, the company executives who approved its use or the operations managers who implement the system? This scenario illustrates why clear accountability frameworks need to be established before deploying AI systems that impact public services. Accountability ensures there is a structure to manage such dilemmas.

Speaker 1: 4:48

Here is how it works 1. Executive sponsors Think of them as the visionaries. They make sure the AI strategy aligns with the long-term business goals. For example, when UPS rolled out its Orion Route Optimization System, top executives made sure it supported their sustainability objectives. 2. Program Managers Program managers oversee implementation of AI systems that align with business goals. Their responsibilities include monitoring AI deployment, coordinating between teams and addressing any operational challenges. For instance, if an AI model designed for customer demand prediction in a retail chain begins generating inaccurate forecasts, such as underestimating demand for a popular product or overstocking slow-moving items, the program manager steps in to recalibrate the system. They work with data scientists to fine-tune algorithms, adjust training data and refine predictive models to prevent disruptions in the supply chain. 3. Technical Leads the coders and engineers. They're the ones tweaking algorithms and ensuring the AI behaves as intended. At Tesla, technical leads continuously update the autopilot system based on real-world data, ensuring continued robustness of the self-driving Teslas.

Speaker 1: 6:18

4. Ethics Committees the conscience of the operation. Let us say, an AI hiring tool starts favoring certain demographics. Ethics committees help identify and address such biases before they become PR disasters. Organizations must ask themselves how they will prevent AI-driven bias or discrimination and what safeguards they will put in place to mitigate these risks. Managing business risksks Without Losing Sleep Risk management in AI is not just about avoiding catastrophes. It is about staying agile in a rapidly evolving landscape. Regular risk assessments can flag early signs of issues like an algorithm drifting away from expected behavior. For instance, a hiring AI might gradually introduce demographic biases, or a recommendation system could start suggesting increasingly extreme content. These assessments allow organizations to identify and correct minor deviations before they escalate into significant problems.

Speaker 1: 7:25

Ensuring AI systems remain dependable, ethical and aligned with their original objectives. For instance, financial institutions deploying fraud detection AI run constant tests to ensure the system doesn't start flagging legitimate transactions by accident and when risks do emerge, escalation should ensure that the right people are looped in fast to ensure there is no fumbling around in a crisis Handling incidents. The blueprint Incidents will happen, whether it is a technical glitch or an ethical oops. The key to handling incidents is having airtight protocols in place. For example, when OpenAI detected ChatGPT's privacy bug in 2023, they had a response team ready to contain the issue, communicate with stakeholders and roll out a patch within days. Their transparency earned them trust instead of backlash. A comprehensive incident response protocol should include Clear definitions what counts as a critical failure, response teams, who jumps in when things go south? Post-incident learning, how do we make sure it does not happen again? With real-world stakes and clear accountability, organizations can turn AI into a powerful ally instead of a rogue element.

Speaker 1: 8:52

Transparency and Explainability Transparent AI systems are essential for building trust and ensuring accountability. Organizations must establish comprehensive documentation practices that track key aspects of AI development, including system deployment decisions, training, data sources, pre-processing methods, model architecture and parameter choices. Additionally, performance metrics, validation results and any changes or updates to AI systems should be meticulously recorded. To further enhance accountability, audit trails and logging systems must capture all significant AI system decisions, user interactions, performance metrics, compliance with ethical guidelines and resource usage data. Effective stakeholder communication is also crucial, as diverse groups have distinct information needs. Technical teams require in-depth system details, business users need operational insights, customers seek assurance regarding ai's role and regulators demand compliance evidence. By implementing these measures, organizations can foster transparency, maintain ethical AI practices and build confidence among users and stakeholders.

Speaker 1: 10:14

Ethics and compliance Creating ethical AI guidelines should be a collaborative process that involves input from various stakeholders, including policymakers, industry experts and affected communities. Key considerations include ensuring fairness and non-discrimination, maintaining transparency and accountability, upholding privacy protection standards, assessing environmental impact and fostering social responsibility. By incorporating these principles, organizations can develop AI systems that operate responsibly and align with societal values. Bias detection and mitigation require continuous attention to prevent unintended consequences. Organizations must conduct regular audits of AI system decisions to identify potential biases, ensure the use of diverse training data and implement biased testing protocols. Additionally, effective correction mechanisms should be in place to address identified issues, along with ongoing monitoring of long-term impacts. To ensure AI systems remain fair and unbiased over time, ai governance must align with established industry-specific regulations, data protection laws and AI-specific legislation, as well as adhere to international standards and reporting requirements. Compliance with these frameworks not only ensures legal adherence, but also enhances trust among users and stakeholders. To meet or exceed industry benchmarks, organizations should adopt relevant ISO standards, comply with industry-specific frameworks and actively participate in AI governance initiatives. Regular assessments against best practices, coupled with continuous improvement processes, ensure that AI systems remain ethical, transparent and accountable as they evolve.

Speaker 1: 12:17

Agent transformation projects Strategic planning Setting the stage for success. The first step in transforming a business with agentic AI is a solid, comprehensive plan that aligns AI ambitions with business goals. This is like laying the foundation for a skyscraper you cannot build upward without stability underneath. For example, when Walmart began implementing AI for supply chain optimization, they were careful not to compromise operational integrity. They conducted an extensive readiness assessment. Operational integrity they conducted an extensive readiness assessment, pinpointing inefficiencies in its inventory systems and ensuring its technology infrastructure could manage the change. This resulted in faster restocks and fewer stockouts in stores, directly improving the customer experience. A successful approach to AI integration involves breaking ambitious goals into strategic, manageable milestones. Consider Walmart's roadmap short-term inventory improvements, medium-term cost reductions and a visionary long-term goal of end-to-end supply chain automation.

Speaker 1: 13:29

Process Reimagination, rethinking Workflows. Automation Process reimagination Rethinking workflows. Ai is not just a plug-and-play tool. It is a chance to reimagine how the work gets done. Leaders must start by analyzing every corner of their operational workflows. Where is the bottlenecks? Where could automation have the most significant impact? For example, in response to the growing demands of modern e-commerce, dhl began reimagining its warehouse operations around 2019 to address the challenges of increased order volumes and tighter delivery deadlines. Recognizing the need for faster, more efficient fulfillment processes, DHL integrated robotics and agentic AI into its warehouses, increasing order fulfillment rates by up to 180%. They did not simply add a robot onto an old process. They rebuilt their workflows around what the AI could do best, equipping People for Change.

Speaker 1: 14:30

Ai cannot thrive in a vacuum. People need to evolve alongside tech innovation. Organizations must identify gaps between current employee skills and the expertise needed for AI-driven roles. John Deere developed autonomous and AI-driven machinery for precision agriculture tasks like planting and fertilizing. As these intelligent machines were put on the field, agricultural experts transitioned into roles managing AI-enhanced tractors. Employees were upskilled with training such as monitoring and maintaining intelligent machinery. The result Higher job satisfaction and increased productivity. As AI systems become more integrated into daily operations, new roles are emerging to ensure their effective and responsible use. Ai operations specialists oversee the day-to-day management of AI systems, making sure they function smoothly and efficiently. Meanwhile, human AI collaboration coordinators focused on optimizing interactions between human workers and AI, facilitating seamless task handoffs and enhancing productivity. Additionally, ai ethics officers play a crucial role in identifying and addressing issues related to bias, fairness and responsible AI deployment. Together, these roles reflect the growing need for specialized expertise in managing, refining and ethically governing AI technologies in various industries.

Speaker 1: 16:10

Change Management Leading the Charge in various industries. Change Management Leading the Charge Rolling out AI is as much about people's hearts and minds as it is about machines. Stakeholders at every level need to buy into the vision. This human-centric approach is critical because AI transformation touches every aspect of an organization Frontline workers may fear job displacement, middle managers might worry about changing or losing their roles, and executives are concerned about ROI and competitive positioning. Success requires transparent communication, demonstrated value and a clear path forward that acknowledges and addresses each concern. And a clear path forward that acknowledges and addresses each concern.

Speaker 1: 16:56

For example, when the UK's NHS deployed AI for early cancer detection, it faced skepticism from staff, primarily from clinicians, eg radiologists and oncologists, concerned about diagnostic accuracy, as well as administrative staff anxious about potential job displacement. Leaders tackled this by highlighting success stories like how the AI helped doctors diagnose patients faster. Forums were set up to hear concerns, ensuring staff felt heard and involved in the transformation. The successful adoption of AI in the NHS was driven by several key factors. These included providing regular updates on progress, helped keep staff informed and engaged, addressing uncertainties and reinforcing trust in the technology. In addition, teams focused on celebrating quick wins, such as faster and more accurate diagnoses. Wins demonstrated the tangible benefits of AI and further encouraged its adoption. Most importantly, transparency about AI's role ensured that healthcare professionals understood it as a tool to enhance rather than replace their expertise. By emphasizing these strategies, nhs leaders were able to foster a collaborative environment that supported the integration of AI into clinical practice.

Speaker 1: 18:23

Technical Infrastructure Building the Backbone. Your AI is only as good as the infrastructure that supports it. Scalable cloud systems, robust data pipelines and airtight security are non-negotiable. For example, at the heart of Netflix's seamless viewing experience lies a sophisticated AI recommendation engine quietly working behind the scenes to analyze billions of data points every day. This intricate system thrives on a robust and scalable infrastructure, ensuring it can manage the immense demands of its global audience. Without this carefully designed architecture, the engine would struggle to keep pace, leaving millions of users searching for their next favorite show in frustration or, more likely moving to a competitor at the click of their mouse.

Speaker 1: 19:17

Moveworks, a company specializing in AI solutions, successfully implemented agentic AI to revolutionize IT support. Ai agents were programmed to resolve IT issues, such as password resets and software installations, without the need for human intervention. This transformation led to increased efficiency. For human intervention, this transformation led to increased efficiency, allowing IT personnel to focus on more complex tasks, but also their increased motivation. Moveworks' approach exemplifies the effective integration of agentic AI into organizational workflows results in enhanced productivity and employee satisfaction.

Speaker 1: 20:00

Scaling Agententic AI Critical Success Factors. Scaling agentic AI successfully across an organization is not just about expanding its technical footprint. It requires strategic alignment, robust infrastructure and a culture ready to embrace innovation. Robust infrastructure and a culture ready to embrace innovation. Organizations that scale AI effectively unlock new efficiencies, gain competitive advantages and often redefine their industries. Some of these factors are listed below 1. Strategic Alignment with Business Goals. Scaling agentic AI begins with aligning its capabilities with the organization's objectives. Ai should be implemented where it can directly impact key performance indicators such as revenue growth, operational efficiency or customer satisfaction. For example, ups's Orion system is an agentic AI that optimizes delivery routes. By scaling this system across its fleet, ups saved an estimated 10 million gallons of fuel annually and significantly reduced delivery times, directly supporting its goals of operational efficiency and environmental sustainability. Of operational efficiency and environmental sustainability. This initiative demonstrates how tying AI systems to measurable business outcomes drives success.

Speaker 1: 21:29

2. Robust Infrastructure and Scalability. A scalable infrastructure is the backbone of agentic AI systems. Organizations must invest in high-performance cloud computing, scalable data pipelines and secure AI platforms to manage increasing workloads. For instance, amazon's agentic AI systems power its fulfillment centers by coordinating robotic arms, managing inventory and predicting order demand. With its Scalable Cloud Infrastructure, aws, amazon ensures its AI can operate seamlessly across global warehouses, handling billions of orders annually. This setup highlights the importance of infrastructure in scaling AI without disruptions.

Speaker 1: 22:18

3. Continuous monitoring and feedback loops. Scaling AI is not a set-it-and-forget-it task. Continuous monitoring and improvement are essential to address performance gaps, emerging risks and evolving business needs. For example, tesla's Autopilot AI uses over-the-air updates and real-time data from millions of vehicles to continuously refine its capabilities. By actively monitoring system performance and incorporating feedback, tesla ensures its AI remains state-of-the-art and adapts to new driving conditions.

Speaker 1: 22:56

4. Leadership and Cultural Readiness. Scaling agentic AI requires leadership to champion its adoption and foster a culture of trust and collaboration. Leaders must clearly communicate the AI's purpose, address employee concerns and promote its benefits. Ping An integrated agentic AI across its operations, using intelligent agents for tasks like fraud detection and customer service. Leadership played a critical role by transparently communicating AI's goals and ensuring employees understood how the technology would complement their roles rather than replace them. This cultural readiness facilitated a smooth scaling process, boosting both productivity and employee morale.

Speaker 1: 23:45

5. Metrics and Outcomes. Successful scaling is measurable. Organizations must track metrics like ROI, operational efficiency, error reduction and employee adoption rates to evaluate their progress. However, failing to effectively manage metrics and outcomes can lead to costly missteps. For instance, general Electric. Ge invested over $7 billion in its Predix platform, an ambitious agentic AI system designed to predict equipment failures and optimize manufacturing processes. Despite the promise of real-time performance data and significant savings in downtime and maintenance costs, the platform struggled due to overly ambitious goals, internal cultural resistance and the complexity of building a comprehensive industrial ecosystem. These challenges caused the platform to fall short of expectations, serving as a cautionary tale of how insufficient alignment with measurable outcomes can derail even the most well-funded initiatives. Conclusion Navigating the future of agentic AI.

Speaker 1: 25:00

Ai is already transforming how we work. To scale effectively with Agentic AI, organizations need three things careful planning, clear goals and strong ethics. Success comes from building robust and secure systems, encouraging new ideas and staying focused on tangible results. As agentic AI continues to evolve, its impact will deepen across industries. By focusing on governance, transformation and scaling, organizations can confidently embrace this technology, unlocking possibilities of the AI-driven future. The time to act is now Decisively, responsibly and with a vision for what is possible.