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Explore Careers Within Careers Search Jobs Early Careers Culture, Values, and Inclusion Experienced Professionals Locations Events Operations AI-First Enterprise Operations: Reinventing the Operating System of Work By  Marcus Wittig ,  Alfonso Abella ,  Olivier Bouffault ,  Robin Anders ,  Alexander Duerloo ,  Alexander Noßmann ,  Stephen Robnett ,  Salvatore Cali ,  Yann Letourneux ,  Christoph Schmitt , and  Juan Martin Maglione Article June 15, 2026 8 MIN read Key Takeaways BCG’s agentic deployments show that end-to-end process redesign is what separates organizations that achieve 60% cost reductions from those that capture less than 20%. The near-term objective is not to deploy more agents. It is to build the foundations for scale and start the redesign process. Companies that delay agentic process redesign face a compounding disadvantage. Save It For Later Saved To My Saved Content Download Article Share Link copied Subscribe Stay ahead with BCG insights on operations Stay ahead with BCG insights on operations You are subscribed to BCG's eAlert on operations. Manage Subscriptions Subscribe Enterprise operations are at a pivotal moment. Many organizations have taken their first steps with AI —deploying copilots, bots, and an “automation layer”—yet 60% have yet to capture material value at scale. In early waves, we saw organizations delivering productivity improvements of just 10% to 20%. In contrast, our first agentic clients are demonstrating a three-fold increase in productivity, an 80% reduction in cycle time, and 60% or more in long-term cost reductions. The reason for this gap is clear: most organizations introduce AI but leave the operating system of work untouched. This improves task-level efficiency but does not structurally reinvent how processes are designed, governed, and executed. The organizations generating real value with AI do not have superior AI models. Instead, they have redesigned processes end-to-end for agentic AI, with outcome-managed agents and agent-driven control flow and orchestration. While agentic AI is still a maturing technology , its exponential trajectory creates urgency: Now is the time to build the operational and technical foundations for value realization. Agentic Enterprise Operations Rewrite the AI Paradigm Over the past 18 months, the thinking about AI deployment has evolved rapidly. It is clear that copilot-style tools that add a speed boost to human workflows achieve only modest results. The only way to transformative change is AI-driven execution with agents executing tasks end-to-end. The latest and most advanced stage of this evolution is not about better models; it shifts the unit of change from tasks to end-to-end processes where reusable, multi-agent systems increasingly orchestrate tools and decisions by default, under defined governance, controls, and accountability. (See the exhibit.) For example, a leading European bank deployed BCG’s OpsAI Agent as part of a holistic, zero-base process transformation in retail lending. The agent processes loan applications through five capabilities, which include handling unstructured data: Document recognition and classification File splitting and data sync across systems Autonomous data extraction, interpretation, and correction Integrated consistency and fraud checks Signature recognition and contract validation The results have been significant: end-to-end automation of more than 90% for consumer loans, more than 70% for mortgage loans, and, more broadly, productivity gains across retail lending processes exceeded 50%. Weekly Insights Subscription Stay ahead with BCG insights on operations Subscribe Manage Subscriptions From Optimizing Tasks to Designing Autonomy Traditional process optimization was built around a scarce resource: human capacity. The focus was reducing waste through standardization, deviation control, and flow optimization. Agentic systems change the paradigm in enterprise operations: When execution becomes nearly instant, and capacity is no longer a constraint, the design question shifts from “How do we optimize the flow?” to “How do we govern outcomes?” Leaders need to address three implications: Deviation becomes part of the design. Instead of eliminating exceptions, agentic systems are engineered to dynamically detect, route, and resolve them. Ownership changes. Process steering shifts toward a single agentic process owner accountable for end-to-end outcomes, embedding coordination and control into the system itself. Business models evolve. Near-instant execution and personalization at scale enable new value propositions, such as outcome-based offerings and tailored services. Read More How AI-First Companies Are Winning The Five Elements of Successful Agentic Enterprise Operations Across our client work, five actions consistently distinguish transformations that scale agentic operations and unlock true value from those that stall. Focus on outcomes. Review processes, focusing on the outcomes before optimizing as-is operations. Don’t layer AI onto unstable or fragmented processes. Start from the outcome and intent, and redesign back to ensure autonomy amplifies value rather than structural weaknesses. Build an “agentic process transformation factory” to train the transformation muscle. Follow a standard playbook for an end-to-end process shift that centralizes funding, guardrails, and prioritization. Ensure a continuous feedback loop and evaluations that are owned at the C-level and run by integrated business–tech teams, measuring value realization. Make tech choices a C-level priority. Elevate ecosystem orchestration; build an AI layer and prepare IT service management for agentic scale. This also helps power up the agentic factory, orchestrating the multivendor ecosystem and building a solid AI layer, founded in data, workflows integration, infrastructure, and testing. It also gradually accelerates the agentic delivery life cycle to match adoption pace, including release/evaluation processes, monitoring, and auditability. Place a platform bet. Pick one agentic platform and get started; lock-in is not a major concern as portability is expected to increase as agentic AI matures. Design for interoperability because vendor capabilities and product boundaries will most likely evolve; next-generation architectures will increasingly mix agents across platforms, with shared protocols enabling cross-platform coordination. Start the journey early and evolve governance and change management along the way. Focus on building agentic processes across one or two high-value domains and a path to scale. Alternatively, build a greenfield agentic attacker to disrupt your own business and learn how to transfer learnings to your brownfield operations. Use pilots to set up and harden governance and data products. Then address the changing challenges from redefining roles, and align on risk thresholds and institutionalize continuous improvement before scaling. A common blocker to multistep autonomy at scale we see across our clients is the tech and data foundation. In practice, scaling AI in enterprise operations requires: Trusted data access and quality, including permissions and regulatory compliance. Production-grade AI runtime and orchestration beyond the LLM, such as gateways, container services, OCR, observability, and security/auditability. Governed integration into workflows and core systems, with clear interaction layers and read/write controls. AI-specific testing and validation that go beyond the traditional software delivery life cycle and second-line model checks. These foundations are vendor-agnostic, and they determine whether an agentic platform can run reliably in production. With these foundations in place, platform choices become pragmatic decisions, guided by a set of beliefs that keep the focus on value realization. Success does not just come from action; agentic AI will deliver most value in organizations where senior leaders have settled on some key beliefs: There is currently no one-size-fits-all agent platform. Most enterprises will likely land on a hybrid, and platforms will likely converge. Scale is expected to come from structure. Unified orchestration and life cycle discipline tend to outperform ad hoc builds. Context and access are likely to become scaling bottlenecks. As such, agentic platforms should sit close to the data platform. A platform-adoption-first approach has so far proven to be the fastest path to value. Build selectively where differentiation is clear. Treat evaluation and monitoring as production infrastructure. This means establishing it from day one. Different Transformation Paths to Success with Agentic AI Operations There is no one-size-fits-all pathway to agentic enterprise operations. However, journeys typically depend on two design choices: Greenfield versus brownfield. Organizations must choose between building autonomy-first workflows from scratch or progressively deploying agentic capabilities on top of stable cores, prioritizing continuity and controlled risk. Full shift to agentic AI versus partial. Here, organizations must choose between committing to an end-to-end redesign to unlock maximum value or a gradual approach to autonomous operation that reduces disruption and upfront investment. Across all pathways, execution must be holistic. Programs that scale align these building blocks from day one: Strategic Ambition. Define value ambition, transformation principles, and governance philosophy. Program Orchestration. Set up transformation governance, roadmap sequencing, value assurance, and change management. Agentic Operations Design. Redesign end-to-end processes starting with outcomes and working back to processes, deploy agentic workflows, and establish monitoring and agentic governance. Agentic Foundations. Build enabling AI capabilities across tech, data, processes, talent, and governance. Where to Start with Agentic Operations The near-term objective is not to deploy more agents. It is to build the foundations for scale and select the right wedge into end-to-end redesign. At a high level, companies must start by assessing agentic maturity and value potential across key end-to-end processes, then selecting one or two priority domains for redesign. For each, they must set clear transformation principles alongside the governance philosophy that will guide execution. Agentic AI is still maturing, but its exponential development makes it essential to build capability now. Organizations that move early to reinvent their operating system of work will be the ones that benefit first from the shift from incremental task gains to transformational reinvention. DOWNLOAD THE SLIDE DECK Authors Marcus Wittig Managing Director & Partner Cologne Alfonso Abella Managing Director & Senior Partner, BCG X; Global Lead, Deep Customer Engagement AI Madrid Olivier Bouffault Managing Director & Senior Partner; Global Leader, Operations Practice Paris Robin Anders Project Leader Düsseldorf Alexander Duerloo Managing Director & Partner Amsterdam Alexander Noßmann Managing Director & Partner Düsseldorf Stephen Robnett Managing Director & Senior Partner Seattle Salvatore Cali Partner and Associate Director Rome Yann Letourneux Partner & Director Paris Christoph Schmitt Partner Hamburg Juan Martin Maglione Partner Madrid Related Content Artificial Intelligence Article July 8, 2026 The Agentic Leadership Playbook: A Scaling Strategy for CTOs and CIOs Most CTOs and CIOs are still asking what agentic AI can do. Leaders are rethinking how the business operates. Learn More AI Agents Article December 15, 2025 How Agents Are Accelerating the Next Wave of AI Value Creation A CEO’s roadmap for scaling agentic AI—the actions to take now, the disruptions to prepare for next, and the guiding principles to always follow. Learn More Artificial Intelligence Article July 21, 2026 Scaling Agentic AI in Procurement Is an Organizational Challenge New BCG research shows that most enterprises are wrestling with how to adapt the procurement organization’s design to make the best use of agentic AI. Learn More Artificial Intelligence Article July 20, 2026 Building Enterprise AI Agents in Regulated Industries Scaling agentic AI in a regulated business calls for a common enterprise platform that balances innovation with governance. Learn More AI Agents Article November 18, 2025 Leading in the Age of AI Agents: Managing the Machines That Manage Themselves Agentic AI is both software and colleague—a form of artificial intelligence that acts. Organizations that swiftly adapt their management playbook to make full use of this transformative technology will gain the edge. Learn More Marketing and Sales Article November 25, 2025 The Agentic Marketing Race Is On. CMOs That Move First Will Win. Agentic AI is ushering in a new era for marketing, one where autonomous systems learn, decide, and act alongside human teams. Learn More ABOUT BOSTON CONSULTING GROUP Boston Consulting Group bridges the gap between ambition and outcomes for the world's leading companies and organizations. We are built for this era of unprecedented change — bringing strategic clarity rooted in over 60 years of deep domain knowledge, combined with applied AI shaped by our practitioners. BCG works shoulder-to-shoulder with CEOs across industries and geographies to deliver transformative impact at scale: stronger returns, transferred capabilities, and change that sticks. For more information, visit bcg.com . 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