Building Momentum: How to Scale Agentic AI Platforms Across the Enterprise

AI is moving from experiments into core business systems. The focus keyphrase "scale agentic AI platforms" captures the new imperative: companies must not only pilot agents, but build repeatable, secure platforms that deliver real value. This post explains what that means, why it matters, and how leaders can act now to turn agentic AI into sustainable advantage.

## Why businesses must scale agentic AI platforms

Enterprises today are seeing a shift: isolated AI pilots no longer cut it. Therefore, leaders must think bigger. IBM’s new Enterprise Advantage service shows a practical route. It packages reusable AI assets, playbooks, and consulting expertise so companies can build internal platforms that host agentic assistants. Additionally, IBM emphasizes that this approach works with existing cloud providers and models. That matters because it removes heavy migration or replatforming as barriers to adoption.

The IBM example includes a marketplace of industry-specific agents and a proven delivery platform that reportedly boosted consultant productivity by up to 50%. For companies, that translates into faster prototype-to-production cycles and clearer ROI pathways. For example, a lifelong learning company used the service to combine human experts with agentic assistants to manage routine work and decisions. Similarly, a manufacturer mapped high-value use cases, tested prototypes, and aligned leaders on a platform-first strategy.

However, building a platform is not just a technical task. It is a change program that touches processes, skills, and governance. Therefore, the immediate impact is twofold: faster delivery of AI capabilities, and a safer, more repeatable path to scale. Looking ahead, firms that treat agentic AI as a platform will be better positioned to multiply use cases and control costs as adoption grows.

Source: IBM Think

Building a platform-first approach to scale agentic AI platforms

Many executives say AI will be central to revenue by 2030. However, most lack a clear map from pilots to profit. The IBM Institute for Business Value study found that 79% of surveyed executives expect AI will significantly contribute to revenue by 2030, yet only 24% know where that revenue will come from. Therefore, a platform-first approach helps close that gap.

A platform-first strategy starts with three moves. First, identify high-value business workflows that agents can improve. Second, create reusable assets—data connectors, APIs, and tested agent templates—that accelerate repeat deployment. Third, align leadership and teams on measurable outcomes rather than one-off experiments. Additionally, investment trends suggest companies plan to increase AI spending substantially through 2030. That means early platform choices will have long-term consequences for cost, vendor lock-in, and speed.

Integration is critical. The IBM study warns that 68% of executives worry their AI efforts will fail because they are not integrated with core business systems. Therefore, platforms should be designed to plug into existing ERP, CRM, and decision systems. This reduces friction and helps agents act on real data. Moreover, the skills question matters. Firms need people who can translate business needs into agent behavior and governance, including a blend of product managers, data stewards, and software engineers.

Consequently, companies that plan investments around a platform model can move from experimentation to scale while managing risk and costs. Over time, that will be the difference between pilot fatigue and sustained AI-driven growth.

Source: IBM Think

Governance and compliance for scale agentic AI platforms

Agentic AI introduces autonomy. Therefore, governance can no longer be an afterthought. At the World Economic Forum, a recent collaboration between e& and IBM highlighted a new class of enterprise-grade agentic systems meant to move beyond simple chatbots. This signals a growing emphasis on governance, compliance, and operational controls for agents that make or recommend decisions.

Good governance starts with clear rules of engagement for agents. For example, teams must define what actions an agent can take autonomously and what requires human approval. Additionally, traceability is essential: organizations need logs and explainability tools so they can audit agent behavior. The e& and IBM announcement points to enterprise-grade implementations, which implies attention to secure environments, role-based access, and compliance checks that align with industry rules.

However, governance is also cultural. Leaders must establish decision protocols and regular reviews so that agents remain aligned with business intent. That includes periodic risk assessments and testing against edge cases. Moreover, vendors and platforms that support multi-model and multi-cloud deployments can simplify compliance by allowing organizations to keep sensitive workflows in controlled environments.

Consequently, governance enables scale. When compliance, auditability, and human-in-the-loop checkpoints are built into the platform, legal and risk teams can approve broader deployments. That, in turn, unlocks the ability to roll agents into customer service, finance, and operations with confidence.

Source: IBM Think

Financial and operational shifts: AI as core infrastructure

Large organizations are already rethinking where AI sits in their budgets. For instance, a major bank has publicly reclassified AI spending as core infrastructure, on par with payment systems and data centers. Consequently, this reframes budgeting, procurement, and operating models. Treating AI as infrastructure leads to steady capital investment and ongoing operating expenses rather than one-off project costs.

This shift has practical implications. First, it encourages centralized platforms and standards, which reduce duplication and create economies of scale. Second, it changes vendor relationships: firms may favor fewer, strategic platform providers and demand robust SLAs and security guarantees. Additionally, human resources must adapt. Employees will need new roles focused on platform reliability, model lifecycle management, and incident response.

However, adopting AI as infrastructure is not only about spending. It is about resilience. Infrastructure thinking implies redundancy, monitoring, and continuous improvement. Therefore, platforms for agentic AI must include observability, rollback mechanisms, and lifecycle controls that mirror other critical systems. Firms that do this are better prepared to absorb change while maintaining service quality.

Moreover, this approach supports faster scaling. When AI is budgeted and governed like infrastructure, leaders can plan long-term capacity and integrate agents deeply into operations. Consequently, the enterprise moves from pilots to production-grade deployments with predictable cost and risk profiles.

Source: Artificial Intelligence News

Personal assistants and app-level integration: what it means

Consumer experiences are also shaping enterprise expectations. Google’s Gemini feature now scours apps like Gmail, Photos, YouTube, and Search to answer personal queries. Therefore, users are growing accustomed to assistants that access multiple data sources and act across apps. Enterprises should take note.

This capability shows what agentic assistants can do when they have controlled access to internal systems. For employees, similar assistants could surface customer history, summarize meeting notes, or prepare briefings by pulling from multiple enterprise apps. Additionally, the consumer model highlights the need for privacy controls and clear consent flows. Businesses must ensure that agents access only approved data and that usage is logged and reviewable.

However, consumer-grade integration also raises expectations for speed and fluidity. Users will expect agents to be proactive, context-aware, and capable of handing off to humans seamlessly. To meet that bar, platforms must support secure connectors, permissions models, and consistent user experiences. Vendors that can bridge consumer-style convenience with enterprise-grade control will likely gain traction.

Consequently, the rise of app-scouring assistants is both a signal and a template. Enterprises can learn from consumer features while building the guardrails required for regulated environments. This balance will determine whether internal agents become productivity multipliers or sources of risk.

Source: AI Business

Final Reflection: From pilots to platform-led transformation

Together, these stories paint a clear arc. Companies are moving from isolated AI experiments to platform-led strategies that treat agentic AI as both product and infrastructure. Therefore, successful scale depends on three linked capabilities: practical platforms that reuse assets and integrate with existing clouds; robust governance that makes agents auditable and compliant; and a financial mindset that treats AI as long-term infrastructure. Additionally, consumer advances in app-level assistants raise user expectations and provide a blueprint for productive integrations.

Looking ahead, organizations that focus on measurable business outcomes, invest in platform building blocks, and bake in governance will unlock the most value. However, this is not a single-team project. It requires cross-functional collaboration across IT, risk, and the business. Consequently, leaders who act now to align strategy, investment, and operations will find themselves ahead—able to scale agentic AI platforms that are both powerful and responsible.