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Agentic AI AND Business process improvement

Agentic AI is rapidly becoming the most commercially important phase of enterprise AI because it moves the conversation from content generation to business execution.

 

Most organisations have already experimented with generative AI. They have seen value in summarising reports, drafting emails, accelerating research, and helping teams retrieve information faster. But those gains, while meaningful, still sit largely at the edge of the workflow. Agentic AI changes that. It enables AI systems to interpret goals, break work into steps, use tools, make decisions based on feedback, and operate with a degree of autonomy inside defined controls. Stanford HAI describes agentic AI in exactly those terms: systems that can interpret goals, plan and sequence actions, use tools, and adapt over time to complete tasks.

That distinction matters at board and executive level because most operating inefficiency is not caused by a lack of information. It is caused by the delay between knowing what needs to happen and actually making it happen. Teams chase context across systems, manually reconcile records, repeat the same review steps, and wait on handoffs that add cost without adding value. Agentic AI is compelling because it targets that gap directly. Instead of only helping staff think faster, it can help the enterprise move faster.

At Neo, that is where we see the strategic opportunity. The value is not in deploying AI for novelty. It is in redesigning processes so they become leaner, more responsive, and more scalable without losing governance, oversight, or control.

From AI Assistance to AI-Enabled Execution

The first wave of enterprise AI was largely assistive. It improved the front end of knowledge work by helping individuals write, search, and summarise. The next wave is more operational. Agentic AI can participate in the process itself.

That means an AI capability can do more than answer a question about an exception in a workflow. It can identify the exception, retrieve the relevant data, compare the case against policy or expected rules, prepare a recommendation, trigger the next workflow step, and escalate where human judgement is required. Databricks now explicitly positions Mosaic AI around enterprise-grade generative AI applications including AI agents, and its documentation describes Agent Bricks and the Mosaic AI Agent Framework as tools for building and deploying production-quality agents, including retrieval-augmented and multi-agent systems.

This is a major shift in enterprise design thinking. Once AI can participate in execution rather than only interaction, businesses can begin to redesign processes around speed, coordination, and reduced manual overhead. That does not mean uncontrolled autonomy. It means structured autonomy, where routine steps are accelerated, information is assembled faster, and people are brought in where approval, judgement, or accountability matters most. NIST’s AI Risk Management Framework is highly relevant here because it emphasises trustworthy and responsible AI use across design, deployment, and operation.

Where the Commercial Value Becomes Most Visible

The strongest commercial use cases for agentic AI are not necessarily the most glamorous. They are the ones where work is repetitive, context-heavy, cross-system, and slowed down by handoffs.

Operational exception management is one of the clearest examples. Many businesses still rely on people to review alerts, gather evidence, assess severity, and decide what should happen next. Agentic AI can compress those steps by triaging issues, collecting the supporting context, applying business logic, and routing the case appropriately. The result is faster response, lower noise, and more consistent handling.

Reconciliation and data quality workflows are another natural fit. These processes are full of repeated comparisons, validations, exceptions, and follow-up tasks. They consume time because the process is not only rules-based; it is also evidence-based. Agentic AI is strong in exactly that space because it can gather records, check patterns, surface anomalies, and prepare actions at speed.

There is also substantial potential in compliance-heavy and regulated environments. Here the goal is not to replace human accountability. It is to improve the quality and speed of preparation. Agentic AI can gather relevant records, align them to policies or obligations, highlight discrepancies, and prepare evidence packs for review. That is especially valuable in sectors where the cost of delay, inconsistency, or weak traceability is high.

Customer experience benefits as well, even when the AI is not customer-facing. Many customer pain points begin in back-office processes: document checks, service requests, case management, approvals, and follow-up actions. When those internal workflows become faster and more coordinated, the customer experience improves as a direct consequence.

The leadership takeaway is straightforward: the biggest value from agentic AI is not usually in making existing tasks slightly faster. It is in compressing the end-to-end process cycle.

What It Takes to Make Agentic AI Real

This is where many organisations will either create durable advantage or end up with another wave of promising pilots that never scale.

Agentic AI does not succeed because the model is clever. It succeeds because the operating environment is ready. Businesses need trusted data, access to the right systems and tools, clear permissions, strong monitoring, and an explicit control model for when humans stay in the loop.

Trusted data is foundational. If the inputs are stale, fragmented, or poorly defined, the AI will simply accelerate bad process outcomes. System connectivity is equally important. Agents become useful when they can interact with data platforms, business applications, APIs, and workflow tools where the real work happens. Governance is non-negotiable because enterprises must know what the AI accessed, what logic it applied, and what action it proposed or took. NIST’s framework is explicit that AI risk management must be practical, adaptable, and operationalised across the AI lifecycle.

This is why the most successful implementations will not be the ones that start with the broadest ambition. They will be the ones that target a specific high-friction process, define clear task boundaries, measure outcomes rigorously, and scale from there.

Why Databricks Matters in an Agentic AI Strategy

Databricks is increasingly important in this conversation because agentic AI needs more than a model endpoint. It needs a governed execution environment around enterprise data.

Unity Catalog is a strong example. Databricks describes it as a unified governance solution for data and AI assets, with centralised access control, auditing, automated lineage tracking, data discovery, quality monitoring, and secure sharing. That matters because once AI systems begin to participate in processes, governance can no longer sit off to the side as a separate workstream. It has to be built into the environment where the data, models, and decisions live.

Databricks also positions Mosaic AI as an integrated platform that unifies the AI lifecycle from data preparation to production monitoring, including AI agents, retrieval-augmented generation, model serving, and no-code and code-based tooling for building enterprise AI applications. That integrated model is strategically valuable because most organisations still struggle with fragmentation: data in one stack, governance in another, models elsewhere, and process workflows disconnected again. Databricks is clearly aiming to reduce that fragmentation.

For leadership teams, that matters because platform fragmentation is one of the biggest hidden costs in AI adoption. It slows execution, increases control risk, and makes it harder to move from pilot to production. A more unified model improves the odds that agentic AI becomes an operating capability rather than an isolated experiment.

The Leadership Agenda from Here

Agentic AI should not be framed as the next chatbot trend. It should be framed as a leadership question about operating model design.

Where are the processes in your business that are slowed by handoffs, fragmented data, repeated review, and delayed action? Which of those processes are valuable enough to improve, bounded enough to govern, and measurable enough to scale? Where would faster process execution create a material lift in margin, service quality, or control effectiveness?

Those are the right questions, because the organisations that win with agentic AI will not necessarily be the ones with the most advanced demos. They will be the ones that connect AI to operational reality. They will identify the processes where speed, consistency, and context matter most. They will build on governed data foundations. They will apply structured autonomy rather than uncontrolled experimentation. And they will treat AI not as a side capability, but as part of the enterprise operating model.

That is also Neo’s view. The real opportunity is not simply to introduce AI into the organisation. It is to use AI to redesign how work gets done — faster, leaner, and with better control. That is where agentic AI moves from technical promise to commercial advantage.

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