Provided byReltio
AI agents are progressing beyond mere coding aides and customer support chatbots, embedding themselves within the operational core of businesses. The return on investment looks promising, but autonomy without alignment can lead to disorder. Business leaders must establish essential foundations at this juncture.
The explosion of agents is imminent
Agents are autonomously managing comprehensive processes like lead generation, supply chain enhancement, customer assistance, and financial reconciliation. A medium-sized company could easily operate 4,000 agents, each making choices that influence revenue, compliance, and customer satisfaction.
The shift towards an agent-driven company is unavoidable. The financial advantages are too considerable to disregard, and this potential is materializing quicker than anticipated. The challenge? The majority of businesses and their underlying frameworks are ill-equipped for this transition. Early adopters have faced significant obstacles in scaling AI initiatives effectively.
The reliability gap hindering AI progress
Organizations are investing significantly in AI, yet tangible returns remain elusive. According to new findings from Boston Consulting Group, 60% of organizations report negligible revenue and cost improvements despite considerable investment. Conversely, leaders indicated they experienced fivefold revenue growth and threefold cost savings. Clearly, a significant advantage exists for those at the forefront.
The distinction between leaders and others doesn’t lie in their spending or the models they adopt. Before expanding AI deployment, these “future-oriented” organizations established crucial data infrastructure capabilities. They invested in foundational elements that allow AI to operate reliably.
A model for agent reliability: The four quadrants
To comprehend how and where enterprise AI can falter, consider four vital quadrants: models, tools, context, and governance.
Consider a simple scenario: an agent that orders pizza for you. The model interprets your request (“order me a pizza”). The tool performs the action (connecting with the Domino’s or Pizza Hut API). Context offers personalization (you typically request pepperoni on Friday evenings at 7 pm). Governance checks the outcome (did the pizza actually arrive?).
Each dimension signifies a potential failure point:
- Models: The core AI systems that decipher prompts, generate replies, and forecast outcomes
- Tools: The integration layer linking AI to enterprise systems, including APIs, protocols, and connectors
- Context: Before making decisions, agents require a comprehensive view of the business landscape, encompassing customer histories, product catalogs, and supply chain networks
- Governance: The policies, controls, and processes that assure data quality, security, and adherence to regulations
This model aids in diagnosing where reliability gaps appear. If an enterprise agent fails, which quadrant contains the issue? Is the model misinterpreting intent? Are the tools malfunctioning or unavailable? Is the context lacking or contradictory? Or is there no system to confirm that the agent performed as expected?
Why this is a data challenge, not a model challenge
The instinct is to assume that reliability will naturally enhance as models evolve. Nonetheless, model capabilities are improving exponentially. The costs of inference have fallen nearly by 900 times in just three years, hallucination rates are decreasing, and AI’s ability to handle lengthy tasks doubles every six months.
Also, tooling is progressing rapidly. Integration frameworks like the Model Context Protocol (MCP) significantly simplify the connection of agents to enterprise systems and APIs.
If models are potent and tools are evolving, what then impedes adoption?
To echo James Carville, “It is the data, stupid.” The root issue with most malfunctioning agents is misaligned, inconsistent, or incomplete data.
Organizations have amassed data debt over many years. Mergers, custom systems, departmental applications, and unmanaged IT have resulted in data spread across silos that rarely align. Support systems don’t correspond with marketing systems. Supplier information is replicated in finance, procurement, and logistics. Locations may have various representations depending on their source.
Introduce a few agents into this environment, and they will initially function remarkably since each is assigned a curated subset of systems to interact with. But as more agents are added, discrepancies widen, with each one constructing its unique fragment of truth.
This pattern has occurred before. When business intelligence evolved into self-service, everyone began creating dashboards. Productivity surged, yet reports often diverged. Now envision that scenario not with static dashboards but with AI agents capable of taking action. With agents, data inconsistency results in real business implications, not merely discussions among departments.
Companies that create a unified context and strong governance can confidently deploy thousands of agents, assured that they will collaborate harmoniously and adhere to business protocols. Organizations that bypass this foundational development will find their agents producing conflicting results, breaching policies, and ultimately undermining trust more swiftly than they create value.
Utilize agentic AI without the disorder
The focal point for enterprises revolves around organizational preparedness. Will your company establish the data foundation necessary for effective agent transformation? Or will you invest years troubleshooting agents, addressing one problem at a time, perpetually pursuing issues stemming from infrastructure you never established?
Autonomous agents are already revolutionizing work processes. However, the enterprise will only realize the advantages if these systems operate from the same truth. This guarantees that when agents reason, plan, and act, they do so based on accurate, consistent, and current information.
The organizations deriving value from AI today have built upon purpose-fit data foundations. They recognized early that in an agent-led world, data serves as critical infrastructure. A solid data foundation transforms experimentation into consistent operations.
At Reltio, the aim is to construct that foundation. The Reltio data management platform integrates core data from across the enterprise, ensuring that every agent has immediate access to a unified business context. This cohesive strategy allows enterprises to accelerate operations, enhance decision-making, and unlock the full potential of AI.
Agents will shape the future of the enterprise. Contextual intelligence will dictate who leads it.
For leaders navigating this forthcoming wave of transformation, consult Reltio’s practical guide:
Unlocking Agentic AI: A Business Playbook for Data Readiness. Obtain your copy now to discover how real-time context becomes the crucial advantage in the era of intelligence.
