- Explainable AI (XAI) and LLM observability are crucial for scaling GenAI deployments and ensuring trust in AI-generated outputs.
- Organizations must prioritize XAI tracing, multidimensional observability, and continuous evaluation to improve GenAI reliability.
The growing importance of explainable artificial intelligence (XAI) will drive large language model (LLM) observability investments to 50% of GenAI deployments by 2028, up from 15% currently, according to a recent report from Gartner.
Gartner defines XAI as a set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behavior and identifies any potential biases. LLM observability solutions monitor, analyze and provide actionable insights into the behavior and performance of LLMs. They go beyond standard IT measurements, such as response times to look at specific LLM metrics such as hallucinations, bias and token utilization.
These tools are used by teams that develop and operationalize AI systems, and increasingly by IT operations and SREs responsible for the performance and resilience of these systems in production.
The Importance of XAI
Pankaj Prasad, Senior Principal Analyst at Gartner, stated that as enterprises scale GenAI, the trust requirement grows faster than the technology itself.
“XAI provides visibility into why a model responded a certain way, while LLM observability validates how that response was generated and whether it can be relied on,” said Prasad in a statement. “Without robust XAI and observability foundations, GenAI initiatives will be restricted to low risk, internal, or noncritical tasks where output verification is easily managed or inconsequential, severely limiting the potential return on investment.”
Why the Growing Need for XAI and LLM Observability
Gartner forecasts the global GenAI models market will exceed $25 billion in 2026 and reach $75 billion by 2029, driven by rapid adoption across industries. As usage increases, so does the need for mechanisms that verify AI-generated content and protect against hallucinations, factual inaccuracies and biased reasoning.
“Traditional observability is focused on speed and cost, but the priority is now moving toward deeper quality measures such as factual accuracy, logical correctness and sycophancy,” said Prasad. “This shift requires new governance-focused metrics and evaluation methods, such as human-in-the-loop validation of the generated content’s narrative and citation accuracy.”
To improve the reliability, transparency and business value of GenAI use cases, Gartner advises organizations to prioritize the following steps:
- XAI Tracing for High Impact Use Cases: Mandate verifiable XAI tracing for all high impact GenAI use cases to document the model’s reasoning steps and the source data behind each output.
- Multidimensional LLM Observability: Prioritize observability platforms that monitor latency, drift, token usage and cost, error rates, and output‑quality metrics to ensure reliable GenAI performance.
- Continuous LLM Evaluation in CI/CD Pipelines: Integrate LLM evaluation metrics, including factual‑accuracy benchmarks and safety checks, into continuous integration (CI)/continuous delivery (CD) pipelines for continuous validation before deployment.
- Stakeholder Education on Explainability Requirements: Educate legal, compliance, and other key stakeholders on explainability requirements to ensure alignment on risk, governance expectations, and implementation challenges.
“Explainability turns a GenAI output into a defensible, auditable insight. LLM observability ensures the model behaves as expected over time,” said Prasad. “Without both, GenAI cannot mature beyond controlled lab environments.”
AI-Driven Sales Enablement Will Deliver 40% Faster Sales
By 2029, sales organizations with AI-driven enablement functions will achieve 40% faster sales stage velocity than those using traditional enablement approaches, according to Gartner.
Findings from a Gartner survey of 227 chief sales officers (CSOs) underscore why this shift is becoming urgent. Sales organizations completed an average of four transformations in the past 12 months, making the ability to drive performance through continuous change a core requirement for CSO success.
The survey additionally found that sales organizations that collaborate on enablement content with other functions, such as marketing and service, are 2.4 times more likely to achieve strong commercial growth than those that do not.
How to Keep Up
To keep pace with constant transformation and rising revenue pressure, sales leaders must move beyond static content and training to deliver in‑workflow, data‑driven guidance; align enablement across sales, marketing and service to drive consistent revenue execution; and leverage AI and automation to scale performance through continuous transformation.
“Traditional enablement was built as a reactive support function, not as a system engineered to drive measurable seller performance,” said Shayne Jackson, VP Analyst in the Gartner Sales Practice. “As CSOs face ongoing transformation and heightened revenue pressure, enablement must become an AI‑driven function that orchestrates seller behavior in real time. Organizations that fail to make this shift will struggle to improve deal velocity and sustain growth.”
