Enterprise AI adoption is not a technology problem—it is a trust problem. A 2025 Harvard Business Review study revealed that organizational barriers, not technical limitations, are the primary blockers to AI deployment. The Nielsen Norman Group's July 2026 article, 'Crafting AI Explanations for Every Role in Your Enterprise,' provides a critical framework: AI explainability must be tailored to three distinct enterprise roles—AI consultants and governance leads, builders, and domain experts. The article's shared scenario of configuring an AI help-desk agent demonstrates how a single explanation style fails. For example, a governance lead evaluating pre-deployment readiness needs global, model-based explanations like audit summaries and compliance-ready dashboards. A builder debugging an escalation error requires local, interactive explanations showing which inputs drove a decision. A domain expert reviewing test responses needs plain-language explanations with policy anchors and feedback widgets. This taxonomy-driven approach, grounded in IBM's 2019 explainability taxonomy, shifts AI from a black box to a collaborative oversight tool. The stakes are high: misconfigured AI can cascade into compliance failures, operational inefficiencies, and eroded employee trust. Organizations that fail to implement role-specific explainability risk stalled adoption, regulatory penalties, and competitive disadvantage.
The Three-Role Taxonomy: A Strategic Imperative
The article defines three categories of enterprise AI users, each with distinct jobs-to-be-done. AI consultants and governance leads (e.g., centers of excellence, solution architects) operate at the system level, defining best practices and evaluating risk. They need global explanations: performance trends, edge-case handling, and audit trails. Builders (platform admins, developers, configurers) translate business needs into working solutions. They require local, interactive explanations to debug and iterate—like change-impact summaries and example-based comparisons. Domain experts (process owners, business managers) contribute specialized knowledge to improve AI outputs. They need plain-language explanations tied to familiar workflows, with feedback mechanisms to correct errors. This taxonomy is not rigid; roles overlap, but the job-to-be-done determines the explanation type. For instance, a builder debugging a help-desk AI escalation issue needs to know: 'This request was escalated because the user’s account is flagged for multi-factor authentication review, which falls outside the standard password-reset scope defined in your system prompt (confidence: 84%).' A domain expert reviewing the same system needs: 'This response was based on 14 similar past tickets that directed users to the legacy portal before the migration. The current policy documentation was added 3 weeks ago and may not yet be reflected in the agent’s training data.'
Strategic Consequences: Winners, Losers, and Market Shifts
Winners: AI vendors that embed role-specific explainability into their platforms will gain a competitive edge, especially in regulated industries like finance and healthcare. IBM, as the taxonomy creator, positions its AI products as the standard for transparent enterprise AI. Domain experts—often overlooked in AI design—become empowered contributors, driving adoption and iteration. Losers: Builders face increased complexity in implementing multiple explanation types, potentially slowing development cycles. Small AI vendors without resources to build sophisticated explanation toolkits will struggle to compete. Market impact: Role-specific explainability is becoming a compliance requirement. The EU AI Act and similar regulations demand transparency; organizations that adopt this framework early will reduce legal risk and accelerate deployment. The article's reference to a 2025 study on workload and multitasking performance underscores that even Level 1 XAI (basic explanations) can affect user reliance—meaning explanation design directly impacts operational outcomes.
Outlook: What Executives Must Do Now
Over the next 30 days, organizations should audit their AI systems for explanation gaps. Key indicators: Are governance leads receiving global audit summaries? Can builders trace outputs to configuration choices? Do domain experts have plain-language feedback widgets? If not, adoption will stall. The article's framework is a starting point, not a formula—but the principle is clear: treat explainability as a design problem, not a technical afterthought. Companies that invest in role-specific explanations will build trust, reduce liability, and unlock the full value of enterprise AI.
FAQ
AI consultants and governance leads (system-level risk and compliance), builders (debugging and configuration), and domain experts (workflow context and output validation). Each requires a distinct explanation type: global, local-interactive, and plain-language with feedback.
Different roles have different jobs-to-be-done. A governance lead needs audit summaries, a builder needs traceability to configuration choices, and a domain expert needs policy anchors. A single explanation style leaves critical gaps, eroding trust and slowing adoption.
Start by mapping each AI system's stakeholders to the three roles. For governance leads, build dashboards with global performance trends and compliance summaries. For builders, provide interactive debugging tools with local explanations. For domain experts, offer plain-language outputs with feedback widgets.
