How Support Teams Can Maximize Impact Through Smarter Investments and Better Metrics

In 2025, the promise of AI-driven support and automation continues to inspire hope for greater scalability and efficiency. Yet despite remarkable technological advances—especially in generative AI and large language models—most organizations are still falling short of delivering the seamless, high-impact support experiences these tools can enable.

The reason: AI cannot deliver on lofty expectations unless organizations make strategic investments in three core areas—tools, model training, and most critically, content. Language models and generative AI are only as effective as the knowledge they are trained on and the content they can access. Without well-structured, accurate, and consumable content, even the most advanced AI falls flat.

AI is not a strategy—it is a tool.

But content and tooling aren’t the only challenges. One of the most significant inhibitors to realizing better outcomes from AI, self-help, automation, and knowledge management is the lack of clear definitions and measurement strategies for success. Many teams deploy digital tools with no consistent way to evaluate their effectiveness. As a result, initiatives are judged by activity metrics—page views, interactions, search hits—rather than by their ability to deliver effective resolutions to customers.

To scale support credibly and sustainably, organizations must define consistent success standards to determine when AI-driven support and automation deliver results—and to what extent these efforts reduce the burden on assisted support channels.

Defining What AI and Automation Success Looks Like

The missing ingredient in most support strategies today isn’t more technology—it’s accountability. Until organizations apply consistent, outcome-based metrics, AI and automation will underperform.

To assess effectiveness, support organizations must adopt a unified definition of success: the delivery of an Effective Resolution—an outcome that is timely, satisfies the customer, and fully resolves the issue without further effort.

Whether delivered by a support engineer, a chatbot, or a knowledge article, Effective Resolutions should be the performance standard across all support channels.

Deflection and Enablement: Two Measures of Success

A core metric for evaluating self-help and automation is Deflection—the rate at which these tools fully resolve issues that would have otherwise required assisted support. To qualify, a deflected case must:

• Be submitted by a customer entitled to assisted support
• Be resolved with no follow-up required
• Require no validation by human staff

A second measure—Customer Enablement—captures cases where self-help or automation delivers useful information that enhances product use but does not eliminate the need for assisted support. While enablement may not reduce case volume, it builds product competency, user confidence, and long-term trust.

The Deflection Gap

While 71% of support demand now begins with self-help, only 22% of issues are resolved without human involvement—a decade-long average that has not improved. This persistent Deflection Gap reveals a fundamental failure to turn usage into resolution.

Why does the gap persist?

1. Content Gaps – Many answers don’t exist in usable formats.
2. Comprehension Barriers – Customers struggle to understand or apply available solutions.
3. Confidence Issues – Customers often seek human validation, even when a correct answer exists.

Measuring and Closing the Gap

To close the deflection gap and unlock the full potential of AI and automation, support organizations must:

• Analyze customer behavior and usage data to understand how and when digital channels are used.
• Identify high-traffic topics with ineffective or missing content.
• Create content in customer-friendly formats (e.g., videos, interactive guides).
• Tailor knowledge to align with user intent and context.
• Ensure content is accurate, complete, structured, and optimized for AI consumption and regeneration.
• Build trust in self-help tools through branding, endorsements, and consistent quality.
• Allocate sufficient resources to create, maintain, and govern support content and systems.

The Role of Knowledge Management in 2025

For AI to succeed, organizations need a content strategy that feeds and sustains AI.

Yet most knowledge management (KM) efforts still fall short. Critical knowledge remains undocumented, unstructured, or unintelligible to customers. KM must evolve from a background process to a core support function—one that enables scalable, resolution-focused service.

KM must evolve from a repository mindset to a resolution-enablement function.

Support organizations should take the following actions to strengthen knowledge management for AI-enabled support:

1. Elevate KM as a Core Support Function
   Knowledge creation should not be an afterthought to case closure—it must be a structured effort to capture and share insights from customer interactions. Knowledge should be treated as a strategic output, not a byproduct.
2. Invest in Dedicated KM Roles and Skills
   Leverage subject matter experts (SMEs) to contribute knowledge, but assign ownership to knowledge strategists, content designers, and KM professionals to manage structure, governance, and continuous improvement.
3. Adopt an AI-First Approach to Knowledge Design
   Design knowledge for reuse and regeneration—not just reading. Build structured, modular, intent-driven content that LLMs can consume, retrieve, and present effectively.
4. Measure KM with Outcome-Based Metrics
   Move beyond views and thumbs-up ratings. Track resolution contribution, deflection, AI-assisted success rates, and knowledge-driven enablement to evaluate content value.
5. Source Content from Across the Organization
   Integrate useful insights from Sales, Education, Community, and Professional Services to enrich the support knowledge base and reduce redundancy.
6. Implement a Continuous Feedback Loop
   Use failed searches, chatbot fallbacks, and customer feedback to identify gaps. Maintain processes for reviewing and updating content to reflect evolving user needs and product changes.

Rethinking Support Resource Allocation

To realize the full potential of AI and automation, support leaders must rethink how teams are staffed and structured. Today, the majority of support staff are focused on assisted support—while only about 10% of FTEs are dedicated to self-help, automation, or knowledge roles.

This is unsustainable in the face of rising support demand and proportionally lower staffing levels.

The future requires a leveraged model, where more resources are directed toward building and sustaining scalable support capabilities powered by AI tools and robust KM processes.

This shift demands new roles and expertise, including:

• A Digital Support Owner | Manager
• Knowledge Strategists
• Content Developers
• AI Trainers
• Automation Workflow Designers
• Support Insights Analysts
• Digital Experience Designers

Conclusion

The future of support hinges not just on deploying AI, but on using it wisely. That means investing in better content, reallocating resources toward scalable solutions, and holding every channel—assisted, automated, or self-help—to the same standard of delivering Effective Resolutions.

When customers can solve their issues through intelligent tools and trusted knowledge, support efficiency improves, agent load decreases, and business outcomes accelerate.

But until support teams bridge the deflection gap and build confidence in digital channels, the full promise and potential of AI will remain unfulfilled.