In today's tech landscape, companies are rapidly integrating AI into their products. While many implementations deliver genuine value, others seem to prioritize having an "AI feature" over truly enhancing the user experience.

At XtendOps, where we build AI solutions for customer support, we've observed this trend and developed a perspective on what separates high-value from low-value AI implementations.

When AI Features Fall Short

Consider the productivity software space. Tools like Notion have added AI features for summarizing content or assisting with brainstorming - capabilities that often feel incremental rather than transformative.

Design platforms aren't immune either. When tools like Figma implement AI to handle what were previously straightforward template selections, it raises questions about whether this represents meaningful innovation or simply repackaging existing functionality.

These examples illustrate what we might call "Low Value AI" — features added primarily to align with market trends rather than to address significant user needs.

Evaluating AI Feature Value

How can organizations determine if an AI feature is worth implementing? One practical test we use: if you can achieve comparable or better results using simpler approaches or base models, the feature may not justify the investment.

Many companies could benefit from applying this filter. Resources spent developing AI components that don't meaningfully outperform traditional solutions might be better allocated elsewhere.

Creating Meaningful AI Implementation

Our approach at XtendOps is driven by a clear goal: harnessing AI to enhance our agents' capabilities and efficiency in customer support.

Rather than implementing AI broadly, we focus on specific applications that help us achieve more with fewer resources. Our goal is to leverage technology that enables 2,000 people to accomplish what traditionally required 20,000 in the BPO space.

Our SmartAgents platform embodies this philosophy. Each AI feature we develop must demonstrably make our agents more effective. If it doesn't meet that standard, we reconsider the approach.

Moving Toward Purposeful AI

The potential of AI to transform industries remains significant, but realizing this potential requires moving beyond surface-level implementations.

Companies would benefit from shifting their thinking from "How can we add AI to our product?" to "What problems can AI uniquely solve for our users?"

As the market matures, users will increasingly distinguish between AI features that deliver value and those that don't. Organizations that prioritize meaningful AI implementation focused on solving real problems will likely see more sustainable success.

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What's your experience with AI features in the products you use? Have you encountered examples that genuinely improved your workflow, or instances where the AI addition seemed unnecessary? I'd appreciate hearing your thoughts in the comments.

And if you're interested in diving deeper, request a pass to CX Sh/ft Philippines 2025, where industry experts will be breaking it all down.

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