Bridging the AI productivity gap: from investment to impact

AI investments are off the charts. Spending on these systems surged from $2.3B in 2023 to $13.8B in 2024, and there’s no sign of a slowdown in 2025. 

But while AI-related expenses continue to skyrocket, returns on these investments leave much to be desired. In fact, less than one in four executives are realizing significant business impact—and the AI productivity gap is largely to blame. 

The discrepancy between how much leaders are spending on AI and the value they’re reaping from it stems from underinvesting in the essential foundations needed to translate investments into impact. Only businesses with data-backed work models, real-time employee guidance, and scalable growth pathways will be able to turn AI’s seemingly limitless potential into profitability. 

What is the AI productivity gap?

The AI productivity gap represents the growing disconnect between the substantial investments organizations are making in AI and the actual business value these investments generate. This gap manifests when companies enthusiastically adopt AI solutions but fail to establish the necessary organizational foundations to translate these technologies into measurable productivity improvements.

At its core, the AI productivity gap occurs when:

• Organizations lack visibility into AI impact across the enterprise and are unable to measure how effectively AI systems integrate with existing workflows and drive business outcomes
• Work processes remain unchanged despite AI implementation, missing the opportunity to redesign and optimize workflows at the task level for maximum effectiveness
• Employees aren’t equipped or aligned for AI-powered work because they lack both the skills to leverage AI tools effectively and reallocation opportunities 

This gap isn’t merely a technology implementation challenge; it’s a fundamental business problem that threatens to undermine the transformative potential of AI across industries. While most organizations are eagerly investing in cutting-edge use cases, they’re simultaneously underinvesting in the essential foundations needed to convert their AI investments into meaningful business impact.

Why leaders should care about the AI productivity gap

For executives and organizational leaders, the AI productivity gap represents both an immediate financial concern and a long-term strategic threat. The consequences of this gap extend far beyond wasted technology investments. 

Organizations caught in this cycle risk falling behind competitors who successfully operationalize AI, potentially losing market share and struggling to attract top talent who seek employers at the forefront of innovation. Some top concerns include:

#1. Financial impact

The gap directly affects return on investment for AI initiatives. Organizations pouring millions into AI solutions without corresponding productivity gains are effectively burning resources that could be directed toward other strategic priorities.

#2. Competitive vulnerability

As some organizations successfully bridge the gap and begin realizing AI’s full potential, those caught in the implementation cycle will find themselves at a growing competitive disadvantage. The productivity differential between AI leaders and laggards will likely widen over time.

#3. Employee productivity

When AI tools are introduced without proper integration into workflows, employees often experience them as additional burdens rather than productivity enhancers. This can lead to resistance, reduced adoption, and ultimately, implementation failure.

#4. Innovation capacity

Organizations stuck in the AI productivity gap often find themselves in perpetual “implementation mode,” limiting their ability to explore new innovations or respond quickly to market changes.

#5. Strategic credibility

Repeated failures to generate returns from AI investments can damage leadership credibility and make it increasingly difficult to secure resources for future digital transformation initiatives.

Building the foundation for AI readiness

True AI readiness goes beyond just buying new tech. It’s built on three essential pillars: a data-backed work model, real-time employee guidance, and scalable growth pathways.

Organizations must map their existing workflows to target high-impact AI investments efficiently. Employees need practical, contextual support to effectively use AI in their daily tasks. Meanwhile, structured opportunities for developing AI competencies must be established to advance careers. Without these foundations, the AI productivity gap will widen, wasting resources and undermining transformation efforts

5 steps to close the AI productivity gap 

If you’re looking to turn your AI investments into tangible impacts, prioritize the following action items to ensure your organization is becoming AI-ready: 

#1. Create a task-level view of work 

To understand AI’s potential impact and prioritize AI investments with the highest ROI, organizations must first model the work tasks that power their operations. This involves leveraging AI to identify the tasks performed across roles within the organization, creating a comprehensive, data-driven map of the work tasks currently driving the business.

This detailed work model is crucial because it provides critical visibility into which tasks have high automation potential and, conversely, which tasks inherently require human skill. With this fundamental understanding, businesses can strategically prioritize AI initiatives with the most significant returns and prepare their workforce for intentional transformation, rather than making AI investments blindly.

#2. Prioritize high-yield workflows 

Focus your AI investments strategically on workflows that promise the greatest potential impact rather than diluting resources across too many initiatives. By identifying and prioritizing high-yield work streams, organizations can maximize ROI and accelerate their AI transformation journey while avoiding the common pitfall of spreading resources too thinly.

#3. Establish success metrics

Define clear, measurable indicators of work efficiency to accurately gauge AI’s true impact on productivity across your organization. By establishing concrete success metrics, companies can move beyond vague promises of transformation to demonstrate tangible ROI from their AI investments, enabling data-driven decisions about future technology adoption.

#4. Forge impactful human-AI collaborations

Successful AI implementation isn’t about replacing humans but creating effective partnerships between people and technology. Organizations must develop frameworks that clarify how employees and AI systems can best complement each other’s capabilities.

#5. Develop targeted upskilling strategies

As AI transforms work processes, employees need opportunities to develop new competencies. Rather than generic “AI awareness” training, organizations should create precision-targeted programs that drive true AI mastery through hands-on experiences embedded into the flow of works.

Accelerate your AI transformation with Gloat Signal

Recognizing the challenges organizations face in bridging the AI productivity gap, our newest innovation is designed to quantify AI’s potential impact, calculate return on investment, and identify high-priority opportunities before competitors can capitalize on them.

Gloat Signal helps organizations:

• Quantify automation potential: Get concrete data on which tasks and processes are most suitable for AI enhancement and automation 
• Calculate precise ROI: Rather than vague promises of efficiency, the Gen AI Index delivers specific financial projections for AI investments 
• Identify strategic priorities: The Index identifies the highest-impact opportunities, allowing organizations to focus resources where they’ll generate the greatest returns 

Want to say goodbye to AI productivity gaps once and for all? Test drive Gloat Signal for yourself to see how you can turn your workforce into an AI-ready organization.