The AI Execution Gap: Why Enterprises Are Investing in AI but Failing to Scale — And the Operating Model FixCorporate investment in artificial intelligence is reaching record highs, yet most of these projects remain stalled in the pilot phase. For enterprise leaders, the challenge has shifted from acquiring the technology to integrating it into the business's operational fabric—a disconnect known as the AI Execution Gap.While approximately 78% of organizations report implementing AI in some capacity, only a small fraction—estimated at roughly 5%—are realizing significant financial or operational value at scale. While companies often encounter technical hurdles, the failure to scale is primarily driven by the organizational operating model. To bridge this gap, leadership must transition from treating AI as an experiment to integrating it into the core business strategy and prioritizing AI adoption across the organization.
Defining the AI Execution Gap
The AI Execution Gap describes the space between a successful "proof of concept" (PoC) and a fully integrated, value-generating production environment. In a controlled pilot, AI often performs well because the data is clean, the scope is narrow, and the stakeholders are highly motivated. However, scaling often requires AI to be integrated with legacy systems, disparate data sources, and workflows involving manual tasks.Recent industry analysis suggests that 60% of companies see minimal returns on their AI investments. This stagnation is typically driven by three operational failures:- Dead End Pilots: Many projects are designed to prove technical feasibility rather than business scalability. Without a strategy for integrating AI into business processes, these projects may stagnate and fail to reach production.
- The Precision-Usability Gap: While model accuracy is essential for establishing user trust, technical precision alone does not guarantee business impact. Accurate models will not deliver value until teams learn how to properly leverage the new technology to improve business processes.
- Organizational Silos: When data scientists, IT departments, and business units work in isolation, AI tools often fall short of meeting business stakeholder expectations. When stakeholders are not aligned, technical solutions often fail to address the specific business challenges that drive enterprise ROI.
The Solution: Establishing an AI Center of Excellence (CoE)
To close the execution gap, enterprises require a centralized structure to manage the complexity of AI deployment. The AI Center of Excellence (CoE) is the primary mechanism for this "Operating Model Fix."High-functioning AI CoEs are typically cross-functional teams composed of technical leaders, business strategists, and legal/compliance stakeholders. The CoE mandate is to bridge the gap between technical potential and business results by providing a unified framework for the entire organization.Strategic Functions of the CoE- Standardization: The CoE defines the enterprise "AI stack," ensuring that different departments aren't using incompatible tools or redundant data sources.
- Governance and Risk Management: Centralized oversight allows the company to manage data privacy, security, guardrails, and bias at scale, rather than leaving these critical risks to individual project teams.
- ROI Prioritization: The CoE acts as a filter, directing resources toward use cases with the highest potential for impact, such as high-volume business process automation.
Scaling ROI Through Intelligent Process Automation
While AI has broad applications, Business Process Automation (BPA) remains one of the strongest use cases for driving immediate and measurable ROI. Traditional automation was limited to rigid, rule-based tasks. AI evolves this into "Cognitive Automation," where systems can interpret unstructured data—such as emails, legal documents, and images—to handle complex enterprise workflows.A mature operating model utilizes AI-driven BPA not just for efficiency, but as a catalyst for end-to-end process transformation. By integrating AI with core systems and data sources, organizations position themselves to enable high-value, autonomous decision-making. To achieve this level of execution, AI tools leverage not only structured enterprise data but also unstructured data that is siloed in spreadsheets, documents, and even emails.Operating Model Dependencies
In addition to the creation of a CoE, leaders must establish core principles to ensure AI can scale.1. Unified Data DefinitionsAI performance is dependent on the quality and consistency of data. Scaling becomes complex when different departments use conflicting definitions for the same metrics. The CoE should shepherd the creation of a "common data language"—a unified set of definitions that ensures the AI interprets information consistently across the organization.2. Active GovernanceScaling AI introduces certain regulatory and reputational risks. Traditional governance models, which often review projects after development is complete, are too slow for the AI lifecycle. Instead, governance must be "baked in" to the development process. The CoE ensures that AI solutions include checks for bias, data lineage, guardrails, and compliance from the start.3. End-to-End Workflow RedesignEnterprises often make the mistake of "paving the cow path"—using AI to automate a process that is already broken or inefficient. To scale successfully, business leaders must be willing to reimagine workflows from the ground up. This often means transforming a process with many manual steps to one with automated tasks, utilizing a human-in-the-loop approach for orchestration and decision-making.Case Studies: Success vs. Operational Failure
Success: Air India’s Integration StrategyAir India transitioned from simple chatbots to a generative AI agent integrated into core operational systems. By centralizing data access and securing an executive mandate for the agent to execute transactional tasks—such as flight bookings and seat reassignments—the airline automated 97% of nearly 4 million interactions. This deep integration allowed the carrier to maintain service levels even as passenger traffic doubled.Failure: Zillow’s Valuation ModelZillow’s “Offers” project failed because it scaled advanced pricing models without a robust operational governance framework. While the algorithms were technically sophisticated, they lacked the human-in-the-loop overrides necessary to adjust for sudden market volatility. The lack of centralized oversight led to systematic overpayment for properties, resulting in losses exceeding $880 million before the program was terminated.5 Actionable Tips for Business Leaders
- Charter a Balanced CoE: Your Center of Excellence must have equal representation from the business and technical sides. If it is purely an IT function, it may fail to deliver significant business value.
- Prioritize High-Impact, Low-Complexity Quick Wins: Instead of solving the most difficult problems immediately, start with projects that offer measurable impact with less effort and shorter timelines. These successful pilots build organizational confidence and provide the internal buy-in necessary to fund and tackle larger initiatives.
- Establish Unified Data Standards: The CoE should be a catalyst for creating a common data language across all departments. Standardization is important for ensuring that AI-driven results are both trustworthy and operationally sound.
- Automation and Transformation: View AI projects as opportunities to move beyond the automation of existing tasks. Use these initiatives to redesign business processes—improving efficiency, quality, and the measurability of outcomes.
- Cultivate AI Fluency in Management: Scaling AI requires leaders who understand the value of leveraging AI agents across the organization. Invest in training for middle management to ensure they can effectively oversee AI-augmented teams.
Conclusion: Adapting the Organization to the Technology
The AI Execution Gap is not a technical problem; it is a management challenge. Enterprises that successfully scale AI do so when they stop viewing it as a standalone tool and start treating it as a core operational discipline. By establishing a Center of Excellence and improving the operating model, leaders can finally turn AI's potential into a scalable, high-ROI reality.Is your organization ready to move from pilot to production? Contact OSF to learn about our AI Strategy Assessment.Author: John Coniglio
John Coniglio is a Senior Consultant in OSF’s Digital Strategy group. As a digital commerce and marketing technology expert, John has 20+ years of experience helping companies drive incremental revenue and improve operations with strategic technology initiatives. John helps OSF’s clients create strategic plans that are aligned with their business goals.
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