Most enterprises are stuck in proof-of-concept purgatory with generative AI—only 10% of projects reach production. Survey data from enterprise leaders reveals the real barriers: unclear ROI, fragmented data, and leadership hesitation. This research-backed guide shows how to bridge the gap between AI experimentation and enterprise transformation.
Generative AI promises transformative improvements in efficiency, customer engagement, and innovation. Yet most organizations struggle to move beyond pilots, with common barriers stalling progress at every stage.
Insights derived from survey of enterprise leaders attending EIS webinars and participating in LinkedIn professional networks. Respondents represent business and technology leaders actively exploring AI adoption.
Only 10% of AI projects typically advance to production, with another 11-25% seeing partial success. This slow progression indicates fundamental challenges in scaling AI initiatives.
Survey insight: Most organizations are in exploration/proof-of-concept phase, reflecting high interest but uncertainty about large-scale implementation.
Distribution breakdown:
Organizations remain stuck in pilot mode due to technical readiness gaps, leadership hesitation, and operational misalignment. They lack foundational elements needed to scale.
The small segment that has transitioned to deployment focuses on achieving measurable outcomes through dedicated budgets, leadership buy-in, and clear scaling strategies.
Survey finding: Top barrier cited by respondents.
The challenge: Difficulty quantifying GenAI benefits, especially for long-term, cross-functional impacts. Traditional ROI frameworks don't capture AI's compounding value.
The impact: Projects can't secure funding or executive support without clear financial justification.
Survey finding: Nearly equal to ROI concerns as primary obstacle.
The challenge: Poor data governance and fragmented systems hinder AI performance and scalability. AI amplifies data quality problems rather than solving them.
The impact: Models trained on bad data produce unreliable outputs, eroding trust and stalling adoption.
Survey finding: Third most cited barrier.
The challenge: Lack of skilled personnel slows both experimentation and deployment. Organizations can't find or afford talent with AI, data science, and domain expertise.
The impact: Projects depend on expensive consultants or move too slowly to maintain momentum.
Survey finding: Significant factor in project stagnation.
The challenge: Limited executive sponsorship creates roadblocks for organizational alignment, resource allocation, and cross-functional collaboration.
The impact: AI remains siloed in IT or innovation groups without business ownership.
Survey finding: Competes with expertise and leadership as key barrier.
The challenge: AI initiatives compete for funding with established priorities. Without clear ROI, budgets get cut when economic uncertainty increases.
The impact: Start-stop cycles damage momentum and waste initial investments.
Data security and regulatory compliance concerns temper enthusiasm, especially for customer-facing or sensitive applications.
Survey finding: Many organizations need general AI technology overviews, especially for management.
The gap: Leadership doesn't understand AI capabilities, limitations, or appropriate applications. This creates unrealistic expectations or excessive caution.
Survey finding: Significant number need help choosing right vendors.
The need: Trusted advisory role to guide decision-making through crowded, confusing market with overlapping vendor claims.
Survey finding: Training for AI-related tasks is critical need.
The recognition: Workforce readiness determines adoption success. Technical skills matter, but so do change management and new ways of working.
Survey finding: Organizations need help calculating and justifying ROI.
The requirement: Better financial models, case studies, and frameworks for measuring AI's business impact across time horizons.
Survey findings on Retrieval-Augmented Generation:
Interpretation: RAG recognized as valuable but adoption remains early-stage. Customer-facing use requires highest confidence.
Survey distribution:
Trend: Moving from exploration to selective internal deployment, with customer-facing applications still rare.
Concerns over data privacy, compliance, and output quality prevent aggressive scaling. Leadership support varies—some invest heavily, others adopt wait-and-see approach.
Enterprises delaying GenAI adoption risk losing ground to competitors. Those embracing it without addressing foundations face operational or reputational risks.
Survey correlation: Organizations with clear financial commitments see faster, more sustainable progress.
Why it matters: Budget signals executive commitment, enables proper staffing, and allows multi-quarter planning horizons AI requires.
Survey correlation: Executive support enables departmental alignment and resource allocation.
Why it matters: AI crosses organizational boundaries. Without top-down support, cross-functional collaboration fails.
Survey correlation: High-quality, well-managed data systems underpin effective implementations.
Why it matters: AI performance ceiling is data quality. No amount of sophisticated modeling compensates for bad data.
Survey finding: Organizations stuck in PoC phase lack frameworks for moving successful pilots to production.
Why it matters: Pilots prove possibility. Scaling requires different disciplines: governance, standardization, change management.
Before deploying GenAI, insurance organization implemented knowledge engineering framework to structure and tag content systematically, ensuring AI operated with reliable "ground truth."
Leveraging structured content, company introduced GenAI to enhance customer-facing chatbots, delivering contextually accurate responses and reducing manual help desk reliance.
Ongoing governance and performance tracking ensured system effectiveness and adaptability. Regular content repository updates kept AI outputs aligned with evolving business needs.
Strong leadership support and dedicated budget fostered culture of innovation and continuous improvement.
30% reduction in manual help desk operations, freeing resources for complex, high-value customer issues. Scalable, sustainable AI capability achieved through foundational investment.
Based on ROI barrier findings:
Based on data quality barrier findings:
Based on expertise gap findings:
Based on leadership support findings:
Based on compliance concern findings:
Survey data shows enterprises embracing GenAI strategically—addressing foundational barriers, prioritizing ROI clarity, engaging leadership—will be positioned to capture market share, reduce costs, and drive innovation.
Moving from pilot purgatory to production success requires systematic approach: build business case, fix data foundation, engage leadership, develop workforce, govern continuously, measure relentlessly.
AI systems capable of generating human-like text, images, code, or other content based on training data and user prompts.
AI architecture combining generative models with retrieval systems, grounding outputs in specific enterprise content rather than relying solely on model training.
Small-scale pilot project testing AI feasibility and value before committing to full implementation.
AI system operating at scale serving real business processes or customers, not experimental or pilot use.
Policies, processes, and ownership structures ensuring data quality, security, compliance, and appropriate use.
Financial measure comparing benefits gained from AI implementation to costs incurred, including both tangible and strategic returns.
Executive-level support and sponsorship providing resources, organizational alignment, and strategic direction for AI initiatives.
Employee skills, training, and cultural preparedness to work effectively with AI systems and adapt to new workflows.
Framework and processes for expanding successful AI pilots to broader organizational use while maintaining quality and governance.
Ongoing processes ensuring AI systems adhere to regulatory requirements, ethical guidelines, and corporate policies.
Discipline of structuring, tagging, and organizing content to make it machine-readable and usable by AI systems.
Coordination between different departments (IT, business units, legal, compliance) necessary for successful enterprise AI implementation.
Earley Information Science is a specialized professional services firm supporting measurable business outcomes by organizing data—making it findable, usable, and valuable.
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