The growing investment in artificial intelligence — particularly generative AI — across all industries underscores a universal aspiration to use technology for operational advancements and competitive advantage. The potential of AI is highlighted by International Data Corporation (IDC) projections that anticipate global expenditure doubling to reach $631 billion by 2028. But there is a gap between the AI future we imagine and the actual AI tools we have rolled out.
A recent analysis surveying 100 senior AI and data executives across North American organizations provides important insights into this bottleneck. The findings in “AI’s Time-to-Market Quagmire: Why Enterprises Struggle to Scale AI Innovation” consistently point toward extended timelines that hindered progress in expanding AI initiatives beyond pilot projects — the underutilization of AI lifecycle automation and inconsistent robust governance frameworks. An astounding 56% of the enterprises interviewed said it took between 6 and 18 months to get a generative AI project into production.
Inefficiencies in managing the flow of AI projects, a lack of clear ownership across development stages, and a flawed perception that governance stifles, rather than supports, widespread AI adoption are identified as key obstacles. These factors extend the time needed to bring AI concepts to life and limit the sheer volume of AI efforts enterprises can effectively oversee with the necessary reliability, accountability, and speed.
Let’s look at the main issues holding companies back from scaling their AI projects: integrating fragmented or multiple systems, governance that feels too big to tackle, no governance policy or framework currently in place, replacing manual processes so they can be scaled, and regulatory or compliance hurdles. By examining these dynamics, organizations can better understand how to construct agile, automated AI pipelines that accelerate innovation, effectively manage inherent risks, and unleash the full spectrum of value promised by their AI investments.
From Conception and Fruition: An Operational Bottleneck
There is a disconnect between generating AI ideas and successfully bringing them to market. Enterprises report a considerable backlog of generative AI use cases in the initial planning stages, but most only have a handful of initiatives in production today. This contrast underscores an appetite for exploring AI possibilities with the challenge of bringing AI to market at scale and realizing its full potential.
Getting AI projects off the ground is a real challenge. As mentioned above, for more than half of these generative AI projects, it takes about 6 to 18 months from the first idea to actually launching it. Although this timeframe is generally shorter than that seen for traditional AI and machine learning projects, it still creates a sizable delay in a rapidly evolving technological landscape. Time-to-market can postpone the ROI and introduce the risk of solutions becoming less relevant as business needs and technological capabilities shift. The capacity for rapid iteration of AI solutions is crucial for maintaining agility and securing a competitive advantage.
I’ve recently observed increasing pressure on leadership to demonstrate tangible returns on AI investments. There is a need to demonstrate foresight, drive organizational change, and establish a unique market position that necessitates a scalable pathway to operationalize AI — delays undermine objectives and contribute to a perception of AI initiatives as resource-intensive and slow to yield concrete business outcomes.
The Automation Advantage: A New Engine for the AI Lifecycle
All of this underscores the need for the strategic adoption and comprehensive implementation of AI lifecycle automation as the linchpin for overcoming the challenges of scaling AI. The reliance on manual and disjointed methodologies for managing the AI pipeline is demonstrably inadequate for handling the growing volume and complexity of AI endeavors.
Let’s consider the initial phase of the AI lifecycle: the intake of potential use cases. The survey reveals a common reliance on multiple, often manual, systems for managing these proposals. A lack of a unified approach, with some still using basic tools like spreadsheets and email, introduces inefficiencies, elevates the risk of data loss, and complicates the crucial processes of prioritization and tracking. AI lifecycle automation platforms offer a centralized, standardized framework for submitting, evaluating, and prioritizing AI ideas, reducing administrative overhead and accelerating the initial stages of the AI journey.
The later development and validation stages are often hindered by manual tasks associated with data preparation, model construction, rigorous testing, and deployment to production. These manual interventions are time-intensive, prone to errors, and create bottlenecks that slow the speed and efficiency of transitioning AI models into production environments. AI lifecycle automation tools can coordinate these intricate workflows, automating routine tasks, ensuring consistency, and substantially accelerating the model lifecycle.
Automation takes centerstage when addressing the persistent challenge of model documentation. The often-reluctant engagement of data scientists in the labor-intensive process of manual documentation can lead to complications in later stages, particularly during critical audits. However, AI lifecycle automation platforms can seamlessly integrate documentation processes directly into the development workflow, automatically capturing essential information and alleviating the documentation burden on data science teams, providing a reassuring solution to this challenge.
Scalable Governance: Automation as the Essential Infrastructure
The research also underscores the critical symbiotic relationship between AI lifecycle automation and effective governance. Many organizations view AI governance as an impediment to the rapid pace of innovation. The reality is that achieving robust and scalable governance without the foundation of automation is a near impossibility, especially when managing an increasing number of AI models and diverse applications.
Manual governance procedures, often relying on disparate spreadsheets, email threads, and isolated systems, lack the inherent scalability required to operationalize AI enterprise-wide. The consistent enforcement of policies across numerous teams and a growing portfolio of AI initiatives becomes an overwhelming and error-prone undertaking. AI lifecycle automation provides the essential infrastructure to embed governance controls seamlessly throughout the entire AI pipeline. This includes the automated enforcement of predefined policies, continuous risk monitoring, proactive bias detection, and adherence to compliance requirements at various critical junctures across the lifecycle.
Expert perspectives stress that getting effective AI governance at scale depends on applying AI lifecycle automation to coordinate the teams and systems involved in bringing AI to market while ensuring consistent adherence to internal guidelines and external regulatory mandates. By automating governance processes, organizations can establish consistent oversight, reduce the potential for errors and non-compliance, and cultivate greater trust in their AI initiatives. This, in turn, fosters a more supportive environment for the widespread and confident scaling of AI innovation.
The survey’s findings regarding AI assurance further illustrate the crucial role of automation in achieving scalable governance. The prevalence of decentralized assurance practices, lacking comprehensive enterprise-level oversight, introduces the risk of duplicated efforts and inconsistent reporting. AI lifecycle automation platforms can establish a centralized framework for AI assurance, ensuring standardized testing, validation, and continuous monitoring across all AI initiatives, thereby providing a holistic and accurate view of risk and performance at the organizational level.
Automation is the Strategic Enabler for AI Expansion
AI lifecycle automation transcends the notion of mere efficiency enhancement; it’s fundamental for achieving truly scalable AI innovation within the enterprise. The current difficulties experienced by organizations in translating AI ambitions into widespread, impactful solutions are deeply rooted in the limitations of manual, fragmented, and inconsistently governed AI workflows.
By strategically implementing AI lifecycle automation, organizations can:
- Optimize their AI pipeline
- Establish governance at scale
- Enhance operational efficiency and accuracy
- Improve cross-functional collaboration and enhance visibility
- Facilitate engineer’s continuous monitoring and iterative improvement
Realizing the full transformative potential of AI at an enterprise scale is linked to the strategic and comprehensive adoption of AI lifecycle automation. Building automated, consistently governed AI pipelines is the best way to overcome an innovation stalemate, ultimately reshaping operations and securing a sustainable competitive advantage in the evolving technological landscape. The future of impactful AI innovation hinges on the pervasive integration of automation throughout the entire AI lifecycle.
Pete Foley is CEO of ModelOp, the leading AI lifecycle automation and governance software, purpose-built for enterprises. It enables organizations to bring all their AI initiatives—from GenAI and ML to regression models—to market faster, at scale, and with the confidence of end-to-end control, oversight, and value realization.
