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Why 90% of AI Projects Fail to Hit ROI Targets (And What to Do About It)

Why 90% of AI Projects fail to hit ROI targets | Wallaroo.AI
Why 90% of AI Projects Fail to Hit ROI Targets (And What to Do About It) | Wallaroo.AI blog

Transforming a model into a fully functioning component of a production system is complex, and not every project makes it across the finish line. The stark reality is that about 90% of AI projects miss their ROI targets, and only 10% of organizations today have AI projects currently deployed and operational. This begs the question: Why do so many AI projects struggle to make it to production, let alone achieve ROI?

This is a trillion dollar question. After all, an AI initiative that doesn’t contribute to the bottom line is hard to justify.  

Current State of MLOps: Challenges in AI Deployments

AI Production Pipeline

While MLOps has made significant strides in streamlining the deployment of AI models, several challenges persist, as shown by a recent CDO survey:

  • Toolstack Complexity & Integration Challenges: AI deployments involve piecing together multiple tools, leading to a complex and often clunky toolchain. This complexity not only adds to the deployment time and cost but also complicates maintaining consistency throughout the AI lifecycle. This complexity also makes it difficult to maintain consistency across different stages of the AI lifecycle. The effort required to integrate tools is a major challenge for 51% of CDOs.
  • Lack of Observability & Monitoring: Once a model leaves the development environment, its performance can be unpredictable due to the new production conditions. The lack of advanced monitoring and observability tools, as indicated by 60% of CDOs, hampers the ability to track and quickly rectify performance issues, making it challenging to maintain model accuracy and reliability.
  • Lack of Resources: Deploying and maintaining AI models in production often requires more resources—both computational and human—than anticipated. The competitive job market makes it difficult to hire and retain the needed expertise, with 64% of organizations facing challenges in recruiting essential personnel for AI projects.
  • Skills & Communication Gap: A significant barrier to successful deployment is the disconnect between model developers and deployment teams. Nearly half (47%) of organizations report difficulties bridging this gap, underscoring the need for improved communication and shared understanding to ensure smooth transitions from development to deployment.
  • Managing Time & Costs: Deploying AI models efficiently is crucial for achieving quick value realization and managing costs effectively. Yet, 38% of CDOs are worried about the time it takes for models to generate value, affected by factors such as model complexity and infrastructure choices. Additionally, 46% face challenges in cost management.

How MLOps Challenges Affect ROI

The challenges encountered by organizations in the deployment phase in MLOps can significantly impact the return on investment in several key ways:

  1. Increased Costs: Deployment challenges often lead to delays and require additional resources to troubleshoot and fix issues, increasing the overall project cost, stemming from delays, the need for additional debugging, and optimization efforts. Efficient deployment strategies, which minimize rework and streamline the integration process, are crucial for keeping costs in check.
  2. Delayed Time to Market: In a fast-paced business environment, getting models from concept to production quickly can be a competitive advantage. Deployment bottlenecks, such as compatibility issues with production environments or difficulties in model optimization, can slow down this process. This delay not only impacts the project’s ROI by shortening the window for value generation but can also result in missed market opportunities.
  3. Suboptimal Model Performance: Deployment issues might force teams to make compromises on model complexity or data processing capabilities to fit the constraints of the production environment. This can result in models that perform suboptimally compared to their initial design, leading to less accurate predictions or insights and, consequently, a lower impact on business outcomes.
  4. Operational Inefficiencies: Especially if manual interventions are required for deployment or maintenance, inefficiency can absorb engineering resources, detracting from the time available for innovation or enhancement of existing models. Automation and MLOps practices are key strategies for mitigating these inefficiencies, enabling scalable and sustainable operations.
  5. Misalignment with Business Objectives: It’s essential for AI initiatives to align with the strategic objectives of the organization. Without this alignment, even technically successful projects may not deliver the expected business value, leading to perceived failures. If deployment complexities lead to significant changes in how a model functions in a production environment or excessive delays, the final product may no longer align with the original business goals, reducing its value to the organization.
  6. Scalability Challenges: As the use of AI applications increases, the ability to scale models to meet growing demand without compromising performance is critical. This requires scalable infrastructure and efficient model design to ensure that systems remain responsive and reliable under increased loads.

Operationalizing AI for Maximum ROI 

The bottom line: transition from developing models in a lab setting to deploying them in real-world production environments is complex. This process requires the seamless coordination of different skills and the integration of various tools, many of which may not be specifically designed for AI applications. 

Enter AI Production platforms, built-for-purpose tools that specifically target the operational aspects of deploying AI models. Consolidating tools and processes into a single platform reduces complexity and streamlines the deployment process in multiple ways:

  • Accelerating Time to Market: Fast-tracks  models from the drawing board to production, drastically cutting down the time to market.
  • Improving Cost Efficiency: Merges various functionalities into one platform, reducing both the direct and indirect costs associated with managing standalone tools.
  • Boosting Revenue: By enabling faster deployment of AI models, it empowers businesses to capitalize on market opportunities and to iterate more quickly. 
  • Simplified Process: Centralizes tools and processes, cutting through the complexity of deploying AI models and making management more straightforward.
  • Scalability and Performance: Leverages distributed computing to reduce infrastructure demands, ensuring quick and efficient data processing.
  • Real-time Optimization: Equips teams with advanced tools for continuous monitoring and optimization, ensuring models perform at their peak.
  • Seamless Ecosystem Integration: Ensures compatibility with existing MLOps frameworks, facilitating smooth integration into current workflows.
  • Enhanced Collaboration: Promotes better communication across teams, aligning data scientists, developers, and business stakeholders towards shared objectives.

Focusing On The Operational Side of AI: The Key ROI for AI Projects

AI production platforms that follow the LowOps approach aim to simplify the entire AI lifecycle, from development to deployment and beyond, making it straightforward and efficient. With operational complexities handled by the Low Ops AI platform, organizations can focus more on strategic business objectives rather than getting bogged down in technical details of AI deployments. This focus helps ensure that AI projects are directly contributing to business value, enhancing overall ROI.

For CDOs looking to drive more value from their AI initiatives, embracing an operational to AI approach could be the game-changer. By mitigating common deployment challenges, AI production platforms provide the necessary edge to transition AI initiatives from the the lab into operational solutions that drive real business impact.

Reach out if your team is looking to start getting models into production, or if you already have a few models in production but are looking to scale.

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