A staggering 90% of companies struggle to scale AI across their enterprises, and about half of AI projects fail.
Businesses must manage vast and diverse datasets, ensure that their infrastructure can handle the computational demands of advanced AI models, and seamlessly integrate AI solutions into existing workflows and systems. Additionally, the complexity of AI, ML, and Data Science models grows as they scale, necessitating more sophisticated optimization techniques.
To overcome these challenges and achieve successful AI scaling, enterprises need a comprehensive strategy. At N-iX, we understand the intricacies involved in scaling AI across enterprises. With experience in delivering over 60 data projects and a team of more than 200 data experts, we are well-equipped to provide scalable AI solutions for various industries, including logistics, e-commerce, finance, and more.
Importance of scaling AI
According to Accenture, companies that deploy AI at scale achieve approximately three times higher returns on their AI investments than those at the PoC stage. However, Gartner predicts that by 2025, at least 30% of generative AI projects will be abandoned after the PoC phase due to poor data quality, inadequate risk controls, rising costs, and unclear business value.
AI scaling refers to expanding the capabilities and deployment of Artificial Intelligence systems from initial proofs of concept (PoCs) or small-scale implementations to widespread, enterprise-level applications. This involves not only increasing the computational power and data used by AI models but also ensuring that these models can reliably and efficiently handle larger volumes of data, more complex tasks, and a broader range of use cases.
While a PoC might demonstrate the feasibility of an AI application, AI scaling ensures that the benefits such as increased efficiency, enhanced customer experiences, and better decision-making, are realized across the organization.
Success of scaling AI involves many parts in data management, data science, and business process management, often categorized under machine learning operations (MLOps). Enlarging AI from a Proof of Concept to full-scale deployment involves a comprehensive and structured approach. At N-iX, we typically follow three strategic ways to scale AI:
- Prioritize AI use cases based on potential business value, forming specialized units to accelerate the scaling of these high-impact projects;
- Foster skill development and build capabilities across teams, assigning dedicated groups to develop and enhance the necessary expertise;
- Implement AI use cases through agile build and validation cycles, collect early end-user feedback, and scale successful minimum viable products.
Let's explore how the N-iX team proceeds with one of the approaches.
Stages of AI scaling
Assess current AI maturity and infrastructure
N-iX begins by conducting a comprehensive assessment of your present-day AI capabilities and infrastructure. This involves evaluating existing AI models, data quality, computational resources, and the overall AI strategy to understand your organization's AI maturity. Our engineers cover all aspects of AI consulting services, from deployment and data collection to model implementation and performance monitoring.
We identify gaps in data quality, model performance, and computational resources. Poor data quality can lead to inaccurate model predictions, while inadequate computational resources hinder the ability to efficiently process large datasets or complex models.
For example, consider a current model like Meta's Llama 2, trained with 10 billion parameters from various sources such as social media and web data. If the assessment reveals that increasing the dataset to 100 billion tokens could significantly reduce error rates, but existing computational resources are insufficient to handle this volume, addressing the computational gap becomes a priority.
Another example is Qualcomm's approach with sub-10 billion parameter models running on edge devices. These models, while smaller, provide useful outputs and demonstrate the importance of optimizing both data and computational resources. Techniques like knowledge distillation, which transfer knowledge from a large model to a smaller one, help maintain accuracy while reducing computational demands, illustrating the critical balance needed for effective AI scaling.
Select and prototype use cases
To effectively scale AI within an enterprise, N-iX starts with a comprehensive needs assessment. Our AI experts collaborate with various departments to pinpoint critical areas where AI can add significant value by addressing pain points and opportunities.
Each use case is evaluated through a business impact analysis, considering metrics like cost savings, revenue generation, efficiency improvements, and customer satisfaction. We rank use cases offering the highest value by quantifying their impact and understanding broader implications.
For example, if an enterprise identifies customer service as a critical area, we could prototype an AI chatbot to handle common inquiries. A business impact analysis might show that automating these responses could reduce customer service costs by 30% and improve customer satisfaction scores by 20%. Prototyping this use case with a clear set of metrics and expected outcomes provides a tangible starting point for scaling AI solutions across the organization.
Create a data strategy
A robust data strategy starts with integrating data from various sources into a centralized data ecosystem. Our data team implements comprehensive data governance frameworks. We define data ownership, set access controls, and establish protocols for data lifecycle management.
At N-iX, we design data architecture to handle large volumes of structured and unstructured data efficiently. Employing advanced analytics and data lake technologies facilitates real-time data processing and insights generation. Additionally, leveraging automated data pipelines streamlines data ingestion, transformation, and storage processes, enabling faster and more accurate data availability for AI applications.
We implement a scalable metadata management system to enhance the data strategy's robustness. This system catalogs and manages data assets, providing comprehensive visibility into data lineage, usage, and quality metrics. Such a system aids in data discovery and governance and improves data reliability by ensuring that any changes in data are tracked and documented.
Enhance data management and governance
N-iX begins by creating a unified data environment to ensure that all data is readily accessible and efficiently used for various AI applications. This centralization facilitates better data integration and consistency, guaranteeing that AI models are trained on the most comprehensive and up-to-date information.
Our approach to data governance involves setting standards and procedures for data handling. We define roles and responsibilities for data management, establish access controls, and regularly audit data usage to detect and mitigate any risks.
We benefit from data lakes by simplifying the storage of vast amounts of raw data in their native format, making it easier to handle large-scale datasets and enabling flexible data processing. In contrast, data warehouses are optimized for query performance, providing valuable insights through complex data analysis. Together, these technologies help organizations to process and analyze data more efficiently.
Optimize and standardize AI models
Creating AI models can be compared to manufacturing: while the prototype is bespoke, scaling up production requires a standardized and optimized process. Many companies struggle with standardizing AI processes, often reinventing the wheel with each model deployment. This can lead to inefficiency and significant issues once research models are moved into production.
We focus on developing a repeatable method for building models and a well-defined operational process. Our team develops standardized protocols for model development, deployment, and monitoring to ensure consistency across AI projects. This includes best practices for data preprocessing, model training, evaluation, deployment, and robust monitoring systems to track model performance over time.
Using MLOps, we automate and streamline the AI lifecycle. MLOps integrates DevOps principles with machine learning, providing a framework for continuous integration and delivery of AI models. Data engineers help to automate repetitive tasks, reduce errors, and ensure efficient model deployment and maintenance. For instance, employing standard libraries for AI model validation encourages consistent testing and validation.
We deploy techniques such as hyperparameter tuning, model pruning, and quantization to enhance model performance while reducing computational requirements. Leveraging automated Machine Learning tools helps systematically optimize models, enabling data scientists to focus on more complex tasks.
WHITE PAPER
AIOps vs. MLOps: Explore the differences between approaches and discover their applications in our guide!
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Invest in scalable AI infrastructure
We recommend adopting a hybrid cloud strategy, combining on-premises resources with cloud-based solutions to achieve scalability, flexibility, and cost-efficiency. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure offer scalable computing power and storage solutions that can adapt to the dynamic needs of AI workloads. Implementing edge computing solutions can further enhance real-time data processing capabilities, particularly for applications requiring low latency.
To train large-scale AI models, we implement high-performance computing environments. Advanced GPUs and TPUs to accelerate the training process and efficiently handle complex computations. Furthermore, utilizing containerization and orchestration tools like Docker and Kubernetes can streamline the deployment and management of AI models across diverse environments.
Сhallenges of AI scaling
Data quality and integration hurdles
Many companies struggle with data that is incomplete, inconsistent, or inaccurate. Data silos within different departments further complicate this issue, making it difficult to create a unified data set. Additionally, integrating data from various sources and formats requires sophisticated data processing and cleaning techniques. AI models can produce unreliable results without addressing these data quality issues, leading to potentially costly errors.
Our solution: N-iX implements robust data governance policies and advanced data processing techniques for data variety (structured and unstructured data), volume (large datasets), and velocity to ensure data integrity, consistency, and quality across all sources.
Infrastructure and computational resources limitations
Advanced AI models, especially those based on deep learning, demand high-performance computing power for training and inference. Many organizations face limitations with their existing infrastructure, which may not be capable of handling the large volumes of data and complex computations required by AI models.
Our solution: N-iX leverages scalable cloud solutions and high-performance computing resources, providing seamless integration with legacy systems.
Scope management
One of the most critical challenges in generative AI scaling projects is defining and managing the scope effectively. As AI initiatives transition from small-scale experiments to broader implementations, the scope tends to expand, encompassing more variables, datasets, and integration points. This growth can lead to scope creep, where projects continuously grow beyond their original parameters.
Our solution: N-iX employs a structured project management approach to clearly define project scopes, manage variables, and control scope creep.
Time constraints
Successful AI projects can span from three to 36 months, depending on scope and complexity. This extended timeframe includes phases like model selection, deployment, and ongoing monitoring in controlled settings. Data provisioning for large-scale AI systems significantly contributes to project timelines.
Our solution: We accelerate AI project timelines by leveraging open-source tools, libraries, and cloud services to streamline data integration, model deployment, and performance monitoring.
Change management and organizational resistance
Seamless integration of AI models into workflows requires significant changes to existing systems and processes. Resistance to change and limited AI literacy can impede these efforts. Effective integration necessitates a comprehensive change management strategy, including training programs, clear communication of benefits, and ongoing support.
Our solution: N-iX provides comprehensive employee training programs and continuous support for efficient generative AI scaling.
Best practices of scaling AI from N-iX experience
Adopt AI everywhere
To achieve comprehensive AI integration, incorporate AI into every aspect of your organization, including customer service, operations, product development, and marketing. This widespread implementation ensures that AI-driven insights and efficiencies enhance all business areas, leading to overall improvement and fostering innovation.
For retail companies, we can integrate AI models into
- customer service through a chatbot that handles common inquiries;
- operations by optimizing supply chain logistics using predictive analytics;
- product development by leveraging AI for predictive maintenance of manufacturing equipment;
- marketing by personalizing customer interactions based on behavioral data.
Acquire data science talent
Building a robust AI capability starts with acquiring skilled AI and data science professionals. These experts bring the necessary knowledge to develop, implement, and scale AI solutions effectively, ensuring your projects are both innovative and technically sound.
A healthcare organization hires data scientists proficient in natural language processing to develop AI-driven solutions for analyzing patient records and predicting healthcare outcomes, thereby improving decision-making processes and patient care - simplifying diagnosis procedures.
Dive headlong into data science experiments
Forwardly tackling data science projects can encourage a culture of experimentation and innovation. This approach helps uncover new insights and applications, fostering an environment where AI can evolve and adapt to meet emerging business needs.
A fintech startup can conduct data science experiments to analyze transaction data and identify patterns for fraud detection, leading to the development of an AI-powered fraud detection system that significantly reduces financial losses.
Focus on data users, add engineering to data science
Prioritize the needs of data users by integrating engineering principles into your data science efforts. This combination enhances the usability of AI scaling solutions, making them more accessible and impactful across the organization.
For example, a manufacturing company such as Bosch combines data science with engineering principles to develop AI-driven predictive maintenance solutions for machinery. By integrating real-time sensor data with predictive models, they optimize maintenance schedules, reduce downtime, and extend equipment lifespan.
Adopt AI wherever it adds value
Implement AI in areas with the most significant impact, such as automating routine tasks, optimizing processes, or enhancing decision-making. By focusing on high-value applications, you can demonstrate tangible benefits and drive wider acceptance of AI within the enterprise.
A logistics company like Uber deploys AI for route optimization, reducing fuel costs and delivery times. They also use AI-powered demand forecasting to optimize inventory management, ensuring products are stocked efficiently across their distribution network.
N-iX success stories
N-iX has scaled AI and enhanced data science capabilities for a Fortune 500 industrial supply company by migrating its on-premise data solutions to a scalable, cloud-based infrastructure. By leveraging AWS and Snowflake, N-iX developed a unified data platform that integrated over 100 data sources, enabling efficient data management and advanced predictive analytics. This transformation optimized the client's data operations and provided the flexibility to scale AI initiatives seamlessly, ensuring the infrastructure could handle growing data volumes and complex analytics workloads.
Another of our clients, a rapidly growing brokerage firm managing billions of dollars in assets, faced the challenge of streamlining numerous routine tasks slowing down their employees. N-iX designed and set up an internal web portal powered by generative AI, enabling employees to perform various tasks more efficiently. The solution included Single Sign-On based on MS Azure Active Directory for user authentication and a custom .NET-based API Gateway for orchestrating business workflows.
In addition to these achievements, N-iX delivered the following:
- Managing the machine learning lifecycle and providing ongoing support;
- Integrating advanced language models into client operations;
- Implementing multiple layers of protection, including VPN connection, network firewall, and data encryption;
- Using GPUs and analyzing various large language models to fit client needs while reducing infrastructure expenses.
Final thoughts
Even small performance improvements from scaling may justify the rising costs, as they could unlock significant or risky capabilities that warrant policy intervention.
Scaling generative AI is a complex and multifaceted process that demands a strategic and structured approach. While only 36% of companies have successfully moved an ML model beyond the pilot stage, the journey to scaling AI involves persevering through challenging phases. Many organizations initially see only marginal gains from early AI efforts, yet the most successful ones understand that real breakthroughs in AI emerge with persistence and strategic execution.
At N-iX, we understand the complexities of scaling AI and have a proven track record of helping businesses successfully navigate this process. Our team of experts can provide the strategic guidance and technical expertise needed to scale your AI efforts effectively and efficiently. If you're facing challenges with scaling, we're here to help.
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