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What is Scaling AI? What are the benefits of scaling AI? What does scaling AI require? What are the key technologies in scaling AI? What are the challenges in scaling AI? How can AWS support your scaling AI efforts?

What is Scaling AI?

Scaling AI is increasing AI utilization and scope across all aspects of an organization's operations to maximize business value. Most organizations start with a few AI projects focused on solving specific problems. Scaling AI moves beyond projects to integrating AI both widely and deeply into an organization’s core service, product, or business processes. 

This process requires enhanced technical capabilities—you must develop and train different AI models with diverse datasets, and then deploy them systematically for change management and bug fixing. Apart from solving technical challenges, AI scaling also requires a mindset and process shift to drive innovation in every aspect.

What are the benefits of scaling AI?

Scaling AI means shifting from experimental to applied artificial intelligence. It has broad enterprise applications and can disrupt industries. It is a game-changer that fundamentally shifts the competitive landscape. Organizations can deliver more value at less cost, gaining a competitive edge in their sectors. We outline some key benefits below.

New revenue sources

AI systems are already contributing to product and service improvements. For instance, generative AI technologies are being used to speed up product design, and chatbots are changing how customers access and receive support and services. With that in mind, enterprise-wide AI adoption could drive innovation far beyond this scope. For example, Takenaka Corporation, Japan’s leading construction company, uses AI to develop a Building 4.0 Digital Platform. This lets workers easily find information from construction industry laws to regulations, guidelines, and best practices. The platform improves internal efficiency and creates a new revenue source for the organization.

Enhanced customer satisfaction

Enterprise-wide AI adoption allows organizations to deliver value at every step of the customer journey. From personalized recommendations to faster delivery and real-time communication, organizations can solve customer problems and meet changing customer requirements. For example, FOX, a major media company, is accelerating data insights to deliver AI-driven products that are contextually relevant to consumers, advertisers, and broadcasters in near real-time. Advertisers can use the system to target product placements at specific and relevant video moments—which translates to more value from their relationship with Fox. At the same time, viewers also receive product recommendations that are most relevant to them at the right time.

Reduced wastage

Scaling AI means introducing AI capabilities from customer-facing areas to back and middle office tasks. It can reduce administrative workload, freeing up employees for more creative work and better work-life balance. Similarly, AI systems can also monitor critical processes to identify and remove bottlenecks or choke points. For example, Merck, a research-intensive biopharmaceutical company, has built AI applications for knowledge mining and market research tasks. Their goal is to reduce manual, time-intensive processes that detract from more impactful work all across the pharma value chain.

What does scaling AI require?

Experimenting with one or two AI models differs significantly from running your entire enterprise on AI. Complexities, costs, and other challenges also increase as AI adoption expands. To successfully scale AI, you must invest resources and time in three key areas: people, technologies, and processes.

People

AI projects are usually the domain of data scientists and AI researchers. However, AI at scale requires a broad range of skills—from domain expertise to IT infrastructure management and data engineering. Organizations should invest in creating multi-disciplinary teams that can collaborate for various AI implementations across the enterprise. There are two approaches: pod and department.

Pod

Small teams of machine learning experts, data scientists, and software engineers take up AI product development for specific enterprise departments. Pods can accelerate AI development but also have pitfalls. They may result in knowledge silos and varied collections of different AI technologies and tools being used ad hoc across the enterprise.

Department

A separate AI division or department that prioritizes, oversees, and manages AI development across the organization. This approach requires more upfront costs and may also increase time to adoption. However, it results in more sustainable and systematic AI scaling.

Technology

Scaling AI requires building and deploying hundreds of machine-learning models across various environments. Organizations must introduce technology that efficiently transitions the models from experimentation to production while facilitating ongoing maintenance and productivity. The technology should integrate with the existing IT infrastructure and software development practices. It should support collaboration between data scientists and other stakeholders within the organization.

Processes

AI development is an iterative process that requires constant refinement. Data scientists prepare the data, train and tune the model, and deploy it to production. They monitor the output and performance and repeat the steps to release the next version. The entire process requires standardization to scale efficiently. Organizations must implement machine learning operations (MLOps), a set of practices to automate and standardize processes across the AI lifecycle. Governance of the entire lifecycle is also vital to ensure secure, regulated, and ethical AI development.

What are the key technologies in scaling AI?

Specialized technologies and tools are a must for progress in AI. We give some examples below.

Feature stores

Feature stores facilitate the reuse of features across different ML models. Features are individual measurable properties derived from raw data. They can be simple attributes such as age, income, or click-through rate, or more complex engineered features created through transformations and aggregations.

A feature store organizes and manages these features and their metadata like definitions, computation logic, dependencies, and their usage history. Data scientists and machine learning engineers can reuse, share, and discover features efficiently, reducing duplication of effort.

Code assets

Reusable code assets like libraries, frameworks, and custom codebases increase efficiency. By standardizing certain libraries and frameworks, organizations can ensure that their AI solutions are developed using best practices and are more maintainable over time. Reusable code assets also promote consistency across projects. They reduce repeat work and provide the framework for innovation.

Operational automation

Automations like automated testing and continuous integration/continuous deployment (CI/CD) are invaluable in the AI scaling process. They allow organizations to rapidly iterate on AI models and enhance the agility of their AI implementation. Practices like RAG can be used to enhance the existing training of large language models in generative AI, instead of training new ones from scratch. Streaming data technologies are a must for automating data processing tasks—things like the preparation and analysis for real-time data processing that machine learning operations require.

Cloud computing

Cloud computing and scalable infrastructure offer flexible, scalable resources that can be dynamically allocated to meet the needs of AI workloads. The ability to scale resources up or down based on demand ensures that organizations can efficiently manage costs while meeting AI model performance requirements. For example, you can use high-performance computing (HPC) instances for training complex models and scalable storage solutions for managing large datasets. AWS cloud services also include specialized AI and machine learning tools that can further accelerate development and deployment.

What are the challenges in scaling AI?

Successful AI scaling requires organizations to overcome the following challenges.

Model operationalization

Developed models do not realize their full potential as operational tools for a number of reasons, some of which we list below:

  • Developing a model was largely a one-time process unrelated to real business outcomes.
  • The model hand-off between teams occurs without documentation, process, and structure.
  • The model development process exists in a silo without input from end users, broader organizations, or subject matter experts.
  • Models are deployed individually on legacy systems.

Models backed by static one-time data pulls quickly become stale and inaccurate. Without continuous improvement practices, a model’s performance eventually degrades, or it risks becoming obsolete.

Cultural resistance

Adopting AI at scale requires significant changes in organizational culture and workflows. Resistance to change and lack of understanding of AI capabilities impede the process. Integrating AI into existing business processes and IT systems can also be complex due to compatibility issues or legacy systems. Data teams may struggle to maintain productivity due to increasing complexity, inadequate collaboration across teams, and lack of standardized processes and tools.

Increasing complexity

Operational AI models must remain accurate and effective in changing environments. Ongoing monitoring and maintenance—like regular updates and retraining with new data—is a must. However, as AI models become more sophisticated, they require more computational resources for training and inference. Making changes or fixing bugs becomes more expensive and time-consuming in later iterations.

Regulatory concerns

Ensuring the security and privacy of data and AI models is a challenge. Experimental AI projects have more flexibility in using the organization's data. However, operational success requires meeting all regulatory frameworks applicable to the enterprise. AI development requires careful management to ensure authorized data access at every step. For example, if an unauthorized user asks an AI chatbot a confidential question, it should not reveal confidential information in its answer.

How can AWS support your scaling AI efforts?

AWS can help you at every stage of your AI adoption journey, offering the most comprehensive set of artificial intelligence (AI) services, infrastructure, and implementation resources. You can scale AI faster and more efficiently across the enterprise. For example, you can use:

  • Amazon Bedrock to select, customize, train, and deploy industry-leading foundational models with proprietary data. 
  • Amazon QDeveloper to accelerate software development by generating code, analyzing codebases, debugging issues, and providing architectural guidance based on AWS best practices—all through natural language interactions within your IDE or the AWS Management Console.
  • Amazon Q to get fast, relevant answers to pressing questions, solve problems, and generate content. You can also act using the data and expertise in your company's information repositories, code, and enterprise systems.
  • Amazon SageMaker Jumpstart to accelerate AI development by building, training, and deploying foundational models in a machine learning hub. 

You can also use Sagemaker for MLOps tools to streamline AI development processes. For example:

  • Use SageMaker Experiments to track artifacts related to your model training jobs, like parameters, metrics, and datasets.
  • Configure SageMaker Pipelines to run automatically at regular intervals or when certain events are triggered.
  • Use SageMaker Model Registry to track model versions and metadata—such as use-case grouping and model performance metrics baselines—in a central repository. You can use this information to choose the best model based on your business requirements.

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