Updated: 10 September 2024
Contributors: Matthew Finio, Amanda Downie
Scaling artificial intelligence (AI) for your organization means integrating AI technologies across your business to enhance processes, increase efficiency and drive growth while managing risks and elevating compliance.
Using AI at scale has moved beyond digital native companies to various industries such as manufacturing, finance and healthcare. As companies accelerate their adoption of AI technologies, they are progressing from isolated AI projects to full digital transformation, implementing AI systems across multiple departments and business processes.
Common AI projects include modernizing data collection and management as well as automating and streamlining IT service management (AIOps). In addition, generative AI—AI that can create original content—is transforming high-volume work and boosting productivity. This includes modernizing code, automating workflows and using AI-powered chatbots to reinvent customer experience and service.
AI is most valuable when deeply woven into the fabric of an organization's operations. However, scaling AI presents distinct challenges that go beyond deploying one or two models into production.
As AI implementation expands across an enterprise, the risks and complexities grow, including potential performance degradation and limited visibility into AI model behavior. As generative AI proliferates, data volume continues to expand exponentially. Organizations must take advantage of this data to train, test and refine AI, but they must prioritize governance and security as they do so.
For this reason, organizations committed to AI scaling need to invest in key enablers such as feature stores, code assets and machine learning operations (MLOps). These help to effectively manage AI applications across various business functions.
MLOps aims to establish best practices and tools for rapid, safe and efficient AI development, deployment and adaptability. It is the foundation for successful AI scalability, and requires strategic investments in processes, people and tools to enhance speed-to-market while maintaining control over deployment.
By adopting MLOps, businesses can navigate the challenges of scaling AI and unlock its full potential to drive sustainable, data-driven innovation and growth. Also, using AI platforms such as cloud services and large language models (LLMs) through application programming interfaces (APIs) can democratize access to AI and ease the demand for specialized talent.
Companies must adopt an open and trusted technology architecture, ideally based on a hybrid cloud infrastructure, to scale AI securely across multiple IT environments. This architecture supports AI models that can be used across the organization, promoting secure and efficient collaboration between various business units.
Successful AI scaling requires a holistic enterprise transformation. This means innovating with AI as the primary focus and recognizing that AI impacts—and is fundamental to—the entire business, including product innovation, business operations, technical operations, as well as people and culture.
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Scaling AI involves expanding the use of machine learning (ML) and AI algorithms to perform day-to-day tasks efficiently and effectively, matching the pace of business demand. To achieve this, AI systems require robust infrastructure and substantial data volumes to maintain speed and scale.
Scalable AI relies on the integration and completeness of high-quality data from different parts of the business to provide algorithms with the comprehensive information necessary for achieving wanted results.
Also, having a workforce ready to interpret and act upon AI outputs is crucial for scalable AI to deliver its full potential. An AI strategy that puts these essential elements in place enables an organization to experience faster, more accurate, personalized and innovative operations.
Here are the key steps commonly used to successfully scale AI:
- Start with data science: Start by working with data science and machine learning experts to develop algorithms tailored to your business objectives. By using the appropriate APIs, you can train large language models that suit your needs. Data scientists play a critical role in designing models that ultimately drive innovation and efficiency.
- Locate and ingest data sets: Identifying and ingesting the right data sets is essential for training AI models. These data sets include internal sources like customer records, transaction histories and external sources such as market trends and research reports. Ensuring data quality and relevance is crucial for accurate AI model performance.
- Involve stakeholders across departments: Engage stakeholders from various departments, such as customer service, finance, legal and others. Collaborating with the data science team, these stakeholders can help guide the AI model's development and alignment with specific business needs and challenges.
- Manage the data lifecycle: Develop secure and standardized data structures that integrate and update data sources. Managing the data lifecycle keeps data relevant and up-to-date for training and validating AI models.
- Optimize and simplify MLOps: Opt for an MLOps platform that aligns with the skill sets of your data science and IT teams while supporting your organization's IT infrastructure and primary cloud provider. Streamlined MLOps enhances AI deployment and maintenance.
- Assemble a cross-functional AI team: Form a multidisciplinary AI team that includes stakeholders from various business areas to promote cross-departmental collaboration. This approach enriches AI initiatives and fosters a comprehensive understanding of business goals.
- Select projects with high success potential: Choose projects with a high likelihood of success to provide early wins and build momentum for future, more ambitious projects. Example use cases can include customer service, talent management or app modernization. Establishing an AI center of excellence can also support these efforts.
- Incorporate governance and compliance: Integrate AI governance and reportability from the outset. Make sure that tools for data management, data science and business operations include built-in governance features. Incorporate compliance and reportability into all AI processes to maintain ethical standards.
- Employ the right tools: Use cloud-based data science platforms to facilitate collaboration between data scientists, IT engineers and businesspeople on AI governance and compliance issues. Provide environments where trainers can experiment with, develop and scale AI models effectively. Such environments support the efficient deployment and maintenance of AI systems.
- Monitor AI models end-to-end: Track AI models end-to-end, considering metrics such as speed, cost, reasoning and value to users. By monitoring in real-time and tracking key performance indicators (KPIs), organizations can swiftly identify potential issues and optimize performance over time.
Scaling AI within an organization can be challenging due to several complex factors that require careful planning and resource allocation. Overcoming these challenges is crucial for the successful deployment and adoption of AI at scale.
AI relies heavily on data, which can come in various forms such as text, images, videos and social media content. Data engineering, which includes data management, data security and data mining—the organizing and analyzing of massive data sets—requires specialized expertise and investment in scalable data storage solutions like cloud-based data lakehouses. Ensuring data privacy and security is paramount to protect against both external and internal threats.
Scaling AI involves an iterative process that requires collaboration across multiple teams, including business experts, IT and data science professionals. Business operation experts work closely with data scientists to make sure that AI outputs align with organizational guidelines. Retrieval augmented generation (RAG) can optimize AI outputs based on organizational data without modifying the underlying model.
The tools used to scale AI fall into three categories: tools for data scientists to build ML models, tools for IT teams to manage data and computing resources, and tools for business users to interact with AI outputs. Integrated platforms such as MLOps streamline these tools to enhance AI scalability and facilitate monitoring, maintenance and reporting.
To find individuals with the deep domain knowledge required to design, train and deploy ML models can be challenging and expensive. Using cloud-based MLOps platforms and APIs for large language models can help alleviate some of the demand for AI expertise.
When progressing from pilot projects to scaled AI initiatives, consider starting with a manageable scope to avoid significant disruption. Early successes will help build confidence and expertise, paving the way for more ambitious AI projects in the future.
Moving AI projects beyond the proof-of-concept stage can take significant time, often ranging from three to 36 months depending on complexity. Time and effort must be dedicated to acquiring, integrating and preparing data and monitoring AI outputs. Using open-source tools, libraries and automation software can help accelerate these processes.
By addressing these six key challenges, organizations can navigate the complexities of scaling AI and maximize it's potential to improve operations and drive business value.
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