AI Scaling: 4 Best Practices for Organizations in 2024

Artificial intelligence (AI) is transforming industries and how businesses function with hundreds of use cases. In order to realize its full potential, companies should scale their AI initiatives and widely integrate AI applications into their existing business processes. According to Accenture, companies that deploy AI at scale achieve ~3x higher return on AI investments than companies that are in the AI proof-of-concept stage.

However, scaling AI initiatives from the proof-of-concept phase to the enterprise level is a major challenge for many businesses. This is because AI at scale involves much more than plug-and-play AI models. In this article, we explore 4 key steps for businesses looking to successfully adopt AI at scale.

1. Invest in your data strategy

Data is the lifeblood of AI and ML models and an organization-wide AI strategy should start with data. As AI is scaled, it becomes increasingly challenging to manage, cleanse, maintain, and utilize data. Therefore, without proper methods and tools for managing the various aspects of a data lifecycle, it is nearly impossible to deploy AI at scale across an organization.

Some challenges of dealing with big data are:

• Data silos: This is data that is accessible by one or a group of departments but is isolated from the rest of the company. This reduces transparency and efficiency within the organization.
• Incompatible data: Data collected from different sources has different formats and must be standardized before usage.
• Inaccurate data: Large datasets necessarily include inaccurate, outdated, and other problematic data that must be cleaned for an accurate analysis.

Organizations must implement a robust data management strategy to successfully scale their AI initiatives that covers all components of a data lifecycle including collection, storage, integration, and cleaning. 

A key component of a scalable data management strategy is automation. Organizations should adopt DataOps practices to efficiently automate data orchestration.

There is also a wide range of tools that cover parts or the entire data lifecycle management. Feel free to check out our hub for data management tools ecosystem.

2. Streamline AI processes with MLOps

Similar to managing data-related processes, you should also standardize and streamline how you build, deploy, and manage your models. It’s relatively easy to build a few ML models that work well for specific business problems, but things can get complicated quickly if you want to adopt AI systems across the enterprise. This is because:

• Building machine learning models require lots of trial and error for optimal models, datasets, hyperparameters, codes, etc. This is especially challenging for complex models such as deep learning, natural language processing (NLP), or computer vision, trained on large datasets.
• Building a model is quite different than bringing it to real use: Only 36% of companies were able to deploy an ML model beyond the pilot stage.
• All models should be monitored in real-time to ensure that they don’t suffer from model decay.

Managing all these processes manually with data scientists from different departments working in silos would seriously hinder an organization’s ability to scale its AI projects.

This is why companies have started to adopt practices known as MLOps to standardize and automate processes associated with building and managing ML algorithms. MLOps helps organizations to:

• Streamline machine learning lifecycle with automated pipelines,
• Create a unified framework to follow which facilitates improved communication and collaboration between stakeholders.

Feel free to check our in-depth article on MLOps for a comprehensive account.

3. Build multidisciplinary teams

A small team of data scientists may be enough to manage a couple of models. But AI scaling requires a wide range of skill sets, including data engineers, IT and cybersecurity specialists, project managers, and so on. More importantly, technical staff must be connected with business professionals who would determine specific use cases according to business needs.

For this purpose, companies have started to establish AI Centers of Excellence (AI CoE) to close the gap between executive decision making and AI implementation within an organization. These business units bring technical experts from different departments and coordinate and oversee organization-wide data science and AI initiatives.

For more on AI Centers of Excellence, check our article on the topic.

4. Build an enabler company culture

Successfully scaling AI requires new tools and technologies but adapting the company culture and ways of working is just as important. This is because:

• Employees may fear being replaced by AI which can slow down the transformation.
• A large-scale AI transformation requires new skills as it fundamentally changes the way employees interact with machines.

In order to address these challenges, companies should:

• Create opportunities for reskilling and upskilling for employees,
• Restructure business processes, workflows, and policies,
• Improve top-down communication to ensure that everyone understands what is changing, why it is changing, and what the expectations are.

These can help companies prepare for changes resulting from the large-scale adoption of AI capabilities and increase employee confidence and engagement.

Feel free to check our article on the importance of company culture on digital transformation initiatives.

You can also check our data-driven lists of:

• AI Platforms
• AI Consultants
• AI Development Services

to explore AI solutions. If you have other questions about how to successfully scale AI in your company, feel free to ask: