How to Scale AI in Your Business

Your business will be affected by artificial intelligence. For AI to work for you and not against you, you’ll need to move initiatives beyond the pilot stage and into day-to-day operations.

Early movers are showing the way: They use AI to increase the speed and accuracy of accounts payable and receivable processes, summarize legal briefs and other research, and add a layer of assurance to critical tasks such as reading X-rays. These leaders use AI to detect fraud amid millions of financial transactions and make lightning-quick decisions in frenetic warehouses and on demanding manufacturing floors. They use AI chatbots to handle ever more complex support calls and guide salespeople to the best next step for each client.

And all this is just the beginning.

For each of these wins, however, the organization had to work through a scaleup process, bringing together tools and people and making the technical and cultural adjustments required for AI to work in the real world.

Below, we’ll look at the many facets of the challenge of scaling up AI for business.

What Is Scalable AI?

Scalable AI is the ability to use machine learning (ML) algorithms or generative AI services to accomplish day-to-day tasks at a pace that keeps up with business demand. It requires that algorithms and generative models have the infrastructure and data volumes they need to operate at the speed and scale required. Beyond that, scalable AI requires data from many parts of the business that’s integrated and complete enough to provide algorithms with the information needed to derive desired results.

Just as important are people who are prepared to use AI outputs in their work. With all these requirements in place, scalable AI can help business operations move with more speed, security, accuracy, personalization, and even creativity.

Key Takeaways

• Scaling AI can greatly improve a wide variety of business operations.
• Success involves many working parts in the areas of data management, data science, and business process management. These are often gathered under the heading of machine learning operations (MLOps).
• MLOps can include building and training ML models or training current algorithms or large language models (LLMs) to achieve a business goal.
• Businesses need to consider data security, data privacy, and regulatory reporting as they bring AI into daily operations.

Why Is It So Hard to Scale AI?

Scaling AI takes investment and commitment. It requires new skills and technologies, heavy-duty computing power, and changes in how your organization operates. Scaling AI goes way beyond building and training models; it means bringing them into production-grade applications that run at scale and provide business users with monitoring and reporting features.

There are six major challenges to overcome on your way to scaled AI:

1. Data: Data is the lifeblood of AI. It refers to the information used to train ML algorithms as well as the information those algorithms scan to deliver outputs. The data that ML models use comes in many forms. It can reside in the rows and columns of a relational database, and also in text documents, images, videos, or social media.
   
   Acquiring, organizing, and analyzing often massive data sets requires expertise in data management and investments in tools and cloud services, such as a scalable cloud-based data lakehouse. The security and privacy of data are primary concerns of any scaled AI. Data must be protected from external and insider threats, the same as sensitive data stored by any company. AI operations teams have an additional responsibility: Ensure that sensitive information in training data doesn’t appear in AI outputs.


2. Processes: Scaling AI is an iterative process that involves at least three groups:
   
   
   1. Experts in each relevant business operation, be it customer service, shipping logistics, product design, radiology, or accounting.
   2. The IT team, which integrates, secures, and standardizes the operational data and assembles the necessary compute power and networks.
   3. The data science team, which creates ML features, selects the model and tunes the parameters until the AI is ready to deploy and scale up. Your business operations experts will work with data scientists to ensure the AI outputs conform to guidelines. Teams should investigate retrieval-augmented generation (RAG), which provides a way to optimize the output of an LLM based on the organization’s data without modifying the underlying model itself.


3. Tools: The collection of tools used to scale AI come in three flavors—tools that data scientists use to build ML models, tools the IT team uses to manage data and support compute-hungry algorithms, and tools that help businesspeople use AI outputs in their daily tasks. Building a single ML model can require a dozen specialized systems, often assembled by data science practitioners from a wide variety of open source and proprietary tools.
   
   More recently, tech companies have organized data science, data management, and AI operations tools into integrated platforms for scaling AI. The effort is popularly called machine learning operations, or MLOps, and includes tools for building, maintaining, and monitoring AI as well as reporting on its outputs to internal stakeholders and regulators.


4. Talent: The expertise needed to design, train, and deploy ML models takes time to acquire, so people with deep domain knowledge in AI are hard to find and expensive to hire. That’s why, so far, it’s been tech giants building AI platforms and large, tech-savvy organizations that have been willing to pay for AI expertise.
   
   Now, however, MLOps platforms are available as cloud services, and LLMs are available through API calls. That’s opening AI to more companies. There will still be a need for data management and data science expertise, but the AI services available through cloud providers could take the pressure off the need to hire people with deep AI model building expertise.


5. Scope: When it’s time to move beyond piloting AI in a corner of the business, how big do you go? Ideally, your AI initiative will be large enough to make a noticeable difference in operations, whether that’s in shipping times, customer experience, or other measurable outcomes. But early efforts in scaled AI shouldn’t be so complex or so tied to the bottom line that you’re tempted to pull the plug if you hit a rough patch, rather than risk disruption. Start smallish in an area where hiccups won’t cause too much harm. The scope of AI initiatives will become more ambitious as expertise and confidence grow within your organization.


6. Time: Nearly 80% of AI projects never move beyond proof of concept, according to CompTIA, and those that do succeed take three to 36 months, depending on scope and complexity. That time is spent selecting and deploying models as well as monitoring AI outputs in a controlled setting.
   
   Business decision-makers also need to consider the time and effort required to supply the data that a large-scale AI system needs. Data scientists and IT teams will need to acquire, integrate, store, prep, and stream data through machine learning algorithms and monitor the outputs. A growing list of open-source tools and libraries as well as automation software and cloud services can help accelerate this cycle. As the field matures, so will the tools.

Why Is Scalable AI So Important?

Although scaling AI is difficult, business leaders are betting that the challenges and upfront costs eventually will be offset by business gains. According to McKinsey, AI will add an estimated $13 trillion to the global economy by 2030. There are several reasons. First, to take advantage of AI, more companies will take on “digital transformation” projects where they can use their data to become more innovative and competitive in the digital economy. AI will compound those competitive advantages and lead to further innovations. Companies that have already scaled AI see benefits including higher customer satisfaction and workforce productivity as well as more efficient use of assets such as ships, trucks, manufacturing equipment, and warehouses.

How to Scale AI in Your Business

Bringing AI into the rough-and-tumble world of business operations can be daunting, but it’s worth it for the right project. Start with data science, where libraries of machine learning algorithms can be tailored to meet your business objectives. This is also good advice if you’re using APIs to access and train large language models provided by vendors such as OpenAI and Cohere.

The next step is to find and ingest the data sets on which your AI will be trained. They may consist of internal or external data or a mix of both. For AI to work in a business setting, bring together stakeholders and advocates, whether they’re in customer service, finance, legal, or any other department. Those advocates will work with the data science team, so the trainers understand “a day in the life” of the people in the target business function. Those advocates will then work with their colleagues or partners to help prepare for the AI-driven process and push for wide adoption when it launches. With ML models, data flows, and business processes in line, it’s time to scale AI in your business.

7 Best Practices to Scale AI

Scaling up the use of AI in a business process comes with many challenges. The following are established best practices to help you succeed:

1. Focus on the data lifecycle

Before data scientists can build ML models and the business can scale those models, there must be a data structure that integrates and updates data sources and provides a secure, standardized format.

2. Standardize and streamline MLOps

Choose an MLOps platform that fits the skill sets of your data science and ML operations teams and matches your IT infrastructure or that of your primary cloud provider.

3. Create a collaborative, multidisciplinary AI team

AI initiatives cut across disciplines and departments. Bring together stakeholders from across the business to help.

4. Choose initial projects that are likely to succeed

Bringing AI to any business process is a complex endeavor. Begin with a project that gives a quick win and points the way for more ambitious future projects. Consider standing up an AI center of excellence to help ensure success.

5. Plan for governance and reportability

Choose tools for data management, data science, and business operations that have governance built in. Understand relevant security and privacy regulations, and build compliance and reportability into your process.

6. Track models end to end

Look for features that can help you track the speed and cost of your AI outputs as well as the reasoning behind them and their value to end users.

7. Use the right tools

To scale AI in your business, you’ll need a collection of tools that make it easy for data scientists to work with IT engineers and for both groups to work with businesspeople on AI governance and compliance issues. Cloud-based data science platforms can give teams of data scientists a place to build, train, deploy, and manage machine learning models and notebooks—interactive computational environments that combine code execution with data visualization and textual commentary. The key is providing spaces where trainers can experiment with models, develop them, and scale their use.

Scale AI with Oracle

When you want to scale AI in your business, Oracle Cloud Infrastructure (OCI) is a smart choice. It can help you get the benefits of AI in the way that makes the most sense for your company and that scales with your needs. You’ll find a range of SaaS applications with built-in ML models and available AI services as well as best-in-class infrastructure to build, train, and deploy ML models at scale. Oracle also makes it easy to access generative AI models based on Cohere’s state-of-the-art LLMs.

For data scientists, a fully managed data science platform helps build, train, deploy, and manage machine learning models using Python and other open source tools. Oracle offers a JupyterLab-based infrastructure to experiment with models, develop them, and scale up model training with NVIDIA GPUs and distributed training. The cloud is ideal for training generative AI, including conversational applications and diffusion models.

With OCI, you can take models into production and keep them current with MLOps capabilities, such as automated pipelines, model deployments, and model monitoring. Contact Oracle today, or try these services for free.

Consumer-grade AI may attract the lion’s share of attention, but businesses are actively implementing AI and ML. Technology platforms and business processes are rapidly emerging to help scale up enterprise AI, enabling more projects to move from proof of concept to full-scale production. Challenges remain, but companies that overcome them will achieve improved efficiency, accuracy, data security, personalization, and innovation.

How to Scale AI FAQs

How do you scale an AI product?

Scaling an AI product is a team effort involving stakeholders from across the organization. These include data science experts, data management and IT pros, and people with intimate knowledge of the business processes in which the AI product will be used. Often, an MLOps platform will help bring this group together to design, train, deploy, and fine-tune ML algorithms.

How do you scale an AI startup?

Scaling an AI startup is based on making the right decisions early about data acquisition, ML models or LLMs, and compute infrastructure, either on-premises or cloud-based. Startups need to procure a large number of GPUs to train large data sets and run complex AI infrastructure with the performance and reliability to deliver results in a timely manner.

What is the scalability of an AI system?

A scalable AI system has enough speed and accuracy for the rough-and-tumble world of business operations. These systems go beyond the experimental or proof-of-concept stage and are able to scale to serve a group of users.

What is AI scaling?

Scaling is the term given to any compute-intensive service that can grow to meet the needs of the business. If more computing resources are required by an application, the IT infrastructure that supports the application must ramp up to deliver. In some cases, scaling also refers to scaling down when infrastructure isn’t needed. For example, some applications have seasonal or quarterly spikes in use. A scalable cloud infrastructure can scale up to meet these needs and then scale down so the company isn’t paying for infrastructure it isn’t using.