Don’t Let Tooling and Management Approaches Stifle Your AI Innovation
It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services.
The possibilities for AI use grow almost daily, so it’s important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management. This article presents guidance for avoiding proprietary lock-in in enterprise AI, and why it is important to do so.
AI grew out of data analytics that later evolved to business analytics and business intelligence. Early analytics were dominated by a few proprietary solutions, which had a limited ecosystem of innovative companies developing complementary tools and technologies to enhance the vendor platform. That slowed analytics adoption.
The AI market is the Wild West in comparison. Hundreds of companies, ranging from some of the best-known names in tech to incredibly innovative startups are offering solutions for every stage of AI and the AI model life cycle. Enterprises don’t have to use the same limited tools and techniques as their competitors, and they are taking advantage. In 2021, 81% of financial services companies were using more than one AI model development tool, and 42% were using at least five, according to the 2021 State of ModelOps report. That doesn’t even count the tools and solutions used for other stages of AI and the model life cycle.
Many tools are oriented to developing models for a specific purpose (e.g. fraud detection, assisted shopping) or execution environment (e.g. in-house hardware or AWS and other hyper cloud services). Having multiple purpose-built, best-of-breed model development options available to data scientists has broadened the scope of use cases that are possible and valuable, which has helped AI use grow within organizations.
AI lock-in occurs when organizations rely on the same tool they used to create models to also run and manage them in production. That may have been OK in the early days of analytics when models and model operations were more transparent, but models are too sophisticated now. The requirements and tools for model development are very different from those for model operations. Models in production must interact with many more systems, including business applications, risk management frameworks, IT systems, information security controls and more.
Model development tools are designed for data scientists to simplify the model creation effort. Model operations solutions are designed for IT to efficiently and effectively manage the operations of all enterprise models throughout their life cycle.
The processes, tasks and users change when a model goes from development to production, not to mention the need for checks and balances between development and production environments and operations.
