Artificial Intelligence and Machine Learning (AI/ML) technologies are rapidly advancing, with new breakthroughs being made every day. Businesses in nearly every industry sector are rushing to take advantage of AI/ML. Yet large enterprises, especially in finance, banking, and insurance, seem surprisingly reluctant. 

Why are enterprise-level companies dragging their feet when it comes to adopting and using AI/ML technologies? 

In this guest post, Dmitrii Evstiukhin, Director of Managed Services at Provectus, lists roadblocks preventing large enterprises to keep up with the rapid pace of AI/ML development and innovation compared to startups and medium-sized businesses and offers a few solutions. Let’s dive in!

Roadblocks to Adopting AI/ML Technologies in Large Enterprises

We begin with a brief overview of the hurdles that must be overcome in order to adopt AI/ML. Many of these obstacles are natural consequences of being in business for a long time and achieving success. However, in order to maintain a competitive advantage in the field, it is necessary to overcome a number of limitations, including but not limited to:

• Hierarchical structure of large enterprises

• Cumbersome legacy IT infrastructure

• Lack of the right knowledge and skills among personnel

• Insufficient resources to implement, manage and scale AI/ML solutions

• Constraints due to regulations and compliance mandates

• Bureaucratic and risk-averse nature of large enterprises

• High costs associated with large-scale adoption of AI/ML

• Inability to take risks and innovate stifles AI/ML implementation

Some of these issues can be easily fixed with investments, while others may be show-stoppers.

Recipe for Success

Does this mean that large companies can never successfully implement AI/ML initiatives? Not at all. But they must be willing to take measures that pave the way for success. 

1. It is necessary to accept the fact that AI/ML adoption in enterprises is almost exclusively managed top-down. A technology-first approach does not work. Strategy comes first, and C-level people should be the first to immerse themselves in AI, to define a winning game plan.

2. The ability to democratize data is essential, and it must be made readily available for experimentation while keeping security in mind. This can be done by implementing a self-service data platform with a data catalog, such as Open Data Discovery (ODD), to ensure access to the right data sets.

3. ML engineers and data scientists should be able to experiment with the technology and data faster and on a larger scale, to gain the best results. In practice, this means the implementation of a robust end-to-end machine learning infrastructure. 

Large enterprises can take steps to lay a strong foundation, to fully leverage the potential of AI/ML technologies and remain competitive. However, technology in general, and AI/ML in particular, is not a one-time investment. On top of a solid foundation, ongoing maintenance, support, and improvement are still necessary. 

To achieve success, C-level executives must be the driving force behind the adoption and implementation of AI/ML technologies. They must provide the right resources and personnel to ensure the successful implementation and utilization of AI/ML, along with the necessary data sets for experimentation. 

Large enterprises should also ensure that their processes and procedures remain flexible enough to keep up with the rapid pace of AI/ML development, and that their personnel possess adequate expertise and experience to use the technology to its fullest potential. With the right combination of resources and personnel, large enterprises can make the most of AI/ML technologies and stay ahead of the curve in their respective industries.

Standardization Is Key

Once a large company successfully embarks on its AI/ML journey, it is important to establish standards that help the company remain organized and efficient on its path to AI transformation. 

When building an AI/ML organization within a large organization, it's important to establish a set of general standards to ensure efficient and effective operations. These standards typically include:

• Data management practices, such as data collection, storage, and quality control.

• Development processes, such as development methodology, coding practices, version control, and testing procedures.

• Model training and deployment practices, such as training frameworks, deployment methods, and infrastructure.

• Governance and ethics standards, including policies and procedures for data privacy and security, and compliance with relevant laws and regulations.

• The skills and training required for AI/ML projects, including job roles, responsibilities, and certification programs.

• Tools, such as data science platforms, machine learning frameworks, cloud platforms, code editors and IDEs, and collaboration tools.

By standardizing these areas, organizations can ensure that AI/ML projects are developed and deployed efficiently, while minimizing risk and ensuring compliance with relevant laws and regulations. But the key to establishing successful standards is to focus on the user journey. Standards should be geared toward enabling developers, and providing a path from data discovery to production inferences. This requires the right tools and personnel to ensure that the standards are properly implemented and utilized. 

Additionally, it is important to foster a culture of experimentation and innovation, as well as proper training, to ensure that standards are followed. By focusing on the user journey and the necessary tools and personnel, large enterprises can pave a path for new ideas and reduce Time To Value for AI/ML projects.

Ready to embark on your AI/ML adoption journey? Discover Managed AI Services and reach out to me if you are interested!

*This post was written by Dmitrii Evstiukhin, director of managed services at Provectus. We thank Provectus for their ongoing support of TheSequence.