Open source artificial intelligence: The key for business transformation

Artificial intelligence (AI) is becoming ubiquitous across all functional areas of a business. It’s fair to say that AI is the most disruptive innovation of our lifetime. According to IDC research, AI adoption and spending is on the rise. Large enterprises spend an average of $200 million annually on AI. Early adopters report approximately 35% improvement in innovation and about 32% improvement in employee and customer experiences with the rollout of AI solutions.

Enterprises across industries are embracing AI/machine learning (ML) for a breadth of use cases. AI/ML in financial services is helping to improve loan underwriting and reduce risk. AI can also lessen financial crime through advanced fraud detection and spotting anomalous activity. Deep learning (DL) algorithms are being used to shave down the time it takes to diagnose serious illnesses. In manufacturing operations, machinery maintenance and quality are the leading AI transformation projects today. AI is making predictive maintenance a reality for industrial IoT.  While most AI systems so far have been used for classification and predictions, the rise of generative AI has the potential to be a major game changer for businesses due to its ability to be creative.

The opportunity of AI

The application of generative AI in enterprises is just starting to unfold along with a world of opportunity. Enterprises can leverage generative AI for code assistance, automated software quality, contextual chatbots, knowledge discovery, and content generation for tasks across sales, marketing, HR, finance, and customer service. Businesses that can effectively leverage technology are likely to gain a significant competitive advantage.

As advancements in AI/ML rise, business leaders are embracing open-source culture and technologies. There are many reasons for this, including multi-organizational collaboration, accelerated innovation, and social responsibility. A software-maker culture inspires established companies to experiment with AI and create platforms that align with their business needs.

These companies already have the data that fuels AI; they need the freedom to build and deploy models as rapidly as the business context changes. Using an organization’s data to train models gives organizations the context to make better business predictions and decisions. Open source helps these companies become co-inventors of software, not just buyers of it. Open-source AI has been one of the most important developments within the technology industry, and it is continuing to grow.

Organizations typically embrace one or more of these options to support their AI/ML initiative life cycle: fully managed service, self-managed service, or commercial offerings they can customize to their needs. According to IDC research¹, about one-third of organizations reported using open-source DIY or self-managed service, and two-thirds reported a combination of various options.

While AI/ML initiatives can deliver significant benefits, they are not without challenges and risks. Among the challenges:  cost, lack of tools for ML operations, and lack of responsible AI tools are the top inhibitors to scaling AI initiatives. Downloading open-source software may introduce the risk of lack of governance and security or lead to shadow IT and drive associated risks. Yet when embraced appropriately with the right security and management layers, AI/ML can drive significant business capabilities including flexibility, agility, portability, and overall time to value.

Open source innovation has huge promise for MLOps.  A proper MLOps platform that helps manage and even automate AI/ML life cycles collaboratively between data science teams can speed up the development and deployment of AI-enabled applications.

Advancements in ML, natural language processing, conversational AI, and computer vision AI are at the forefront of AI software innovations. AI initiatives offer more than cost savings: They help organizations predict and shape future outcomes, and they allow people to improve productivity, automate and optimize processes and decisions, and reimagine new business models. Ultimately, these capabilities lead to increased revenue, improved profit margins, and reduced risks.

Still, AI is not a panacea for all enterprise problems. Along with its significant benefits, AI holds inherent risks for security, privacy, and equity if not mitigated properly.

For more on our best practices and recommendations on open-source AI platforms as well as real-world customer scenarios, download the IDC White Paper, Why Open Source Artificial Intelligence Platforms Help Enterprise Business Transformation, sponsored by Red Hat.

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¹ IDC Blog, Why Open Source Artificial Intelligence Platforms Help Enterprise Business Transformation, October 31, 2023