Scalable Embeddable AI

Slow and steady may win the race, but sluggish AI adoption impedes organizations from solving important challenges.

AI capabilities are crucial to responding to some of the most critical imperatives of our time, including mitigating climate change, advancing medical research, supporting front line healthcare workers, and protecting against pervasive cybersecurity threats. But AI is also a valuable tool for supporting essential daily business operations and meeting changing employee and customer expectations.

Ronald van Loon is an IBM partner and is applying his perspective as an industry analyst to discuss the role of embeddable AI in resolving challenges associated with AI adoption.

Globally, the AI market is predicted to expand over the next three years to reach $126 billion by 2025 . Organizations wanting to leverage the benefits of AI need to address the barriers of building and deploying AI in their applications, processes and solutions.

Embeddable AI provides a simpler route to developing and scaling AI solutions and enables businesses to explore opportunities for business growth and speed up time-to-market for new products and services.

Development Challenges Demand a Shortcut to AI

One of the top cited barriers to enterprise-wise AI adoption and integration is lack of technical talent and skills; recent research indicates that AI skill shortages came in second among the most common types of IT skill deficits at 36%. Ironically, the lack of talent can prevent organizations from deploying AI to resolve the widespread skills and talent shortage.?

Another impediment to building and deploying AI within business solutions is that minimal organizations have made significant investments to address bias or establish AI trust . Because AI systems recognize patterns in existing data, the output can mirror any bias contained in the datasets from which the AI system learns. This can lead to gender disparity in hiring systems, or wrongful loan rejections, for example. Yet the process of making AI fair and transparent is highly complex.?

IBM’s embedded AI is a portfolio of enterprise grade AI technologies in an agile form factor that simplifies the approach to embedding AI into business solutions. The new libraries include IBM Watson Speech to Text library, which facilitates accurate speech transcription in customer service applications; IBM Watson Text to Speech library, which helps transform written text into audio across various languages; and IBM Watson Natural Language Processing Library, which helps glean context in human language through emotion and intent.

Embedded AI Capabilities?

Gartner suggests that for industries like finance, for example, buying software with embedded AI capabilities rather than attempting to build in-house AI solutions allows organizations to rapidly develop pilots for specific business problems and use cases.?

IBM’s Embedded AI capabilities can aid Independent Software Vendors (ISVs) in efficiently and economically building and scaling their own AI-driven solutions, whether in hybrid, on prem or multi-cloud environments. Basically, it distills aspects of Watson into digestible pieces while giving developers the ingredients needed to infuse AI into their own products. For example, developers who might lack the resources to develop machine learning models can still incorporate machine learning into workflows.?

Here’s what else you should know about Embedded AI :

• Speeds up model developer time by double digit percentages while performing model build up.
• Developed with trust to ensure transparent, responsible AI.
• Can be efficiently incorporated in applications so deep learning can be used to procure accessible insights from disparate unstructured data.
• Can help ISVs in use cases like improving customer engagement in customer care domains via sentiment analysis capabilities, or deploying visual inspection capabilities for manufacturing quality checks via distributed AI.
• Supports intelligent operations in important areas like enterprise observability for incident predictions.?
• Offsets AI adoption obstacles in developing machine learning and AI models from the ground up.
• Provides developers and IT teams with the means to build innovative products without any data science specialization.

Embedded AI is Accessible AI

Ultimately, it’s a resource-intensive, investment-heavy process to develop and deploy AI at scale. Having AI capabilities at their fingertips can streamline this demanding process and contain development costs.?

Check out IBM for more insights on embeddable AI and how it can help speed up the AI journey.