IBM's VP of Build on Where Embeddable AI Stands to Benefit

The integration of AI into core business solutions is rapidly becoming the next frontier for many companies. As organisations move beyond experimentation with AI tools and models, they are seeking ways to fully embed this technology into their products and services. 

Enter, embeddable AI. This shift towards presents numerous opportunities for businesses to enhance efficiency, reduce costs, and improve user experiences. So where are the main areas embeddable AI could stand to optimise?

To find out more, we spoke with Dawn Herndon, VP of Build at IBM EMEA, about the potential of embeddable AI and its implications for businesses across various sectors.

Embeddable AI refers to a set of scalable AI models in a flexible form factor that developers can easily integrate into commercial products and enterprise applications. These AI-enabled solutions can provide automation, productivity improvements, and enhanced end-user experiences.

"With an embeddable AI approach, you get a set of scalable AI models in a flexible form factor that developers can easily integrate into commercial products and enterprise applications to provide AI-enabled automation, productivity improvements, and enhanced end-user experiences, like content extraction and summarisation, or automatically transcribing voice messages and video conferences to text," Dawn elaborates.

This approach to AI integration offers significant advantages for businesses looking to incorporate AI capabilities without the need for extensive infrastructure changes or additional staffing. It allows companies to add specific AI functionalities to their applications or workflows without expanding their technology stack, hiring more data science talent, or investing in expensive supercomputing resources.

Benefits and use cases

The benefits of embeddable AI are manifold, ranging from process optimisation to cost savings. Dawn highlights that "embedding such AI algorithms into commercial products and enterprise systems has the potential to optimise processes, reduce resource demands, deliver cost-savings and enhance efficiency and productivity."

One compelling use case Dawn shares involves the call centre industry. "If you're an Independent Software Vendor (ISV) and you're developing a solution that's used in a call centre, for example, you may want to develop AI that enables an agent to take action inside of a conversational AI chatbot; a solution that assists your agents during the interactions with clients," she explains. This application of embeddable AI can lead to significant productivity gains, decreased call and resolution times, and increased call centre efficiencies.

Another example Dawn provides is from the legal sector, where a customer is using natural language processing to automate contract privacy. "Usually, redacting information within a contract is a manual process involving a person going line by line to mark passages for redaction. With embeddable AI they created a natural language processing tool to find and mark sensitive information automatically, saving significant time," Dawn notes.

The potential for innovation through embeddable AI extends far beyond traditional business applications. Dawn shares an exciting example of how this technology is being applied in space exploration:

"We're working, for example, with Ubotica Technologies, an Ireland-based company that is doing some fascinating work on deploying AI on satellites," she reveals. "They're using the technology to simplify the process for a developer to get their application running onboard a satellite. These space-borne AI models are then used to generate insights from data in space, delivering increased autonomy and decision-making capabilities at the edge with reduced dependence on ground systems."

This example illustrates the vast potential of embeddable AI to transform industries and push the boundaries of what's possible in various fields.

Ensuring trust and safety in AI applications

As the adoption of embeddable AI grows, so do concerns about data trustworthiness and safety. Dawn emphasises the importance of governance and ethics in AI implementation:

"When I'm having conversations with ISVs that are looking for the best and most cost-effective way to incorporate or embed AI into their offerings, governance and ethics is top of mind. Business leaders want responsible, transparent and explainable AI," she states.

Dawn stresses the need for confidence in mitigating perceived risks associated with AI and monitoring models for issues such as hallucinations, bias, and drift. She advocates for AI governance as essential for achieving compliance, trust, and efficiency in developing and applying AI technologies.

"A solution that provides indemnification, for example, and focuses on the importance of governance – will help organisations have confidence that AI-native products designed, developed, and brought to market today will be adaptable and relevant for tomorrow," Dawn concludes.

As businesses continue to explore and implement embeddable AI solutions, the focus on responsible development and deployment will be crucial. By prioritising governance, ethics, and safety, companies can harness the full potential of AI while maintaining trust and reliability in their products and services.

******

Make sure you check out the latest edition of AI Magazine and also sign up to our global conference series - Tech & AI LIVE 2024

******

AI Magazine is a BizClik brand ​​​​​​​