“The urgency to innovate for greater efficiency and productivity has heightened due to the competitive corporate landscape and changing consumer behaviour.”
The skills gap remains a major obstacle to AI adoption. In fact, a study conducted by IBM last year found that 34% of respondents cited limited AI skills, expertise or knowledge as hinderances to their business successfully implementing AI. It was on this backdrop that IBM is addressing the concern by investing in embeddable AI technology to enable IBM Ecosystem partners to overcome the high skills and infrastructure costs.
Today, the urgency to innovate for greater efficiency and productivity has heightened due to the competitive corporate landscape and changing consumer behaviour. From bringing new products to market to enhancing customer experience; organisations are racing to build and deploy AI applications that can help them reach new markets, grow revenue and achieve business prosperity.
This environment has driven many organisations towards using AI from IBM’s embeddable AI software portfolio, which allows them to focus on creating differentiated AI models and applications while leveraging the industrial strength of IBM AI platform.
Embed to empower
But just what is embeddable AI and how does it work? Embeddable AI is akin to a pre-fabricated car engine that can be tailored for different needs. It empowers and equips Independent Software Vendors (ISVs) with trustworthy AI that can be embedded in commercial solutions.
“With embeddable AI, ISVs get a set of flexible, fit-for-purpose AI models that developers can use to create enhanced end-user experiences.”
With embeddable AI, ISVs get a set of flexible, fit-for-purpose AI models that developers can use to create enhanced end-user experiences. The current generation of embeddable AI technologies from IBM can be harnessed through a portfolio of watsonx, AI libraries, applications and APIs developed by IBM researchers.
The technology has created new opportunities for businesses and is driving innovation in a wide range of industries. Case in point can be seen with Australian call recording software provider Dubber. With embeddable AI, Dubber uses speech-to-text, tone analyser, and natural language understanding to capture and transcribe verbal exchanges for data mining through keyword search. Phone calls and video conferences are automatically translated to text. On top of that, the sentiment of each conversation can be categorised into positive, negative or neutral to help provide customers with a consistent experience.
Other instances of embeddable AI can be seen in financial services and customer care organisations including QuantumStreet AI, who embedded AI to make smarter investment decisions. CrushBank and Sherloq are depending on embeddable AI to improve customer care service and to identify marketing leads through web 3.0 compliance and intelligent website design.
Another great example is SAP embedding IBM Watson capabilities to power SAP Start and help customers benefit from intelligence at the point of decision-making.
Embed with generative AI
Mobile security firm, Zimperium worked with Krista Software, an IBM Business Partner, to automate its entire scheduling and deployment process. The work was completed within two months which benefitted Zimperium with the ability to release updates faster, address human error and regulatory requirements, improve efficiency and reduce risk with no data science and coding requirements. Zimperium saw significant cost savings and increased efficiency as it helped protect its clients against both known and unknown cybersecurity threats.
The success has also spurred Krista and Zimperium to optimize Zimperium’s order-to-cash process and automate its international customer support. Krista also plans to continue to deepen its work with IBM, including integrating IBM’s watsonx AI and data platform, to help clients like Zimperium unlock AI’s true potential.
With watsonx, organisations can use open source frameworks and tools for code-based, automated and visual data science capabilities to tune models for specific business needs. For example, they will have access to Hugging Face’s open source transformers library of over 100,000 GitHub stars, more than 230,000 models and over 40,000 data sets (and counting) for prompt engineering.
Businesses like Zimperium have gained an edge through their partnership with Krista, without having to expand their technology stack, hire more developers and invest in expensive computing resources.
Organisations can now look forward to bring new, revenue generating product and services to market faster at reduced costs with fit-for-purpose AI for business solutions.
Put AI for Business to work
IBM is bringing its core AI and automation technology to partners to embed into their solutions to better serve their clients. These include:
- watsonx.ai: Brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a powerful studio spanning the AI lifecycle.
- Watson Assistant and Watson Orchestrate: Core digital labour products that have been supercharged with the NLP foundation model to enhance employee productivity and customer service experiences.
- IBM Watson NLP Library for Embed: Designed to help developers provide capabilities to process human language to derive meaning and context through intent and sentiment.
- IBM Watson Speech Libraries for Embed: A set of containerized text-to-speech and speech-to-text APIs which provide more flexibility and greater capabilities to build voice transcription and voice synthesis applications and deploy them in any hybrid multicloud environment.
About the author:
Kalyan Madala is the CTO for IBM Australia, Southeast Asia, New Zealand & Korea (ASEANZK). He enjoys peeling off layers of tech red tape to uncover gems that help businesses implement sustainable solutions.