Red Hat announces Ansible Lightspeed with IBM Watson Code Assistant, a new generative AI service designed to help drive IT automation adoption. The solution makes it easier, even for inexperienced users, to automate tasks by removing the burden of creating low-value tasks for automation experts.
New generative AI service and the IT skills shortage
In an era characterized by evolving sector dynamics and economic uncertainty, companies are called to do more with less. They need the skills to stay resilient, drive innovation, and unlock the value of hybrid cloud and automation investments. however deficiencies in fundamental IT knowledge can be a challenge obstacle to the achievement of these results.
I future problems
According to IDC, “90% of global organizations will experience the IT skills crisis by 2025”. Which means that “by 2026, companies that have not effectively addressed the digital skills and talent gap within their organization will limit opportunities for revenue growth by 20%”. However, “Due to the skills shortage, CIOs who invest in digital adoption platforms and machine learning technologies will see a 40% increase in productivity by 2025, ensuring faster access to skills”.
New generative AI service
Ashesh Badani, senior vice president and chief product officer, Red Hat
Innovation has been and always will be a human-centric story. It starts with the right idea, flourishes with the right team, and reaches its full potential with the right tools. That’s what we’re doing with Ansible Lightspeed. That is to give people technology that is capable, intelligent, yet understandable enough to allow developers and operators to use automation in new ways, and users to use their knowledge to bring a vision to life. In this way AI is born already with the characteristics of the environment in which it will be used. And most importantly, it works immediatelyprecisely because it was formed on specific experiences.
An aid to developers and operations teams
Kareem Yusuf Ph.D., Vicepresidente senior, Product Management and Growth, IBM Software
The speed with which many organizations are adopting AI requires solutions that give cloud developers and operations teams security and flexibility. Necessary to scale and accelerate AI across the IT organization. IBM Watson Code Assistant, based on watsonx, will exploit the carefully curated data in the Red Hat Ansible domain to help developers and operations teams of all skill levels write syntactically correct code with AI-generated recommendations.
New generative AI service
Ansible Lightspeed is the next phase of the Project Wisdom initiative, now accessible to Red Hat users, contributors, customers and the partner ecosystem. Using natural language processing, the service will be integrated with Watson Code Assistant – available by end of the year – to access IBM Foundation Models and quickly build automation code. This is the added value that Watson Code Assistant (currently in technical preview) wants to bring to businesses. That is, address skills gaps and generate the efficiencies needed to accelerate the time to value of automation.
Power of experience and technical innovation
Designed specifically for developers and automation workers, Ansible Lightspeed delivers a productivity boost for Ansible users. And this by translating their domain expertise into YAML code for creating or modifying Ansible Playbooks. Users can also provide their feedback, contributing to model training. The artificial intelligence of the domain combina the power of first-hand experience with technical innovation, integrating into domain technologies, such as automation, to address specific challenges.
New generative AI service designed for IT automation
Being taught by users with in-depth knowledge of real-world applications, Ansible Lightspeed delivers more consistent, higher-quality recommendations for specific challenges. Can provide quickly real value ⎯ by sector, language and function. Red Hat and IBM aim to provide an Ansible Lightspeed experience based on transparency, collaboration, and choice. Proper matching of source code is a top priority, and upstream content contributors will be able to choose whether or not their work contributes to the fine tuning of the model.