There’s widespread agreement that generative artificial intelligence (genAI) has transformational potential. Although genAI made its debut in the form of chatbots that targeted a general audience, its value for knowledge workers, managers, executives, and developers quickly has become apparent.
Yet there’s also a pervasive sense of trepidation. Enterprises want to enjoy genAI’s many advantages and gain a competitive edge, but they need guidance on putting genAI to work and reassurance that it delivers tangible business benefits.
Thanks to the staggering pace of innovation, companies have developed solutions for practical genAI use cases along with best practices to pave a smooth path for deployment. Some of the most promising early applications address common enterprise pain points, including overburdened staff and escalating operational costs.
Compelling genAI use cases
Self-service portals are in use at many enterprises and typically resemble their public-facing websites. Although self-service portals look sleek, individuals often find them challenging to navigate. That’s where genAI can help; a fleet of these digital assistants can respond to conversational queries and carry out tasks more efficiently through existing collaborative platforms like Microsoft Teams or Slack.
GenAI can be used to smooth and streamline other tasks. For example:
- Knowledge workers have traditionally had to toggle between multiple browser tabs, applications, and forms to find the data they need. GenAI technology can pull together the right information for them.
- IT teams can use genAI to quickly detect problems, identify root causes, and recommend solutions — easing their workloads, while also ensuring systems are up and running smoothly.
- Senior IT management can customize data views and build their own applications with the help of genAI’s analytics capabilities — without having to rely on an overburdened data scientist.
Read about other genAI use cases here.
Easing toward adoption
Enterprises considering how genAI can work for them may be stymied by one of the first decisions they need to make: whether to opt for a closed or open approach. With the closed approach, the enterprise typically allows its data to be migrated to the vendor’s cloud for use with its AI services.
The open approach allows more flexibility and control over enterprise data, as well as adoption of best-of-breed models. BMC Software uses this approach, working with its customers’ existing solutions and models and offering compatibility with both open and closed AI models. Enterprises can choose to connect to public clouds, private clouds, and on-premises systems.
Rapidly get started with BMC HelixGPT, which runs on the BMC Helix platform. Built with containerized architecture, BMC HelixGPT provides:
- Portability among cloud services options
- Robust data security and privacy
- The ability to customize features
Soon, organizations will be able to deploy BMC HelixGPT applications on Snowflake’s scalable and secure data cloud, where they will gain the benefits of end-to-end encryption, multifactor authentication, and support for regulatory compliance.
Calculate the cost and time savings
For those businesses wary about investing in a new technology, not much imagination is required to determine the ways that genAI can save time, increase productivity, and positively impact ROI. For example:
An enterprise that uses genAI to summarize ticket resolutions may save three minutes, on average, per ticket. Multiply that by the three million tickets generated annually with employees earning $20/hour, and the annual savings would be $3 million — just for that one task.
Businesses can confidently explore genAI’s promise and begin their journey to transformation with careful planning and the right partner.
BMC Software — the first to embed genAI capabilities across its AI-driven service and operations management portfolio — should be that partner for your enterprise.
The most important next step: Bring genAI to life for IT service and operations teams. Learn how here or contact BMC.
