Enterprises need generative AI tailored to their unique needs, with their own unique data

In less than a year, we’ve gone from the “run your business and apply AI to help” paradigm to a reality where enterprises in every industry are navigating how to embed AI into the fabric of their strategies. Generative AI based on foundation models has brought us to this inflection point. In fact, new research from IBM’s Institute for Business Value CEO study found three out of four (75%) CEOs surveyed believe the organization with the most advanced generative AI wins, and 43% of responding CEOs say their enterprises are already using generative AI to inform strategic decisions.

In the past, scaling and operationalizing AI has been challenging for organizations. According to a Gartner® press release (link resides outside ibm.com), “The Gartner survey revealed that on average, 54% of AI projects make it from pilot to production.” Enterprises need transformation partners with the right expertise and capabilities to help them on their paths.

Generative AI can be applied to an array of use cases, such as sorting and classifying written input, transforming domain-specific text into personalized summaries, identifying and extracting essential information from unstructured data, and generating code, marketing content and more.

There are a few key areas that are relevant to most enterprises and are ripe for quick gains in productivity and time to value:

Talent

HR departments are embracing generative AI to manage their workloads more efficiently. By training their models with company-specific HR data, HR professionals can use AI to help with tasks like creating job postings, summarizing groups of incoming resumes and helping professionals better understand new policy documents.

Customer care

Organizations can combine customer data and generative AI to create personalized experiences at scale through chatbots and digital assistants. AI has been successful in handling call center calls, to improve service and enable human agents to focus on more complex tasks.

Application modernization

Engineers can use AI to generate and build upon starter code and playbooks. In fact, in the realm of application modernization and enterprise IT operations, this can be linked to an increase in productivity.

Businesses are exploring options for implementing AI—they can build their own models from the ground up or use a combination of propriety and open-source models. Enterprise-ready platforms, end-to-end tooling and technical expertise can help them get started, but there are factors to keep in mind when adopting AI:

Building trustworthy AI is critical

As businesses tread new AI territory, they need assurance that the AI they’re using for mission-critical decisions and outputs is built to be trustworthy, ethical and reliable. It must be designed to be explainable, fair, robust and transparent, and prioritize and safeguard consumers’ privacy and data rights to help engender trust.

Solutions should be tailored to enterprises’ unique needs

The key to businesses’ differentiation in AI—whether it’s based on Machine Learning or foundation models—is customizing and adapting technology to their customers’ specific needs and priorities. The advantage of foundation models is rooted in their ability to be tuned to an enterprise’s unique data and domain knowledge with specificity that was previously very difficult and highly labor intensive.

AI environments should have governance and flexibility at their core

AI initiatives must evolve based on changing demands and opportunities. At the same time, it’s crucial for organizations to implement AI within an environment that upholds governance, transparency, and ethics to effectively navigate the complexities of regulatory and compliance demands. A hybrid multicloud approach enables easier scalability and adoption of new processes and workflows on a larger scale.

IBM is committed to empowering a generation of businesses, spanning every industry, to embed AI into the core of their strategies. We provide open and targeted value creating AI solutions for businesses. IBM watsonx—our enterprise AI and data platform—offers a seamless, efficient, and governed approach to AI deployment across a variety of environments.

Whether enterprises are using open-sourced models, creating their own or deploying AI on-premises or in the cloud, IBM is ready as a transformation partner to empower all businesses in the age of AI.