For a little over a year, consumers and businesses alike have been fascinated by the possibilities of generative AI. With customers already familiar with the fluency of conversational tools like ChatGPT, software companies are working to develop AI into a robust business tool. Part of the challenge is packaging the new technology—with a reputation for sometimes giving incorrect answers and behaving unpredictably—into a form suitable for the lucrative but conservative enterprise market.
“There’s probably a lack of trust in the algorithms themselves,” says Iavor Bojinov, assistant professor at Harvard Business School. “People don’t really understand what these algorithms are doing.”
Companies also have concerns about potentially biased data used to train AI systems, and whether copyrighted work used in training can be regurgitated in the output, potentially exposing users to liability. As with all cloud-based systems, big companies also have concerns about security, and a desire to understand and control what sensitive data is shared with the systems. And there are simply questions about how to integrate AI platforms with existing processes, workflows, and company data.
One approach software companies have taken is offering AI as an add-on in applications companies are already using. Microsoft is probably the most prominent example, integrating AI Copilots into software from GitHub to Microsoft 365, so an assistant is just a click away as people work on familiar tasks like building PowerPoint presentations and writing code.
Similarly, Canva has integrated AI image generation and editing into its graphic design software, and Outreach has added AI assistance throughout its sales management platform, helping with everything from emailing prospects to closing a deal. Enterprise search company Elastic added AI capability to its products, giving customers access to generative AI through a system they already trust to index and search voluminous and sometimes sensitive company data, from product listings to security logs. Even users of the powerful but notoriously abstruse Unix command line can now have an AI assistant, thanks to Warp.
AI providers have also taken steps to help businesses feel more comfortable sharing data with AI and relying on the answers it provides. IBM’s Watsonx platform, for example, lets businesses build, train, and test AI models and instruction prompts, as well as governance features to help meet compliance requirements, test AI for bias, and generally gain understanding of what AI is doing under the hood. IBM’s toolkit also includes data management software to help securely store and prepare datasets for processing by AI.
Some companies have even built AI systems that can help look for issues elsewhere in company processes, like XStereotype’s AI that can scan content for racial bias and EcoVadis’s AI for tracking supply chain risk.
Perhaps equally critically, AI vendors including Microsoft, Canva, and IBM have begun offering indemnification against copyright cases arising from some uses of their AI technology.
“After that came out, people are willing and ready to adopt those technologies faster than they were before,” says Andy Thurai, VP and principal analyst at Constellation Research.
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