Taking the time to make a use case for AI will propel companies further and improve the return on investment in this fast-changing technology. The market is officially three years post ChatGPT and many of the pundit bylines have shifted to using terms like "bubble" to suggest reasons behind generative AI not realizing material returns outside a handful of technology suppliers. In September, the MIT NANDA report made waves because the soundbite every author and influencer picked up on was that 95% of all AI pilots failed to scale or deliver clear and measurable ROI. McKinsey earlier published a similar trend indicating that agentic AI would be the way forward to achieve huge operational benefits for enterprises. At's Technology Council Summit, AI technology leaders recommended CIOs stop worrying about AI's return on investment because measuring gains is difficult and if they were to try, the measurements would be wrong. This places technology leaders in a precarious position-robust tech stacks already sustain their business operations, so what is the upside to introducing new technology?

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Ironclad yesterday unveiled a new version of its contract platform embedded with an AI layer in an effort to improve business workflows throughout the lifecycle of a contract. Organizations can create contracts 60% faster by automating the contract creation process, according to Jason Boehmig, the company's CEO and co-founder. They will also have the capability to "slice and dice" all the operational data in previously executed contracts, he said.

For the majority of organizations, continuously delivering and integrating AI solutions within enterprise applications and business workflows is a complex afterthought. On average, it takes about eight months to get an AI-based model integrated within a business workflow and for it to deliver tangible value. However, to reduce AI project failures, organizations must efficiently operationalize their AI architectures. Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives. Organizations should consider model operationalization (ModelOps) for operationalizing AI solutions.

Digital transformation is the flavor of the season. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market. McKinsey senior partner Kate Smaje says that organizations are now accomplishing in 10 days what used to take them 10 months. With data powering better and faster decisions, she says, the road to recovery is paved with data. As a result, most organizations are trying to adopt data-driven decision-making.

We are excited to announce our MonkeyLearn integration with Zapier! Wouldn't be amazing if you had a simple idea on how to automate a manual workflow with AI and just try it out in a couple of minutes? Labeling your emails, tagging customer support tickets or organizing billing invoices are just a few examples of manual human processing that are time consuming and boring. Those tasks should be automated, but they usually involve some degree of human intervention to read and understand the content. In order to automate that, you must add a layer of Machine Learning to make machines understand that content.

Artificial intelligence and machine learning are often associated with tech giants like Google and Amazon that have created the most popular machine learning libraries and platforms. Since efficient AI/ML solutions require vast volumes of costly data to train, small companies are often reluctant to integrate AI into their business workflow. These worries are overblown though. These days, turning your small business into a full-fledged data-driven company might be easier than you think. To prove this, here are five easy tips on how to kickstart AI transition of your small company right now.

Machine learning algorithms are enabling organizations to supercharge workflow processes across their enterprises. They center around technology that has the ability to learn without being explicitly programmed: machines that can study their mistakes and reprogram themselves to improve their performance over time. Lots of big names are investing R&D dollars into machine learning. Here are some ways it can help improve business operations. A workflow process is the backbone of just about every common business activity, whether it centers around finance, inventory or another back-office task.

Machine learning algorithms are enabling organizations to supercharge workflow processes across their enterprises. They center around technology that has the ability to learn without being explicitly programmed: machines that can study their mistakes and reprogram themselves to improve their performance over time. Lots of big names are investing R&D dollars into machine learning. Here are some ways it can help improve business operations. A workflow process is the backbone of just about every common business activity, whether it centers around finance, inventory or another back-office task.