Workflow automation platforms powered by AI can help distributed teams streamline their processes, improve communication, and better collaborate for remote and global teams.
By integrating AI deeply into an organization’s existing workflows, teams can get the full benefit of the technology — with AI-generated intelligent insights, content creation, and automating tasks with AI agents.
This reduces unnecessary friction and bottlenecks, whether they’re due to skills gaps like coding expertise or conflicting time zones.
These AI-powered automation platforms also include a human-in-the-loop experience that helps other decision makers participate in the end-to-end process.
All these lower the gap between business and the technical sophistication traditionally required to build automations.
These allow more users across the organization to create their own automations, and more easily share their requirements with remote and global teams.
Users that build these automations provide their instructions in natural language including guardrails specified during design time.
These include soft guardrails (e.g., a description of what the AI should do), and hard guardrails (e.g., what resources are available for the AI itself – for example is it allowed to send an email or not).
Ashvini Sharma, director, Power Automate for Microsoft, explained the automation runtime breaks this high-level description of the automation into multiple steps using deep planning and reasoning capabilities, with continuous validation against the provided guardrails.
“The automation runtime does aggressive checks against hallucinations, tracks the overall execution against the dynamic plan and execution of each action,” he said.
If allowed by the user, these actions can include human in the loop actions like sending a request for approval and waiting for the response which could happen several days after the approval was sent.
This step-by-step planning and reasoning are provided for automation owners to review and update as necessary.
“Usually, we see customers adding more specificity to their instructions as they run these automations through different scenarios,” Sharma said.
John Gordon, SVP and president, HPWS Managed Solutions, said the expansion of low-code and no-code platforms has been invaluable for non-technical users.
“They dramatically accelerate the ability of mildly technical business users to streamline processes that incorporate AI,” he said.
He added he has used several platforms to do everything from developing career development programs to generating program-specific web presence in no time.
“These platforms deliver rapid development, high quality, democratized access,” Gordon said.
Sharman explained many business users are experts in their function but find coding a barrier for converting their needs into solutions.
“Some settle for learning just enough coding to be able to copy/paste code from the net without completely understanding the side effects, with the goal of moving their business forward,” he said.
However, low-code/no-code tools take a fundamentally different approach by providing easy-to-use functionality that minimizes/removes the need for coding.
“Many of these tools are also designed to be collaborative, allowing a group of users to work together on building comprehensive automation, together,” Sharman said.
He added a critical component of this democratization platform includes security and governance that allows large organizations to nurture and deploy these tools with confidence, accelerating the overall output of their teams.
“Robust low-code, no-code platforms provide a significant depth of governance capabilities that include data loss and sharing prevention policies, deep reporting, lifecycle management, role-based access controls, to name just a few,” Sharman said.
Param Kahlon, executive vice president and general, automation and integration at Salesforce, said AI algorithms start with data analysis to gain a contextual understanding of each task within automated workflows.
But to do this well, companies first need to collect relevant structured and unstructured data from various sources and unify them in one platform, which allows them to glean the full context and extra insights after building this unified foundation.
“This creates AI-powered experiences to help employees prioritize with intelligent apps, using the building blocks made possible by composable architectures, to drive efficiency and productivity within every workflow,” he said.
Kahlon said unifying enterprise data across sources and capturing every customer interaction enables AI-powered workflow platforms to learn from user behavior in real time.
“Consumers expect companies to respond and interact with them in real time, meaning businesses must build faster communication methods between applications to meet this demand,” he said.
Gordon said with most workflow automation platforms able to incorporate APIs into their design, the potential for high value automation is nearly endless.
“Gen AI is great at interpreting our ideas and offering us suggestions, which can inspire each of us innovate more quickly,” he said. “However, we constantly need to watch for hallucinations that can cause us to take the wrong path.”
Sharma said as organizations get more comfortable with the ability to use AI capabilities, usually the next challenge is to identify which scenarios will give them the highest returns.
“One of the common challenges we hear from customers is significant pressure from their management on faster results from Gen AI,” he said. “They are also concerned about the decisions made by AI and want the ability to align the behavior of these models to their norms and expectations.”
