Domo Pushes Business Intelligence Beyond Dashboards Into Action
As enterprises chase real returns from AI, data platforms are shifting focus from visualization toward workflow-driven action
Enterprises rolling out artificial intelligence at scale are increasingly discovering that dashboards alone do not change outcomes. While business intelligence tools have long promised visibility, the next phase of enterprise AI is focused on something more operational: translating insight into action without removing human oversight.
That shift is now reshaping how data platforms are designed and sold. Rather than treating analytics, data integration, and AI as separate layers, vendors are consolidating them into end-to-end systems designed to support decisions across the organization, from data preparation to execution.
Against that backdrop, Neil Corbett, Enterprise Account Executive at Domo, Inc., said the company’s approach is to treat business intelligence as a foundation rather than an endpoint.
“We’re able to add the business intelligence layer on top of the data foundation, providing reports and dashboards for business use, and then take that one step further from an AI perspective by moving from static reports and dashboards into informed decisions,” he told TechJournal.uk in an interview. “Our agentic solutions and workflows are looking at the data and prompting the user to take valuable actions and insights, so maintaining a human in the loop.”
That emphasis on guided decision-making reflects a broader reassessment inside enterprises about where AI delivers real value. Instead of autonomous systems operating in isolation, many organizations are prioritizing tools that augment existing workflows, highlight opportunities, and recommend actions while keeping accountability with people.
For data platform providers, this approach also addresses a persistent challenge: fragmented data environments that limit the usefulness of AI models. Without clean, connected data, even the most advanced analytics struggle to move beyond experimentation. Corbett said this problem is often underestimated during early AI deployments, when teams focus on model performance rather than data readiness.
In practice, the lack of integration between operational systems, cloud platforms, and legacy databases can slow projects and dilute returns long before AI reaches production scale.
Corbett shared the comments during an interview at Momentum AI 2025, a two-day enterprise technology conference organized by Reuters in London. The event brought together technology vendors, enterprise buyers, and analysts to discuss how organizations are benchmarking progress in AI adoption and scaling production deployments.
During the discussion, Corbett outlined how Domo positions itself as a unified data and AI platform rather than a standalone business intelligence tool.
“We’re an end-to-end data products and AI platform, so we can do everything from data connectivity and integration of data,” he said. “We have 1,500 pre-built connectors you can easily connect into, and we also partner with Snowflake, Redshift, and Databricks to combine that data together.”
He said the goal is to reduce the operational friction that often slows analytics initiatives by bringing disparate data sources into a single usable layer.
“We also have the ability to pull all of that data together via drag-and-drop ETL functionality and model that data so it can get into a usable state,” Corbett said.
Breaking silos
For many large organizations, data remains scattered across departments, applications, and cloud platforms, creating bottlenecks for analytics teams and limiting who can access insights. Corbett said customers typically approach Domo with that exact problem.
“That might be hard-to-connect data sources and providing data into one usable state, removing the need for disparate data sources and siloed data sets,” he said. “It’s about combining that into one specific, usable data source that the whole business can benefit from.”
By focusing on consolidation, platforms like Domo are targeting a long-standing enterprise pain point: the reliance on specialist teams to prepare data before it can be analyzed. Reducing that dependency is increasingly seen as a prerequisite for scaling AI beyond pilots.
Corbett said enterprises are increasingly looking for ways to shorten the time between a business question and a usable answer. By simplifying data preparation and making insights accessible to non-technical users, organizations can respond more quickly to operational changes without adding complexity to their technology stacks.
Once data is unified, the next step is how insights are delivered. Rather than expecting users to interpret dashboards on their own, Domo’s approach centers on workflow-driven agents that surface recommendations at the point of decision.
“Our agentic solutions and workflows are looking at the data and prompting the user to take valuable actions,” Corbett said. “It’s about maintaining a human in the loop, but using workflows and agentic solutions to enhance productivity.”
That framing mirrors a broader trend in enterprise AI toward assistive systems with guardrails, as organizations remain cautious about granting algorithms full autonomy in regulated or mission-critical environments.
Rather than pursuing fully autonomous decision-making, many enterprises are opting for incremental adoption, using agentic tools to support planning, monitoring, and execution while retaining clear lines of responsibility. Corbett said this approach makes it easier to build trust internally and demonstrate value to business leaders.
Company background
Domo, Inc. is an American cloud software company based in American Fork, Utah, that provides a Software-as-a-Service (SaaS) platform spanning artificial intelligence, business intelligence, and data visualization.
Founded by Josh James in 2010 and initially incorporated as Shacho, Inc., the company rebranded to Domo in 2011 following the acquisition of Corda Technologies, with the goal of building a unified business intelligence platform that connects data, systems, and people across organizations.
Over time, Domo has expanded that vision to include data integration, analytics, and AI-driven automation within a single cloud-native platform, positioning itself as a data experience layer for enterprises seeking faster, more consistent decision-making.
That evolution reflects broader shifts in the business intelligence market, where customers are demanding platforms that can support real-time decision-making across multiple functions. As organizations operate in increasingly dynamic environments, the ability to act on fresh data, rather than retrospective reports, has become a competitive requirement.
The next challenge is translating that real-time visibility into concrete outcomes.
Looking ahead
As enterprises look to justify AI investments, platforms that shorten the distance between data and action are likely to face growing scrutiny. For many organizations, the next phase of AI adoption will be less about experimentation and more about whether tools can reliably support day-to-day decisions at scale.
That transition also places greater emphasis on governance and trust. As agentic systems become embedded in operational workflows, enterprises are paying closer attention to how recommendations are generated, how data is governed, and how accountability is maintained.
Rather than replacing human judgment, platforms are increasingly expected to document decision logic, surface context, and make it clear why a particular action is being suggested. As enterprise AI strategies mature, those that focus on usability, transparency, and operational impact are likely to shape how data platforms compete.