Domo Accelerates AI Into Production at Bissell — Five Operational Workflows Built in Two Days
Bissell used Domo to build five AI workflows in two days, replacing hours of manual reporting with automated insights for customer service and product teams.
Bissell, the floor-care and cleaning-products company, and analytics vendor Domo staged a rapid, two-day AI sprint that yielded five working workflows and agents designed to remove repetitive work and surface actionable insights. The experiment is notable not because it produced a flashy prototype, but because it translated AI conversation into tangible automation that employees can use immediately. The Domo-powered sprint targeted real business problems across customer experience, product design, marketing and sales reporting — showing how a focused, cross-functional effort can turn analytics and AI tools into operational systems rather than theoretical pilots.
Why Bissell Turned to Domo for a Rapid AI Sprint
Organizations often struggle to move from curiosity about AI to operational deployments. Bissell’s team confronted that gap directly. Rather than letting teams experiment with consumer-grade generative tools in isolation, the company partnered with Domo to run a structured, hands-on workshop that emphasized immediate utility and integration with existing data. Domo’s platform combines data ingestion, transformation, visualization, and low-code automation capabilities, making it a practical choice for teams that want to attach AI agents and workflows to business metrics without building a custom stack from scratch.
The case reflects a broader pattern in enterprise AI adoption: success often depends less on the novelty of models and more on how quickly stakeholders can map AI capabilities to specific operational pain points, then iterate with real data. For Bissell, that meant bringing together product, customer support, engineering, sales and marketing stakeholders and giving them the time and tools to build, test, and refine working workflows.
Designing Five Use Cases: Concrete Problems, Concrete Outputs
The sprint did not aim to solve abstract AI puzzles. Instead, Bissell defined five discrete business problems and assigned cross-functional owners for each. These included:
- Post-launch excellence (PLE) tracking and analytics to monitor product rollouts and customer feedback.
- Customer service issue tracking to identify repeat problems and accelerate resolution.
- Marketing reporting to consolidate campaign metrics and reduce manual assembly of dashboards.
- Resource planning for product design to align staffing and timelines with real-time demand signals.
- Sales reporting focused on top-selling products to speed analysis for commercial teams.
Each use case represented a task that previously required significant manual labor — sometimes hundreds of hours a year — such as toggling between dashboards, extracting data from PDFs or slide decks, and compiling trend analyses. By centering the sprint on measurable business outcomes, the team forced choices: what data sources mattered most, how an agent should present results, and what level of automation would be genuinely useful to the people doing the work.
How the Two-Day Domo Sprint Was Structured and Run
The workshop approach was deliberately compact. Key personnel were removed from their day-to-day responsibilities for two full days, with executive backing to ensure focus. Domo consultants worked alongside internal owners so that domain knowledge and platform expertise were available in the same room. The sprint favored lightweight, iterative development: build a minimum viable agent, validate it against a real data set, then expand features based on stakeholder feedback.
This cadence produced rapid feedback loops. For example, a sales analytics agent that aggregated Amazon sales data was assembled in around an hour, demonstrating how prebuilt connectors and low-code interfaces can collapse integration time. The goal was not to ship a polished enterprise system at the end of day two, but to create operational prototypes that reduce manual effort immediately and can be hardened afterward.
Inside the PLE Agent: Trends Discovered and Time Saved
One of the most striking outcomes was the PLE agent. Before the sprint, analysts spent eight to ten hours gathering scattered information — dashboards, documents, and presentations — to spot emerging product issues post-release. The Domo agent consolidated data and surfaced three clear signals within minutes of deployment:
- A roughly 20% increase in users seeking supplemental resources such as instructional videos and manuals.
- Approximately a 27% rise in general product-related questions from customers.
- About a 15% uptick in troubleshooting requests tied to device power and functionality.
These findings, surfaced quickly by the agent, allowed teams to prioritize content creation, customer communications, and engineering triage more rapidly than previous, manual approaches. What used to be an all-day extraction exercise turned into a near–real-time insight pipeline, reducing lead time from discovery to action.
Operational Gains Across Marketing, Product, and Sales
Beyond PLE, the other four workflows produced comparable efficiency and clarity gains. The marketing reporting workflow automated the collection and normalization of campaign KPIs, removing the need for repeated manual merges and recalculations across channels. Product design teams used resource-planning automation to reconcile staffing levels with anticipated workloads, helping managers avoid bottlenecks and optimize time-to-market. Sales teams gained a streamlined top-sellers dashboard that reduced the turnaround for revenue analysis and decision-making.
The shared pattern across these projects was pragmatic: lightweight automation and AI-integration were applied incrementally to high-friction tasks. That approach preserved trust among domain experts — people who had been skeptical after playing with consumer AI tools — because they saw immediate reductions in busywork and tangible improvements in how they could act on insights.
Data Readiness and Governance: The Foundation for Productive AI with Domo
A recurring theme from the sprint was that AI work stalls without clean, accessible data. Teams that toy with models often discover the real bottleneck is data wrangling, not model selection. Bissell’s experience reinforced this: the productivity gains from building agents were realized only because relevant data sources had been prepared and integrated into the Domo platform.
This raises two operational imperatives for organizations adopting Domo or similar analytics platforms:
- Invest in a data fabric that centralizes and standardizes sources so agents can query reliable information without ad hoc extraction.
- Couple automation with governance controls — access policies, audit trails, and quality checks — to ensure outputs are trustworthy for operational decisions.
Failure to address data readiness steers teams back into manual mode, where promising AI efforts are consumed by pipeline repairs rather than generating impact. For CIOs and analytics leads, the lesson is practical: allocate time to integrate and sanitize core datasets before announcing broad AI initiatives.
Who Should Run This Kind of Sprint and Which Teams Benefit Most
The sprint model suits organizations that meet a few conditions. First, they should have clear, recurring manual processes — reporting, triage, trend detection — that a workflow or AI agent could reasonably streamline. Second, leadership must grant protected time for domain experts to participate; rapid prototyping requires sustained attention. Third, a minimum set of platform capabilities is necessary: connectors, data transformation tools, visualization, and automation primitives.
Teams that typically gain the most are customer support, marketing operations, product management, and commercial analytics. Smaller entities or departments inside larger firms can use a lightweight version of the sprint model to target a single high-impact workflow, while enterprise teams may scale multiple simultaneous sprints across business units.
Developer and Integration Considerations for Domo-Based AI Workflows
From a technical perspective, Domo’s value proposition during the sprint was its access to prebuilt connectors and its capacity to host automation and agent logic close to the data. Integrators and developers will want to evaluate:
- API access and rate limits for upstream systems such as CRMs, e-commerce platforms, and cloud logs.
- Mechanisms for extending agent behavior, whether via embedded prompt templates, serverless functions, or integrations with internal microservices.
- Monitoring and alerting hooks so that automated agents feeding business processes are observable and can be debugged when they diverge.
- Security posture — encryption at rest and in transit, role-based access, and audit logging — so automation does not introduce new compliance risk.
Teams with engineering resources can bind Domo outputs into internal tooling — for instance, pushing priority tickets into a support queue, triggering resource allocations in planning systems, or populating marketing dashboards used by campaign managers. The easier this integration, the less fragmented the user experience and the faster the ROI.
How to Avoid AI Hype and Build Practical Automation
Bissell’s sprint provides a counterpoint to overblown expectations around AI. Rather than chasing broad transformational narratives, the sprint prioritized demonstrable, incremental value. Practical steps for teams trying to replicate that success include:
- Start with a narrowly scoped pain point that is well understood by a business owner.
- Ensure data availability for that use case before building agent logic.
- Protect time for cross-functional stakeholders so decisions can be made rapidly.
- Build a minimum viable agent that returns useful results quickly, then iterate.
- Measure time saved, error reduction, or response improvements — quantify the benefit so subsequent investments are justified.
This pragmatic posture mitigates the “AI toy” problem where staff tinker with consumer models in their spare time but fail to translate experiments into operational change.
Wider Industry Implications and What This Means for Developers and Businesses
The Bissell–Domo sprint illustrates several trends that resonate across the software and enterprise landscape. First, low-code analytics and automation platforms are lowering the barrier to productionizing AI by removing infrastructure complexity. That shift redistributes the work: success depends less on building custom pipelines and more on orchestration, change management, and domain expertise.
Second, short, focused sprints can democratize AI adoption. They reduce the initial investment and risk associated with large centralized AI programs, enabling departments to pilot validated workflows that can later be scaled. For developers, this means the role is moving toward building robust connectors, APIs, and governance layers that allow business users to safely produce operational automation.
Third, organizations that prioritize data readiness will outpace peers. As Bissell showed, even simple agents deliver outsized benefit when data is accessible and clean. The implication for CIOs and data leaders is clear: invest in data engineering and governance as the first order of AI business value, not last.
Finally, there are implications for vendors and the broader tooling ecosystem. Platforms that combine ingestion, transformation, visualization, and automation — and which enable easy injection of AI reasoning or natural-language interfaces — are likely to see increased adoption. This converges with developments in CRM, marketing automation, and product analytics, where AI can be embedded to reduce manual assembly and accelerate insight-to-action cycles.
Practical Checklist for Running Your Own Two-Day AI Sprint Using an Analytics Platform
If you’re considering a similar sprint, here’s a practical checklist based on the Bissell experience:
- Define 1–5 high-friction business problems with measurable outcomes.
- Secure executive endorsement and protected time for participants.
- Assemble a core team: a business owner, a data lead, a platform expert, and an engineer or two.
- Line up the data sources and verify basic quality checks before day one.
- Use platform connectors to centralize the data; avoid manual file sharing when possible.
- Build minimum viable agents that prioritize clarity and actionable outputs over model complexity.
- Validate agent outputs with domain experts and iterate in short cycles.
- Document assumptions, data lineage, and governance controls for each workflow.
- Plan a roadmap to harden the most valuable agents after the sprint, including monitoring and SLAs.
Treat the sprint as a discovery and value-proving exercise — not the final production release. The point is to transition from skepticism to repeatable practice.
Bissell’s sprint also underscored the importance of accessible prompt and template libraries for professionals. Teams that codify effective agent prompts and response structures accelerate subsequent sprints and reduce reinvention.
The Domo engagement shows how an analytics platform can shortcut the time between ideation and impact, provided data is ready and teams are empowered to move fast. For companies struggling to produce tangible AI outcomes, the two-day sprint is a replicable pattern to convert interest into operational systems that reduce manual labor and surface meaningful signals.
Looking ahead, expect more organizations to adopt short-form, cross-functional AI sprints as a practical route to productionizing automation. As analytics platforms evolve to include richer AI primitives, the differentiation will shift to how well companies prepare their data, govern automated decisioning, and integrate agents into everyday workflows — not to the novelty of the models themselves.
