ARTICLE SUMMARY
A workflow automation platform is the software a company uses to standardize, automate and orchestrate its workflows end to end. In the agentic era, the differentiator is no longer task automation, but the governed orchestration layer that connects systems, people and AI Agents.
By the time a workflow automation platform reaches your shortlist, the demos all look alike. Every vendor shows drag-and-drop builders, a library of connectors and an AI Agent that drafts an email. On a feature grid, the differences are hard to see.
The difference that matters only shows up later, in production: can the platform run a full process across your legacy ERP and dozens of accessory systems, with governance and a result your CFO can measure? That is where most tools stall, and where the return separates them.
That return is now documented. Forrester’s The Total Economic Impact™ study of Pipefy found up to 260% ROI on the platform, with payback in under 6 months. For a buyer comparing options, the useful question is not whether a platform can automate a task, but whether that automation compounds into a measurable outcome.
This article answers that with evidence. We pulled 10 companies, from US fintechs to global manufacturers, that moved past isolated automation and orchestrated their workflows with AI on Pipefy.
They are a sample of the 30+ cases in a Pipefy exclusive e-book, and together they show the one thing a feature grid never will: companies don’t scale with loose prompts, they scale with orchestrated processes.
[E-Book] 30+ companies that orchestrated their processes with AI in Pipefy
How to evaluate a workflow automation platform in the agentic era
Most operations run their processes at mixed maturity levels, from fully manual to partly automated, and almost none reach the top. In the second edition of the Pipefy AI Research, a study of AI adoption across companies, only 6.6% of US organizations run processes that are fully orchestrated and integrated end to end.
Reaching that stage is the real test of a platform. When evaluating options, three questions separate a platform that orchestrates the process from one that only automates tasks.
1. Does it run the whole process, or just the easy step?
A real platform coordinates the flow end to end across the systems, bots and integrations you already own, instead of replacing them. Each step behaves as a deterministic state machine, with an entry trigger, an explicit rule, an output and an owner, and AI Agents operate inside that step, inheriting its rules, context and permissions.
2. Can you prove what the AI did, and predict what it costs?
Governance is the dividing line. The platform should add human approval at critical points and keep an audit trail of every action, so AI never runs as Shadow AI and cost stays predictable instead of varying with every token consumed.
3. Who builds the next workflow, IT or the business?
If every new automation becomes another ticket for the technical team, adoption stalls. The business should configure and adjust flows in no-code, within IT’s rules, and choose the AI model, including the one the company already runs.
Use these three questions as a scorecard. The table below turns them into the difference a buyer actually sees between a tool that automates tasks and a platform that orchestrates the process:
| What to check | Automation only | What orchestration looks like |
| Process coverage | Each bot solves one isolated step | A single flow runs end to end across systems |
| AI Agents | Run loose, without process context | Inherit the rule, context and permission of each step |
| Governance | No audit trail, cost varies per token | Full trail and predictable, bounded cost |
| Ownership | Every change goes back to IT | Business configures in no-code, under IT’s rules |
| Outcome | Local wins, hard to measure | A result leadership can audit and report |
This is the principle behind Pipefy, as André Kano, Business Unit Manager at Pipefy, frames it:
The unit that delivers value to companies is the process. Companies don’t produce results by running a lot of specific tasks across their departments. They deliver results when they establish processes that are repeatable and produce results at scale, consistently.
André Kano
Business Unit Manager at Pipefy
- Learn more: To see how these questions apply to a specific flow, the guide on agentic orchestration connects the criteria to practice.
10 companies that scaled their workflows with AI on Pipefy
Every case below starts from the same place, work trapped in spreadsheets, email threads and disconnected systems, and ends at a number leadership can report. The industries differ; the shape of the gain does not.
- isaac (Fintech): in Accounts Payable, a Pipefy AI Agent took over data extraction and payment processing, integrated with the ERP and Slack, cutting processing time by 50% and operating cost by $420k a year.
- Stanley (Manufacturing): the manufacturer put 5 AI Agents into production in credit and risk analysis, shrinking the cycle from 25 to 7 days and reclaiming capacity equivalent to roughly 48 FTEs.
- ZEISS (Optics): by standardizing back-office processes on the platform, the company lifted productivity per FTE by 144%, freeing teams for higher-value work.
- Capgemini (Professional Services): in the Order to Cash cycle, orchestrating the flows on Pipefy raised the automatic approval rate to 50%, cutting manual triage and rework.
- Dasa (Healthcare): the largest integrated healthcare network in Brazil orchestrated its supply-chain workflows on Pipefy and recorded a 295% increase in operational efficiency.
- Algar (Telecom): MIA, the AI Agent built on Pipefy, validates contracts, creates purchase requests in SAP and processes invoices automatically, running more than 140 active automations.
- Coplacana (Agribusiness): one of the largest sugarcane cooperatives in São Paulo orchestrated its IT, Credit and Finance workflows on Pipefy and reached 157% ROI, saved more than 1,700 hours and cut credit analysis from 25 to 8 days.
- CTA Smart (Fleet Fueling): a leader in fleet fueling control standardized more than 40 commercial workflows in Pipefy’s CRM, grew sales by 300% and cut the sales cycle from 90 to 42 days.
- Grupo Delta (Construction): it structured its entire commercial operation on Pipefy, with AI Agents from lead qualification to target tracking, and scaled revenue 10x in 4 years.
- James Delivery (Retail): it orchestrated operations that once lived in spreadsheets and email and reached 134% ROI in 2 months.
What the numbers have in common
Read side by side, the cases point to one buyer takeaway: the gains do not track the number of bots deployed, they track whether a single layer governs the whole process. When the process runs the operation, the impact lands in the indicators, from ROI to cycle time, not in perception. The patterns that repeat:
- 300% sales growth at CTA Smart and 10x revenue in 4 years at Grupo Delta.
- 157% ROI at Coplacana and 134% ROI in 2 months at James Delivery.
- 295% supply-chain efficiency at Dasa and 144% productivity per FTE at ZEISS.
- 50% automatic approval in Capgemini’s Order to Cash and a 25-to-7-day credit cycle at Stanley.
- More than 1,700 hours saved at Coplacana and $420k a year in cost reduction at isaac.
The common denominator is never the count of automations. It is the layer that connects them, the core premise of any workflow orchestration platform and every business process automation software: value comes from orchestrating the work, not from stacking isolated tools.
Why Pipefy wins on orchestration, not just automation
What these cases share is the platform underneath them. In the agentic era, value shifts away from the AI model and toward the layer that organizes and governs the work between people, systems and AI Agents.
Pipefy is built to be that agentic orchestration layer for scaling governed processes. Rather than adding one more system to the stack, it connects ERPs, accessory systems, people and AI Agents into one traceable flow, where each step generates the data that proves the return.
For IT, that changes the buying calculus. Architecture, integration and vendor risk get validated once and then enable many areas. The legacy stays in place, and business teams configure flows in no-code without overloading engineering, the same model behind the gains in IT operations.
The reason this layer is the real moat is simple: AI models can be replicated, but the context of how your company operates, its rules, exceptions and approvals, cannot. Ignoring that layer carries a market-sized cost. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, driven by escalating costs and unclear business value.
So the competitive edge is not building an agent, it is orchestrating one. As Renato Hartz, Head of Business Units at Pipefy, puts it:
Building an agent is easy. There are more than 100 players in the market today, globally, that build AI Agents. The hard part is orchestrating that intelligence. That is the real challenge, and it is where Pipefy places its bets.
Renato Hartz
Head of Business Units at Pipefy
That layer adapts to each operation. For instance, in credit, the Credit Decisioning AI Studio orchestrates the full workflow in one flow, as in the Coplacana and Ademicon cases.
In procurement and back office, the P2P AI Studio automates end-to-end purchasing against the ERP, as Algar shows.
The same platform holds up in highly regulated industries, such as financial services and insurance, with the governance they demand. As João Moreira, General Manager of the Insurance BU at Pipefy, puts it:
Your company doesn’t run on prompts, it runs on processes. And we are able to orchestrate AI Agents and automate processes with governance in a highly regulated industry like financial services and insurance.
João Moreira
General Manager of the Insurance BU at Pipefy
See all 30+ cases before you shortlist a platform
The 10 stories above are a sample. In this exclusive e-book — 30+ companies that orchestrated their processes with Artificial Intelligence in Pipefy —, we bring together 30+ cases in full, across different industries and areas, each with the challenge, the solution and the proven results.
Inside, you will find:
- Cases from Sales & CRM, Credit & Risk and Accounts Payable & Finance.
- Cases from HR & Onboarding, Support & Customer Success and Back office.
- The ROI, cycle-time, freed-capacity and cost-reduction numbers each company reported.
- The pattern that repeats: from isolated automation to orchestrated operations at scale.
