Enterprise AI, Automation & Workday Financials
Prevvi Team
Client
A national healthcare services organization
Industry
Healthcare Services
What was delivered
- NetSuite-to-Workday Financials migration
- Enterprise AI and automation program
- Data and integration architecture
- Identity and security operations
- Program leadership
Founder-led enterprise project. This work was delivered by Prevvi's founder in an in-house leadership role before Prevvi was founded. It is shared here as part of the experience behind how Prevvi operates, not as a Prevvi client engagement.
Before founding Prevvi, our founder led technology programs at a national healthcare services organization with approximately 16,000 employees. Two of those programs define how Prevvi thinks about enterprise change today: a financial systems transformation from NetSuite to Workday Financials, and an enterprise AI and automation program that put generative AI to work across Finance, HR, Operations, and IT.
The challenge
Large organizations do not get to pause while their systems change. The finance team was closing books in NetSuite while the migration to Workday Financials was designed, built, and tested around them. Meanwhile, departments across the company were drowning in manual work, repetitive requests, report assembly, cross-system data wrangling, exactly the workload where automation and generative AI can compound.
Both programs shared a constraint: at 16,000 employees, mistakes multiply. Integrations, data quality, security, and compliance with HIPAA, SOX, and NIST-aligned controls were not side quests; they were the terrain.
What was delivered
NetSuite-to-Workday Financials transformation
- Program leadership across financial systems architecture, stakeholder coordination, and operational transition
- Data migration from NetSuite into Workday at enterprise scale
- Integrations connecting Workday to the platforms around it
- Testing and reporting so finance operations landed on the new platform without losing a step
Enterprise AI and automation program
- Generative AI and automation introduced across Finance, HR, Operations, and IT
- Hundreds of manual hours eliminated through automated workflows
- Faster internal response times, with selected response processes improved by approximately 30%
- Growing automation adoption across departments as early wins built trust
The connective tissue
- Enterprise data and integration architecture linking Workday, ADP, Google Cloud, finance, HR, and operational reporting systems, so decision-makers stopped reconciling fragmented data by hand
- Identity and security operations for the full workforce: MFA and identity management, application access, vendor and MSP management, and HIPAA, SOX, and NIST-aligned controls
How it was approached
Transformation is a people program with a technical core
A financial systems migration succeeds or fails on stakeholder alignment: finance leadership, accounting teams, auditors, and integration owners all have veto power in practice. Program leadership meant sequencing the technical work inside a communication structure where every stakeholder knew what was changing, when, and what was expected of them.
Automate where the hours actually go
The AI program did not start from what the technology could do; it started from where employees spent repetitive hours. Processes were selected for automation based on volume and pain, which is why the program produced measurable results, hundreds of hours returned, response processes about 30% faster, instead of demos.
Governance makes AI adoption durable
In a healthcare organization, AI touches data governed by HIPAA and processes governed by SOX. Every automation shipped inside guardrails: defined data handling, human review where judgment matters, and controls that satisfied the same audit regime as the rest of the enterprise. Adoption grew because the safe path was also the easy path.
What smaller businesses can take from enterprise AI programs
The lessons scale down better than the budgets:
- Pick processes by hours, not hype. The best first automation is the boring one that eats the most repetitive time.
- Measure before and after. “Hundreds of hours saved” is only knowable if you baseline the process first.
- Put guardrails in from day one. Data-handling rules and human review points are much easier to build in than to bolt on.
- Let wins recruit the next department. Adoption spreads through demonstrated results, not mandates.
This is the same playbook Prevvi now applies for small and midsize businesses through our AI solutions practice: find the hours, automate with guardrails, measure, and expand. When the change also involves platforms and process, our IT consulting work covers the program side.
The outcome
The organization moved its financial core from NetSuite to Workday Financials, connected previously fragmented HR, finance, and operational data, and built an AI and automation practice that eliminated hundreds of hours of manual work while improving response times across multiple business functions, all inside HIPAA, SOX, and NIST-aligned controls.
Key takeaways
- Enterprise transformations are led through stakeholders; the technology is the easier half.
- Choose automation targets by measured hours, and baseline before you build.
- Compliance guardrails are what let AI adoption survive contact with auditors.
- Fragmented data undermines every downstream decision; integration architecture pays for itself.
Frequently asked questions
By measured hours, not by hype. The best first automation is the repetitive process that eats the most staff time. Baseline the process before building so the result is provable; that discipline is why this program could report hundreds of hours saved and roughly 30% faster response processes rather than vague wins.
Yes, when every automation ships inside guardrails: defined data-handling rules, human review where judgment matters, and controls that satisfy the same HIPAA, SOX, and NIST-aligned audit regime as the rest of the enterprise. The safe path has to also be the easy path, or adoption stalls.
Stakeholder alignment more than technology. Finance leadership, accounting teams, auditors, and integration owners all hold practical veto power, so the technical work has to run inside a communication structure where everyone knows what changes when. Data migration, integrations, and testing are necessary but not sufficient.
Directly. Pick targets by hours, baseline before building, put data-handling guardrails in from day one, and let early wins recruit the next department. The budgets scale down; the playbook does not change.
When HR, finance, and operational data live in disconnected systems, every report requires manual reconciliation, numbers disagree across departments, and decisions ride on stale spreadsheets. Integration architecture connecting platforms like Workday, ADP, and cloud data environments is what turns reporting from archaeology into information.
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