AI consulting · Software
AI consulting for software and SaaS companies
Native helps software and SaaS companies put AI into production across support, sales operations, finance and engineering, with forward-deployed engineers inside each team. At Avalara, that meant 150+ deployments across 8 departments in under six months. A first deployment in one team usually lands within weeks.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 3 min read
Where AI has an impact in software companies
Support triage
Tickets classified, answered from the docs where possible and routed with a summary when not.
Sales credit and territory inquiries
Questions about who gets credit for a deal picked up from the CRM and the rules, with a first response in an hour rather than twelve.
Manager coaching
An assistant in Slack that helps managers prepare for meetings, onboard hires and find policy.
Workflow governance
Every automation scanned for security and compliance issues before it reaches production.
AI code review
Every pull request reviewed by an agent before an engineer looks at it.
Finance operations
Contracts and invoices read into billing and revenue schedules, with exceptions flagged.
Customer onboarding
Implementation checklists run and chased, so new customers go live sooner.
Product feedback
Calls, tickets and reviews summarised into themes the product team can act on.
Why software companies stall on AI
Software companies adopt AI tools faster than anyone, and that is often the problem. Every team runs its own experiments, nobody measures them the same way, governance arrives late and slows everything down, and the work depends on a few enthusiasts rather than on a system. The company has a lot of AI and very little of it in production.
The fix is an operating model more than a model choice: one way to bring ideas in, one measure of value to rank them, someone in each department accountable for delivery, and governance that checks work before it ships instead of blocking it.
How Native helped Avalara
At Avalara, forward-deployed engineers worked inside each department, every initiative was scored in hours saved per year, and a risk scanner checked workflows for security and compliance issues before they reached production. In under six months the program put 179 agents into production across 8 departments.
In RevOps, first response on sales credit inquiries fell from 12 hours to 1. An assistant in Slack supports 600+ managers with meeting preparation, coaching and onboarding. The governance scanner covered all 720 workflows and found that 84% had issues, which surfaced them before release rather than after.
What agents do in each team
| Team | Agents handle | People keep |
|---|---|---|
| Support | Triage, first responses from the docs, ticket summaries | Escalations and customer relationships |
| Sales and RevOps | Credit and territory inquiries, CRM hygiene, deal research | Forecasts and negotiations |
| Finance | Invoice and contract data, revenue schedules, collections follow-up | Close sign-off and judgement calls |
| Engineering | Code review, small changes as draft pull requests, tests and docs | Architecture and merges |
| People and managers | Policy questions, onboarding, meeting preparation | Performance decisions |
Engineering goes first
In a software company, engineering is usually the team that moves fastest and shows the others what working with agents looks like. At Avalara, an automated platform took software development cycles from 12 months to 30 days. Our page on AI-native engineering covers how that works: AI review on every pull request, small changes drafted by agents, and engineers who review and merge.
The other departments follow on the same intake model and the same measure, so a support agent and a finance agent can be compared and prioritised side by side.
How to measure the impact
- Hours saved per year, per initiative, as the one shared measure
- Deployments and agents in production, by department
- First response and resolution times on internal and customer requests
- Share of workflows passing the governance scan before release
- Development cycle time from spec to production
Case study · Avalara
Avalara put AI into production across 8 departments in under six months.
Native helped Avalara move from scattered AI experiments to a repeatable way of deploying them, with forward-deployed engineers in every department, one shared measure of value, and 150+ deployments across the business.
Frequently asked questions
How are SaaS companies using AI internally?
Across every department: support triage, sales operations, finance, engineering and manager enablement. The companies that get the most impact run it as one program with one measure, rather than as separate team experiments.
What does an AI consultant do for a software company?
An AI consultant sets up the operating model for getting AI into production, then builds the agents with each team. At Native, forward-deployed engineers work inside each department and stay accountable for delivery.
How do you measure the ROI of AI at a software company?
Pick one measure and use it everywhere. At Avalara every initiative was scored in hours saved per year, which made support, sales and engineering work comparable and set the order of work.
How do you govern hundreds of AI workflows?
Scan them before they ship. At Avalara a workflow risk scanner covered all 720 workflows and found issues in 84% of them before they reached production.
How fast can a software company get AI into production?
The first deployment in one department usually lands within weeks. Avalara reached 150+ deployments across 8 departments in under six months.
