Quick summary
AI can compress enterprise development timelines, but speed without structure creates technical debt. This guide covers how to define requirements, select production-ready infrastructure, manage compliance, automate workflows, and deliver software that enterprise buyers trust. Six steps, no shortcuts.
What does it take to build enterprise software with AI?
Most teams that build enterprise software with AI fall into one of two camps.
The first moves fast, spins up a prototype in days, then discovers it can’t handle compliance, scale, or paying customers without a near-complete rewrite. The second spends months evaluating platforms and assembling committees, only to have the market move past them. Neither works.
What does work is using AI with intent while building on production-ready infrastructure from the start. This Launchpad guide walks through how to do that, step by step, without the false starts that waste budget and burn out teams.
Before we begin…
Why listen to us?
At Launchpad, we don’t just write about building enterprise software. We provide the platform that B2B software companies use to do it. Backed by Pega's 40 years of enterprise workflow automation, Launchpad powers production applications for companies like Fielo (serving Google and Audi), Quavo (25x revenue growth), and Connex.
Every founder building something today should believe they’re changing the world. Launchpad gives us the best chance of doing that.
-Brian Ratcliff, CEO, Rulescom
Why AI changes the economics of enterprise software
By 2028, 75% of enterprise software engineers will use AI code assistants, up from less than 10% in early 2023, according to Gartner, Inc. These aren’t incremental improvements. They represent a structural shift in how software gets built in this technology-driven world
For B2B software companies, the implications are that development cycles that used to take three sprints can now be compressed into one. What’s more? Repetitive infrastructure work that consumes senior engineering time can be automated. And the barrier to building sophisticated, multi-tenant applications has dropped significantly.
But speed without structure creates problems. A Veracode analysis of code generated by over 100 LLMs found that 45% contained security vulnerabilities. For Java, the failure rate was 72%.
Moving fast with AI-generated code is only valuable if the underlying platform handles the security, compliance, and scalability requirements that enterprise customers demand.
Step-by-step guide on how to build an enterprise software with AI
Enterprise buyers don’t want experiments. They want systems that pass audits, scale predictably, and integrate cleanly with their existing stack. This is where AI amplifies outcomes, but only when paired with durable foundations today.
Whether you're starting from scratch or rethinking an existing product, these six steps will help you move from idea to production without the false starts that derail most teams.
Step 1: define what you’re actually building (and for whom)
The foundation stage is where most AI-assisted projects go off the rails. The temptation with modern AI tools is to start building immediately. In a minute, you describe your app to an AI agent, watch it generate code, and iterate from there.
That works for consumer prototypes and internal tools. For enterprise-grade software that customers will pay for and depend on, however, skipping this step is costly.
Here is what to clarify before writing a single line of code:
- State your business problem: Be clear on what problem you want to solve and for whom you are proffering the solution. The specificity matters because it determines your architecture, data model, and compliance requirements from day one.
- Outline your customer’s technical environment: Per Sopro, nearly 8 in 10 buyers and almost 9 in 10 enterprise buyers know what they want before their research begins. This shows that first impressions matter and influencing buyers early is crucial. Enterprise buyers operate within constraints: existing tech stacks, security policies, procurement processes, and integration requirements.
- Create your differentiation playbook: AI makes it easy to build generic software fast. That's exactly why it’s becoming commoditized. The true value of your product lies in the domain expertise, workflow design, and proprietary logic you bring to the problem. Protect that and automate everything else.
Let's look at a practical example of how this plays out:
Say two teams are building expense management software for healthcare organizations. Team A jumps straight into an AI code generator and produces a working prototype in a week. It looks polished, handles basic expense submissions, and demos well.
Team B spends the first week mapping the compliance workflows specific to healthcare procurement: approval hierarchies tied to grant funding sources, audit-trail requirements for federally funded programs, and integration points with existing EHR systems. Then they build on a platform that natively supports those requirements.
Team A's prototype needs six months of rework before it passes a single hospital’s vendor security review. Meanwhile, Team B ships to their first customer in the same timeframe. The big difference is clarity of scope, not the coding speed.
Step 2: choose a platform that’s production-ready from day one
This is the decision that compounds the most over time and what most teams often get wrong. AI-powered development platforms currently fall into three categories: AI code generators, traditional enterprise platforms, and purpose-built enterprise development platforms.
Ask these questions before choosing between the three categories:
- Can I ship to enterprise customers on day one? If the platform requires you to rebuild for production after prototyping, you're paying for the same work twice.
- Is the pricing predictable as I scale? Credit-based pricing models can spiral out of control when AI agents consume resources unpredictably. Look for usage-based models tied to actual value delivery rather than opaque credit systems.
- Do I own the differentiation? If the platform locks your IP into a proprietary ecosystem, your product's value is partially captured by your infrastructure provider.
AI-accelerated coding tools deliver 20-30% productivity gains, yet those gains do not translate into lower R&D spending or faster product cycles, according to AlixPartners. Tools like Replit, Lovable, and Bolt are excellent for rapid prototyping because they can produce functional applications from natural language prompts in minutes. But the output typically requires significant rework to meet enterprise security requirements.
On the other hand, traditional enterprise platforms, like Salesforce, ServiceNow, and SAP, offer the compliance and scalability enterprise buyers expect. The trade-off is development speed and flexibility. Customization tends to be expensive, slow, and locked into the vendor’s ecosystem.
Purpose-built enterprise development platforms like Launchpad bridge the gap by combining the speed benefits of AI-assisted development with the infrastructure rigor required for enterprise software. These platforms offer pre-built multi-tenant architecture, compliance certifications, managed cloud infrastructure, and AI-powered workflows.
Step 3: use AI to accelerate development (not replace engineering judgment)
Once the foundation is in place, AI becomes genuinely valuable. It is most effective when applied to repeatable, well-understood work, like generating standard application patterns, accelerating test creation, maintaining documentation, and supporting refactoring as the codebase grows. Used this way, AI frees senior engineers to focus on architecture, domain logic, and system design.
Where teams get into trouble is when they allow AI to make judgment calls it is not qualified to make. Architectural decisions, security reviews, domain-specific workflows, and user experience design still require human expertise.
Given that nearly half of AI-generated code contains security flaws despite appearing production-ready, every AI-produced code block needs a security review before it reaches production. This is important for enterprise software because AI-generated interfaces tend toward generic patterns.
The interaction design that makes enterprise software genuinely useful (clear information hierarchy, efficient workflows, appropriate defaults) still requires human designers who understand the end user’s context.
Step 4: build with compliance and security as defaults
Enterprise software that treats compliance as an afterthought doesn’t survive procurement.
The companies that build AI-powered enterprise software treat security and compliance as foundational constraints, not features to bolt on only when a sales cycle requires them.
Rather than building on a general-purpose platform and adding compliance layers later, choose infrastructure that includes SOC 2, ISO 27001, GDPR, and HIPAA compliance from the beginning.
This eliminates months of security hardening work that would otherwise block enterprise sales. Platforms like Launchpad include all four certifications by default, so teams can engage with enterprise buyers without separate security audits delaying the timeline.
Multi-tenant enterprise software requires granular permissions at the architectural level. Retrofitting access control into a prototype-grade codebase is one of the most common and costly sources of technical debt.
Pro tip: You can integrate GenAI directly into your business workflow to deliver compliant, dependable, enterprise-grade software that automates compliance checks against policy documents in real time.
Step 5: automate your workflows beyond coding
Here's where many AI-assisted development efforts miss the larger opportunity. Generating code faster is valuable. But the biggest productivity gains in enterprise software come from automating workflows surrounding development, including deployment pipelines, testing sequences, environment management, data migrations, and operational monitoring.
At the same time, the most defensible products extend automation outward. They apply AI to customer-facing workflows, not just internal systems. This is where long-term differentiation compounds.
Enterprise software requires 24/7 monitoring, alerting, and incident response processes. Platforms with built-in observability and auto-scaling can reduce operational overhead compared to configuring those layers separately.
This is where purpose-built platforms show their advantage. Platforms with built-in workflow orchestration handle deployment, scaling, and operational management as default features rather than custom engineering projects. That frees your team to focus their automation efforts on the product-specific workflows your customers actually care about.
Step 6: ship, measure, and iterate your launch process
The teams that build lasting products treat their first release as the beginning of a feedback loop. Here’s what to measure after launch:
- Adoption metrics: Track active usage, feature adoption, and workflow completion. These signals tell you whether your product is solving the problem it was designed to solve.
- Infrastructure performance: Monitor response times, error rates, and resource utilization. AI-generated code can sometimes introduce performance regressions that only surface under real-world load. Catch them early.
- Customer feedback loops: Enterprise buyers will tell you exactly what they need if you create structured channels for that feedback. Build these into your product (not just your sales process) from the beginning.
- Development velocity: Track how quickly your team ships improvements over time. If velocity is declining, your architecture may be accumulating technical debt that needs attention. If it's increasing, your platform and process choices are compounding in your favor.
Teams that built their initial version with heavy AI assistance sometimes struggle to maintain it, because even though it works, no one on the team fully understands it. When something breaks in production, debugging takes longer because the codebase doesn't reflect how your engineers naturally think about the problem.
The goal is to treat AI-generated code the same way you'd treat code from a new contractor. Review it, understand it, and refactor anything that doesn't meet your team's standards for readability and maintainability.
Common failure patterns in AI-built enterprise software
Most teams don’t fail because AI slows them down. They fail because early speed hides structural gaps that surface later.
The most common patterns look like this:
- Treating prototypes as foundations: AI-generated demos ship fast but are rarely designed for multi-tenancy, access control, or auditability. Rework becomes inevitable once real customers are involved.
- Optimizing for build, not procurement: Tools that feel powerful during development often struggle in security reviews, compliance checks, and pricing scrutiny.
- Deferring compliance: Security and governance are pushed downstream until sales demand them. This delays deals and forces rushed retrofits.
- Letting AI own critical logic: Code works, but no one on the team fully understands or owns it. When something breaks, velocity drops.
The difference between building software and building a software business
AI has lowered the barrier to building functional software. But the barrier to building enterprise software that customers trust, rely on, and renew year after year hasn't changed as much as the prototype-in-a-weekend narrative suggests.
Enterprise buyers evaluate security certifications, uptime guarantees, data handling practices, integration capabilities, and vendor stability. The teams that succeed use AI to move faster without compromising the fundamentals. They automate the commodity work and invest their engineering talent in the differentiation that justifies their price point.
Launchpad was built for exactly this kind of team. With enterprise-grade infrastructure, AI-powered development workflows, and Pega's proven orchestration engine, B2B software companies have the foundation to build, launch, and scale products their customers can depend on.
Start with Launchpad today and build enterprise software that's ready for the real world from day one.
FAQs
1. Can AI replace engineers when building enterprise software?
No. AI can accelerate execution, but it cannot replace engineering judgment. Enterprise software requires architectural decisions, security reviews, and domain expertise that AI is not equipped to own. Teams that treat AI as a force multiplier rather than a decision maker ship faster and avoid costly rework.
2. Is it risky to use AI-generated code in enterprise applications?
It can be, if it is not reviewed. Studies consistently show that a significant portion of AI-generated code contains security vulnerabilities. The risk is manageable when AI output goes through the same review, testing, and security scanning as human-written code. For enterprise software, this is table stakes.
3. Can AI-first prototypes fail in enterprise environments?
Yes. That’s because they optimize for speed, not for the constraints that matter to enterprise buyers. Prototypes built on general-purpose or demo-focused platforms often lack compliance, multi-tenancy, and access controls. When teams reach the procurement or security review stage, they discover that the foundation cannot support production use.
4. What should teams prioritize when choosing a platform for AI-driven development?
The ability to ship to enterprise customers without later rebuilds. That means production-ready infrastructure, predictable pricing, built-in compliance, and ownership of your product’s differentiation. If a platform saves time early but forces a migration later, it rarely delivers real ROI.