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BySix
Oct 23, 2025
From prototype to product: shipping GenAI in the enterprise
In today’s fast-moving world of software development and AI, many enterprises have run promising prototypes of generative AI solutions, but few manage the leap from pilot to production. For an AI software development company offering AI software development services, helping clients cross that chasm is where real value lies.
Why shipping GenAI matters
The global enterprise generative AI market is estimated at around USD 2.94 billion in 2024 and is projected to reach nearly USD 19.8 billion by 2030, growing at a CAGR of about 38.4%.
At the same time, surveys show that 84% of business leaders believe artificial intelligence (AI) will disrupt traditional practices and unlock new ways of working.
However, many projects stall: statistics suggest that a large portion of innovative initiatives never become full-scale products. This emphasises why robust delivery of generative AI solutions is so critical.
Key phases in moving from prototype to product
Define business value and product-fit: Identify clear use-cases with measurable KPIs (cost reduction, revenue uplift, improved productivity). Without this, a prototype risks being shelved.
Architect for scale from the start: Beyond the model itself, build supporting infrastructure – data pipelines, governance, monitoring, continuous deployment – so the solution is production-ready and sustainable over time.
Embed into operations and workflows: Genuine enterprise value from generative AI arises when the system is embedded into workflows (sales, customer service, operations), not merely demonstrated in isolation.
Governance, data management, and model lifecycle: Ensure that as you move from prototype to product, you have the right data quality, versioning, feedback loops, drift monitoring, and model retraining protocols.
Partner with a capable provider: An AI software development company that understands both the science of generative AI and the discipline of production-grade software development gives you the best chance of success.
Why many fail and how to avoid it
Many enterprises get stuck at the prototype stage. According to recent findings, while 88% of organisations measure value derived from AI, 75% say adoption is challenging, and 69% report many AI projects never reach live operational use.
The reasons include: unclear business case, lack of production readiness, insufficient operational integration, inadequate change management, and absence of end-user adoption.
By contrast, success comes when a prototype is treated not as a research exercise but as a product launch: thinking through software development lifecycles, user experience, scalability, and enterprise-grade reliability.
Best practices for generative AI productisation
Adopt modular architecture and strong software development practices: version control, CI/CD, observability.
Select use-cases where generative AI solutions deliver real differentiation, not just hype.
Prioritise embedding value into workflows: the model output must be actionable and integrated.
Monitor operational metrics: latency, accuracy, drift, data freshness, and user adoption.
Choose a partner in the AI software development services space that brings a full-stack mindset—from data engineering to model deployment to operational feedback loops.
Transitioning from a prototype of generative AI to a full-fledged enterprise product is a journey, one that demands more than a clever model. It requires strong software development, well-conceived infrastructure, deep operational integration, and clear alignment with business outcomes.
At BySix, we specialise in bridging that gap: combining best-in-class generative AI capabilities with a robust product development discipline. With our expertise as an AI software development company, we help clients deploy generative AI solutions into production, delivering measurable value and sustainable impact. If you’re ready to move beyond prototype and scale your AI product with confidence, we’re here to partner with you.
