Generative AI (GenAI) is quickly changing how businesses operate and scale. Three years since the introduction of GenAI tools, nearly 90% of organisations are now regularly using them. However, with investments growing exponentially, many organisations are betting on seeing a return on investment this year. At the same time, concerns about lack of return on investment from AI are persistent, making it even more important for enterprises to think carefully about how they adopt and integrate these solutions to ensure tangible results are delivered.
With GenAI at the core of many enterprise workflows, including customer service, content generation and financial analysis, executives are left with a choice – do they build their own GenAI solutions, or do they buy off-the-shelf models?
There are clear upsides and downsides to both approaches. The right path depends on budgets, long-term ambitions, and an organisation’s appetite for complexity and patience.
The argument for off-the-shelf AI
For many organisations, buying GenAI is the simplest place to start. Off-the-shelf tools offer speed, cost efficiency and far less operational complexity. In practice, this often means using GenAI to support customer service teams, automate the summarisation of reports and meetings, or improve how employees find and use information across internal systems.
These models are pre-trained, tested at scale, and designed for ease of implementation, while most also boast proven performance. Buying also offers access to continuous innovation as vendors can push out updates and improvements faster than most in-house teams can manage.
Yet, limitations are becoming more apparent as enterprise use cases mature. Pre-trained models are typically designed for the average user, rather than the edge cases or proprietary requirements of specific industries or highly regulated environments.
Financial services offer a clear example. Banks and investment firms often need GenAI systems to work with sensitive, proprietary data, apply strict compliance rules and produce outputs that are auditable and explainable. Off-the-shelf models, optimised for broad applicability, can struggle to meet these demands when workflows deviate from standard patterns or require deep domain context.
In these scenarios, generic tools may fall short of supporting end-to-end operations smoothly, pushing organisations to consider building or customising GenAI solutions.
There is also the issue of data privacy and vendor lock in. Many GenAI models operate as black boxes, requiring data to be sent off-premises, which introduces concerns around security and compliance. The more important GenAI becomes to your operations, the more exposed you could be to licensing costs, dependency and widespread security risks.
When building in-house makes sense
By contrast, building your own GenAI solution helps guarantee it will have the nuanced, custom functionalities and features your company needs. A custom-built model can be tailored to your data, domain and workflows, integrating with existing systems and giving engineering teams the ability to iterate and improve the model over time. This can offer a competitive advantage while ensuring the privacy of your data.
But the costs are steep. Building an LLM model in house ranges from at least $1 to $2 million in the first year, with additional costs for maintenance, storage and updates making the overall cost close to the multi-million mark annually. And as AI capabilities continue to evolve rapidly, the requirement for regular updates, retraining and optimisation is only set to increase.
While a proprietary GenAI model might be a game-changer, it is inevitably a massive investment, not to mention the longer time to market and the risk of failure. It also requires the relevant team of skilled members to get the job done.
Part of the reason building in-house is so expensive is the cost of the specialised talent needed to make these models work at scale. The high-end GPUs used to train and run large models are scarce and costly, while the AI engineers, machine learning specialists and data scientists capable of delivering production-grade systems command premium salaries.
These pressures are being compounded by a widening technical skills gap across engineering. Demand for experienced AI and machine learning talent continues to outstrip supply, making teams difficult to hire and even harder to retain. According to a data analysis by LinkedIn, the average time to hire an engineer is now 49 days, longer than in many other professions, including finance, IT and healthcare, slowing progress and adding further cost to in-house GenAI initiatives.
Moving from AI pilots to long-term value
As generative AI cements itself within business strategy, the build-versus-buy dilemma becomes less about the technology and more about prioritisation. Neither method is universally correct, but the wrong choice can slow progress. The businesses that will succeed are those who assess their data maturity and risk tolerance alongside clear ROI goals and long-term value.
