Alongside driving greater usability, a wave of new models and on-demand services is making it easier for in-house IT teams to craft their own solutions, even with limited resources. Yet while this accessibility boost is good for those who want refined customisation and control, that doesn’t mean the fully bespoke route is always the best option.

Before diving into in-house development, budding businesses must carefully consider whether buying or building is right for them by weighing multiple factors, including potential strategic gains and time-to-value, costs, and the need for continual maintenance.

AI for everyone: the new easy-build era

The biggest factor that has made creating AI tools a more realistic prospect is the evolution of AI itself.  This technology has progressed far beyond completing set tasks within strict parameters, now providing  the opportunity to deliver more complex workflows end-to-end. AI is also revolutionising research, providing quick access to rich insights and facilitating better decision making. Some of the most exciting advances have come in areas such as prototyping, allowing almost instantaneous visualisation of ideas that can be tested with users, incorporating feedback quickly, and significantly shortening the development time.

In addition, software development is being streamlined in very exciting ways. Instead of spending hours writing code manually, developers can use AI tools powered by powerful large language models (LLMs) to generate code in minutes by describing what they want. AI can also help perform quality reviews, identify and fix bugs, and refactor existing code for better speed and maintainability. According to recent government trials, AI assistants can help coders and tech engineers save as much as one hour of working time per day, with the realistic near-term potential to multiply productivity many times.

Already exceeding $30 billion globally, the market for these low and no code AI tools is growing rapidly as businesses embrace platforms that allow non-technical employees to craft bespoke applications. Also typically fuelled by LLMs, these platforms enable intuitive app creation and assembly through pre-built components and drag-and-drop interfaces.

Together, these shifts mean building solutions with AI can now be achieved faster on smaller budgets by teams with varying (and even little) technical skills; opening the self-build option to companies of all sizes. Indeed, building might feel like the ideal choice for startups that often have finite funds and are already in the ideal test-and-learn mindset to fuel agile innovation. But while this is an exciting time, as many organisations are now able to experiment with AI, there is still a question around whether they should.

When is build or buy the right call?

What’s best for each company  depends on its unique goals, needs, focus areas, and in some cases, compliance needs if the industry vertical demands it. To ensure decisions have a solid base, there are three essential pillars that most will need to bear in mind.

Strategic advantage

When a business has unique needs or handles sensitive information, building its own, bespoke tools can sometimes be the smarter path. Home-grown models can be a good fit when important workflows rely on private data, such as employee details, operational metrics, or customer records. Creating systems in-house also gives teams a closer view of how the data is used and how the models perform, making it easier to identify problems early and adapt the tools according to business needs.

Buying, however, is frequently shrewder when it brings stacks up to par. Take customer care, where convenience is increasingly seen as key to meet modern expectations. In this arena, adopting platforms that allow smooth switches between interacting via conversational AI and human agents will likely prove the best choice for new market entrants looking to get on an even footing with established rivals who have similar capabilities as standard.

Cost efficiency

Off-the-shelf AI tools are usually the cheaper option. Many basic features that businesses look for already exist, so some may question the need to invest resources into building them internally. The challenge  arises when a tool almost meets a company’s needs but lacks a few niche functions. In those cases, it is worth comparing the true cost of adapting an existing product with the cost of building a custom solution. Sometimes an external tool can be adjusted or modified at a reasonable price. Other times, a unique use case may actually be more cost-effective to support with a custom model.

Long-term effectiveness

Constructing tools is one thing; maintaining and evolving them is another. Even with low and co-code solutions fuelling relatively affordable builds, ensuring lasting rewards will inevitably demand further investment.

The eye-watering AI investment budgets of big tech firms do highlight the ongoing commitment that comes with building AI. Those choosing the self-managed route must be ready for constant assessment and adjustment, including measuring impact against specific KPIs and fine-tuning solutions to improve value.

This means it’s still paramount for teams to look at their resources and determine if they have the necessary funds and people to run projects successfully, especially as crucial knowledge and abilities change over time. If they can’t be confident of keeping pace, it’s arguable that leveraging externally managed and maintained solutions might be the wisest move.

To an extent, AI is helping to solve the tech arms race it has created by providing tools that allow startups and small companies to extend their smart stacks. But building isn’t necessarily the right choice long-term, and finding technology partners that care about your business, and ideally your vertical, can be a more sustainable choice. Ensuring a stable balance of costs, benefits, and longevity will mean thinking practically and deeply about what capabilities are truly needed and whether it is better to build or buy.

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