Businesses increasingly turn to AI technologies - from Large Language Models (LLMs) to computer vision systems - to transform their operations. Typical applications include process automation, advanced data analytics, and personalised customer experiences. Almost every industry will benefit from what AI has to offer. However, successful implementation requires careful consideration of the technology and the organisation's data strategy, technical capabilities, and long-term objectives. However, many business leaders ask if they should build their own AI software from scratch or buy an off-the-shelf solution. Each option has advantages and challenges, and the right choice depends on the organisation’s needs, technical capabilities, and business goals.

Customisation 

One of the most significant distinctions between building and buying AI-powered software is the level of customisation needed. Sometimes, off-the-shelf software won’t cut it, and you need a custom solution that sets you apart or caters to your unique needs. Creating AI software in-house allows you to tailor every feature to align precisely with your company’s unique business objectives. For example, in the live entertainment space, we’ve seen companies interested in the level of individualisation that AI offers by building unique custom fan experiences to deepen their audience's connection to their events, sports teams and artists. This type of AI-powered software is designed and engineered to meet the specific needs of the fans on their favourite devices. For instance, one entertainment client leveraged AI to analyse live sports feeds to pull highlights out in real time and serve them on an individualised basis, meeting fans' exact tastes.

Off-the-shelf AI solutions often cater to common use cases like customer service chatbots or predictive analytics. While these solutions excel at standardised tasks like sentiment analysis or basic forecasting, they may need to improve when dealing with industry-specific challenges or unique data sets. Buying might make more sense if your needs align well with these standard functionalities and you’re looking to implement a solution immediately. 

Cost

Cost is another major factor to take into consideration. Building custom AI-powered (or any software in general) can carry significant expenses. You’ll need to hire AI architects, data scientists, AI engineers, product designers and product managers to do this right. Initial development costs typically range from $200,000 to $1.5+ million depending on complexity, with ongoing development averaging 15-20% of the initial investment annually. You can do this in-house if you already have these roles or hire outside talent on a project basis. Then, you must also remember the ongoing cost of maintenance, upgrades, and updating as data changes.

By contrast, buying an off-the-shelf solution reduces these up-front costs. Many AI SaaS companies offer subscription models which allow for predictable budgeting. However, in the end, it may only partially do what you need because it was built to serve the masses. While a SaaS solution might cost $50,000-$150,000 annually, scaling costs can increase dramatically with user count or API calls, potentially exceeding custom development costs over a 3-5-year period.

Data Ownership  

Building AI-powered software internally gives you greater control over data handling and model customisation, which is especially important in industries with strict data privacy regulations. You own the intellectual property and can adjust the model as needs evolve, which is a significant advantage in highly regulated fields like finance and healthcare. This becomes particularly crucial when dealing with sensitive data like HIPAA-protected health records or GDPR-regulated personal information, where data lineage and processing transparency are mandatory.

When buying off-the-shelf solutions, control over data usage, privacy, and updates lies primarily with the SaaS company. They may use aggregated data from all clients to improve their models, which could challenge data security. Additionally, vendor lock-in becomes a genuine concern - migrating your data and retraining models with a new vendor can be costly and time-consuming.

Implementation Speed

While traditional AI development cycles could take 12-18 months, modern AI-enhanced development practices have dramatically reduced this timeline. AI-integrated SDLC leverages:

• AI-assisted code generation and testing

• AI-assisted code review and optimisation

• Continuous model evaluation

For example, engineers are now using AI tools to supercharge their workflows by automating repetitive tasks so they can focus on writing new features. Product managers and product owners are also using AI to develop well-defined user stories that are force-ranked and constantly reprioritised to ensure that the engineers are spending their valuable time on the most necessary features - and that they are building those features precisely as the product owner intended. If your business seeks a unique solution to gain a competitive edge, investing in a build is worthwhile and may be quicker than anticipated. 

When buying off-the-shelf AI, many of these solutions come with pre-trained models, which deliver value almost immediately if you want to standardise processes or enhance productivity where differentiation isn’t critical. However, be prepared for a 3-6 month integration period to properly configure, test, and train staff on the new system.

Long-Term Scalability 

Custom AI-powered software offers long-term flexibility, as your team can refine or scale the system as your business grows, adapting to changing industry trends and new data inputs. This includes the ability to:

• Switch between different AI models as technology evolves

• Scale horizontally across multiple cloud providers

• Integrate new data sources and features without architectural overhaul

Buying an AI solution will only be future-proof if the vendor invests in improving their products and is aligned with your evolving needs. So, in this case, you are taking a big chance on the vendor and should ensure long-term scalability, which is important to them before investing. 

Decision Framework 

Before making your choice, consider these key factors:

1. Data Uniqueness: How specific is your data to your industry/business?

2. Competitive Advantage: Will AI differentiation drive business value?

3. Technical Capability: Can you support ongoing AI development?

4. Time to Market: What's your implementation deadline?

5. Budget Structure: Do you prefer CapEx or OpEx investment?

Have you decided to build your custom AI solution? Here’s how we can help…

• We’re Fast. By incorporating AI into our SDLC, we can deliver your solution much faster. 

• We Deliver Quality. By practising TDD and applying our homegrown Continuous Alignment Testing framework, we mitigate the risk of hallucinations without killing the human and creative nature of the AI. 

• We Enable Adaptability: AI is evolving quickly - by engineering your solution appropriately, we can enable seamless transitions to new models as technologies evolve or need change. Our modular architecture allows for easy integration of new AI models and capabilities, ensuring your investment remains future-proof.

Whether you build or buy, AI is no longer optional to stay competitive in today's market. However, custom AI solutions offer unmatched potential for organisations seeking true differentiation and long-term value. With the right partner and modern development practices, building your own AI-powered software isn't just feasible - it's a fast path to industry leadership.