AI is becoming increasingly important for many products and services. This trend has been accelerated by the adoption of LLMs and other generative AIs in the past year, making it easier than ever for people to access and integrate advanced AI into their systems. A great AI can help a company stand out, especially considering that up to 40% of AI start-ups in Europe do not actually have an AI product. The initial decision when building an AI system is often whether to 'build or buy'. However, it is crucial to first define the desired capabilities of the AI system.

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Evaluating AI's Role in Your Business

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The 4 key things to know before you buy or build an AI

There are many things to consider when thinking about building out an AI system. After working with several clients, I have found that these are the top three things everyone needs to consider before building an AI solution:

1. How much good quality data are available? What constitutes good quality data? All AI depends on good data so this is the first place to start.
2. What is the budget (both time and money)?
3. What are the objectives and requirements? What functionality is expected and what infrastructure is available? For example, you may not want a large model if you don't have the infrastructure to serve it at a reasonable speed.
4. What is the plan for improving the model? Any model starts becoming outdated as soon as it is trained and needs to be updated at “regular” intervals.

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Identifying the Right AI Solution for Your Needs

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Have you defined the problem you are looking to solve?

Aim at nothing, and you will hit nothing. If you're thinking about creating an AI tool for your business, it's important to set your goals and think about whether AI is the right solution. Building a tool using the latest techniques and technologies can take a lot of time and resources. The latest AI is not always the answer, no matter how popular it may be. Developing a tool from the latest techniques and technology will require all manner of time and resources. That's why it's worth looking into other, quicker, and more cost-effective ways to solve the problem. Instead of starting with the AI you want think about what a successful system looks like and how its effectiveness can be measured.

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What resources do you have available for this project?

If you are certain AI is the path you want to take next, consider what resources you have available to bring this tool to life. Besides obvious concerns like budget and expertise the main thing anyone in this position needs to consider is data. In many ways this will determine if your project is technically feasible.

• What kind of data do you have?
• Is the data already clean and preprocessed?
• How representative is it?
• Do you have access to all of it?

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Are there any ethical issues to be considered?

As the power of AI grows, ethical considerations are becoming increasingly important. As ethics and governance are beginning to catch up with this fast moving industry it is completely understandable that this is a fresh concept for many of us. With new legislation on AI leading to strict legal and regulatory requirements being enacted it is increasingly important to consider this early in the process. Several aspects warrant careful evaluation, but posing these essential inquiries can start your strategic objectives:

• Are there any biases within the provided data?
• Can these biases be corrected or accounted for in the system design?
• Could this tool potentially cause harm?
• How explainable will the predictions of the AI be and how transparent is its use?
• How will the impact of the AI system be tracked and what measures will be taken to make necessary changes?

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How should you build your AI?

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Choosing Between Buying and Building AI

Once an achievable framework for your AI system is decided the choice now comes on how to create it. This is especially important in the case of generative AI where the models are extremely large, and in many cases impossible to train from scratch.

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Buying an AI solution

Purchasing a third-party AI can be a great option for several reasons. It is particularly useful for prototyping or creating a proof-of-concept, as it eliminates the need to build from scratch, thereby significantly increasing the speed of development. This is because collecting data and training models is both time-consuming and costly. Therefore, using a pre-built model can result in significant upfront cost savings.  Another advantage is that you will, hopefully, be using a proven technology from a company that is a specialist in AI with ample resources, expertise and experience.  

While purchasing an AI solution may seem like the best option, it may not be suitable for everyone.  It's important to consider your specific needs and the limitations of third-party AIs, which may restrict your ability to customise for your use case. Moreover, integrating a third-party solution may result in vendor lock-in, making it more challenging and costly to switch to another AI service or your own in the future. It's important to keep in mind that vendors charge ongoing fees and potentially expensive API calls, which could end up costing more than a custom solution over time. You are also subject to the whims of the vendor, without control in the development or direction of the service you are using.

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Building an AI solution

To create an AI that is completely tailored to your specific needs, a custom model would need to be built.  This sets your product apart from others in the market, giving you a competitive advantage with unique and valuable intellectual property. Developing your own AI also provides complete control and can be more cost-effective over time due to lower ongoing fees and potentially a more efficient model that requires fewer computing resources.  Furthermore, an in-house model can offer greater privacy and security compared to an outsourced model.

Developing your own AI solution may seem daunting due to the large upfront cost in terms of time, money, and resources, which can be a challenge for smaller companies. However, it is important to note that a custom solution can provide a competitive advantage and long-term cost savings. While it may take months or even years to develop a complex AI solution, even with the availability of open-source tools, the investment can pay off in the long run. If a quick solution is needed, a ready-made option may be preferable, even if just in the interim.

It is also important to consider that specialists with expertise in AI, ML, and data science will need to be hired to ensure the success of the project, both in building the system and maintaining and improving it. However, there are other options available. For instance, using a foundation model from a third-party, such as OpenAI’s GPT-4, can be a viable and cost-effective solution to build your AI solution on. However, it is advisable to develop a platform that is agnostic to the foundation model, enabling the use of large models while reducing dependence on a single vendor. Self-hosting foundation models are also worth considering, especially as you collect data and can fine-tune a smaller model that can reach the same performance and is unique to your company.

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A custom or hybrid solution

Finding the right expertise can be difficult and time-consuming, especially if AI is new to your business. Recruiting new employees takes time and money away from the rest of the business, and you may not need the same team to build an AI system as you would to maintain it or even fix it. This is where hiring a third party to build an AI solution with you may be an option. This provides you with access to expertise without the cost of hiring new staff, and this expertise and bank of resources and experience can build an AI tool faster, but still tailored to your needs.

A vendor will have a wealth of data engineers, data scientists and machine learning engineers as well as the expertise to know how to assemble a team to build models and build an infrastructure to host them. The risk is also lower because the third party is responsible for developing and implementing the solution. However, this will lead to some loss of control over the development and direction of the solution, and the sensitivity of the data could become an issue. It is therefore vital to be clear about the security measures and the requirements of the model you want to build.

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Ensuring AI Readiness and Quality Assurance

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How do you know it’s ready?

One thing that is often left until too late is evaluating the quality of your AI solution. This is a clear advantage of building your own AI, as you can evaluate the individual components, as well as the data that goes into it, for quality and representation/diversity. AI models are often a black box and expertise is also required to interpret them, both in terms of the predictions they make and the ethical implications. To ensure the safety and reliability of an AI system, testing, monitoring and safeguarding must be applied, whether the system is in-house or not. Although this is a significant ongoing effort, it ensures both regulatory compliance and a model that will improve over time!

The ongoing evaluation and improvement of AI models is also something to consider in the 'build or buy' dichotomy. Depending on the size and impact of your AI solution, you will also need to consider whether you want to retain the staff to maintain and update your AI solution. This is another advantage of using a third party to build a bespoke solution. Not only do they have the expertise to build and explain the AI system, but they can also help you develop a plan to keep it relevant without the need for your own dedicated team.

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Making the Informed Decision

Choosing whether to build a bespoke AI solution or opt for an off-the-shelf product is a decision fraught with complexities. This choice impacts not only the immediate capabilities of your organisation but also its long-term innovation trajectory. As we've explored, both paths offer distinct advantages and challenges, from the customization and control afforded by building your own AI to the speed and cost-effectiveness of buying a pre-built solution. The key lies in carefully assessing your organisation's needs, resources, and strategic goals.

Consulting with AI experts can illuminate the nuances of this decision, helping you navigate the trade-offs and align your choice with your business objectives. Whether you decide to build a custom solution that sets you apart in the market or leverage the efficiency of an existing technology, the goal remains the same: to harness the transformative power of AI in ways that propel your organisation forward. Remember, the journey toward AI integration is continuous, requiring ongoing evaluation, adaptation, and commitment to excellence.

To help answer between building or buying an AI solution based around your specific needs Deeper Insights offers a month-long explorative programme, known as our Accelerated AI Innovation Plan.