AI is now an integral part of how large businesses operate. It's being used in all sorts of ways from customer service automation right through to financial reporting and the management of internal processes.
However once you've found a good use case then you've got the next question: do you build your own AI, do you buy an existing platform or do you do a bit of both?
This has an impact that extends well beyond the initial budget. It determines the level of control the business will have over its technology, the speed at which the solution can be deployed and who is going to be responsible for the ongoing maintenance.
For CTOs and CIOs the build vs buy Enterprise AI decision should start with one specific business process rather than with some grand organisation wide technology strategy. Understanding the trade-offs of each option and the long term commitments that are involved will go a long way to helping the company make a more sensible investment.
Before you even go down the route of build or buy you need to have a good understanding of the process you're trying to improve.
It's so easy to start off by looking at the various AI platforms, the various models. The technology should be driven by the business need rather than the other way around.
Think of a company that wants to automate their invoice processing.
The current process is to get the invoice, pull the details out, check it against the PO and then send it off for approval.
An AI could potentially do the work manually, but the company would have to know where the problems are first.
Are staff spending too long entering the data? Are the invoices getting delayed because of the complicated approval rules? Is the real issue in fact bad data?
Each problem will have a different solution.
Before you start looking at vendors or development options at all you need to establish the process owner, the desired business outcome, what data is available, the security requirements and the acceptable level of error.
The organisation should also specify how success will be measured.
'Reduce invoice processing time by 30%' is a far clearer objective than 'introduce AI into the finance function'.
Building enterprise AI is about building a solution for the business.
This wouldn't necessarily require training an AI model from scratch.
In many cases businesses will build their apps using existing AI models, APIs, open source frameworks and their own in house systems.
For instance a logistics company could have an AI assistant which looks at the shipment data, spots the delivery issues and then suggests what action to take based on their own internal operating procedures.
The organisation would own the app design, the integrations, the business rules and the user experience.
This can be very useful when you've got a company with a very particular way of working or a set of requirements that aren't easily met by off the shelf software.
However custom development brings with it further obligations.
The business will need the technical expertise to develop, test, deploy, monitor and maintain the solution.
It must also deal with security, model performance, the infrastructure and future updates.
Building gives you greater control, of course, but that control is accompanied by a host of ongoing obligations.
One of the major benefits of building the AI in house is the ability to customise it.
Commercial platforms are built to serve a multitude of customers and as such may not meet every individual business requirement.
A bespoke solution can be built to fit around the organisation's existing workflows, approvers and internal systems.
Another benefit is control over the application.
The company can choose how the information is processed, which AI models are used and how the employees interact with the technology.
This can be of real value to organisations with very stringent security requirements or those with very specific operational processes.
Building may also reduce your dependency on a single application vendor.
If the architecture is well designed then the organisation may be able to swap out individual components without having to rebuild the whole system.
There can also be competitive advantages.
For instance a business with very specific operational data might build an AI application which improves a process that its competitors simply can't replicate.
However the benefits are all contingent on the organisation's ability to maintain the technology over time.
Custom development can be expensive and time consuming.
Even if you're using an existing AI model you still need the engineers, the data people, the security people and the people who understand the business process.
Integration can be a real challenge.
An AI application may need to talk to older databases, internal apps and systems that weren't built with automation in mind.
Testing also requires a lot of attention.
AI systems don't always behave like conventional software. The output can be all over the place and they can produce incorrect or totally unexpected answers.
Organisations need some form of evaluation process, monitoring tools and safeguards to ensure that errors don't have a knock-on effect on the running of the business.
Maintenance is another concern.
When a model provider changes an API or releases an update the internal teams may need to update the app.
Building AI can be a good option, provided the organisation has the requisite technical capability and the long term commitment to support it.
Buying enterprise AI is about buying or subscribing to an AI solution that has already been developed by an external provider.
These platforms may provide out-of-the-box capabilities for customer service, document processing, employee assistance, analytics and workflow automation.
For instance a company wanting to do document summarisation would buy an enterprise AI assistant rather than build their own app.
The vendor will generally be responsible for the bulk of the underlying infrastructure, product development and maintenance.
This can reduce the technical load on the internal teams.
Commercial platforms may also offer the relevant integrations, admin controls and customer support.
However buying a solution doesn't in any way remove the implementation work.
The company may still have to sort out the permissions, get the internal systems connected, get the data in order and train the staff.
Buying is often quicker than building, but the actual deployment time will obviously depend on the complexity of the business process.
Speed is one of the key reasons organisations go down the commercial AI route.
A platform with the required functionality could be deployed orders of magnitude faster than a bespoke application.
This can be particularly useful when a company wants to improve a fairly standard business process.
Buying can also lead to more predictable initial costs.
Subscription fees and enterprise contracts enable the procurement team to get a handle on spend without having to fund the full development of the product.
Another benefit is access to the vendor's own expertise.
The established vendors can provide technical support, security documentation, product updates and implementation support.
For organisations with limited AI engineering in-house these services can be very useful.
Commercial solutions can also incorporate capabilities that would take a long time to build in-house such as admin dashboards, user management and monitoring tools.
The key issue is whether those features actually meet the organisation's requirements.
Buying an AI platform creates a dependency on the vendor.
The provider may control product updates, the features that are available, pricing changes and certain aspects of the underlying technology.
If the company have a specific workflow that the platform doesn't support then the options for customisation are likely to be very limited.
Data handling is another issue.
Organisations must know where the data is stored, how it's processed and whether the vendor's security controls are up to the organisation's own requirements.
Integration limitations can also be an issue.
A platform may be able to integrate quite easily with the major apps but have a real struggle with the more niche or legacy systems.
Over time we may see subscription and usage based charges go up as more people take up the technology.
Vendor lock-in can become an issue if you're in a position where moving to another platform would require you to rebuild your workflows or migrate massive amounts of data.
Before you buy you should look at the contract terms, data portability, the support arrangements and the provider's product roadmap.
The choice isn't necessarily between building it all in house and buying a full platform.
Many organisations can benefit from a hybrid approach.
This is about buying in the existing AI capability and then building the bits that you need to customise.
For instance a company might use a commercial LLM via an API but then build its own application interface, its own business rules and its own integrations.
The org avoids the cost of building a foundation model whilst still maintaining control over how the AI is used.
Another example would be buying an enterprise automation platform and then building your own connectors to your internal systems.
This approach can strike a balance between speed, flexibility and ownership.
However hybrid solutions still need technical management.
The company need to know which of the components are in house and which are reliant on third parties.
Security, troubleshooting, monitoring and updates all need to have clear responsibilities defined.
Without that clarity a hybrid architecture can end up more complex than either build or buy on their own.
Cost is a major factor in the build vs buy Enterprise AI decision, but comparing up front prices can be very misleading.
Building a solution will involve development costs, infrastructure, testing, security and of course ongoing maintenance.
Buying typically involves licensing, implementation, integration, training and then ongoing subscription or usage charges.
Hybrid solutions combine the two.
For example a company looking at an AI assistant for its procurement function.
A bespoke application may require a large up-front investment but can provide a great deal of flexibility in terms of future development.
A commercial platform could have lower setup costs but would then charge you a fee per user or per transaction.
A hybrid solution could cut development time by leveraging an existing AI model with the internal resources required to run the application.
To compare the options fairly we need to work out the total cost over a 3 to 5 year period.
Include staff time, infrastructure, vendor fees, implementation, support and future upgrades.
It's also important to bear in mind the cost of delay.
If it takes you significantly longer to build a solution than it would to buy one then the business is going to be losing out on the potential productivity benefits during the build.
The lowest initial cost isn't necessarily the best financial decision.
Security requirements can have a huge bearing on whether an organisation should build, buy or combine its AI capabilities.
Some businesses have information which is not permitted to be processed outside of approved environments.
Others work under a regulatory regime which mandates specific controls around data access, data retention and auditing.
Building internally may give you greater control over the application architecture and deployment, but it also means you're taking on more of the security responsibility.
Buying can give you access to established security capabilities (you just have to check the vendor's controls).
A hybrid approach needs careful co-ordination between the internal systems and the external services.
For each option assess how the data flows through the system, who can access it and where it's stored.
Ask whether the customer information is used in the training of the model, how long the records are kept for and whether the customer can delete or export their data at any time.
The organisation should also be testing how the AI deals with requests that are not legitimate and potentially malicious instructions.
Security decisions should be based on actual requirements rather than on the assumption that internally developed technology is automatically safer.
An AI solution almost never works on its own.
It may require access to customer records, financial systems, employee databases and internal documentation.
The difficulty of getting the systems to connect can have an impact on the delivery model.
If a commercial platform already does the required apps then it may well be worth buying.
If it's reliant on proprietary systems or very specific processes then building or stitching together solutions may actually be the way to go.
Technical ownership is just as important.
Organisations should work out who is responsible for application updates, model evaluation, infrastructure, incident response and user support.
For a bought platform some of the responsibilities lie with the vendor.
For a bespoke application the majority will still be internal.
A hybrid approach splits the responsibility between the two.
Documenting the arrangements prior to implementation will help avoid confusion if any technical issues do arise.
The right approach is going to be very much dependent on the particular business process in question, what you have available and what your long term needs are.
Build when the process is highly specialised, customisation is key and the organisation has the requisite technical skills to maintain the solution.
Choose to buy when the business requirement is fairly standard, the commercial platforms already have the required functionality and speed of deployment is key.
Choose a hybrid approach where the core functionality is provided by existing AI services but where you have the need for custom integrations, workflows and/or interfaces.
Before making the final decision, shortlist realistic options and test them against the same business requirements.
For example, an organization evaluating AI invoice processing could compare a commercial platform against a small custom prototype.
Both should be tested using similar documents, processing volumes, and accuracy requirements.
The evaluation should measure performance, implementation effort, security, operating costs, and employee usability.
This provides a stronger foundation for the decision than comparing product features alone.
Buying often involves lower initial development costs, while building may offer greater flexibility. However, the cheaper option depends on implementation complexity, usage volume, maintenance requirements, and long-term vendor fees.
Building may be appropriate when the business process is highly specialized, existing products cannot meet essential requirements, and the organization has the resources to develop and maintain the technology.
A hybrid approach combines purchased AI capabilities with internally developed components. For example, a company might use a commercial AI model while building custom applications and integrations around it.
Not necessarily. Commercial platforms may still require technical staff for integration, configuration, security, monitoring, and ongoing administration.
CIOs should compare the approaches using consistent criteria, including business fit, implementation time, total cost of ownership, security, integration complexity, technical resources, and long-term flexibility.
The build vs buy Enterprise AI decision isn’t simply about choosing between internal development and external software.
It’s about finding the right balance between control, cost, speed, and long-term responsibility.
Building offers customization and technical flexibility but requires substantial internal expertise. Buying can accelerate deployment and reduce development work, although it introduces vendor dependencies. A hybrid approach can combine the strengths of both when managed carefully.
For CTOs and CIOs, the most effective starting point is one clearly defined business process.
By evaluating realistic options against measurable requirements, organizations can choose an AI delivery model that supports their operations without creating unnecessary complexity or expense.
