The key factors that will decide the outcome are budget, timeframe and the issue to be tackled.
CMOs across the world are faced with confusion to whether buy or build a martech solution. The confusion is more prevalent amongst organizations dealing with Artificial Intelligence. Most IT leaders state that collaborating with an AI marketing technology SaaS partner will be beneficial to the organization.
At the fork in the route between deciding to build an AI martech solution or buying it from a third-party vendor, is the point of the availability of the resources. Building a solution will be dependent on quality, technical debt, and opportunity cost. Buying a martech solution is feasible only if it guarantees the generation of revenue.
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Identifying the issue
CMOs need to first work with the IT leaders to identify if the problem is localized to the organization or common across the industry. Considering that attracting more clients is an industry-wide issue, most CMOs and IT leaders would rather opt for hiring more consultants, user acquisition managers, or agencies, instead of using AI based apps to connect. They would depend on the manual workforce only to gather data and optimize campaigns. Such a solution will be categorized as a high risk and expensive proposal.
Before opting for a solution, an efficient CMO should first study the strategies embraced by other organizations to solve an issue, take into account any possible third-party solutions that can be used. If the issue is centralized to the organization, finding a pre-existing solution may prove to be difficult. Most organizations don’t prefer building an AI martech solution due to resource constraints. It is a complicated big scale project which requires dedicated resources. Firms generally have a limited number of technical and data personnel who focus on core products.
Budget unavailability
AI martech solutions require a considerable amount of funds and extra resources as well with the possibility that the project goes over budget. CMOs state that most companies do not have the required budget to finance an in-house AI project. They prefer to hire third-party SaaS product vendors and opt for monthly or yearly spending on them.
CMOs need to understand that if they opt to push for building an in-house solution, the budget needs to cover the associated long-term technical mortgage for hosting and maintaining the solution and the down-payment (up-front costs).
Timeline
CMOs need to consult with CIOs on the threat faced by the organization. Is it something that can be improved over time or an immediate threat to the organization’s survival? Depending on the situation, a decision will need to be made.
Risks associated with building in-house AI martech solutions
CMOs need to identify if their organization considers AI marketing as the core competency. Costs associated with AI is high as they need to hire data scientists, build a team of machine learning engineers, data structure building, and maintaining the above resources. Even with the right resources, AI martech solution building is a major undertaking.
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Algorithms need to be updated frequently as machine learning requires constant oversight to ensure the right input. Costs associated are also a major deciding factor in this scenario, and CMOs need to answer the pertinent question, will the organization be able to bear the costs if there is a delay in the solution building. Or a third party vendor trial period to test the solution would be more feasible to test the in-house solution.
A CMO will have to weigh all these factors before deciding the route.
