3 Budget Planning Hacks for AI Projects

May 14, 2020

Contributor: Manasi Sakpal

Identify the major costs of an AI project to avoid unexpected cost overruns.

With economic disruption and volatile markets top of mind, executives might be hard-pressed to fund innovation or new technology. However, COVID-19 has actually increased the need for digital tools such as chatbots, virtual assistants and augmented analytics. 

This trend is mainly due to more remote work traffic. Organizations need a fortified remote work infrastructure. This includes digital tools for performance management, daily task managers, virtual meeting rooms, etc. to reduce work while employees get comfortable with remote working. 

“The caution around incurring new expenses is higher than what it was before COVID-19,” says Melanie Alexander, Director Analyst, Gartner.

“Projects such as artificial intelligence (AI) are typically high cost. So it is important that sourcing and procurement leaders focus on high-priority cost-saving areas and ensure that the budget is used in the most efficient manner.” 

Once given the go-ahead to start an AI project, the key is to focus on three areas that will impact the budget: Software, implementation and governance costs. 

No. 1: Define the use case to determine the software costs

There is a large disparity in pricing for AI software. The most common pricing models are subscriptions and may be consumption-based, requiring different budgeting and negotiation strategies. For example, pricing for a chatbot will be different from developing and deploying machine learning, but can be just as complicated. 

Asking the right questions is critical. Sourcing and procurement leaders can begin with:

  • General questions such as how the software is licensed, the delivery method (on-premises versus SaaS), and what pricing metrics are used and how they are measured.
  • If the pricing metric used is based on application program interface (API) calls, inquire about how this is defined and measured. 
  • If a pricing metric has a consumption-based element, ask for customized options such as a combined fixed and consumption billing approach.

No. 2: Combat cost overruns for AI implementation services

The best implementation service for each organization will depend on budget and individual needs. Services can be small and niche vendors focused on a particular industry vertical or technical domain or they can be large consulting firms. 

 “By using larger service providers, organizations can get end-to-end, strategic consulting in contrast to smaller providers who are focused on depth and flexibility,”says Alexander. 

Although niche vendors offer industry or technology specific expertise, large service providers offer end-to-end capabilities. A managed service provider delivers services, such as network, application, infrastructure and security, via ongoing and regular support, which a niche vendor cannot. Although this option may seem more expensive, after all of the costs of self-providing and self-managing, the price tag may make more sense. 

No. 3: Include forward-looking costs and potential compliance costs

Think beyond software and implementation costs. The majority of organizations that scale implementation spend more than half of their budgets on adoption-driving activities, such as workflow redesign, communication and training. 

“The most common additional cost exposures are maintaining compliance with other applications,” says Alexander. “Once the choice of AI technology is finalized, it is important to determine what existing applications the AI software may need to access. For example, if the intent is to introduce automation to perform tasks that involve data transmission through an ERP system, then additional software licensing may be required.”

Determine the future costs of the AI project in the pre-implementation phase to determine the actual budget. Staffing, security, privacy requirements, public cloud licensing and skill development are some of the common hidden costs that may go overlooked.

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