The current Artificial intelligence (AI) landscape is uncertain. 

With countless emerging AI tools and platforms, it’s difficult for businesses to pinpoint the best options for their unique needs. For CFOs, the question is which tools to invest in and the timing, implementation strategy, and long-term value of AI.

The Uncertainty in Budgeting for AI Development

AI is still in a rapid state of development. New platforms, software, and tools are being introduced almost daily. This constant evolution leaves many businesses in a state of limbo.

Should you invest in the most popular AI software now or wait for the technology to mature? How much of your budget should be dedicated to AI, given the uncertainty of ROI (return on investment)?

CFOs will find themselves grappling with issues like:

  • Which AI tools align with our business goals?
  • What will be the long-term maintenance costs?
  • How can we plan for scalability?
  • Should we invest in building an in-house AI team, or should we consider offshoring development?

Let’s carefully evaluate a few things about your organization’s AI readiness and strategic goals.

Does Your Organization Need AI Right Now?

Before diving into AI budgeting, ask yourself if your organization truly needs AI. While AI is hailed as the future of business, not every company will benefit from adopting it immediately.

Consider your business needs. AI can bring significant advantages in automation, data analysis, and process optimization, but are these areas necessary for your business right now? 

AI adoption may need to provide more immediate value to justify the costs for some companies, especially smaller ones.

Should You Wait Before Adopting AI?

Waiting is a safe strategy, especially given the rapid pace of AI advancement. Many businesses are experimental, trying various AI tools to see what works.

Consider a trial period where you invest in low-cost, scalable AI solutions to assess their impact on your operations. You can get a taste of AI without committing large amounts of capital upfront. 

On the other hand, companies that are ready to dive deeper into AI should start budgeting for it thoughtfully. 

How to Budget for AI Development

1. Plan According to Business Needs

Align the technology with your business needs. Your AI budget should be determined by the problems you’re trying to solve. For instance, if you need AI for customer service automation, your budget would look different than investing in predictive analytics for supply chain management.

Start by outlining specific use cases where AI could provide a clear advantage. 

Will AI be used for process automation, customer insights, or personalized recommendations? Narrowing down your needs will help you focus your budget on the right tools and resources, minimizing unnecessary expenditures.

2. Think Both Short-Term and Long-Term

This is not a one-time investment. Staying effective and competitive requires continuous improvement, refinement, and maintenance. 

Account for the short-term and long-term implementation costs of maintaining and upgrading your AI systems.

Short-term costs include hiring AI specialists, purchasing software licenses, and integrating AI tools into your infrastructure. Long-term costs include scaling your AI applications as your business grows, retraining models with new data, and maintaining the hardware and software infrastructure.

3. Take Refinement into Consideration

AI models need constant refinement to remain effective. This is particularly true for machine learning models, which rely on vast amounts of data to improve their predictions. As your business grows and collects more data, your AI models must be updated and refined to remain accurate.

Make room for the costs associated with data collection, storage, and model refinement in your AI budget. 

Planning for this ongoing process is important, rather than assuming AI implementation is a one-time cost. These refinement costs can be reduced by choosing AI tools that offer automated model updates and easy integration with new data.

4. Think About Having an Offshore, In-House AI Team

Another strategy is to build an offshore in-house AI team. 

You can have more control over your AI development and still benefit from the cost advantages of offshoring. You can also oversee the development process closely and have the final say in all AI decisions to be made. 

It can also provide continuity, unlike outsourced teams that may be project-specific. With your offshore team, you can continually develop and refine your AI solutions, creating long-term value for your business.

5. Plan for AI Management

AI management is often overlooked when budgeting, but we must consider it. 

All AI models and tools require monitoring, governance, and ethical oversight. You must plan for AI management roles that ensure data privacy, algorithm transparency, and regulatory compliance.

The management layer also needs to coordinate between various departments. Whether you assign a dedicated AI manager or involve senior leadership in overseeing AI initiatives, you must budget for this role during your planning stage. 

Conclusion: Embracing Uncertainty in AI Budgeting

The uncertain nature of AI tools and development is a unique advantage. 

The tools and platforms available today are constantly improving, and waiting to see what the next advancement brings could work in your favor. And if you start experimenting early, you’ll better understand the tools you’re using when you decide to commit to AI adoption for your processes. 

This uncertainty is an opportunity to continuously improve, innovate, and scale your AI initiatives as the technology evolves. Ultimately, the best approach balances current needs with future possibilities so your organization can benefit from AI, both now and in the long run.