December 10, 2024 What is the Best Time to Start with AI?

With deals and cross-departmental alignment at year’s end, starting AI discovery in Q4 is essential for organizations looking to adopt AI.

By Chris Stephenson, alliant

As another year comes to a close, your business is likely strategizing and planning for next year’s opportunities. If these conversations do not already include planning an AI implementation for your business, you will be missing out on a golden opportunity to get a head start on the competition.

Why is Q4 the Most Economical Time to Adopt AI?

Once your business has set its sights on the right applications for AI, the next step is figuring out when to slot in implementation amidst all other priorities.

Depending on what industry you operate in, your busiest times may revolve around certain seasons, holidays, or school deadlines – however, most companies use the last quarter of the year to wind down, take stock, and plan for the coming year. Manufacturing operations slow down or even stop. Projections and budgeting are fleshed out.

Thus, the optimum time to get started on AI implementation is in the last quarter of the year. That means right now. This isn’t just because of operational lulls. Several other compelling factors play into the timing:

The AI Cost Landscape

The AI honeymoon we’ve enjoyed over the last few years is fading. Many are discovering that Generative AI, which is trained on datasets and used to generate text, images, or video, is so resource-intensive that many of its providers are operating at a loss. Secondary impacts such as server farm water usage and heat generation are coming to the forefront.

By the end of the fiscal year, a business will already have an idea of its AI budget. Short-term pricing will already be a known quantity. Given the uncertainty in where costs may go, it behooves businesses to lock in current pricing, which will likely be favorable compared to that in Q1 of next year, to ensure that AI implementations don’t go over budget.

Departmental Collaboration

Throughout the year, IT, Finance, Operations, and Sales chug along and meet when necessary; however, during the last quarter of the year, they’re ideally more aligned in purpose, figuring out what the next year will look like. Since the most successful AI implementations function across departmental lines, businesses should capitalize on this unity of vision. By understanding every department’s needs, decision makers can view projects holistically.

Year-End Pricing

Technology vendors have revenue goals of their own to meet. Thus, Q4 gives businesses a chance to not only take advantage of discounts, but also actively negotiate. By making sure to include future costs and potential overruns in mind, this provides a powerful opportunity for businesses looking to modernize. However, this is also a critical time to tie goals to expenditures. With most of the year behind them, decision makers know where they landed with their initiatives, versus where they wanted to be.

How Do I Get Started?

At this point you’re hopefully raring to go or at least wondering where to start. The first step in any AI implementation is a Discovery Session, during which the following is accomplished:

1. Ideation – stakeholders discuss operational challenges and perceived solutions. It is critical to include representatives from all departments, and not just IT and/or finance.
2. Prioritization – decision-makers rank every initiative brought to the table by importance, taking into account potential impact as well as overall urgency.
3. Infrastructure Assessment – the processes, data, and infrastructure of the business are assessed for their readiness to take on proposed initiatives.
4. Roadmap – participants develop implementation timeframes and milestones.

Discovery Sessions are a critical step in any company’s AI roadmap. Not only are they instrumental in the context of AI, but they are also a great opportunity for internal soul-searching. In fact, as a springboard for some potentially expansive projects, they raise some very salient questions:

Is the Desired Implementation Self-Funding?

Your business’s AI implementation should always aim to be a self-funding transformation. In other words, the cost of the technology plus the internal lift to implement it should not exceed the time and expense it ultimately saves. The net gain from the implementation can then be placed into further, bigger initiatives. Rinse, and repeat.

A recent CPA client provides a wonderful example. The firm was struggling with a manual and inconsistent process for collecting outstanding accounts receivable. By better tracking outstanding bills, standardizing the look and feel of outreach, and cleaning up data from undeliverable emails, the CPA firm collected enough in the first three hours to pay for the implementation, while significantly reducing the time that partners spent on collections.

This strategy resonates not only from a P&L standpoint, but from the aspect of starting with a smaller and simpler implementation that allows a business to work out any kinks before embarking on larger projects. In fact, the last few years’ explosion in AI usage has shown that, in many cases, it’s more effective to break a desired operation down into a series of small automations as opposed to asking AI to figure out a process all in one bite.

Are We Fully Utilizing our Tech Stack?

I have seen countless businesses who have invested in sophisticated systems such as Salesforce and then only used half of their available features. Given the complexity of today’s business software solutions, this isn’t very surprising. However, a full investigation of available functionality almost always uncovers opportunities for automations or data analysis.

Discovery Sessions offer the perfect opportunity to cut through the noise and reveal that, sometimes, a simple solution is already available in place of a desired AI implementation. Business owners just need to take the time to find it, and that time is Q4.

The Bottom Line

The AI Discovery process is not only essential before starting an implementation, it also forces a business to brutally and honestly assess its current technologies and processes. But it’s most effective when it is done during the right time of the year, which for most businesses will be right now.

About the Author:
Chris Stephenson is the Managing Director of Intelligent Automation, AI and Digital Services at alliant. Stephenson is a leading AI consultant having delivered on hundreds of internal and client-facing AI and automation products, as well as major transformation projects for companies like Microsoft, Amazon and SAP Concur. His work is well recognized in the tech industry and has led to him winning several accolades including three American Business Awards. Stephenson holds a bachelor’s from College of the Holy Cross.

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