Powerful AI tools created a technological gold rush, with even the most risk-averse companies scrambling to secure their seats on the rocketship. Few leaders today ask if they should adopt AI. Instead, they’re asking how quickly AI can get implemented into business programs.

“I see business leaders getting caught up in the excitement of AI’s possibilities and immediately wanting to integrate AI into their existing tools or building AI apps from scratch,” NetActuate CEO, Mark Mahle,  writes in a recent Forbes article. “But deploying AI involves complex factors that can rapidly deplete thousands or millions of dollars in a company’s budget.”

Issues like the rising costs of Graphics Processing Units (GPUs), which are a key component of AI computing, can make investments even harder to justify.

To mitigate these inherent risks, leaders must understand the basic building blocks behind AI and adopt a strategic approach to appropriately leverage its power. Tapping into his recent observations with early adopters of intelligent technology, Mahle suggests three core considerations to evaluate before jumping into an AI investment.

Power innovation by counting the watts

Flexible resources make cloud-based solutions using AI servers a popular investment choice. But just like an F1 race car uses significantly more fuel to achieve its incredible speeds, a single AI server can consume more electricity than an entire rack of traditional servers in order to deliver its required processing power. 

Data centers power cloud computing, and already account for 1-1.5% of total global energy consumption according to the International Energy Agency. Google’s carbon emissions shot up by 50% because of AI demand. And by 2027, it’s projected that cloud AI servers will consume more energy than some small countries. 

For businesses, this consumption represents a significant cost factor and a risk of shortages due to immediate demand. Choosing a data center closer to your operations may seem minor, but it can create significant cost savings. One that’s located 100 miles away from a business versus 10 miles away costs an extra penny per transaction, translating to potentially thousands or millions of dollars in the long term. 

On-premise hardware might also be an option for specific needs, specifically for businesses concerned with customizations, security, data privacy concerns, however there are also ongoing costs associated with updating hardware.

Choosing cloud or on-premise depends on the company’s goals and needs, and a discussion with an expert can help decide the best approach.

Real-time needs more than the cloud

Not all AI applications need to live in massive data centers. Conversational, interactive AI experiences like chatbots use edge computing, processing data closer to its source via a small computing unit called a node. 

Similar to how GPS apps find the nearest gas station, edge computing uses software that identifies the closest available resource node, creating lightning fast instant responses to improve people’s experiences with apps like ChatGPT. 

Chatbots powered by AI transform customer experiences. IBM recently stated it can increase customer engagement by 44%. This means data and processing speed is critical in these instances to ensure a seamless user experience. Ask these key questions to create a fast, cost-effective and scalable edge model:

  • How is traffic managed to ensure users have access to the closest processing resources?
  • What technology can you use to give end users access to the nearest application resource node and ensure enough nodes to handle requests? 

Find efficiencies through Ensemble Ai 

For greater flexibility and cost-efficiency, a modular approach can be a powerful strategy. Ensemble AI trains each engine for a specific task and acts like a team of specialists working together. These individual engines then collaborate, combining their strengths to deliver against your business and tech goals.

As it’s focused on specialized tasks, ensemble AI can lead to more efficient processing, reduced overall resource consumption and costs compared to a single, do-it-all AI model.

To make it work, use edge computing and choose each AI engine based on its specific expertise. These engines can work together to act as a modular solution to address any scalability and collaboration needs by integrating with other AI apps inside or outside a company’s systems.

The right consultant can identify the best approach

Companies aiming to claim their seat on this AI rocketship can shape the future of business and technology, but without careful forethought, intelligent technology can also drain resources at lightning speed. By strategically assembling your AI using approaches like cloud optimization, edge computing, and ensemble AI, business can strike gold with artificial intelligence while keeping costs under control. 

NetActuate partners closely with businesses to implement bespoke AI strategies to ensure a beneficial and cost efficient experience. Lower hidden costs and reach out today to start this journey the right way.