Technology is the glue that holds the modern workplace together. Collaboration platforms, cloud applications, and project management tools all promise greater efficiency and flexibility. But for small and mid-sized businesses, the digital workplace can introduce friction to everyday operations.
Digital friction is defined as the unnecessary effort employees must exert to use data to complete a task. That friction appears as tension between IT teams trying to maintain scalable systems and employees who want tools that help them work faster. For small businesses, the challenge is managing the friction that comes with technology.
Here are four practical ways you can do exactly that.
Cloud Infrastructure is Hitting Physical Limits
It’s not a hypothetical future problem either. AI workloads require GPUs which are scarce and expensive due to shortages that are continuing today. Data center expansion is happening across the world in areas that aren’t prone to natural disasters but unfortunately are prone to water scarcity. That water scarcity is a huge detriment toward the data centers as a large center can consume up to 5 million gallons of fresh water a day.
If the demand outpaces our existing infrastructure, businesses will face a multitude of challenges to utilize AI.
Computing queues become normal
Delayed inference times and slower AI applications directly correlate to reduced business value.
Latency increases
Real-time AI will become unreliable and customer-facing tools being to degrade.
Pricing becomes volatile
GPU costs will spike and provide the potential for surge pricing models and bidding systems.
Cloud compute could start to look less like a utility and more like a commodity market.
What Can Organizations Do About It?
Just waiting for infrastructure to catch up isn’t a strategy. Companies need to take a more proactive approach now.
Optimize Before You Scale
Introduce token-based economics. This limits the amount of compute used for specific completions and reduces unnecessary tokens. The trade-off for faster and more sustainable answers is being provided with simpler answers. Better AI isn’t always bigger, just more efficient.
Rethink Your Infrastructure Strategy
Not everything needs to live in hyperscale clouds. Businesses have different options. The hybrid approach allows organizations to keep sensitive or high-frequency workloads locally or utilize mini data centers at offices or regional levels. That turns control over cost, latency, and availability into a competitive advantage.
Plan for Cost Volatility Now
Treat AI computing like a variable commodity to help manage its cost structure better than competitors. By building budget buffers, usage caps, and monitoring systems, organizations can prevent unexpected spikes from derailing operations. In this environment, controlling compute usage is just as critical as deploying AI in the first place.
Where AI and Cloud Infrastructure is Heading
AI adoption won’t slow down anytime soon. If anything, it continues to accelerate as more organization moves from experimentation to fully embedded AI workflows. But infrastructure won’t scale in a perfectly linear way to match that demand. There will be periods when it becomes constrained and less predictable.
That shift will change how businesses think about AI. Success won’t depend on which models you use, but on how efficiently and reliably you can run them.
If you’re starting to explore how AI fits into your business, now is the time to evaluate whether your infrastructure can support it in the long term. Fill out our form and our team will get in touch to help you start building a more resilient AI infrastructure strategy.