Artificial intelligence and its capabilities are a boon for businesses but many companies may mistakenly believe that they can’t afford it as technology costs climb, says Martin James, VP EMEA, Aerospike.
But the reality is that AI doesn’t just help a business grow and remain competitive it actually cuts costs and boosts efficiency. Further, implementing AI now will lead to a lower total cost of ownership than if it were delayed.
For many companies, however, the biggest issue for implementing AI is that they don’t understand its care and feeding. It continuously hungers for data as fast as it can get it. Suppose a company isn’t using a real-time data platform to handle real-time data. In that case, latency slows, and mission-critical applications may not receive the required context or situational awareness. A real-time data platform that provides persistent sub-millisecond performance at unlimited scale with low latency and at an affordable price meets the moment. After all, AI is only as good as the data that feeds it.
AI also functions at the highest level with a real-time platform that serves as the organisation’s global data distribution hub. This reduces friction. Data pipelines are kept open and available to authorised users. When data comes in, it is immediately funneled to the right places.
Another advantage of a real-time data platform is that it allows a company to scale easily and quickly.
Benefits of AI/ML
The current market for AI is about $100 billion (€91.83 billion) but that’s expected to skyrocket to nearly $2 trillion (€1.84 trillion) by 2030. A NewVantage Partners study last year of senior data and technology executives finds that 92% of large companies say they are seeing returns on their data and AI investments. That’s in comparison to only 48% in 2017.
If you’re one of those sitting on the fence, let me tell you why all those other companies are jumping into AI and ML:
- Improved efficiency. AI can drive more automation, whether it’s automating control systems in a warehouse, using facial recognition to ensure security, or implementing a chatbot to interact with online customers. Even small businesses can use AI-driven Chat GPT to perform routine tasks and use other employees to fill more critical roles.
- Better decisions. When companies began collecting customer data, they looked at it through a rear-view mirror. In other words: “What happened? Why did it happen?” But when machine learning came into play, a more descriptive and predictive analysis emerged: “What is happening? “What will happen?” Such insight can lead to wiser decisions in real-time and provide a greater understanding of future trends.
- Boosted productivity. A recent study from the Massachusetts Institute of Technology Stanford University of 5,179 customer support agents finds that those workers with access to an AI-based conversational assistant were 13.8% more productive than those employees who did not. The newest workers received the most significant benefit, reporting that the technology enabled them to work 35% faster.
- Smarter security. AI is much better at spotting potential threats and eliminating them. This means fewer IT people devoted to spotting fraudulent activity. PayPal uses a real-time data platform that leverages AI/ML for fraud prevention
Making better tech decisions
Business leaders tell me that while they managed to grow during the challenging years of the pandemic, now they’ve hit the wall with their existing technology in terms of price or performance. They are starting to feel like their margins are being compressed.
But what these companies may not realise is that there are ways to cut technology costs while still using AI and ML. As mentioned earlier, greater productivity and efficiency is a big payoff but there are also ways to cut the tech costs. They can:
- Trim hardware. Software that has a greater capacity for storing data on less hardware means that data centres can be reduced in size or even shuttered. Millions of dollars can be saved just by closing one data centre and eliminating thousands of servers. Choose a real-time data platform that reduces server usage. Criteo, the French ad tech giant reduced its servers by 80%, which also reduced their carbon footprint.
- Use more flexible software. Companies may opt for the cheapest, open-source solutions they can find when they’re just starting out. Or, they may be unsure about their digital needs, so they only think about small-scale on-site capabilities to save money. But what happens as they grow?Customers may go elsewhere when products or services don’t meet their needs fast enough. Software needs to be easily deployed anytime, anywhere. That’s onsite or in any public, private or multi-cloud environment.
- Ensure reliability. As businesses grow, the number of transactions they do online may go from hundreds a day to millions a day. But depending on the technology they’ve implemented, that may or may not be possible. If the technology fails to keep up, it could be harmful even disastrous to the health of the company. That’s why you want to do things right the first time, and then you can scale forever. In other words, will your business aspirations be met by your current technology?
Last year, companies spent some $1.6 trillion (€1.47 trillion) on digital transformation, and that amount is expected to hit $3.4 trillion (€3.12 trillion) by 2026. But I see a greater effort by company decision makers to truly understand what the entire organisation is going to get from these transformations and the money being spent on them.
By looking at the total cost of ownership whether you’re a large company or a small start-up is the best way to ensure that AI and ML pays off now and in the future.
The author is Martin James, VP EMEA, Aerospike.
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