The cloud and artificial intelligence (AI) is a powerful combination for companies. If you’re thinking about building an AI platform that works in the cloud, there are several things to keep in mind to get the best outcomes for your business.
Start by investigating your current cloud provider or choose a new one if necessary. How well-equipped is the service provider to meet your AI needs? Fortunately, many leading cloud companies have artificial intelligence offerings to help customers succeed with their projects.
Consider your current and future needs while evaluating cloud providers to accommodate your AI platform. For example, does the cloud company support scalability by allowing you to select new capabilities as needed? Does the provider have a history of supporting clients like you with similar AI projects? Answering questions like those can increase your confidence in the early stages of making an AI cloud platform for a business.
Market analysis suggests global AI market revenues will reach $500 billion by 2023. One advantage of that growth is it gives people plenty of choices for meeting their business needs with AI and the cloud. A primary consideration is whether it’s better to invest in a custom-built solution or a standardized offering from a service provider.
If you’re new to AI and have a limited budget, consider the emerging AI-as-a-service (AIaaS) sector. It gives people more control over their AI spending and implementation by enabling them to only pay for what they need and allow a service provider to handle most or all of the installation and upkeep for the product.
However, companies make AIaaS products to appeal to the masses, so you may find they lack the desired customization or capabilities to suit your AI cloud project. Make a list of must-haves and nice-to-have features. Then, compare it to AIaaS offerings to see if any are a good fit.
“Global AI market revenues will reach $500 billion by 2023”
Some business leaders initially feel compelled to adopt AI because they believe most of their competitors already have. That assumption isn’t necessarily off-base. Consider how a 2022 worldwide study by IBM indicated 35% of businesses currently use AI and 42% are exploring the technology.
The adoption rates are even higher in certain industries and countries. For example, 60% of IT professionals in China and India say their organizations use the technology. However, you should not merely decide to build an AI cloud platform to join the trend.
Instead, figure out how AI usage directly translates to more value for your business. It could help you drive sales, find more customers or improve quality control. A chatbot could be a good first use case, especially if you run an e-commerce business. Chatbots provide immediate answers to consumers’ questions. Those prompt responses could increase the chances of eventual purchases. Whatever the case, make sure to iron out your intended goals before deploying AI.
“35% of businesses currently use AI and 42% are exploring the technology”
Deploying your cloud-based business AI platform requires a thoughtful process. A Gartner survey showed 54% of AI projects reach production after pilot phases. Even though that finding indicates people are most likely to succeed, they should put the odds further in their favor by creating a step-by-step plan for planning, creating, testing, and deploying the AI cloud platform.
One possibility is to appoint a person or build a team to oversee those stages and troubleshoot problems that arise during them. Another takeaway from the Gartner study was that 80% of executives believe they can automate any business decision. However, many leaders want assurances that AI algorithms are reliable and free from bias.
It’s impossible to steer clear of all unexpected issues when building and using an AI platform. However, people can reduce risks, delays, and other unwanted outcomes by improving oversight. Taking that step is also highly advisable if your AI cloud platform handles sensitive data or makes potentially life or business-altering decisions.
“54% of AI projects reach production after successful pilot phases.”
Building an AI cloud platform for a business is a major undertaking. However, people usually agree on the payoffs if they can see positive results. Platform-related metrics help individuals working on the AI project see if things are moving in the right direction. If not, they have the evidence needed to justify making changes.
Selecting key performance indicators (KPIs) is a great way to get started with AI metrics. The mean time to repair (MTTR) is a commonly chosen metric that illustrates the time required to fix problems. Keep in mind that timeframes will most likely be longer in the early stages of using an AI cloud platform. Expect them to get shorter as people become accustomed to working with the platform.
Another way to identify and measure metrics is to focus on those specifically linked to the AI business case. Maybe you built an AI cloud platform that identifies the most promising sales leads. Has that tool resulted in more people buying the product or taking other desirable actions? Monitoring goals is an excellent way to ensure your AI deployment stays on track and brings the expected results.
You have virtually endless opportunities to use AI and the cloud to improve your company’s competitiveness, resilience, and marketplace success. Apply these five tips to help your project finish on time and boost the likelihood it performs as expected.
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