Did you know that more than 20% of executives plan to incorporate Artificial Intelligence (AI) across their companies?
Over the last couple of years, countless organizations and companies have boasted about their effective AI strategies. However, when the time came to put those strategies into practice, they realized that what they called a strategy was more than tools without instructions.
Brands today have the knowledge, resources, and incentive to create effective strategies behind the AI implementations. Even though there are many capabilities, few businesses take the time to do so. They use physical tools to practice AI, however, they usually fail to invest the same effort into learning why it is important and what challenges AI offers.
By using new technology before creating a strategy that makes the most of it, businesses trying to get ahead of their competitors set themselves back. In order to correct this approach, businesses must design actionable and real strategies before they incorporate AI across their companies.
Imagine a company in the 80s that saw the IT technology coming but decided to create an IT strategy on mainframes. Even if the company’s leaders had the right idea, the execution would not have helped the business grow.
The exact same thing is happening today. Businesses need both the knowledge and the tools to use them properly. Leaders who want to stop relying on tech merchants have the good of their companies in mind, however, a lack of strategy or plan means their initiatives amount to buy orders.
Brands that think Artificial Intelligence will fix their issues risk burning out on some promising tools. In order to avoid the design and fate a strategy that gets the most out of the AI trend, keep these three main concepts in mind:
1.The Business Must Understand Microservices
Think about how Artificial Intelligence innovation works across various layers within your business. For example, in the infrastructure layer, containerization helps businesses implement tools in specific ways without adopting a new infrastructure.
Containerization is also known as microservices. IBM’s Open Banking Platform operates as a plug-and-play option for financial institutions to add microservices into their operations. Such a solution allows participating banks to use microservices as APIs to streamline processes, nurture fintech collaboration, and build revenue streams.
Keep in mind that AI doesn’t operate like other tools. You shouldn’t look at the existing tools and say, “This AI must be able to work with this system”. You need to look to the market with a specific system-agnostic approach. You need to find opportunities for new systems and tools to fix specific issues within your company.
2. The Application Must Complete a Specific Need
A business’s infrastructure layer determines how AI technologies integrate with certain systems. The application layer identifies how those technologies benefit your company.
IBM’s Watson is a very powerful machine, however, Watson is an infrastructure tool. The domain arms (healthcare, financial, and more) represent the applications of Artificial Intelligence. For example, in the banking world, Watson’s advanced robotic intellect helps bankers investigate false positives in money laundering, simplifying customer service times in the whole process.
Successful AI strategies are usually niche-specific. What does this mean?
Rather than seek Artificial Intelligence empowerment throughout your organization, determine a few key areas that could benefit from AI tools before using the tools that perfectly fit those needs. You need to make sure your infrastructure can support the integrations, filling in any gaps of your layer.
3. The Tools Used Must be Real AI
In today’s market, data is everywhere. More than 50% of respondents to a MicroStrategy study say they simplify their decision-making via data.
As you know, real AI uses data, however, it doesn’t need a trial period now access to your business’s database to prove its value or worth.
For example, if a vendor comes and asks for access to data to generate insights based on specific information, the vendor is not a true Artificial Intelligence vendor. Groups like these are data consultants doing professional services.
Good AI vendors support their users with niche applications and general infrastructure. They don’t care where the data (information) comes from. Quality vendors should not have problem unifying, normalizing, and using data to deliver accurate information.
If you think AI is right for your business company, don’t hesitate and give it a try. AI offers entrepreneurs several benefits that can help them scale their businesses.
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