Leveraging AI For Product and Company Growth
Everyone thinks everyone else is deploying and leveraging AI, but in reality, most companies aren’t. The ones that are sometimes have AI initiatives that don’t line up with business growth. AI’s buzz may have everyone rushing to implement their own strategies to use this tech, but if your boardroom isn’t sure what the purpose actually is, you may not see the benefits.
AI is trendy and often misunderstood, but if you can deploy it correctly, AI can offer significant business benefits. Jeremy Karnowski, Director of Product at Insight, provides some pointers for leveraging AI for best result.
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How Does AI Fit into a Business Model?
Our computing power has accelerated, and now the demand for data science-driven initiatives are one of the biggest pieces of Business 4.0. We’re seeing a lot of restructuring with smaller companies building their own machine learning and AI projects and larger companies buying them up to incorporate what they’ve built.
In pop culture and on your Facebook feed, AI is doing a lot of possibly scary things like inventing a language or learning to walk. In the business world, AI means something a little bit different. AI automates business processes that humans don’t want to do to free up humans for higher-order tasks. It’s not just automating any task, however. AI’s primary business function is capturing things humans are already doing for your business beyond just filling in columns.
You need a few critical components to leverage AI in your business. Applying simple machine learning without specific purpose isn’t going to give you the best return on your investment. You’ll just get the buzzword. Thoughtful applications that integrate AI as part of your team, on the other hand, gives you a healthy edge on your competition.
What Are AI Project Components?
The fundamentals of AI range widely in types of companies and kinds of products they deploy. If you’re leveraging your AI product, you’ll have to consider where your organization falls into these different categories.
- Type of company: If you’re an enterprise-level, customer-facing business, your AI model might look a lot different from a smaller business using AI automation to facilitate in-house teams. Solutions can vary depending on your company’s B2B or B2C purpose, but be assured, AI models can adapt to just about any situation.
- Stage of company: Startups and enterprises will have different plans of action and various resources for implementation. Companies with resources for large data science teams will operate differently than organizations without them. Also, the company challenge varies depending on whether you’re in seed series, series C, or something later.
- Users: A critical part of implementation is knowing who will use your product. Small in-house user bases need a different model than larger, customer-facing solutions. This could determine how best to create, deploy, and store your AI solution. Think not only processing power, but whether you’ll use on-premises or cloud solutions for deployment. Consider who your users are, how AI can solve their problems, and how you’ll know it’s working.
- Data: The data available to you will determine a lot of your AI as well. Those of you with large amounts of unstructured data for processing could find deep learning solutions more applicable. If you’ve got a considerable need for interaction with customers on the human level, that could also determine the types of AI solutions you’re building.
- Models and techniques: Finally, the most effective techniques will involve answering questions about the previous and finding techniques that best address those issues. Your data science team can’t just build the coolest thing. They must understand the business needs and apply those techniques to specific KPIs.
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How Do Effective AI Projects Work?
Companies with a clear idea of what their business needs are and how AI can solve specific user problems are best positioned for success. The pop-culture understanding of AI as “really cool robots” or some other human-like entity isn’t the whole picture. Instead, AI is a fundamental part of your team’s toolkit, automating processes that are tedious and prone to user error and freeing up your human team members (or customers) to do what they want.
Focus on the problem you’re solving instead of building something “cool” and your data science team and users will love your product.
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