Digital transformation is creating gold mines of information for enterprises to dig into. Its value is then freed by artificial intelligence (AI) which is capable of pulling out powerful insights into customer behavior, processes, functions and more. This, in turn, allows organizations to further transform their offerings and operations for even greater financial rewards, cost-efficiency and competitiveness.
AI can transform an enterprise’s business faster than ever. It can predict and shape future outcomes, create highly intelligent workflows, empower people to do more and better work, automate decision-making and create personalized business models.
Yet, despite the great promise, many organizations have struggled to implement and fully exploit AI.
What’s going on?
Once reserved for research in areas like genomic and nuclear sciences, AI is now used across industries from marketing and sales to customer service and healthcare. In fact, the following are some examples of use cases in traditional industries where AI is now taking hold.
- Finance: Institutions are using AI to save time, money and bring additional value to customers. It’s used to identify fraud, emerging investment opportunities, assess risk, automate processes. By screening customer records, it can even recommend credit offerings and loans.
- Retail: AI is enabling stock-scanning robots and cashier-less shopping. It’s being used to predict product demand and re-route shipments automatically. Facial recognition can even prompt more personalized in-store service and prevent crime.
- Manufacturing: AI algorithms can unearth patterns to estimate market demand, optimize material purchasing, control inventory and utility usage, and even figure out staffing levels. Additionally, it can ensure uptime with predictive maintenance of systems, smart supply chains and new business models focused on quality, productivity and profit.
Still, while there’s a lot going on, many enterprises aren’t taking advantage of the technology. The issue often is, many jump into AI without a clear problem to solve; they’ve heard the buzz and believe it’s where their company needs to be. While they should be exploring AI, it’s best to start by identifying a specific question you’d like answered – such as “What are the repeat buying characteristics of our most successful customer relationships?” – and the appropriate data sets.
AI can unearth patterns and get the data you’re looking for. And, a successful project is good for getting c-level support. However, that’s only the beginning; more questions and applications will quickly present themselves. With that in mind, an enterprise will likely find they need to scale their AI efforts, fast.
Teams and tools
To quickly grow, you’ll eventually need an AI team, the key members being data scientists. Each of these scientists will likely use individualized, version-dependent and foible-prone toolkits that they have to maintain themselves. But they have to be able to share and collaborate. Further, you don’t want them bogged down in system and code administration at the expense of company-impacting work.
So, for customers pursuing AI, we recommend IBM PowerAI, a complete environment as a service for data science that combines open source deep learning and efficient AI development tools, accelerated by IBM Power Systems.
Since IBM’s introduction of Watson – the AI supercomputing “question answering” machine – they’ve been known as the pioneer, innovator and future of the realm. IBM also works closely with experts of cloud managed services like BlueChipTek to ensure solutions live up to their reputation.
Then, once you have an actual group of data scientists working together on the same tools, you can introduce entry- and mid-level personnel. With the easy-to-learn toolkit, they can assume tasks that don’t required the skills of data scientists versed in programming, statistics, calculus, tensors – as well as their price tag.
Get your teams and tools working together and success will follow.
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