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A deep dive into Artificial Intelligence’s evolution

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The Gartner Hype Cycles provides a graphic representation of the adoption of technologies and how they are potentially relevant to solving real business problems and exploiting new market opportunities.

Artificial intelligence is no exception. That’s why IBM’s Think 2019 conference has been important to businesses and consumers alike. Over four days in February in San Francisco, participants had a choice of 2,000 technical and business sessions with executives from ExxonMobil, Sprint, Honda, KPMG and more.

In addition, more than 800 leaders, 400 developers and 200 distinguished engineers from IBM appeared on stage and presented technical sessions. Speakers also included football great Joe Montana, skateboard pioneer Tony Hawk and astronaut Taylor Richardson.

AI has a strong community, and Think brought the best in the world together to move the technology onto that great plateau of productivity.

Artificial intelligence applications once required data scientists to understand their basic underlying gears. Now, Watson-enabled applications allow anyone within a company to access the power of AI. RBS, for instance, built Cora using IBM technology. Cora collects critical customer data but also answers customer requests. IBM built natural language processing engines, cloud infrastructures and data warehouses so their clients each fit AI right to their business.

How?

AI has certain phases of the operation process:

1. Collect: Finding all of your data, which is usually hidden across the organization
2. Organize: Structuring data into one business-ready foundation with built-in compliance and protection
3. Analyze: Using AI models that surface insights and allow employees to make better decisions
4. Infuse: Putting AI to work across your entire organization

However, companies usually need buy-in at the highest levels. This is why Think was a crucial conference in 2019. The sessions and talks at Think targeted everyone from the C-suite to the IT specialist. Consider an agent at a call center. In 2019, it’s still common for agents to use print manuals as references. When a customer calls, they need to wait as the agent finds an answer to their query.

Call center technology has evolved. Case in point: Autodesk. The company saves thousands of hours in the bottom line each month by pushing questions to their text-recognizing chatbots and call center forms. The answers pull from established resources like manuals, topical information, (like the current APR if the company is in financial services), and most importantly, what the AI learns as it gains experience.

Australia’s UBank’s AI system marks another phase of call center evolution. RoboBrain, as it’s known, helps human customer service reps find answers to consumers’ questions. Advisors can rate the utility of RoboBrain’s answers and improve the systems’ relevance. These solutions can dramatically save workers hours and lead to higher customer satisfaction.

AI not only empowers staff with answers, it frees them to use their own intelligence engine. Customer service agents should be deployed for higher cognitive tasks, not yes or no questions or updating addresses.

In 2019, the most compelling reason to use AI is that if you’re not, your toughest competitor will. As many businesses remain tentative about implementing AI solutions, some are learning how to use these tools to improve their business. As AI continues to improve, they will too.

Get real answers to your AI questions from IBM Think 2019, during our 3/20 Think recap webinar.

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