Building an Intelligent Enterprise with Artificial Intelligence (AI)
Over the next 5 years every mid-large US enterprise will adopt some set of modern technologies such as Artificial Intelligence, Machine Learning, Internet of Things, Big Data, and Advanced Analytics to remain competitive.
This past year, we have seen AI becoming a vital enterprise technology to create new business models, provide a better customer experience, modernize organization’s existing business processes, and reduce cost to build an “Intelligent Enterprise.”
Intelligent enterprises are defined by their use of data to achieve desired outcomes faster and with less risk through better, cognition, automation, and integration. AI helps enable the intelligent enterprise by automating the detailed analysis of large volumes of structured and unstructured data to achieve valuable and actionable insights to help shape business outcomes. According to a recent study, 82% of participants stated that their organisations would be using AI in 2017.
Embracing an Analytics Driven Mindset to Automate
AI provides new opportunities for exploiting data and information. The application of AI helps answer a need, which previous analytics could not.
The first application of analytics embraced by businesses could be classified as Descriptive Analytics (aka Business Intelligence) which largely provides a rear-view window on business performance. For instance — How much sales did we have, what was organizations revenue, how many vehicles recalled, and more. As we started finding answers to afore-mentioned questions, the next logical question that raised was “What will happen next.”
This is where Predictive Analytics plays a role, by projecting future outcomes using past performance. With more advanced techniques in data science, and tools for integrating multiple data streams, Predictive Analytics starts to answer questions like “which product you might buy next”, or “which component might fail next”.
It’s important to be able to make accurate predictions of what might happen next. However, knowing what to do about it is even more critical.
Prescriptive Analytics enables us to find the best course of action for a given situation or scenario. Prescriptive Analytics can also recommend decision options or even automate actions to accelerate a future opportunity or mitigate a risk.
Integrated farming solutions that combine multiple real-time data streams — from weather patterns to soil nutrition — and automate actions like irrigation, harvesting, or soil enrichment are great examples of Prescriptive Analytics that help create significant efficiencies in an otherwise traditional industry segment.
Techniques in deep learning, neural networks, optimization, and decision-analysis methods drive this level of analytics.
Cognitive Analytics — the future of AI — unlocks the hidden insights from your data. Cognitive Analytics applies artificial intelligence, cognitive computing to specific tasks. Utilising such techniques, a cognitive application can become more effective and smarter over time by learning from its interactions with humans and data.
Each analytics progression has created new opportunities, business models for competitive advantage and meaningful engagement. Below infographic gives the analytics progressions and how these technologies are enabling enterprises into an “Intelligent Enterprise”.
Envisioning and Planning Session on AI
WinWire Technologies, a Microsoft Managed Partner and a member of Microsoft’s AI Inner Circle Program is enabling organisations harness the power of AI to drive innovation and adoption.
Are you looking to accelerate your own journey to AI? Ask WinWire about our Envisioning & Planning Session on Artificial Intelligence and Machine Learning to see how you can fast track implementation of AI for your business.
