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Our Take on Gartner’s “Predicts 2018: Artificial Intelligence” – How Will AI Impact Your Business?

Gartner recently released the Predicts 2018: Artificial Intelligence* report. For an overview and methods, Gartner’s clients can access the summary or download the full report from Gartner’s website.

In the past few years, big data analytics, machine learning, and artificial intelligence (AI) have evolved rapidly, allowing enterprises to better use data to produce transformative results. We've seen the expansion of these technologies in our clients’ projects that combine search with Natural Language Processing (NLP), machine learning, and big data to build AI-assisted systems like question answering systems and chatbots. This blog summarizes some key points on “the rise of AI” and how these developments can impact your business.

Applying AI to Your Business Cases

Let’s first address this question: what exactly are the AI technologies that businesses can apply now to improve processes and produce tangible results? 

Experts often break down AI deployments into “weak” AI - technologies simulating human behavior, and “strong” AI - technologies with the qualities of “consciousness” and the capability for original thoughts. 

In the case of improving business operations with real-time analytics dashboards, chatbots, or question answering systems, we are talking about a form of “weak” or limited AI which can still deliver immense value by helping automate and improve information retrievals, customer support, intranets, administrative tasks, etc. Gartner predicts that “By 2022, 40% of customer-facing employees and government workers will consult daily an AI virtual support agent for decision or process support.”* 

The Search for Insights through “the Mind of the Machine”

In this report, one of the findings is that “AI and machine learning strategy development/investment is already in the top five CIO priorities.”* Increasingly, businesses have invested in building AI-enabled applications. Examples range from business chatbots and question answering systems to more complex systems, such as self-driving cars, virtual doctors, and AI-assisted decision-making platforms.

Notice each of these use cases requires the ability to process, analyze, and serve massive amounts of data, fast! The growing demand for big data platforms, AI, machine learning, and NLP applications also comes with the demand for next-generation technologies that can process a huge amount of data to extract insights. And we’ve seen a shift towards search engines becoming the preferred tool for the output and delivery of complex big data and machine learning applications. When Gartner replaced its “Magic Quadrant for Enterprise Search” with its “Magic Quadrant for Insight Engines” in March 2017, we believe it reinforced that search engines play an integral part in weaving analytics, machine learning, and AI capabilities into the fabric of business.

Where Is AI Heading? 

Gartner predicts that “throughout 2018, AI performance will continue to improve. Segments such as speech recognition have leaped in performance, thanks to deep-learning work by major cloud providers during the last few years. Similar advances will occur both in specific vertical domains and in broader horizontal areas, as spreading AI use creates incentives to capture better data with more detail.”

Similarly, the 2017 AI Index**, a Stanford initiative to track, collate, distill, and visualize data relating to artificial intelligence, found that question answering systems (a form of AI systems built on search engines and NLP) have gotten better quicker over time. And they seem to be gradually narrowing the gap between human and AI performance. This trend promises endless possibilities for businesses to truly leverage data and algorithms for improved productivity, analytics, and decision making. 


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The AI Challenges

While AI seems to be one of the most important trends of 2017, as with many evolving technologies, challenges still exist. “CIOs face two key challenges in exploring and adopting AI: the availability of skilled and experienced staff, and the lack of IT and business understanding of AI's potential.”*

Given the first challenge, we believe that formal training and identifying the right personnel will be crucial to the success of AI initiatives. Given the second challenge, we believe that the transparency of AI methodologies and algorithms will be the key to ensure trust and ethics from within and outside of the organization leveraging AI.  

Speaking of Trust… Entering Blockchain

Applying the above technologies now will undoubtedly bring a competitive advantage. But as data grows and AI-enabled systems accelerate, forward-thinking businesses will also need to address an emerging concern: AI systems that produce false or biased results could adversely impact business outcomes and reputation. 

In fact, the Fjord Trends 2018 report***, published by Fjord – a part of Accenture Interactive, has also placed an emphasis on trust in the digital world. We also spotted this trend in the AI Index, which found that the sentiment of articles referencing AI, while dominantly positive, seems to have grown slightly more negative in recent years.


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Consequently, to counteract this tendency, transparency should be a focus of AI technologies. The Fjord Trends report mentioned a developing approach to ensuring trust and transparency in data: blockchain. Blockchain addresses the lack of transparency by securing records of information, making them resistant to modifications and can only be changed when there’s consensus. Still in its early days, but blockchain has the potential to be a major disruptor across all industries and areas of business.

Building a Foundation for Your AI System 

“By 2020, 85% of CIOs will be piloting AI programs through a combination of buy, build and outsource efforts.”* There will be challenges along the way and getting there won’t be easy. Charting out a right path is essential as there is a lot to consider and learn about what is right for your enterprise and what might not make sense. But it is certain that building a solid technology and strategic foundation for AI capabilities can produce transformative innovation and business outcomes. And the existing search, machine learning, and big data analytics tools and techniques today can play an integral part of this transformation.


*Gartner’s “Predicts 2018: Artificial Intelligence” report, authored by Whit Andrews, Moutusi Sau, Chirag Dekate, Anthony Mullen, Kenneth F. Brant, Magnus Revang, and Daryl C. Plummer, was published on November 13, 2017. Gartner’s clients can access the report here: https://www.gartner.com/doc/3827163/predicts--artificial-intelligence.

**The AI Index, a project within the Stanford 100 Year Study on AI, is an initiative to track, collate, distill and visualize data relating to artificial intelligence. For more information, visit https://aiindex.org/.   

***The Fjord Trends 2018 report is the annual trend report published by Fjord, a design and innovation agency that is part of Accenture Interactive. For more information, visit https://trends.fjordnet.com/

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