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Most Trending Technologies - Intelligent Apps and Analytics

June 5th 2018 in Data Science

Intelligent Apps and Analytics

Organizations are applying AI techniques to create new app categories (such as virtual customer assistants [VCAs]) and improve traditional applications (such as worker performance analysis, sales and marketing, and security). Intelligent apps have the potential to transform the nature of work and the structure of the workplace. When building or buying an AI-powered app, consider where its AI impact will be. It's useful to focus on three target domains when exploring how and where to exploit AI:

  • Analytics -
    AI can be used to create more predictive and prescriptive analytics that can then be presented to users for further evaluation, or plugged into a process to drive autonomous action. AI is also being used for augmented analytics.
  • Process -
    AI can drive more intelligent actions by an application. For example, you can use AI for intelligent invoice matching or analysis of email documents to improve service flow. In the future, this can be extended further to identify patterns of work, from which process models can be built and executed.
  • User Experience -
    Natural-language processing used to create VPAs is one application of AI to the user experience. Further examples include facial recognition and other AI applications for understanding user emotions, context or intent, and predicting user needs.
"There is an AI "land grab" from the large vendors making "big bets" and from startups seeking to gain an edge. They all aim to support or replace manual human-based activities with intelligent automation. Vendors such as Salesforce, SAP, Oracle and Microsoft are incorporating more advanced AI functions in their offerings. These vendors are exploiting AI to varying degrees, but they're all focusing on their traditional strongholds."

During the next few years, virtually every app, application and service will incorporate some level of AI. Some of these apps will be obvious intelligent apps that couldn't exist without AI and machine learning. Others will be unobtrusive users of AI that provide intelligence behind the scenes.

There is an AI "land grab" from the large vendors making "big bets" and from startups seeking to gain an edge. They all aim to support or replace manual human-based activities with intelligent automation. Vendors such as Salesforce, SAP, Oracle and Microsoft are incorporating more advanced AI functions in their offerings. These vendors are exploiting AI to varying degrees, but they're all focusing on their traditional strongholds. For example, the main enterprise software vendors are emphasizing sales, service, marketing and ERP as particularly valuable areas for applying AI techniques. Microsoft is focusing on Office 365 and a strong developer ecosystem. Challenge your packaged software and service providers to outline how they'll be using AI to add business value in new versions. Explore how much of the new value will come from bleeding-edge, rather than older, AI technologies. Examine how they use AI to deliver advanced analytics, intelligent processes and new user experiences.

VPAs such as Google Now, Microsoft's Cortana and Apple's Siri are becoming smarter and are a rapidly maturing type of intelligent app. Some chatbots, such as Facebook Messenger, can be powered by AI (for example, Wit.ai) to deliver an intelligent app. These intelligent apps feed into the conversational platform trend to create a new intelligent intermediary layer between people and systems. If you're an early adopter or you're seeking to drive disruptive innovation, begin to implement targeted VCAs and VPAs where a high-value target persona (for example, a doctor, marketing leader or high-profit customer) could achieve significant benefit. If you're a mainstream user or have more modest innovation goals, consider more simple rule-based chatbots. Exploit prepackaged assistants or simple mobile assistants based on the VPA capabilities embedded in smartphones.

Intelligent apps can create a new intelligent intermediary layer between people and systems. They have the potential to transform the nature of work and the structure of the workplace, as seen with VCAs and enterprise advisors and assistants. These models free people to build on and extend the capabilities of the assistant. For example, in healthcare, advanced advisors and other AI-assisted capabilities have the potential to enhance doctors' understanding and their ability to deliver more personalized treatments. Explore intelligent apps as a way of augmenting human activity, and not simply as a way of replacing people.

Augmented Analytics Will Enable Users to Spend More Time Acting on Insights

Augmented analytics is a particularly strategic, next-generation data and analytics paradigm in which AI is having an impact (see Figure 3). It uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists. Augmented analytics will enable expert data scientists to focus on specialized problems and on embedding enterprise-grade models into applications. Users will spend less time exploring data and more time acting on the most relevant insights. They will do so with less bias than in manual approaches.

toptechtrends Source - Gartner - October 2017

Enterprises will need to develop a strategy to address the impact of augmented analytics on currently supported data and analytics capabilities, roles, responsibilities and skills. They'll also need to increase their investments in data literacy. Both small startups and large vendors now offer augmented analytics capabilities that could disrupt vendors of business intelligence and analytics, data science, data integration, and embedded analytic applications. Data and analytics leaders must review their investments. By 2020, augmented analytics will be the dominant driver for data analysis systems. And by 2020, automation of data science tasks will enable citizen data scientists to produce a higher volume of advanced analysis than specialized data scientists.

Intelligent apps constitute a long-term trend that will evolve and expand the use of AI in apps and services through 2037. Establish a process to continually evaluate where your organization can apply AI today and over time. Use persona-based analysis to determine the most appropriate opportunities. Compare the roadmaps for AI exploitation across your packaged app and service provider portfolio. Proceed with caution if your organization is developing applications — the underlying AI elements for creating intelligent apps aren't ready for most application development projects at scale. Ensure such projects have a very high potential business value. The competitive gaps and missed opportunity costs for laggards could be significant.

"Intelligent apps constitute a long-term trend that will evolve and expand the use of AI in apps and services through 2037. Establish a process to continually evaluate where your organization can apply AI today and over time. Use persona-based analysis to determine the most appropriate opportunities. Compare the roadmaps for AI exploitation across your packaged app and service provider portfolio. Proceed with caution if your organization is developing applications — the underlying AI elements for creating intelligent apps aren't ready for most application development projects at scale."