Virtual and Augmented Reality have long inspired the imaginations of futurists. Take, for example, this glimpse of a potential “Domestic Robocop” HyperReality future created by designer and film-maker Keiichi Matsuda in 2010.

Every real-world surface in the short movie was covered with constantly-changing information — with lots of ads and the occasional glitch thrown in.  Six years later, Matsuda has continued to create such movies, typically with a less-than-utopian view of what the future may hold.

But business soothsayers are now more optimistic about the real-world uses of these technologies. According to the analyst group Gartner, the next five to ten years will bring “transparently immersive experiences” to the workplace. They believe this will introduce “more transparency between people, businesses, and things” and help make technology “more adaptive, contextual, and fluid.”

In Gartner’s latest emerging technologies hype cycle, Virtual Reality is already on the Slope of Enlightenment, with Augmented Reality not that far behind. In other words, enterprise uses of virtual reality have started to become more widely understood, and there are real-world enterprise uses of the technology, even if it’s mostly only in pilot projects.

Emerging technology hype cycle from Gartner, as of July 2016.

The Q3 issue of Digitalist Magazine, Executive Quarterly enumerates the many new business possibilities in the cover story From E-Business to V-Business.

So we know these technologies are coming to our organizations. What are some concrete steps organizations we take today to start preparing the integration of these technologies into business workflows?

Dashboards linked to the real world Augmented reality tends to rely heavily on geographical location. A good first step is to ensure than business people can view data displayed on top of maps whenever position data is available.

Data mapped onto geographic locations, courtesy of the thinkingbi blog

 

These types of visualization are particular effective on mobile devices — for example, the managers of an amusement park could see the real-time data for any of their rides, as they tour the park.

 

The next step is to take current location data into account when showing the visualizations — for example, allowing mobile users to show data for only  locations that are closest to them.

 

Then comes augmented reality, where you have the option to overlay information about a particular location onto a view through a camera.

 

For example, you can provide branch managers of retail stores with latest sales figures as they walk up to a branch office:

 

Or let factory managers see the production records for a particular machine:

 

Or let a store manager compare performance of goods displayed inside the store with the goods stored in the shop window:

 

Or let a refinery foreman see safety records of a particular pipeline:

 

Virtual Boardrooms. There has already been work done in the industry on virtual dashboards for business executives — for example SAP’s virtual digital boardroom application, available for iTunes or Google Play.

 

The application allows viewers to move around and manipulate dashboards in a virtual environment — is this a preview of the boardrooms of the future? Here are the reactions of attendees of this year’s SXSW conference in Austin, Texas:

In conclusion: it’s clear V-business is coming — are you ready?

 

The differences in AI applications at the different phases of business growth

We see companies applying AI solutions differently, depending on their growth stage. Here are the challenges they face and the best practices at each stage.

A growing number of companies are seeking to apply artificial intelligence (AI) solutions, whether they want to launch disruptive products or innovate the customer experience. No matter how business is approaching their strategy, they’ll need to label massive amounts of data, like text, images, audio, and/or video, to create training data for their machine learning (ML) models.

Of course, AI isn’t developed with a one-size-fits-all approach. We find that companies apply different strategies based on their size and stage of growth. Over the past decade, we’ve seen companies leverage AI solutions and encounter challenges along the way, as they come to us for data labeling, or the data enrichment and annotation that is required for training, testing, and validating their initial ML models and for maintaining their models in production.

• Startup companies tend to apply narrow AI to tackle specific problems in an industry where they have deep domain expertise. They typically lack data, especially labeled data that is primed and ready to be used for ML training. They may be challenged by choosing the right data annotation tools, and many lack the expertise or funding to build their own data labeling tools.

• Growth-stage companies are using AI solutions to enhance customer experience and drive greater market share. They typically have a fair amount of data and domain expertise, and they may even have the capabilities to build or customize their own data labeling tool, although perhaps without features like robust workforce analytics. At this stage, navigating competing priorities can be a challenge, where technical resources can be easily stretched and operations staff can get dragged into performing low-value data tasks. The companies in this stage that are applying AI most effectively are those that are giving thoughtful consideration to their customers and missions, focusing on their core competencies, and offloading what makes sense to outside specialists.

• Enterprise companies typically are using AI in one of two ways: incorporating AI into a product or using it to innovate business processes to generate better efficiency, productivity, or profit margins. Larger companies often have plenty of data and extensive in-house technical and data expertise. They are spending millions of dollars on data and AI, but siloed communication across products and departments can make it difficult to get a unified snapshot of the data landscape and where there are opportunities for AI to improve the business. In general, enterprise companies are not as advanced on the data maturity curve as they’d like to be.

As companies of all sizes seek to apply AI solutions, the one component that is more important now than ever is the role people play in the process. Data preparation is a detailed, time-consuming task, so rather than using some of their most expensive resources, data scientists, a growing number of companies are using other in-house staff, freelancers, contractors and crowdsourcing to get this massive amount of data work done.

Best practices for AI solutions implementation

At the end of the day, it takes smart machines and skilled humans in the loop to ensure the high-quality data that performant AI models require. That’s a crucial dynamic when you consider some of the real-world challenges the technology is in a position to help solve. From the ability to identify counterfeit goods or reduce vulnerability to phishing attacks, to training autonomous vehicles with hardware upgrades that make them safer, it’s quality data that makes AI truly valuable.

For companies that are looking to apply or develop AI solutions, here are a few best practices we’ve identified that can help ensure efficient, productive data operations: 

• Secure executive support: Leadership is a key factor in success, and lack of leadership leads to 87% of data science projects failing to make it to market.

• Incorporate data science early: Companies that consider data science and data engineering early in their process will see the most success.

• Collaborate often: Direct access to and clear communication with the people who work with data makes it easier to adjust tools and process (e.g., guidelines, training, feedback loops), which can positively impact data quality and the overall success of an AI project.

• Be prepared for surprises: Developing AI is iterative, and change is inevitable. Companies should consider their workforce and process thoughtfully to ensure each one can provide the flexibility and agility they will need to facilitate innovation quickly while maintaining accuracy along the way. When you realize you’re going to need more labeled data than planned, and quickly, it’s critical to have the right foundation for quality at greater levels of scale.

AI requires a strategic combination of people, process and technology

At any stage of growth, it’s important to understand how to strategically combine people, process, and tools to maximize data quality, optimize worker productivity and limit the need for costly re-work. Leveraging best practices from companies that work with data can put an organization in the best position for success as the AI market continues to grow and new opportunities emerge.

Author: Paul Christianson

Source: Dataconomy