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Over the past few years, much has been made of the rise of big data. And yet research from TDWI states that at organizations where 50% of employees have access to business intelligence tools, only 20% of that group actually use them. Part of the problem is that systems are often hard to use. Another challenge is low rates of data literacy.
To get around these issues, many organizations have relied on visualizations to display information gleaned from data. While a picture may be worth a thousand words, the same can’t always be said for these charts and graphs. There are a range of causes for data misinterpretation, including insufficient domain expertise and lack of training in statistical thinking.
All of this suggests that trying to force people to become data literate is an uphill battle. But this is actually becoming less necessary thanks to the rise of artificial intelligence (AI) — and, in particular, advanced natural language generation (advanced NLG), a subfield of AI. Advanced NLG platforms start by understanding what the user wants to communicate. Then these systems perform the relevant analysis to highlight what is most interesting and important, identify and access the data necessary to tell the story, and finally deliver the analysis in a personalized, easy-to-consume way: as a narrative. Gartner predicts that by 2018, advanced NLG will be integrated into the majority of smart data discovery platforms and that 20% of business content will be generated by machines.
Conversations with systems that have access to data about our world will allow us to understand the status of our jobs, our businesses, our health, our homes, our families, our devices, and our neighborhoods — all through the power of advanced NLG. It will be the difference between getting a report and having a conversation. The information is the same but the interaction will be more natural.
Big companies often have big call centers. I know a company in the financial services industry that has a staggering 13,000 employees in its call center. As you can probably imagine, the logistics of managing a call center workforce even a fraction of that size can be tough.
In organizations of this size, individual managers may not have the time or resources to conduct frequent performance reviews and deliver on-going personalized training, though 92% of managers see high value in such communications. On top of that, people don’t want to be given numbers or charts illustrating how they can do better, they want “corrective feedback” defined as suggestions for improvement or explorations of new and better ways to do things.
With advanced NLG, performance and call activity data can automatically be analyzed to generate weekly personalized coaching reports that convey in simple and conversational terms how individual employees are doing, what behaviors to improve, and their progress against goals to inspire and encourage change.
As advanced NLG transforms from a niche emerging technology into the default communication layer that we put on top of data, the idea that every person needs a set of specific, technical skills in order to interact with data will seem ludicrous.
Our expectations of data are rapidly reaching the same tipping point of other types of innovations that we don’t give a second thought to anymore. Phones that allow us to carry limitless knowledge in our pocket, free video conferencing with anyone across the world or smart houses that regulate themselves are now the norm and not the exception.
While we take them for granted now, there was a time when these advances seemed just as improbable. Looking ahead to the next few years, the same shift will occur to data as enabled by advanced NLG. Additionally, this growing movement will help build trust in intelligent systems as information will be delivered in a familiar, conversational way and the systems will be able to explain in clear language why and how they came to conclusions.
People have always communicated through stories and language, why should we expect them to change now?
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1. Voice assistant software is the #1 AI app today
In a survey of corporate executives, 32% of respondents said voice recognition software like Apple's Siri, Alphabet's Google Assistant, and Amazon.com's Alexa is the most used type of AI tech in their workplace. Many of these voice-powered AIs still leave something to be desired in terms of accuracy, and it was surprising that voice assistants outnumbered big data in overall popularity with businesses.
2. AI bots will power 85% of customer service interactions by 2020
Bye bye, call centers and wait times. According to researcher Gartner, AI bots will power 85% of all customer service interactions by the year 2020 .
3. Digital assistants will "know you" by 2018
Also from Gartner , digital customer assistants will be able to "mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, timing and tone, and the ability to respond, add to and continue with a thought or purpose at multiple occasions and places over time." Said another way, digital assistants will know and interact with you like a friend does today, or at least they'll try to.
4. Amazon, Alphabet, IBM, and Microsoft to host 60% of AI platforms
These 4 tech giants already have significant cloud computing businesses, a trend researcher IDC sees as likely to continue.
5. Get excited for self-driving cars
According to a study from leading consultancy McKinsey , the impact of self-driving cars will be tremendous, saving an estimated 300,000 lives per decade by reducing fatal traffic accidents. This is expected to save $190 billion in annual critical care and triage costs. Less miraculous (but still awesome), autonomous automobiles will also save their users as much as 50 minutes each day by allowing them to focus on other tasks while commuting.
6. 20% of business content will come from AIs by 2018
AI-powered software will write as much as 20% of business content in a mere two years' time according to Gartner . Areas like "shareholder reports, legal documents, market reports, press releases, articles and white papers" are among the writing forms most likely to be automated.
7. AI drives a $14-33 trillion economic impact
In a research report to its investors, Bank of America argued that the rise of AI will lead to cost reduction and new forms of growth that could amount to $14-$33 trillion annually, in what it calls "creative disruption impact," and that's just the tip of the iceberg in some expert's view.
8. Robots will be smarter than humans by 2029
According to Alphabet director of engineering Ray Kurzweil , machines will be smarter than us by 2029.
9. Zero people actually know how big an impact AI will have
Researching this article, I found all manner of predictions for how much AI will impact our daily lives, everything from imminent nuclear winter to global immortality waiting around the corner. So while it's certainly easy to get wrapped up in the litany of predictions, it's perhaps most useful to simply keep in mind that AI should have a major economic impact from which investors can undoubtedly benefit from today.
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Over the last few years we’ve been increasingly interested in the impact of a leader’s preference for speed versus a “slow and steady” mode of operation. It’s clear that overall, organizational processes, communications, and human interactions in the world are speeding up. Many organizations are looking for ways to become more agile. Perhaps leaders worry that their organizations cannot move faster if their employees operate slowly.
What does it take for a leader to have both high quality and fast pace? To research this question, we turned to a data set, one that includes information on more than 75,000 leaders. This data set contained 360-degree assessments with ratings from an average of 13 raters.
The analysis identified seven unique factors that appear to identify what it takes to combine these two seemingly contradictory critical leadership goals.
- Provide clear strategic perspective
Leaders rated as having both high speed and high quality were absolutely clear about the vision and direction of the organization. They were also rated as better at taking a longer term, broader view. They were effective at defining that perspective and then sharing their insights with others so the strategy could be translated into challenging, meaningful goals and objectives. - Set stretch goals and maintain high standards
Stretch goals have a natural tendency to increase speed. People will stay busy without stretch goals but will not accomplish as much. Stretch goals can increase our effort. - Communicate powerfully
When everyone understands where they are going, what problems need to be resolved and where projects are in terms of milestones, both speed and quality increase. When people are uninformed, confused or given misleading direction, errors occur and work slows. - Have the courage to change
Speedy leaders with high quality output became the champions of change. They were excellent at marketing projects, programs or products. Slow leaders who produce poor quality resist change. - Consider external perspectives
The leaders who were top in speed and quality are skilled at looking outside the organization and identifying trends and changing mindsets early. - Inspire and motivate others
These leaders have the ability to inspire people in the organization. Direct reports felt they were on a mission and that what they did was essential. - Innovate
Leaders with fast execution and high quality were always looking for a fresher, faster, more efficient way to deliver. Having a desire to increase both speed and quality using standard procedures is often impossible and therefore requires new innovative procedures. Leaders who look for innovative solutions find a way to have the best of both worlds.
An increasing number of roles require high speed combined with high quality. We believe this achievement is possible.
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