Friday, 29 April 2016

Creating new business models through data

By Gloria Lombardi

Big data is, of course, not a new concept. Yet, it is a loose, often confusing, grab-bag term that encompasses many manifestations. Today, it also seems to disappear in favor of SMART Data.   

But, author Bernard Marr beautifully translates this complex subject into something that anyone can understand. In “Big Data in Practice,” he offers insight and real-life examples from some of the most successful businesses of the 21st Century. Through the stories of 45 leading companies, Marr makes the case for delivering extraordinary results out of data. Ultimately, transforming the way businesses work.

With so many use cases, the choice of which story to review was not the easiest to make.
I opted for LinkedIn and Uber since they offer excellent clues on the future of work and communication. Was it worth? Let's see together.

LinkedIn 

I particularly liked the LinkedIn story. Marr wonderfully captures both the benefits and the challenges of the company in leading to big growth through analytics. Big data is key to the way the largest professional social network in the world works. They track every click, page view and interactions of their 414 million members! This, is necessary in order “to ensure their site remains an essential tool for busy professionals, helping them become more productive and successful,” explains Marr.


The company uses machine-learning techniques to make better suggestions for its users, such as “people you may know”. Marr offers an example: “Say LinkedIn regularly gave you suggestions for people you may know who work at Company A (which you worked at eight years ago) and Company B (which you worked at two years ago). If you almost never click on the profiles of people from Company A but regularly check out the suggestions from Company B, LinkedIn will prioritize Company B in their suggestions going forward.” Ultimately, enabling people to build the networks that work best for them.



LinkedIn uses real-time stream processing technology to offer the most updated information. For example, notifications on who started a new job, or useful articles that contacts liked, shared and posted.



Inside LinkedIn


The author gives us an interesting picture of the internal environment that LinkedIn built. They have an impressive team of data scientists who work to improve LinkedIn products and solve problems for members. These employees also publish at major conferences and contribute to the open-source community. And the company encourage them to pursue research in many areas - from  computational advertising to text mining and sentiment analysis.


Marr also describes the changes in the organisational structure. “From a company that employed fewer than 1000 employees five years ago, LinkedIn have grown to employ almost 9000 people. This places an enormous demand on the analytics team. Perhaps in response to this, LinkedIn recently reorganised their data science team so that the decision sciences part (which analyses data usage and key product metrics) now comes under the company CFO, while the product data science part (which develops the features that generate masses of data analysis) is now part of engineering. As such, data science is now more integrated than ever at LinkedIn, with analysts becoming more closely aligned with company functions.”

Marr considers the hiring challenges too. In 2015, the company were looking to hire over 100 data scientists, a 50% increase from the previous year. But, competition is tough even for a giant like LinkedIn. And, “although more people are entering the field [of big data analytics], it's likely this skills gap – where demand for data scientists outstrips supply – will continue for a few years yet.”

Transparency of communication

Throughout the book, the author makes the case for transparency of communication when using individuals' data. For example, he describes the privacy backlash that LinkedIn faced last year. “In June 2015, the company agreed to pay $13 million to settle a class action lawsuit resulting from sending multiple email invitations to users' contact lists. As a result of the settlement, LinkedIn will now explicitly state that their “Add Connections” tool imports address books. And the site will allow those who use the tool to select which contacts will receive automated invitations and follow-up emails.”

Indeed, we can see this example as a frontier for hope. LinkedIn offer a key lesson to businesses to be clear about what data they gather and how they intend to use it.

Uber

How is big data impacting on the workforce? The Uber story is a fine observation on the changing nature of work. In particular, the on-demand economy.

Big data is at the centre of Uber's disrupting transportation business. And, Marr believes that “without their clever use of data the company wouldn't have grown into the phenomenon they are.”


Uber hold a vast database of drivers in all the cities they cover, which allows them to instantly match a passenger with the most suitable driver. Their algorithms check traffic conditions and journey times in real-time. This allows rides' prices to adjust as demand changes. And, it "encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low,” explains Marr.

Data drives Uber's detailed rating system too. Passengers can rate drivers, and vice versa. This should build trust and let both parties make informed decisions about who they want to share a car with. But, the author points out that drivers have to be conscious of keeping their standards high. In fact, “falling below a certain threshold could result in their not being offered any more work,” he writes.

Another work-related metric is the “acceptance rate.” This is the number of jobs that drivers accept versus those they decline. They should maintain this above 80% to provide a consistently available service to passengers.

Challenges

Indeed, the author acknowledges the controversies that Uber had and still is facing. Most notably, regular taxi drivers who claim they are stealing their jobs. And, concerns over the lack of regulation of the Uber's drivers. Uber responded to taxi drivers' protests with the attempt to co-opt them. They added a new class to their service called UberTaxi. This lets people use a licensed taxi driver in a registered private hire vehicle.

There are also legal hurdles for the company to overcome. Their service is banned in some parts of the world. And it is receiving scrutiny in other regions. But, their popularity across the world is a huge incentive for them “to press ahead with their plans to transform private travel.”

Is it worth reading?

It is refreshing to read a book whose author simply puts the big data hype into practice. Ultimately, it offers a comprehensive narrative of why and how data is transforming the way businesses operate.

What makes the stories of LinkedIn and Uber particularly interesting, thanks to Marr's detailed narrative, is just how complex the relationship between work and data looks today. Backed up with some indiscernible fusion of technology, humanity, business, and society.

But, those are just two examples. After diving into “Big Data in Practice” you will gather diverse insights. Somewhat differently, all the companies that the book lists have created new business models. And, they gained a competitive advantage.

Indeed, today we are seeing the emergence of other related innovations such as the Internet of Things (IoT), Artificial Intelligence (AI) and robots. These technologies are already impacting on the use of data, perhaps making the phenomenon even more important.

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Picture of Linkedin office courtesy of photopin 
Picture Uber Taxi courtesy of Pedro Caramuru 

Friday, 22 April 2016

Meet Pepper the emotional robot at work

Gloria Lombardi speaks with the Aldebaran's team at the Millennial 20/20 in London. Their humanoid robot Pepper wants to offer a new approach in the human-machine interaction.


In the movies, humanoid robots don't seem to have any trouble understanding people. The Tin Man in The Wizard Of Oz goes as far as questing for a heart.

In real life though it’s almost impossible to conceive interacting with a machine as if it was a life companion or work collaborator!

Yet, developers at Aldebaran, which is part of the large Japanese
telecommunications company SoftBank, have built the first 'emotional' robot: Pepper.

I met with the Aldebaran's team at the Millennial 20/20 in London to learn more about their creation. Why building a robot with a focus on affection? How could Pepper change the way businesses operate? This is what I found out.

Meet Pepper

Pepper is slender. It is 120 cm tall and weighs 28 kg. It has a fluid silhouette that allows 17 articulations. Its hands model those of humans, with five fingers and a gripping system. Equipped with a 3D camera and multiple sensors, Pepper can move, avoid obstacles, and identify sounds. It moves smoothly in all directions thanks to three wheels at the base. It follows you and even recharges independently. 

In fact, one of the main challenges for Aldebaran to face while developing the technology was to enable the general acceptance of their robot by the society. So, the technology was designed purposefully to make the machine disappears in favour of the emotional interaction. 

Emotional and interactive

Are you sad, angry, happy? Pepper is an artificial machine that progressively learns to understand your mood and adapts accordingly. It shows its own reactions through the color of its eyes, gestures, and its own words. 

“The idea of Pepper is to have a robot that communicates and interacts with people,” says Business Development Manager Adrien De La Tour.

Pepper interprets basic expressions on the human face such as a smile, frown, look of surprise, anger, and sadness. It also understands the intonation of the voice, the context of the words as well as non-verbal language, including the tilt of the head! “The goal is to understand and adapt its reactions to fit people's mood.”

The robot, of course, currently requires assistance. Pepper does not understand everything and sometimes can make mistakes. In fact, the autonomy of Pepper is progressive: it improves over time.

You teach it through your discussions, which help it memorise names, faces, moods, tastes and habits. “As Pepper continues to recognise you, it starts to develop a memory of your relationship together. It will not ask you the same things twice, and gradually creates an emotional connection with the person it interacts.” 

There is a table placed on its heart, which displays additional information to enrich the interaction. At present, Pepper has a large database of questions and answers in Japanese, English and French. Its voice has also been the subject of in-depth work on expressiveness with custom voices created for English, French and Japanese to better adapt to the culture of the country. Currently, it has three shades of different voices: playful, neutral and didactic. 

Tomorrow, according to the Aldebaran team, Pepper will be empowered by an application library. It will access it to find new behaviours, activities, and content to inform and entertain people. 

Pepper at work 

Japan has always had a long history of robotics activity. SoftBank is also Japanese. So, it is not surprising that the launch of Pepper into the business world started in the Japanese market in 2015.

The robot is already working in 2,000 SoftBank Mobile stores, greeting, informing and entertaining visitors in the three different languages it knows. It has not been sold outside the country yet, apart from very rare exceptions. 

De La Tour says that it was important to test it and see how it worked in Japan before going to other regions where its acceptance could be more resisted and questioned. 

Still, we can already see some Peppers in Europe. For example, “there are a few of them in train stations and retail stores in France. Very soon there will be some Peppers in cruise ships for the Costa Group.” All those projects are trials to start with. But, in a few months, Pepper will be available for companies to use. 

In fact, “the robot can work in any kind of client-facing environment,” says De La Tour. It can go from being a sales advisor in retail stores to work in train stations, airports, hotels, hospitals, and restaurants. “Basically, everywhere you have people looking for information or finding something – the robot is there to make the link between what they are searching for and the solution. That is how it is used today.”


Pepper as a work companion

But, why should Pepper - rather than a person – greet and help people in a store?

“The idea is not to replace humans. Pepper, is a machine that assists and complements people's work,” says De La Tour.

He believes that are many advantages in collaborating with Pepper. “First of all, it is a very systematic machine. So it can deliver a unified poll of services to any kind of client coming to a store. Thanks to its large database it can solve a number of problems and situations that are difficult for an individual to handle.”

In some ways, says De La Tour, it is comparable to when an organisation gives its sales person a tablet to work. “It is just another vector of information to enhance the customer experience. But Pepper is not replacing anything. It's an addition.”

The story of Pepper – SoftBank and Aldebaran

The next generation of technology and innovation have always been part of the DNA of SoftBank. In 2011, they decided to invest in personal robotics capable of enriching and simplifying people's lives. It identified several companies worldwide to partner with, but Aldebaran proved to be one of the most experienced in this market.  

Founded by a French entrepreneur Bruno Maisonnier, Aldebaran creates companion robots to support humans in their everyday life. In 2006, they created NAO, which also was at the Millennial 20/20. New versions of NAO emerged over time, leading to some educational uses in schools. For example, today, NAO can support teachers in assisting children with autism and other special needs.  

In 2014, Aldebaran started collaborating with SoftBank for the creation of Pepper, which became the first robot in the world able to read emotions. 


Pepper as a platform 

Indeed, there are still many challenges for the robot. Yet, De La Tour believes that the future of Pepper is bright. “We cannot even imagine all the future use cases. Pepper will continue developing based on a number of tools and updates.”

In fact, Aldebaran is inviting animators, sound, and graphic designers, as well as linguistics to join the community to invent and plan content to make Pepper more interesting. Developers will be soon able to submit their applications on Aldebaran online store and enter their developer program. The community is also given the opportunity to learn in one of the ateliers in Paris and Tokyo. 

“Pepper will evolve over time allowing people to do more things and interacting with them in ways that we haven't discovered yet.”

As research keep focusing on machines that master algorithms and improve over time, it's entirely possible we'll find ourselves communicating and collaborating with robots like Pepper. 

But, this will also demand opening up to a broad dialogue in order to answer some fundamental questions about our future relationship with work. And what needs to change civically to accommodate the change in our society, positively.

  
Pictures courtesy of Aldebaran

Friday, 15 April 2016

How Deloitte Digital is Disrupting the Workplace - Virtual Reality, Robots and more

Gloria Lombardi visits Deloitte Digital's innovative office in London to explore the latest workplace technologies with the firm's 'Disruptor' Ed Greig
 

“The widest trend that we are seeing across all industries is the need for companies to adopt a unified approach to technology, space and talent,” tells me Ed Greig (pictured right), who is known within Deloitte Digital as the Disruptor.

He works at one of the consulting firm's unconventional offices in London, which is all about researching and testing new technologies - from Virtual Reality (VR) to 3D printing, drones and Internet of Things (IoT) devices. “All these technologies are for us to test as well as for our clients. What we want to do with them is to prove that they have a practical use case.”

In fact, the danger with innovations like these is that people can get distracted by the exciting technology and lose sight of the practical business case. But, Deloitte Digital “are always on the look out for real world problems where we can apply these technologies. Whether that is internal within Deloitte or externally with clients.”

Virtual Reality

The opportunities for virtual reality to impact work positively are many. For example, Greig is using it to understand how Project One, which is Deloitte's upcoming building, can deliver great experiences to employees in the future. “We can model the building in a virtual reality environment for our colleagues. We can give them a view of what the new workspace will look like. And we can redesign the employee experience based on their feedback.”

This is key to the way the firm works, which is very iterative and collaborative. Plus, it saves money. “It is relatively cheap for us to do something now in a virtual reality context compared to building the space and then finding out that it does not work for our people. That will be much more expensive.”

Virtual reality for training employees

Another interesting use of virtual reality is for training staff. For example, Greig has just prepared a proof of concept VR training for the risk team. “It puts you into a risky scenario that you might face at work, and then it asks you to identify the risks.”

He believes that the technology shows the user the consequences of their actions in a much more vivid way than just watching it happening to someone else. “It requires you to actually take the actions yourself, which is a powerful driver for changing your behaviour in the future.”

In fact, one type of training that he is looking to improve through the use of VR is e-learning. VR creates an immersive experience. “When employees are watching videos they may not to engage as well with the training. In contrast, with VR you are focused on it 100% because you are inside the headset. That means you have a much better degree of concentration.”

Another type of training that can be improved by VR is with role play situations. “You are bringing human interactions into VR.” That again can save resources. “The more traditional training is expensive and time-consuming as it requires people to actually be on site.”

3D printing

For Greig, 3D printing is useful to test and explain concepts and projects in a practical, physical and a tangible way. For example, he has just printed an Internet of Things (IoT) prototype to demo to a client. It is a connected door lock that links into Deloitte Digital’s smart home proof of concept.

Drones

Greig is also experimenting with drones. For example, “we use them with our real estate team to film different pieces of buildings and getting interesting footage.”

He believes that one of the greatest opportunities for those flying robots is around taking dangerous tasks out of a job. “For example, inspecting wild spaces or disaster zones. Putting a person in those situations can be very dangerous. Now you can use a drone instead.”

Farming is another sector where drones can make a difference. For example, “they are used to monitor the progression of crops to give farmers a precise picture of what is going on in their field. This allows farmers to use fertilisers very accurately, leading to higher yields at a lower cost.”

Tasks automation vs. job creation

New jobs are emerging from the rise of these technologies. “My role, for example, didn't exist two years ago. Even the idea of Deloitte hiring developers to work on virtual reality or build robots would have seemed very bizarre until recently.”

Indeed, it is also important to recognise that other jobs are disappearing too. “The work that we are doing is often about simplifying or automating roles in order to save costs.”

Yet, automation is not necessarily a bad thing. For example, “through machine intelligence, the level of the repetitiveness of some tasks is now much lower. So, the overall job can actually become more creative and productive.”

Creativity or creative thinking?

Greig makes a clear distinction between creativity and creative thinking. With the former, there has already been some recent progress in this area.

Indeed, by looking at some fascinating projects such as the recent The Next Rembrandt we can realise that there is actually a whole sphere of creativity that is emerging where machines can play a phenomenal role. “The computer was able to analyse the specific period of Rembrandt. It identified the artist's top classic portraits and created an original Rembrandt. Then, it was 3D printed to create the same piece of artwork as the original on canvas.”

Indeed, technology today can analyse data and spot patterns within it in ways that people are not just capable of.

But, the creative thinking of humans should always be valued. And there are a lot of jobs where the human interactions are going to remain very important. “For example, nursing, caring, or personal training. And, the ability to express ideas in a clear way.”

The future workplace – between digital and physical

For Greig, the workplace of the future will feel like a completely seamless experience between the virtual and real environment. “Employees will just not notice the technology. They will just do the job and focus on what's important.” That has started to happen in various degrees. For example, “we are seeing the rise of meeting scheduling bots such as Amy, the digital assistant that interacts with you via email.”

And Artificial Intelligence has started to impact on communication and collaboration on internal social networks. “Software is already matching people together based on interests and find connections across the business for you. It suggests you which conversations are relevant to you. And much more is expected.”

He also believes there will always be a role to play for the physical workspace. “The ability to exchange ideas face-to-face and the serendipity that comes from it is unique.” This, for example, is what happens during the Mash Up Days. “Every three months we run an internal hackathon. We have 24 hours to build whatever you like.”

Anyone from Deloitte can join those events, which sometimes lead to some interesting projects such as the “mind control nerf gun.” This wearable device detects the brain waves of an individual. “When you concentrate the nerf gun will fire.”

Fearing new technology

Although he works with clients that are quite innovative and up for disruption, Greig does appreciate that other people can be concerned about the consequences of using these technologies. “That is the reason why it is important for us to test them internally, and learn from it as much as possible. It is very easy to be tempted by the potential prize and then go in the wrong direction.”

And, “it is important to make sure that the change is conducted in a safe way.” Indeed, as technology is given greater power, greater responsibility will also be crucial.

Friday, 8 April 2016

Internal communication is key to employee advocacy

Gloria Lombardi speaks with Ed Terpening, Analyst at Altimeter Group and author of the new study 'Social Media Employee Advocacy: Tapping Into the Power of an Engaged Workforce'.


45% of companies invest in employee advocacy programs in order to engage customers, and 15% of them expect some kind of financial return. But, a positive byproduct of employees sharing information about their organisation is that it boosts engagement with the employer.

The four-month study by Altimeter Group, 'Social Media Employee Advocacy: Tapping Into the Power of an Engaged Workforce', surveyed the opinions of brand leaders, employees and consumers. Its author Ed Terpening (pictured right) is clear about the power of employee advocacy:

"Yes, companies are engaging with consumers. And, yes, there is a financial return to that. But, they also end up with a workforce that better understands the business."

I wanted to speak with Terpening to explore key insights from the study and how organisations could strengthen their employee advocacy efforts. In this interview he shares the motivations for staff to share corporate content, the importance of internal communications, the drivers for successful initiatives and the link between internal and external social media.

Gloria Lombardi: As your study shows, companies have started to realise the benefits of staff advocating the brand on social media. But, what does it take to really motivate an employee to share information about their company, authentically?

Ed Terpening: According to our findings, the number one reason that employees share corporate information is because they believe in the mission of the company.

But, they also said they believe in the product and want to promote what the business does. Another reason is about connecting with other people outside work.

Recruiting is a big motivator too. Many employees would like to help their friends to get a job at their employer. So, they share job postings and what it feels like to work at the company.

GL: What are the key drivers for successful initiatives?

ET: First, is content. Organisations have to provide employees with a stream of information to have them on board and start sharing. But, not just any type of content. They have to align or tailor it to the interests of their employees. The content that they should share with people in Accounting would be different from Sales or Product Development or HR. So, the company has to have a relatively rich content library.

The best content is also curated to support the sector in which the company operates. So, for example, a bank that sells mortgages, might share a story about the housing market or interest rates.

And, it does not need to be content from the brand itself. In fact, usually it is a mix: there is the information that is discovered and curated by the social media team as well as the content created by the internal communications team. But, there is also the content that is created and curated by the employees themselves.


GL: Tell me more about employee generated content.

ET: Generally, there are two ways of doing that. One, it is about creating and sharing thought leadership content. The employees who are thought leaders in a given topic would spend a lot of time thinking about and writing content on their subject area. Let's take security, for instance. A technical engineer of a company that makes security software might write a blog post about a virus or a hack. Then she shares it with her networks, both internally and externally.

But, there is also the average employee who might just post a photo of an event at work such as a fundraising initiative. In the end, there can be a wide range of content from more informal to thought leadership pieces.

GL: What's the link between internal communication and employee advocacy?

ET: Internal communication is very important. In fact, many companies miss that point when they start. We saw some brands starting employee advocacy programs from the marketing department as a way to expand the reach of the company messages on social media. But, many advanced brands quickly learned that it was really more an internal communication vehicle. And, they moved the whole program to internal communication.

GL: When we talk about employee advocacy initiatives we almost instinctively think about external social media - employees sharing content on Facebook, LinkedIn and other channels. But, what about internal social networks? Do they have a role to play?

ET: Interestingly, 80% of the companies with a strong internal social network, also have strong employee advocacy programs. Their employees are already used to creating and posting content for their fellow colleagues inside the company. They know how to share company-related information because the organisation built this culture.

In fact, getting employees to share content is really a cultural thing. It is not the technology. Implementing advocacy initiatives can be easy from many standpoints, but only if you overcome the cultural issues.

We have to remember that many employers did not let employees post about work for so many years. It is only recently, in the past few years, that more companies have started to ask their staff to share.

GL: Which organisations are doing employee advocacy well? Could you give me some examples?

ET: Adobe is a good example. They are focused on recruiting. Being a technology company based in Silicon Valley they have a lot of competition for engineers. And, they have to find any way possible to recruit new talent. So, they created a wonderful program called the Adobe Life where all the employees are trained and allowed to post information about what's like to work at Adobe.

AT&T does very well too. It is one of the brand that have been successful by, over time, moving their employee advocacy program to Internal Communications, a natural fit.

GL: Which are the platforms most used by organisations?

ET: There are individual platforms such as Dynamic Signal and Trapit, which are popular. And, there are platforms that are built into existing social media management systems. For example, Hootesuite have a tool called Amplify.

But, as we saw earlier, sharing is recurring more and more within enterprise social networks. For example, AT&T have a platform where employees can choose to share information with their outside networks without leaving the internal tool. They do not need a separate technology. It is an existing part of their communications.

GL: How about mobile applications for employee advocacy?

ET: Mobile is where companies will be going. I'll give you an example. Target is a large retailer with hundreds of thousands of employees who do not have desktop computers. They distributed a mobile application to employees to keep them informed but also to share corporate news with their friends.

GL: You conducted the survey with companies in North America, the US, the UK, France and Germany. Did you find any difference across the regions?

ET: We found a couple of key differences. In general, Europeans are more focused on privacy. We asked employees in both continents: 'Does your employer make it easy for you to share information with your friends?' In the US, 41% of employees said it was true. In Europe, only 21%.

Additionally, half of the respondents in North America share work-related content because they want their friends to understand what they do. In Europe, only 30% of them said that.

But, even in the US a third of people still do not want to mix personal with professional life.

GL: Could we sum up your findings with some final advice to organisations? What they should think about before launching an employee advocacy program?

ET: There are a couple of things that I recommend. First, even though you may start with a small pilot, involve early groups like Internal Communications, Legal, HR, IT and Marketing. They all have a stake. Sales may have a stake too, particularly if the company has a B2B sales force.

And, set expectations early on. Are you doing this to engage employees? Or to engage customers? Or to have financial returns? Make sure to have a clear objective before starting.

Sunday, 3 April 2016

Artificial Intelligence and the Future of Work

Gloria Lombardi speaks with software startup advisor Steve Ardire to explore the state of AI and its implications for the future of work.

By Gloria Lombardi

How can Artificial Intelligence (AI) help companies operate in the 21st century? How might it impact organisations and employees?

AI has been around for years, but now it seems that it is taking the business world by storm. According to software startup advisor Steve Ardire (pictured right), it will fundamentally reshape organisations. “Human capital will start to shift from mundane tasks and transactions to higher-order and creative work. Along the way, we will see massive businesses where the technology transforms specific job functions,” he tells me.

Artificial Intelligence or Machine Intelligence?

Recently, there has been a lot of debate about the meaning of AI. As research has developed, the definition has changed. “When I called it AI, many people were distracted by whether certain companies were true AI,” Ardire explains. His interest lies in Machine Intelligence (MI), which is Machine Learning (ML) plus AI. His definition is close to the IBM Watson’s cognitive computing. 

Machine Learning is about “getting computers to program themselves, letting data do the work with training data sets. You keep going around and around until eventually it does well.” 

And, when Ardire talks about Machine Intelligence, he means intelligent computers “that process data for pattern discovery, discern context, make inferences, reasons, learns, and improves over time” without supervision by humans.

Machines and the future of work

For Ardire, the future of work is not a “man versus machine.” Instead, he says: "It is people and machines working alongside each other to improve the way they work."

Indeed, he admits that “the employees who do routine manual and routine cognitive tasks will have a high probability of being replaced in the future.” But the rise of the machines also has immense benefits to offer. “AI systems usually replace old tasks and create new ones.” In fact, leaving machines to do the repetitive tasks can go a long way towards enhancing innovation and human creativity. “If your job is not routine cognitive, then machine learning becomes a digital adviser. It will play collaboratively with you as an assistant. Automation of jobs, in that sense, becomes a good thing.”

To support his belief Ardire points to recent research by Narrative Science. According to the study, for 80 percent of enterprise executives artificial intelligence makes workers more productive and creates new jobs. 

But how can AI help companies operate successfully in the 21st century? 

The idea that automation will free companies to focus on higher order and more creative work has been touted for years. According to Ardire, the insights gained from the use of AI technologies will enable workplaces to use knowledge in ways that have been impossible so far. As he puts it: "Using machine intelligence for improved data analytics is just the tip of the iceberg."

Intelligent computers can inject and integrate information from various sources such as the web, social streams and the enterprise. Then, they blend and harmonise it with contextual processes to provide the organisation with “smart data.”

One of the benefits for companies is that they can make smarter decisions. “Knowledge workers can understand complex issues faster, answer difficult questions, solve problems more effectively. They can be more productive. They spend less time on classifying information and more time on interpreting insights and taking critical action.”

Indeed, it’s a key distinction. The opportunity for organisations to access millions of data points today is of no use unless employees can keep up with the overload and improve the delivery of their work.

Enterprise social communications

Another area where machine learning can make a difference is with enterprise social media and collaboration tools. Presently, one of the challenges for some organisations is making sense of the sheer volume of conversations that their social streams can generate. “There is too much noise, which distracts employees from the work they are doing.”

The next set of AI tools could help deliver a competitive advantage to companies. “Powerful Artificial Intelligence can help make sense of the conversations people have on their networks.” Ultimately, helping workers to understand which messages are useful to their job. 
  
The market

AI technology is developing, fast. “In 2016,” Ardire says, “we are already seeing the emergence of applications for human resources, marketing and communications, sales, customer service, market and risk intelligence and more.”

The leading companies are pushing AI towards new horizons. Specifically, among the key trends is open-sourcing machine intelligence designs. For example, Facebook Big Sur, IBM System ML and now Open.

The rapidly emerging AI also includes many startups mostly currently flying under the radar. “AI startups have raised an aggregate of $967M in funding since 2010,” Ardire says. “And, the deal activity almost doubled in the last quarter only. Those investments went to companies in 13 countries and ten industries including business intelligence, e-commerce, and healthcare.”

Machine intelligence in healthcare

In fact, Ardire points to healthcare as one of the leaders in AI. A good example is MD Anderson Cancer Center. In 2014, the organisation started using Insights Fabric, a CognitiveScale-based platform, to improve patients’ experience, employee engagement, and enterprise operations.

Clinicians connect securely and privately with patients in real time. The health history of individuals is constantly updated as the smart technology learns and remembers people’s behaviours and preferences. This helps care givers to make personalised and tailored recommendations that are current, and to alert patients about key requirements precisely when they need it.

The future is unsupervised learning

But, reaching a state where organisations will be “machine intelligence literate” is not so easy. The majority of efforts today are still around “supervised learning,” where computers are given “training instances that are labelled with reinforcement,” according to Ardire. And training the machine takes time.

In addition, there is the challenge of embedding the technology into existing enterprise applications.
But, Ardire believes that machines will one day become intelligent systems just like the human brain.

He says: "Going forward, the future is unsupervised learning where machines can infer what they don’t know about. They will be given no positive or negative reinforcement [by humans]."

Indeed, it was Google Brain’s Jeff Dean who once explained, “Unsupervised learning is an important component in building really intelligent systems – if you look at how humans learn, it’s almost entirely unsupervised.”

If we think of the impact the internet has had over the last 25 years on the wider society, not least on businesses, it has been revolutionary. And, it has had some big implications for organisations and their workers. 

Now, AI is coming. In 25 years’ time might it have the same impact? Perhaps, the sooner we try to understand AI the better.

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This article originally appeared on IBM