Digital Transformation coming full circle – 23 years later

When asked what we do here at Squirro I talk about AI. AI seems to be everywhere. Our version is all about Augmented Intelligence. Then people often go: “Interesting, but (not yet) for me.”

23 years ago, 1995, I joined the Institute of Information Systems of the University of St.Gallen. There I joined the Competence Center Electronic Markets as research assistant. The competence center was joint research with companies such as UBS, CS, St.Galler Kantonalbank, SwissRe, Mercedes, and many more.

Under the guidance of Prof. Schmid, or Dr. Beat in short, we did ground breaking work: At the institute we got at the time a fully working e-banking prototype called Telecounter. Yes fully working, albeit on clunky PCs. Showing that to people outside of the competence center we were often met with disbelieve and sometimes ridicule.

Fast forward to today. When I opened my UBS e-banking account last Sunday. First, I was asked to accept the new all-digital package. No longer will I receive hard copies of my monthly statements, paying bills will only be possible in an all-digital manner – the good old physical payment slips gone, the only connection to my account is the browser interface.

Within the span of two decades retail banking has been fundamentally transformed.

AI will do the same for banking and any other industry with one difference: It will take significantly less time than two decades. Why? The combination of Moore’s law and massive advances in algorithmic computing make this change irresistible much faster.

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Artificial Intelligence to power the next wave of enterprise apps

What is behind the rise in enterprise apps and why has the apps market suddenly been alerted to the analytic power and automation of processes that AI offers?

Despite the hype, vendor noise and press column inches, artificial intelligence (AI) is yet to truly deliver on its undoubted potential. Relatively few organisations are using AI in any real or meaningful sense and for now at least, it is still mostly found within early adopters.

But this will change over the coming months, as organisations begin to realise the potential for AI to transform elements of their business will ultimately outweigh any reservations that have been preventing their adoption to this point. There is one area of business however, that AI has already made a firm impression – as the technology powering the next generation of enterprise apps.

Enterprise apps have been growing in use and popularity, with many tech firms even creating their own app marketplaces. Salesforce has recently launched a $50 million fund to invest in start-ups employing artificial intelligence, and some of these apps are already available in Salesforce AppExchange. What is behind the rise in enterprise apps and why has the apps market suddenly been alerted to the analytic power and automation of processes that AI offers?

The rise of enterprise apps

Mobile applications, commonly known as apps, have been a fixture in the consumer world for at least a decade now. People would use them to do things faster, better and smarter on their mobile handsets, whether that’s booking a train ticket, doing their online banking, updating social media accounts or playing a game to name just a few.

Because apps were so useful and added so much value for consumers, and smartphones were becoming ubiquitous, enterprises started offering them too, creating apps for business functions such as HR, marketing, finance and more, as well as collaboration tool apps and a whole host of other areas. In 2016 Research and Markets launched a report “Mobile Enterprise Application Market – Global Forecast to 2021” that predicted the market would grow from $48.24 billion in 2016 to $98.03 billion by 2021.

This is an opportunity for app designers to expand into a new market, for enterprises to equip and empower their workforce to do their jobs more effectively and for large enterprise tech companies to grow and protect their own eco-system.

Bespoke enterprise app marketplaces

Launching a bespoke apps marketplace has become commonplace for big tech firms. Oracle has its Oracle Cloud Marketplace, there is the ServiceNow Store, the Zendesk marketplace and Microsoft has three app marketplaces, with AppSource the main offering targeted mostly to the enterprise.

However, Salesforce’s AppExchange was the first enterprise apps marketplace and is still very highly regarded, with an active and engaged audience and enormous innovation to be found within the apps. Analyst firm IDC conducted a study of Salesforce customers that stated businesses who are driving innovation with Salesforce App Cloud are achieving 478% five-year ROI, releasing new apps in 59% less time, and increasing revenues an average of $55,000 per 100 users.

With 3,000 partner apps and more than 4 million customer installs, AppExchange is the most comprehensive source of cloud, mobile, social, IoT and analytics technologies for businesses. But the past 12 months has seen a change in many of the apps listed on AppExchange, and indeed Salesforce’s overall focus, with AI now to the fore.

2018 – the year of AI?

Einstein is a layer within the Salesforce platform that helps users to make best use of their data, delivering insight that allows them to truly focus on their customers. It does so by utilising the computing power of AI, a technology at the heart of everything Salesforce is trying to achieve.

Like many observers, Salesforce believes that AI is set to be the dominant technology of the next decade and that understanding customers is best achieved by AI. It allows users to address a number challenges, such as: learning from the data coming into the organisation; improving sales engagement; being proactive in customer service problem solving; becoming more predictive in addressing issues before they become a real problem.

Salesforce Ventures has announced a new $50 million fund to encourage startups to build AI-fuelled applications on top of Salesforce. This overall change of focus is reflected in the apps that are proving most popular within AppExchange.

AI’s ability to automate certain tasks and augment any number of others, and bto ring enormous insight based on big data is behind this rise in AI-based apps. What’s more, when packaged in an app, this data insight is far more readily available to anyone across the business who might need it than it once was.

Apps and data insight

Data is one of the most powerful tools in any enterprise, but its value is highly limited if it is only available to a select few. The true value of data only comes when each employee can benefit from the insight it delivers – the democratisation of data. AI-based apps are making data insight much more accessible to business users, and moving away from the idea that data scientists are required to really understand and extract insight from data.

Few would argue that AI is still in its very early days of coming to maturity. Despite all the hype, no AI technology has yet passed the Turing Test, a test of a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.

But the advances of faster computing multiplied by advances in algorithmic computing have been astonishing and the next great leap forward in adoption is surely imminent over the coming months and years. AI-driven solutions have not yet mastered causation, but if business leaders can apply such solutions to specific elements of their own business – the efficiency driven approach of deploying AI – then results will follow.

Deploying AI-based apps that address a specific task or challenge, is perhaps the perfect way of achieving this. It allows a true focus on a particular element of business, it plays to the on-going trend and appetite for enterprise apps and can really unlock the deep insight found with an organisation’s data.

 

 

Article appeared first on IT Pro Portal

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Data: The world’s most underused valuable resource

The cover story of the May 6th, 2017 issue of The Economist boldly proclaimed data as “the world’s most valuable resource,” which is a new reality that has taken shape over the years.

Technology used to be all about products, but the 1990s brought about the rise of products and services. In the early-to-mid-2000s, our attention jumped to social media services that were all about your relationships with other people. Now the technology industry is focused on data and the insights that can be extracted from it.

Many may agree that data is in fact the world’s most valuable resource, but the numbers show that data is actually the world’s most underused valuable resource. The truth is that only 1-5% of all data is actually used. It might be easy for companies to pat themselves on the backs about the valuable data that they’re collecting, but if it’s not being used, then what’s the point?

Part of the challenge with making use of all of that information is that 80% of data is unstructured in content like documents, messages, social media posts, pictures, videos, and audio. Unstructured data is notoriously difficult to make sense of because it’s not necessarily organized in a way that can be easily processed. Simply said, it’s difficult to compute.

At the cross-section of ever cheaper computing and massive progress in pragmatic computing, often referred to as artificial intelligence and machine learning, the type of analysis required to turn unstructured data in to meaningful insights becomes possible.

Even more than that, the promise—and reality—of AI is that the right data insights can be delivered to the right person at the right moment without requiring them to think of or search for anything. Instead of being standalone systems, these AI components can be purpose-built and plugged in to existing enterprise systems such as Salesforce and ITSM applications so that companies can manage their data and insights in a single location that has the correct context.

All companies—large and small—need to investigate how AI and machine learning can help them make sense of the mountains of valuable data that they have access to regarding their business operations, customers, competitors, and industry trends. With each passing day, there is a rapid increase in the amount of fascinating unstructured data that is being produced and collected, so if you’re behind today, just imagine how many new valuable insights you’ll continue to miss out on as time goes on if you don’t make data analysis a priority.

 You might think that the point of this article is to encourage you to use all of your data at once, but the best place to start is to try to analyze and act on even just a few more percentage points of data than you’re currently using. The overall goal is to make progress and start treating data like the valuable resource that it is.

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RIP enterprise search – AI-based cognitive insight is the future

Screen Shot 2017-11-23 at 15.28.27

Earlier in 2017, The Economist declared that the ‘world’s most valuable resource is no longer oil, but data’. Few would argue with this. Data and information are at the heart of the modern enterprise, empowering knowledge workers with the insight they need to do their job effectively.

Similarly, few would argue that the idea that enterprise search is at best highly flawed and at worst, unfit for purpose. A McKinsey report has found that employees spend on average 1.8 hours every single day searching for and gathering information. This is too long to simply be trying to locate information and is potentially a major blockage in the knowledge economy.

None of this is a huge surprise, given that enterprise search is based on technology from decades ago, and is unsuitable for the needs and requirements of the modern enterprise. Why has enterprise search grown so unfit for purpose, and can cognitive insight bring the desired speed and accuracy to search?

Different content, changing search habits

Only a decade ago or so, most enterprise information and content came in just a few simple formats, easy to file, manage and access. In 2017, there are more file types and formats than ever before, and much of this is ‘unstructured data’, meaning it is not easily recognised and filed by CRM platform and other enterprise systems. Knowledge workers could sometimes be hunting for information that their enterprise does not know it has.

Furthermore, the way in which we search for information is changing rapidly. Instead of actively searching for something, we’re starting to let the computer, website or AI personality anticipate what we want and give it to us proactively.

As is often the way, consumer services are the first to dip their toes in new ways of getting things done, but this experimentation will eventually impact the enterprise after validation with consumers. Given the shift in search behaviour, companies need to prepare for the impending demise of enterprise search.

A new approach to enterprise search

That’s why some of the biggest firms in the world have turned their attention to enterprise search in 2017. Bing for business is a special version of the Bing search engine, currently in private preview, while Google Cloud Search was introduced in February 2017, and is essentially a watered-down version of Google Search.

But search is slow to change.

The principal elements of search were actually mostly developed in the 1980s, but took a long time to come to market. Searching through customer data, industry news, and analytics reports just isn’t an efficient use of company time. Intelligent recommendations are the new search results.

Just consider a recommendation example that most of us are familiar with: Amazon. You show Amazon what you’re interested in through your browsing and purchase history and then relevant recommendations are provided. A majority of the time these recommendations are a good fit, based on your previous behaviours and likely intent.

Intelligent insights

While every enterprise is not Amazon, every enterprise is drowning in data that can be used to provide intelligent insights and recommendations. Search provides isolated pieces of data, but it won’t provide the AI engine that delivers automatic context, insights, and next steps. While basic search points you to data at that point in time, you really need something that takes it to the next level by analysing activity and habits over a period of time.

No matter what your business scenario is, you shouldn’t have to search for information. Instead, you should connect relevant data sources, and just like Amazon does, you can provide some indication of what you’re interested in and let it come to you. Smart companies shouldn’t search for information to make decisions on: they should just make decisions based on data that they already have in front of them at all times.

Context is king

The future of search is linked directly to the emergence of cognitive computing, which will provide the framework for a new era of cognitive search. This recognises intent and interest and provides structure to the content, capturing more accurately what is contained within the text.

Context is king, and the four key elements of context detection are as follows:

Who – which user is looking for information? What have they looked for previously and what are they likely to be interested in finding in future? Who the individual is key as to what results are delivered to them.

What – the nature of the information is also highly important. Search has moved on from structured or even unstructured text within documents and web pages. Users may be looking for information in any number of different forms, from data within databases and in formats ranging from video and audio, to images and data collected from the internet-of-things (IOT).

When – the timing of the search itself, or the date / time that the information was created will both influence the relevancy and accuracy of results.

Where – the location of the user and also of the information – on-premise, in the cloud, within a database, contained in social media – make up the fourth element of the context that is such an integral part of cognitive search.

Many people still think of search as putting words in a box, but this is hugely limiting. In the future, it’s going to seem crazy to us that there was a time when we had to tell computers what we wanted them to do for each and every task. Safe to say, no one will be looking for enterprise search solutions once the productivity benefits of intelligent recommendations are experienced.

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Time for Artificial Intelligence to Really Deliver

Technology is a sector that has often been prone of over-hyping the latest trends that emerge within it. Over the past few years, virtual reality and driveless cars are just two examples of technologies that while rich in potential, have been over-hyped by both tech and business press, when they are in fact some way still from emerging fully.

The fact that industry analyst group Gartner calls one of its market analyses the ‘Hype Cycle’ further highlights the tech industry’s propensity for hype, and perhaps one of the most over-hyped technologies of recent times is Artificial Intelligence (AI). Despite the media attention and the number of vendors claiming to have world-leading solutions, the number of actual end-users deploying AI to good effect is fairly limited.

But that’s not to suggest that AI is without enormous potential. A recent (July 2017) report from the McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier, revealed that tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment. So AI is on the agenda of the biggest and smartest firms in the world. But how can organisations make the best use of AI and ensure it truly delivers on its potential?

Data and AI – the perfect match

The one thing that can help AI emerge from the hype is simple – data. Organisations hold more data than ever before, and thanks to the Internet of Things and the connected world we live and work, this data is growing all the time.

Used effectively, data can deliver unparalleled insight into customers and more, but it has been a challenge until now for organisations to manage this data in a way that enables easy extraction of actionable insight. The answer lies in AI and machine learning (ML).

But before AI and ML can get to work, a big issue for many organisations is the manner in which data is stored. It isn’t uncommon for a business to store and manage data in multiple CRM platforms, as well as a number of other repositories – ERP, other databases, on the server and desktops – across the enterprise.

This can arise through M&A activity, expansion or simply inefficient managing of IT resources, but the result is the same – data held in many siloes. This makes the management and analysis of this data a much harder job than it need be. If you cannot access data than how can you be expected to draw insight from it?

Addressing unstructured data

Furthermore, the sheer volume of big data in modern organisations can be bewildering, and it comes in files and formats that most CRM systems are unable to manage effectively. Unfortunately, this data is often the most valuable, containing rich insight into a particular customer and their specific needs and requirements.

This unstructured data includes: any social content – Twitter, Facebook, LinkedIn, Instagram – by, and relating to that customer; email conversations between the customer and brand; service call scripts that detail any recent or historical issues, and much more besides that doesn’t into the formats used by most CRM systems.

By not deploying unstructured data within a CRM, it can potentially be a major problem. It means that huge swathes of potential customer insight are missing. Utilising technology that captures both structured data in siloes and the masses of unstructured data, means businesses can begin to benefit from AI.

The potential of AI

Used properly, AI it can be hugely transformative and have a genuine and tangible impact on businesses and their customers. For instance, enterprise search is an area that is crying out for the use of AI and ML. Knowledge workers are spending too long searching for information that might not even be filed where they are searching for it.

Using an AI-based cognitive system allows much more complex search queries and can even make relevant and contextual suggestions back to the user. This is AI effectively understanding better than the user what that user is looking for, saving time on searches and delivering better results.

AI can also deepen customer understanding. An issue for larger firms is knowing who holds which relationships at a particular customer. AI technology can look at mases of unstructured data – all emails sent to an organisation, from and to different people, in different departments and multiple locations – and provide a way for a user to take meaningful action with a customer. This outlines clearly and in real-time who knows who within an organisation, invaluable when enterprise relationships can be so complex.

These are just two examples – the potential of AI can go much further than that. But the key to taking AI beyond the hype lies with data, that’s where the potential is at its richest. By deploying AI and ML, organisations can collect data from multiple sources and in multiple formats, extracting fresh and insightful meaning from it and helping to deliver a complete view of that customer.

First appeared in CompareTheCloud.

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Top 100 Digital Shaper

Feeling humbled and honoured to have been selected to be among so many great people shaping Switzerland’s digital future! A great team is the source of this success! #DigitalShapers

http://www.bilanz.ch/digital-shapers

Dorian_100DigitalShaper2017

 

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You can see the AI age everywhere but in the productivity statistics

“At the hospital they introduced a computer to do payroll. Instead of four people in the payroll department they now employ eight plus a few in the new computer department,” my father explained over Sunday lunch over 30 years ago.

My father worked at the time as medical doctor at the citizen’s hospital (Bürgerspital) in Solothurn, a midsize Swiss town. Back in the eighties the first wave of computers made their way into any large organization, quite often payroll was one of the first applications.

Payroll was a great problem for early computing: A relatively easy task – Take the salary number, deduct social security charges and issue a bank wire statement, highly repetitive – every month the same, and a volume play – the hospital employed hundreds of doctors, nurses, and auxiliary staff.

This was exactly what early computers where good at: straight forward repetitive tasks multiplied many times.  While the computers mastered their designated assignment well, the actual challenge was to embed this new technology into the daily routine at the hospital’s payroll department.

This was difficult for several reasons. For starters, computers were not flexible, so you had to adjust processes to the computer and not the other way around. The early computers were quite big boxes and error prone, especially the matrix printers of the time. Next to the new technology challenge you had a change management process on your hands. The answer quite often: You had the old and new processes run in parallel for a while to account for teething troubles.

Economist Robert Solow famously quipped: “You can see the computer age everywhere but in the productivity statistics.”

Some thirty years later we are at the dawn of a similar transformational phase: The advent of Artificial Intelligence, or AI in short. Every day someone claims another AI breakthrough. It’s the age of intelligent machines, automating everything and taking over the world, or so we’re told.

I beg to differ.

Instead I see a bit of history repeating itself here. Let me explain.

While Siri, Cortana, and similar services start to be great at simpler tasks, they stumble easily if asked more complex questions, or questions without context. Elsewhere you see early forms of machine learning and neural networks applied to repetitive tasks such as identifying cat pictures with limited results.

Let’s inject a dose of pragmatism: Currently it’s faster computing multiplied with clever algorithms.

And many of these algorithmic methods have been around for a while. For example, one of our core USPs, the catalyst detection is built on the shoulder of giants. The work of Robertson-Sparck-Jones. The main body of this work has been developed in the 1980 sand 1990s. We expanded the work on probabilistic retrieval and applied machine learning techniques in the period 2012-2014 together with folks from the University of Applied Science of Zurich and ever since.

Here at Squirro we deploy these techniques to do more with data. And some of the results are quite amazing. Have you seen a system not knowledgeable of what the Octoberfest is to accurately predict the next, using no more than a few time buckets of available data?

To quote Marc Vollenweider from Evalueserve: “We’ve tested 25 #AI engines & only @Squirro brought benefit.

Independent evaluation from the market gives rigor and context to such new technologies and helps to distinguish today’s useful and practical AI, from the hyperbole that surrounds it. We were not aware during the initial assessment phase at Evalueserve of their selection process. And obviously humbled and proud about it’s outcome.

The AI story has a long way to go and won’t make that journey unassisted. As Marc explains in his recent book ‘Mind + Machine’ the phase of combining the best of machines and human brains is where we can expect to see great value. I agree.

To a certain extent, it’s the same story from 30 years ago. The promise of AI is real.

Within a limited compound, some AI products already produce stunning results. A good example is the progress made of applying AI to language translation. But they are still limited in scope and limited to this box. The everyday embedding piece is often missing.

We’re living in an age of experimentation. Along the we learn of every stumble and hiccup and that’s a good and necessary thing for any new technology. Exposure of limitations de-mystifies it over time, making it more likely to be accepted, adopted and find its most useful and practical applications.

 

PS: Reach out to us, as we’ve gained considerable experience in deploying AI solutions providing tangible results.

PPS: Would I be an aspiring economist I’d explore the reapplication of Solow’s thesis.

PPPS: Image credits

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Digital Manifesto for Switzerland

The other day Digital Switzerland invited 50 digital movers and shakers to Bern to discuss digitalization and Switzerland and what we think a way forward is. The outcome is this Digital Manifesto (pdf).

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“We’ve tested 25 #AI engines & only @Squirro brought benefit”

As known, I work for Squirro. Our customer Evalueserve just released this short video:

“We’ve tested 25 #AI engines & only @Squirro brought benefit. This is why #robots won’t replace us”

@Evaluserve, 23 August 2016

I agree.

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New York New York, big city of dreams

New York Central Park

The big apple at 6am, just returning from my morning run in the Central Park. It’s already nearly 80 degrees Fahrenheit. And it was busy in Central Park, the center drive full of joggers and bickers.

I am here to join my team for a full week of customer, prospect and partner meetings. We’ve established our New York office last year and have with Peter a fantastic account executive and with Alex an amazing post-sales engineer on board.

Together with our team in Europe we work on a number of customers in the financial services world and beyond. And we broaden the reach daily. Just heading to a prospect together with our friends from Synpulse, our great consulting & integration partner here in the US.

The US is a great place to do business. The forward leaning approach, taking in new technologies to reap rewards early, helps a younger vendor such as us. The positive attitude to risk and risk taking (in considerate amounts) is a sharp difference from the risk resistive approach that I often see across Europe.

So over the next year or two we’ll expect to win quite a number of new customers & and add a few more colleagues to our team. And I expect to come many times more for an early morning moment to the Central Park.

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