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2019 Prediction: Brexit will be abolished and the UK economy will roar back to life

I am not normally one for big grandiose predictions – I’m actually pretty dull when it comes to big hyperbole (I hope).

Honestly, I’d love to declare that AI will destroy a third of the workforce, and then magically perform a 360-degree flip and start creating jobs.  I’d also love to declare that RPA vendors will magically infuse Machine Learning into their apps to produce AI magic.  Because AI is magic, didn’t you know? I’d love to declare that software is eating the world… and then declare that it actually won’t, because a lot of it is actually pretty crap.  I’d also love to declare that Blockchain will radically impact the entire business ecosystem to such an extent I can prognosticate all these business cases with so many holes in them, I might as well start lauding the transformational capabilities of emmental.

However, there is one big bold prediction I am prepared to make:  Brexit will be dead in the water in a few weeks.

I am an analyst, I explore every permutation of almost anything that impacts economies, business, societies until I drive myself mad.  I also work with other half-crazy analysts who do the same.  Just take a gander at our recent analysis of the hazardous implications of Brexit on the UK economy.

So why is Brexit headed for the scrap heap?

It was with “all in” or “all out”.  We did neither.  Seriously, we should have just drawn the guillotine on the EU right after the 2016 referendum. We should have taken the pain then, and we’d probably be OK right now.  Hell, we’d probably be part of NAFTA introducing delicious microwaved fish and chip pub luncheons to the Mexis and some actual real beer to the Canadians.  And we may even finally get decent burritos introduced to the streets of London and poutine finally replacing soggy chips n’ curry sauce. Instead, we dithered, argued, bored ourselves silly arguing until no-one could quite remember what we were doing in the first place.  Instead, we got to see close hand how indecisive, and stupid so many politicians are, how most of these people only care about self-interest than any actual deep driven mission or purpose.  We also had many chances to think “Why are we doing this again?  None of our businesses are happy, the Irish are freaking out, the Scots are ready to bolt, so we’ll only be left with, er, Wales (and even they are making noises)”.  And Mr Trump even thinks it a bad deal… and he was great in the apprentice, so it must really suck.

Brexit is a massive Catch-22. You can’t just compromise on an issue like this, even though 48% rejected it.  There just isn’t any point in doing half-measures with Brexit – both scenarios suck.  The diluted mess Theresa May has served up basically ensures we only get half-screwed by the experience.  We are still tied to the EU, the Irish are still freaking out, we will close our borders in any case, but noone will want to come here anyway, because our economy will stink. In fact, most of the EU immigrant workers will probably flock to Dublin to work in the call centers after the banks have shifted over there… There really isn’t a compromise when the issues are this black and white.

The only current scenario is ‘no-deal disaster’ or ‘go back to the people to make a decision’.  Let’s get to the point – the “deal” on the table is a plethora of half-measures with little upside for anyone.  So that leaves only one Brexit option:  no-deal and an economic calamity. There is no way 52% of the British folks care that much about putting a middle finger up at Brussels to destroy their livelihoods. When May’s deal fails next week, she will really only have one choice – to go back to the people to decide.  And we only need a 3% swing from that heady warm June 2016 evening to fix this calamity.  I occasionally like a bet, and this is one I’d throw a few pounds at…

The Bottom-Line: Parliament will throw this out and the British public will reject a no-deal Brexit… So Auf Wiedersehen Brexit

Firstly, there is no way MPs will vote for the current “soft-Brexit” deal on the table next week.  May must know this too – and will simply go straight to the people to finalize this issue once and for all.  There is no renegotiation with Brussels – that is clear, and there isn’t enough time, in any case, with the deadline being 29th March 2019.  Secondly, Calling a general election with Brexit looming so close would be madness. There are now only two real options:

1) A “Hard no-deal Brexit”

2) No Brexit

So there will be a second referendum and it will swing for option 2.  That won’t be the end of the matter, as a groveling “take us back” negotiation will take place, but the EU leaders all know they need Britain back to keep the EU strong – and this will drive Putin mad (who would love nothing more than a weakened EU).  Trump liked Brexit for similar economic reasons of weakening Brussels’ power, but the US relies on a strong Britain as its gateway to Europe, and may now prefer an EU including the UK than one without.

There will also be considerable public fall-out as half the country did vote “leave” and they will feel betrayed by shambolic politicians.  However, a “deal” was never going to be done and a transition organized in two short years – May was always on a hiding-to-nothing, and the only real takeaway is that referendums on complex issues never work.  You know who loved referendums? One A Hitler… when information was easily controlled and people easily to brainwash.  In today’s age of hyper-connected everything, you simply can’t control anything!

 

(Cross-posted @ Horses for Sources)

Nokia reinvented: Decline, resurrection, and how CEOs get trapped

Although the Nokia brand is iconic and well-known, most people are unaware of the disruption story behind that name.

In 2007, Nokia held 50 percent market share in mobile phone handsets. The company was considered a national pride for Finland, having been founded 150 years ago in 1865.

By 2013, Nokia’s handset market share decreased to less than 5 percent with 40,000 employees reported in 2012. At that time, the company also faced the near-certain likelihood of bankruptcy. In 2013, Microsoft bought Nokia in an ill-fated deal to save the Windows phone. By 2014, Nokia revenue had dropped to $6.3 billion. Eventually, Microsoft restructured its smartphone business and took a write-down of more than $8 billion.

The following chart shows Nokia’s handset market share from 2007-2012:

nokia-market-share

Nokia handset market share 2007-2013

In sharp contrast to this history, Nokia today has 103,000 employees with revenue of $29 billion. Interestingly, less than one percent of current employees worked for the old Nokia and the company’s primary business today is supplying network infrastructure around the world. For example, the company recently announced a €2 billion deal with Chinese telecom carriers.

Nokia presents a classic case in market disruption that can be summed up in one word: Apple. In 2007, Apple introduced the iPhone, starting the modern smartphone era.

The following graph shows the growth of Apple market share compared to that of Nokia. Once the iPhone picked up traction in the market, Nokia’s handset business was doomed:

 

Apple - Nokia revenue

Noka / Apple handset revenue 2010-2012

The decline and resurrection of Nokia is a classic story of corporate disruption. The story is complex, involving factors such as:

  • Corporate leadership did not respond sufficiently rapidly to changing consumer demands
  • Legacy distribution and business model, selling handsets through telecom carriers, which forced Nokia technology to remain cumbersome and difficult to use, even as the iPhone created a new form of user experience
  • Outmoded leadership culture that slowed decision-making

The survival and re-shaping of Nokia — from dominant handset maker to near-bankruptcy to successful supplier of network infrastructure to the telecom industry — is a story of strategy and transformation.

To learn what happened and uncover management lessons, I spoke with Nokia’s chairman, Risto Siilasmaa, on episode 314 of the CXOTalk series of conversations of the world’s most innovative leaders in business and technology.

Risto wrote a book describing his tenure at Nokia. In it he’s reflective and does not hesitate to point fingers at both himself and other leaders at Nokia. He also describes the inner workings of Nokia’s negotiations with Steve Ballmer at Microsoft. It’s also interesting to note that a third party, HMD Global, is bringing new Nokia phones to market under a licensing agreement.

Siilasmaa has taken a hands-on approach toward ensuring that Nokia remains on top of important technology trends such as machine learning. After decades away from programming, he put on his old developer hat to understand machine learning. His popular machine learning video is now standard fare for Nokia technologists.

You can watch the entire conversation with Risto Siilasmaa, which is embedded above, and read edited excerpts below. The complete transcript is also available on the CXOTalk page for episode 314.

What was Nokia’s market position when you joined the Nokia board in 2008?

Risto Siilasmaa: I joined the Nokia board at the time when Nokia was on the top of the world, but iPhone had been launched the previous year, and Android would be launched the same year that I joined the board. The financial crisis hit that fall. So, the air was so full of debris from these different sources that it was really difficult in the boardroom to understand what was the root cause of what and what was happening.

In 2008, Nokia did well. A few years earlier, Nokia was about 40+ percent of the world’s handset market. At its best, the Symbian platform, which was the Nokia smartphone operating system, had over 70 percent market share in the world until iPhone and Android devices came from behind and bypassed the Nokia platforms. That started the decline of the Nokia business so that, come 2012, the situation was dark, and the press was speculating on the timing of Nokia’s bankruptcy. It was not if; it was when.

Between 2008 and 2012, Nokia went from being the national pride of Finland to facing bankruptcy. Why?

Risto Siilasmaa: It tends to happen in multiple industries. Even in hindsight, there are only a few moments and only a few things that one could say with some degree of confidence that would have kept Nokia in the handset business. Even that is unsure because when you have built the whole company around a particular way of operations, a particular offering delivered in a particular way, and then those basic structures are shifting, it is really, really hard to adapt to that.

Just as an example, our core customers, operators, they were used to working in a particular way. For example, they wanted to warn their users when the smartphone might incur data charges. The Symbian platform was forced by the operators to display all these warning signs, which meant that sometimes when you launched an application, you had to click on, “Yes,” eight times before the application launched.

Apple couldn’t care less. They just wanted to make it as easy to use as possible. They told operators that if you don’t like the iPhone, then we’ll just sell it to your competitor. They all accepted, but they didn’t want to accept similar changes from Nokia. There are powerful forces aligned against you when you want to disrupt yourself.

You talk about the “toxicity of success.” What is that?

Risto Siilasmaa: When a company is hugely successful in its own business, people change their behavior. It is very, very hard to resist. You may see that as sort of complacency, which doesn’t mean that you wouldn’t work hard. You shift your attention to different things, which may not challenge your thinking as much.

That’s natural. It happens to the best of companies. Also, your customers are used to dealing with you the way that you have always dealt with them, and they will resist any change.

When a new player comes into the market, they don’t have any existing contracts. They don’t have any legacy baggage that they carry with them. Their code is all new. They don’t have what we call development debt, which means that sort of old code and old structures that accumulate. You need to rewrite your code in a fairly frequent way to get rid of that debt.

Is there a trap that CEOs can fall into?

Risto Siilasmaa: Of course, there are many different answers. There are many different types of CEOs as well.

I think one way of looking at that is the way we look at CEOs. They are on a pedestal. They are demigods. And, we assume that they know everything, and we force them to pretend that they know everything.

We make it very difficult for them to ask stupid questions, and we make it very difficult for them to admit that there’s something that people assume they know, but they actually don’t. That can create a culture where people don’t go to tell the CEO bad news because they assume that they already know. Also, why would I hurt that demigod’s feelings or why would I go and make his day a bad one?

Why did you learn to program machine learning yourself?

Risto Siilasmaa: I was one of those CEOs or chairmen who became trapped by the role I have. The role I have is one where things are explained to me. I don’t need to dig for explanations myself. There is a team that does a ten-page presentation. Then I read that. Maybe I memorize ten different slogans. Then I can confidently speak to large audiences about difficult topics. I just repeat those slogans that I have heard others say, but I don’t actually understand the topic.

I realized that I was approaching this the wrong way. I was a captive of my role. I had forgotten that I, myself, can study. Then, maybe I can explain to others in a way that I would have wanted somebody to explain to me how machine learning works.

In Nokia, we decided that every single one of our 100,000-plus employees will have to do a course on machine learning; not a complicated one. They just have to understand the essence of it so that they can ask the right questions. When they bump into a business problem, they can have the intuitive feeling that, hey, maybe this problem could be solved using machine learning. Then they can go and talk to experts. But, you have to have that initiation first and, for that, you need to learn. It’s like a code of conduct training for Nokia employees.

CXOTalk offers in-depth conversations with the world’s top innovators. Be sure to watch our many videos!

(Cross-posted @ ZDNet | Beyond IT Failure)

Sympathy for the DevRel

I recently gave the closing keynote at DevRelCon in London, run by Matt Revell and Tamao Nakahara. The conference is building momentum nicely – the first was held above a shop in Shoreditch in 2015, but this year it was held at the QEII center in Westminster.

I really wanted to do a good job, because the Developer Relations community are our people. Matt had originally asked me to talk about walking that fine line between the business and human side of developer relations. How to maintain the humanity of a community while also scaling up. Is professionalisation the enemy of empathy? I had come up with what I thought was a good title – Empathy for the Devil, with the thought in my mind that big company mentality could be seen as making a deal with the devil, but not everything about scaling is bad. I put a set of slides together I was happy with.

Then, the day before the event, my friend Alexis Richardson – who has the naming gene – (and coincidentally is Tamao’s boss) simply said: “Oh that should be… Sympathy for the DevRel”

Queue much face-palming and gnashing of teeth. That. Title. Is. So. Much. Better. Things only got worse when Alvaro Videla, who we were hanging out with, said: “every slide should be a Rolling Stones song.” Of course it should.

And so I began Operation Rework Slides. By the next day late afternoon I was ready to go.

So what was my Sympathy for the DevRel presentation about? Well, I did indeed talk about how bigger companies think about and support Developer Relations – with lessons from IBM, and Microsoft, but also younger firms including MongoDB, Stripe, and Twilio.

The Big Idea in my presentation is perhaps that the related disciplines of Developer Experience, Developer Relations, and Developer Advocacy can be understood in terms of when the empathy is applied.

Developer Experience (DX) puts the empathy up front. It’s in the code of conduct (always have and enforce a code of conduct!), it’s in the documentation, it’s in the design of the API. DX is about having empathy for the experience of the developer, pre-empting complaints, getting it right with design and code samples for onramps that work for everyone.

Developer Relations is when you’re in the room, up on stage presenting to people, trying to understand their needs, doing the demo, making sure people understand what you’re saying. Listening and learning and teaching, with empathy for the people you’re teaching and engaging with.

Developer Advocacy is about post facto empathy. You’ve learned from developers and the community, when you’re working with them. You’ve seen complaints on twitter, blogs, and Stack Overflow, you’ve seen folks cut themselves while trying to replicate your demo. You’re learning from developers and now you’re taking that knowledge back to the product engineering team to make things better for developers in future.

Ideally organisations do all three, but often they are better at one or two of the related disciplines. I am not totally sure this framework works, but I figured using it with a bunch of dev rel people was a good way to learn more.

So case studies, a big idea, and finally empathy by me for Developer Relations professionals. I worry for my friends. I see them eating badly, drinking too much, travelling too much, not giving enough attention to their girlfriends, boyfriends, spouses and kids.

Developer Relations do so much emotional labor, and it takes a toll. Burnout is real. Imposter Syndrome is common, and in devrel you’re always putting it out there. So yeah I asked my people to look after themselves. The most important thing you can say in developer relations in no. Say no to your bosses. Say no to that conference organiser. Say no to everyone except your family and friends.

You can’t get no satisfaction.

It’s not that your employer doesn’t care about you, but you’re eager to please. It’s why you got into Dev Rel in the first place. You love to help, to teach, to be there for people.

I am well aware that a lot of the unhealthy behaviour applies to me. I will be at Kubecon in December. I did five trips to the USA in six weeks this Autumn. But I also say no. I take long vacations. At RedMonk we try to put family first, but we could do better.

Bottom line though – everyone is hiring. If you’re in DevRel you have choices. So say no, and look after yourself.

This thread by Mary Thengvall, who wrote the book on the Business Value of Developer Relations, is a great capture. Thanks Mary.

 

(Read this and other great posts  @ RedMonk)

RPA will reach $2.3bn next year and $4.3bn by 2022… as we revise upwards our forecasts

Well… it’s been quite a 2018 in the fantasy world of RPA (RPA plus RDA), where some of the fantasy dollars have magically become real, as the market hit $1.7bn – an increase of $250m from our forecast last year.  So when the more conservative of forecasters (HFS) undershoots the market by 17%, you know RPA has been sneaking down the growth hormones of late.

So why is RPA growing above initial analyst estimates?

  • RPA vendors, in particularly UiPath and Automation Anywhere (AA), have been able to recognize more revenues than expected. Bots licenses are being sold and deployed faster than we envisaged, due to effective training programs and aggressive support from third-party services firms;
  • The slowdown in new business process outsourcing engagements is driving more focus from enterprises in discrete strategies to drive efficiencies and digitize processes (and encourage more bots plus humans engagements);
  • The shift in the focus of RPA from job elimination to augmenting talent, digitizing processes and extending the life of legacy IT systems has increased the appetite of operations executives to fast-track RPA training programs and invest in broader intelligent automation strategies – even though most enterprises are still in the “tinkering phase”;
  • The initial adoption of “attended RPA”, which makes up the majority of RPA and RDA engagements currently in play will eventually drive more “unattended RPA” where the increased value will be created and genuine alignment between RPA models proving to be a gateway to broader AI engagements;
  • The ramp up from service providers and consultants to support enterprise adoption has continued unabated, especially with the flattening of outsourcing investments and the waning interest in Global Business Services models. This reliance on third parties has proven to be a key dynamic behind the growth in RPA as solution providers prefer to sell through the services channel for larger enterprise deals and accelerate client training and development. The strong focus from the likes of Accenture, Capgemini, Deloitte, EY and KPMG has given the RPA market immense credibility;
  • Rapid funding of RPA vendors (in addition to rapid revenue growth) has encouraged these longer-term investments of many enterprises previously skeptical of investing in very small software boutiques. Largest examples have been AA and UiPath, attaining capital investment rounds as high as $250/$300m, but also some lesser-known niche RPA tools firms, such as Softomotive, which recently had a $25m investment round announced;
  • Increased focus from major ERP / orchestration software vendors with Pega’s acquisition of Openspan and SAP’s first foray into RPA adding Contextor.

 

 

 

 

RPA Definition: 

Example use-case: automating invoice processing across multiple business applications handling rule-based exceptions. RPA is different from traditional automation software as it is inherently capable of recognizing and adapting to deviations in data or exceptions when confronted by large volumes of data. In effect, it can be intelligently trained to analyze large amounts of data from software processes and translate them to triggers for new actions, responses, and communication with other systems. RPA describes a software development toolkit that allows non-engineers to quickly create software robots (known commonly as “bots”) to automate rules-driven business processes. At the core, an RPA system imitates human interventions that interact with internal IT systems. It is a non-invasive application that requires minimum integration with the existing IT setup; delivering productivity by replacing human effort to complete the task. Any company which has labor-intensive processes, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation.  Much fr RPA is self-triggered (bots pass tasks to humans), but requires human intervention for judgment-intensive tasks and robust human governance and to make changes / improvements.

Similarly, RPA offers enough advantage to companies which operate with very few people or shortage of labor. Both situations offer a welcome opportunity to save on cost as well as streamline the resource allocation by deploying automation. The direct services market includes implementation and consulting services focused on building RPA capabilities within an organization. It does not include wider operational services like BPO, which may include RPA becoming increasingly embedded in its delivery.

RDA Definition:

In addition to RPA, the other software toolset which comprises the emergence of enterprise robotics software is termed RDA (Robotic Desktop Automation).  Together with RPA, RDA will help drive the market for enterprise robotic software towards $1.5bn in software and services expenditure in 2018 (with close to three-quarters tied to the services element of strategy, design, transformation and implementation of enterprise robotics).  HfS’ new estimates are for the total enterprise robotics software and services market to surpass $3 billion by 2021 as a compound growth rate of 39%.

Example use-case: automating transfer of data from one system to another. RDA is essentially surface automation, where desktop screens (whether desktop-based, web-based, cloud-based) are “scraped”, scripted and re-programmed to create the automation of data across systems.  A well-designed RDA solution can automate workflows on several levels, specifically: application layer; storage layer; OS layer and network layer. Workflow automation on these layers requires equally specific technologies but provides advantages of efficiency, reliability, performance and responsiveness. Much of this automation needs to be attended by humans as the automation is triggered by humans(humans pass tasks to bots), as data inputs are not always predictable or uniform, but adaptation of smart Machine Learning techniques can reduce the amount of human attendance over time and improve the intelligence of these automated processes.    Similarly to RPA, RDA requires human intervention for judgment-intensive tasks and robust human governance and to make changes / improvements.

 

The Bottom-Line: Automation and AI have a significant part to play in engineering a touchless and intelligent OneOffice

However which way we spin “digital”, the name of the game is about enterprises responding to customer needs as and when they occur, and these customers are increasingly wanting to interact with companies without physical interaction.  Moreover, the onus is moving to the most successful digital enterprises being able to anticipate the needs of their customers even before they occur, by accessing data outside of the enterprise across the supply chain, or economic and market data that can help predict changes in the market, or emerging offering that customers will want to purchase.

This means manual interventions must be eliminated, data sets converged and process chains broadened and digitized to cater for the customer.  Hence, entire supply chains need to be designed to meet these outcomes and engage with all the stakeholders to service customers seamlessly and effectively.  There is no silver bullet to achieve this, but there is emerging technology available to design processes faster, cheaper and smarter with desired outcomes in mind.  The concept was pretty much the same with business process reengineering two+ decades ago, but the difference today is we have emerging tech available to do the real data engineering that is necessary: However, if these firms rest on their laurels, this market dominance will be short lived.  Once the digital baseline is created, enterprises need to create more intelligent bots to perform more sophisticated tasks than repetitive data and process loops. Basic digital is about responding to clients as those needs occur, while true OneOffice is where enterprises need to anticipate customer needs before they happen (see below).  This means having unattended and attended interactions with data sources both inside and outside of the enterprise, such as macroeconomic data, compliance issues, competitive intel, geopolitcal issues, supply chain issues etc.

 

In short, every siloed dataset restricts the analytical insight that makes process owners strategic contributors to the business. You can’t create value – or transform a business operation – without converged, real-time data. Digitally-driven organizations must create a Digital Underbelly to support the front office by automating manual processes, digitizing manual documents to create converged datasets, and embracing the cloud in a way that enables genuine scalability and security for a digital organization. Organizations simply cannot be effective with a digital strategy without automating processes intelligently – forget all the hype around robotics and jobs going away, this is about making processes run digitally so smart organizations can grow their digital businesses and create new work and opportunities. This is where RPA and RDA adds most value today… however, as more processes become digitized, the more value we can glean from cognitive applications that feed off data patterns to help orchestrate more intelligent, broader process chains that link the front to the back office.  In our view, as these solutions mature, we’ll see a real convergence of analytics, RPA and cognitive solutions as intelligent data orchestration becomes the true lifeblood – and currency – for organizations.

Do take some time to read the HfS Trifecta to understand the real enmeshing of automation, analytics and AI.

 

(Cross-posted @ Horses for Sources)