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Using Machine Learning To Find Employees Who Can Scale With Your Business

By on December 12, 2018

Machine-learning-and-hiring

  • Eightfold’s analysis of hiring data has found the half-life of technical, marketable skills is 5 to 7 years, making the ability to unlearn and learn new concepts essential for career survival.
  • Applicant Tracking Systems (ATS) don’t capture applicants’ drive and intensity to unlearn and learn or their innate capabilities for growth.
  • Artificial Intelligence (AI) and machine learning are proving adept at discovering candidates’ innate capabilities to unlearn, learn and reinvent themselves throughout their careers.

Hiring managers in search of qualified job candidates who can scale with and contribute to their growing businesses are facing a crisis today. They’re not finding the right or in many cases, any candidates at all using resumes alone, Applicant Tracking Systems (ATS) or online job recruitment sites designed for employers’ convenience first and candidates last. These outmoded approaches to recruiting aren’t designed to find those candidates with the strongest capabilities. Add to this dynamic the fact that machine learning is making resumes obsolete by enabling employers to find candidates with precisely the right balance of capabilities needed and its unbiased data-driven approach selecting candidates works. Resumes, job recruitment sites and ATS platforms force hiring managers to bet on the probability they make a great hire instead of being completely certain they are by basing their decisions on solid data.

Playing The Probability Hiring Game Versus Making Data-Driven Decisions

Many hiring managers and HR recruiters are playing the probability hiring game. It’s betting that the new hire chosen using imprecise methods will work out. And like any bet, it gets expensive quickly when a wrong choice is made. There’s a 30% chance the new hire will make it through one year, and if they don’t, it will cost at least 1.5 times their salary to replace them. When the median salary for a cloud computing professional is $146,350, and it takes the best case 46 days to find them, the cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. The average size of an engineering team is ten people so only three will remain in 12 months. These are the high costs of playing the probability hiring game, fueled by unconscious and conscious biases and systems that game recruiters into believing they are making progress when they’re automating mediocre or worse decisions. Hiring managers will have better luck betting in Las Vegas or playing Powerball than hiring the best possible candidate if they rely on systems that only deliver a marginal probability of success at best.

Betting on solid data and personalization at scale, on the other hand, delivers real results. Real data slices through the probabilities and is the best equalizer there is at eradicating conscious and unconscious biases from hiring decisions. Hiring managers, HR recruiters, directors and Chief Human Resource Officers (CHROs) vow they are strong believers in diversity. Many are abandoning the probability hiring game for AI- and machine learning-based approaches to talent management that strip away any extraneous data that could lead to bias-driven hiring decisions. Now candidates get evaluated on their capabilities and innate strengths and how strong a match they are to ideal candidates for specific roles.

A Data-Driven Approach To Finding Employees Who Can Scale

Personalization at scale is more than just a recruiting strategy; it’s a talent management strategy intended to flex across the longevity of every employees’ tenure. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. Personalization at scale has succeeded in helping companies find the right person to the right role at the right time and, for the first time, personalize every phase of recruitment, retention and talent management at scale.

Eightfold is pioneering the use of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The taxonomies supported in the corporate candidate database make it possible for hiring managers to define the optimal set of capabilities, innate skills, and strengths they need to fill open positions.

Lessons Learned at PARC
Russell Williams, former Vice President of Human Resources at PARC, says the best strategy he has found is to define the ideal attributes of high performers and look to match those profiles with potential candidates. “We’re finding that there are many more attributes that define a successful employee in our most in-demand positions including data scientist that are evident from just reviewing a resume and with AI, I want to do it at scale,” Russell said. Ashutosh Garg, Eightfold founder, added: “that’s one of the greatest paradoxes that HR departments face, which is the need to know the contextual intelligence of a given candidate far beyond what a resume and existing recruiting systems can provide.”  One of the most valuable lessons learned from PARC is that it’s possible to find the find candidates who excel at unlearning, learning, defining and diligently pursuing their learning roadmaps that lead to reinventing their skills, strengths, and marketability.

Conclusion

Machine learning algorithms capable of completing millions of pattern matching comparisons per second provides valuable new insights, enabling companies to find those who excel at reinventing themselves. The most valuable employees who can scale any business see themselves as learning entrepreneurs and have an inner drive to master new knowledge and skills. And that select group of candidates is the catalyst most often responsible for making the greatest contributions to a company’s growth.

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Using worktech and science for good.

By on December 10, 2018

Workplace harassment is evil.  It can make lives a misery.  Victims often feel unable to speak out. If they do, they face an inquisition, often by the very HR department they turn to help from. In many cases they risk their careers and livelihoods and face ridicule if they  speak up.  If things get to court, every memory recollection is microscopically interrogated. No wonder it  usually goes unreported, as do other forms of harassment and abuse.  The recent supreme court hearing in the US highlighted just how harrowing and difficult reporting harassment can be.

The recent protests at Google merely hint at just how endemic workplace harassment is. The Google workforce gets my thumbs up for its collective action efforts to force change there.  But this is not a Google problem, it is potentially an every employer problem. The #metoo movement has helped surface some of this, but it is really only the surface.  There are many forms of workplace harassment, it is all around us.

Lately I have been suffering from a bit of AI disillusionment in HR/work  tech. Most of what I’d seen recently labelled AI was really just adequate analytics with a new label, or chatbots replacing screens with rather cumbersome text based chat.   Also the role of AI as a magnifier or enabler  of workplace bias was laid open with the recent Amazon recruiting incident. Approaches to AI in HR tech lurch from underwhelming to cavalier.

I opened the November UK edition of wired magazine, and I was pleasantly surprised to read about Dr Julia Shaw’s start up, called Spot. Julia is a psychologist, an expert in false memory and memory based evidence.  Spot aims to make a record of harassment that is “reliable, complete and contemporaneous”.  It uses a natural language chatbot to guide victims through a cognitive interview, producing a more robust documentation of the incident. Spot then enables the victim to communicate with their employer through a secure service, and provides employers with better reporting.   More details on the site.

I wish Julia and her team every success.  Every organization, corporation, public sector, institution or NGO should take a serious look at this.  I’d like to see VC and PE “strongly encouraging” their portfolio companies to deploy tools like these.   Thanks for curing my AI despondency.  Thanks too Wired for writing about it.

(Cross-posted @ Otter Advisory)

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

By on December 10, 2018

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)

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High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019

By on December 10, 2018

Bottom Line: High-tech manufacturers need to urgently solve the paradox of improving supply chain security while attaining greater visibility across supplier networks if they’re going make the most of smart, connected products’ many growth opportunities in 2019.

The era of smart, connected products is revolutionizing every aspect of manufacturing today, from suppliers to distribution networks. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50 percent of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in).

Smart, connected products free manufacturers and their supply chains from having to rely on transactions and the price wars they create. The smarter the product, the greater the services revenue opportunities. And the more connected a smart product is using IoT and Wi-Fi sensors the more security has to be designed into every potential supplier evaluation, onboarding, quality plan, and ongoing suppliers’ audits. High-tech manufacturers are undertaking all of these strategies today, fueling them with real-time monitoring using barcoding, RFID and IoT sensors to improve visibility across their supply chains.

Gaining even greater visibility into their supply chains using cloud-based track-and-trace systems capable of reporting back the condition of components in transit to the lot and serialized pack level, high-tech suppliers are setting the gold standard for supply chain transparency and visibility. High-tech supply chains dominate many other industries’ supplier networks on accuracy, speed, and scale metrics on a consistent basis, yet the industry is behind on securing its vast supplier network. Every supplier identity and endpoint is a new security perimeter and taking a Zero Trust approach to securing them is the future of complex supply chains. With Zero Trust Privilege, high-tech manufacturers can secure privileged access to infrastructure, DevOps, cloud, containers, Big Data, production, logistics and shipping facilities, systems and teams.

High-Tech Needs to Confront Its Supply Chain Security Problem, Not Dismiss It

It’s ironic that high-tech supply chains are making rapid advances in accuracy and visibility yet still aren’t vetting suppliers thoroughly enough to stop counterfeiting, or worse. Bloomberg’s controversial recent article, The Big Hack: How China Used a Tiny Chip to Infiltrate U.S. Companies, explains how Amazon Web Services (AWS) was considering buying Portland, Oregon-based Elemental Technologies for its video streaming technology, known today as Amazon Prime Video. As part of the due diligence, AWS hired a third-party company to scrutinize Elemental’s security all the way up to the board level. The Elemental servers that handle the video compression were assembled by Super Micro Computer Inc., a San Jose-based company in China. Nested on the servers’ motherboards, the testers found a tiny microchip, not much bigger than a grain of rice, that wasn’t part of the boards’ original design that could create a stealth doorway into any network the machines were attached to. Apple (who is also an important Super Micro customer) and AWS deny this ever happened, yet 17 people have confirmed Supermicro had altered hardware, corroborating Bloomberg’s findings.

The hard reality is that the scenario Bloomberg writes about could happen to any high-tech manufacturer today. When it comes to security and 3rd party vendor risk management, many high-tech supply chains are stuck in the 90s while foreign governments, their militaries and the terrorist organizations they support are attempting to design in the ability to breach any network at will. How bad is it?  81% of senior executives involved in overseeing their companies’ global supply chains say 3rd party vendor management including recruiting suppliers is riskiest in China, India, Africa, Russia, and South America according to a recent survey by Baker & McKenzie.

PriceWaterhouseCoopers (PwC) and the MIT Forum for Supply Chain Innovation collaborated on a study of 209 companies’ supply chain operations and approaches to 3rd party vendor risk management. The study, PwC and the MIT Forum for Supply Chain Innovation: Making the right risk decisions to strengthen operations performance, quantifies the quick-changing nature of supply chains. 94% say there are changes in the extended supply chain network configuration happening frequently. Relying on trusted and untrusted domain controllers from server operating systems that are decades old can’t keep up with the mercurial pace of supply chains today.

Getting in Control of Security Risks in High-Tech Supply Chains

It’s time for high-tech supply chains to go with a least privilege-based approach to verifying who or what is requesting access to any confidential data across the supply chains. Further, high-tech manufacturers need to extend access request verification to include the context of the request and the risk of the access environment. Today it’s rare to find any high-tech manufacturer going to this level of least-privilege access approach, yet it’s the most viable approach to securing the most critical parts of their supply chains.

By taking a least-privilege access approach, high-tech manufacturers and their suppliers can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and operating costs across their hybrid manufacturing ecosystem.

Key actions that high-tech manufacturers can take to secure their supply chain and ensure they don’t end up in an investigative story of hacked supply chains include the following:

  • Taking a Zero Trust approach to securing every endpoint provides high-tech manufacturers with the scale they need to grow. High-tech supply chains are mercurial and fast-moving by nature, guaranteeing they will quickly scale faster than any legacy approaches enterprise security management. Vetting and then onboarding new suppliers needs to start by protecting every endpoint to the production and sourcing level, especially for next-generation smart, connected products.
  • Smart, connected products and the product-as-a-service business models they create are all based on real-time, rich, secured data streams that aren’t being eavesdropped on with components no one knows about. Taking a Zero Trust Privilege-based approach to securing access to diverse supply chains is needed if high-tech manufacturers are going to extend beyond legacy Privileged Access Management (PAM) to secure data being generated from real-time monitoring and data feeds from their smart, connected products today and in the future.
  • Quality management, compliance, and quality audits are all areas high-tech manufacturers excel in today and provide a great foundation to scale to Zero Trust Privilege. High-tech manufacturers have the most advanced quality management, inbound inspection and supplier quality audit techniques in the world. It’s time for the industry to step up on the security side too. By only granting least-privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment, high-tech manufacturers can make rapid strides to improve supply chain security.
  • Rethink the new product development cycles for smart, connected products and the sensors they rely on, so they’re protected as threat surfaces when built. Designing in security to the new product development process level and further advancing security scrutiny to the schematic and board design level is a must-do. In an era of where we have to assume bad actors are everywhere, every producer of high-tech products needs to realize their designs, product plans, and roadmaps are at risk. Ensuring the IOT and Wi-Fi sensors in smart, connected products aren’t designed to be hackable starts with a Zero Trust approach to defining security for supplier, design, and development networks.

Conclusion

The era of smart, connected products is here, and supply chains are already reverberating with the increased emphasis on components that are easily integrated and have high-speed connectivity. Manufacturing CEOs say it’s exactly what their companies need to grow beyond transaction revenue and the price wars they create. While high-tech manufacturers excel at accuracy, speed, and scale, they are falling short on security. It’s time for the industry to re-evaluate how Zero Trust can stabilize and secure every identity and threat surface across their supply chains with the same precision and intensity quality is today.

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Conquering the three big fears in enterprise sales

By on December 7, 2018

I can’t believe that 2018 is nearly over ! I have been checking in one folks that I mentor for the last couple of weeks to see how well everyone is doing before we all head for vacation time. Those with a sales quota that hasn’t been retired generally are on an overdrive of emotions in December – positive or negative.

Even for experienced sellers – occasionally, fear of not retiring their quota becomes a paralyzing issue . For inexperienced ones – I have noticed it to be fairly routine. I thought I will share my thoughts on this topic here

There are three big fears to overcome in enterpise sales

1. Fear of inertia

This has two flavors to this – external and internal .

Most customers hate change unless they are sitting on a burning platform. It’s not enough that you think there is value in what you are selling – you need to validate (and sometimes repeatedly) with the customer first hand that they see the value in moving to your solution.

The internal flavor can cause just as much anxiety – especially in larger companies with complex and rigid approval workflows . I have fallen victim to this when I started out – I was more afraid of our internal finance and ops reviews than about negotiating with actual client. That fear went away thanks to a mentor showing me that no reviewer knows my deal as well as I do and that if I can confidently explain it , the inspection session can be turned into a coaching session . As I gained experience – it also helped me challenge some processes and get them changed

2. Fear of competition

From my own experience as a young seller many years ago, and then later as a leader of sellers – I have data to prove that the biggest threat to your proposal not getting ink on paper is customer inaction, and not competition . Ergo – the lack of thoughtfulness in crafting your value prop , and lack of customer relationship are way more serious threats to your success than your competition . Those are both within your control to get better at !

There is one thing I insist with my team all the time – ALWAYS respect your competition , but NEVER fear them . If you don’t respect them – you just are increasing your blind spots and you won’t know what hit you. If you fear them – you will focus more on their strengths and weaknesses and not yours .

Price is a big reason why people worry about competition. A larger competitor might be able to offer a lower price than you could . Even the client might tell you that they are just looking for the lowest price . However – that doesn’t mean you will win if you have the lowest price ! There are always more parameters to differentiate and it’s your loss if you become one dimensional and fight the price war and race to the bottom. The one who wins is the one who understands the customer the most , not the one who understands competition the most !

3. Fear of the clock

 

Especially in December – this is the primary fear for most sellers . Will I be able to close this deal before end of the year ? Even if customer sees value and has recognized that you have a better proposal than others – they still may not choose to buy immediately.

This happens commonly when your value prop doesn’t account for the time dimension at all. If you only realize this late – you can still get a deal done by creating incentives like discounts , or asking for a favor with the relationships you have hopefully built . But those are all suboptimal in general for you and the customer . The better way to do it is to understand what is the most logical time for the customer to do the deal and then consciously (without time pressure) determine a good way to accelerate the deal closure .

Conquering the three fears

 

It’s absolutely possible to overcome these fears . I suggest the following as a starting point

1. Spend your energy qualifying your deals every step of the way from the moment you identify them .

2. Invest in understanding your client, your competition and your internal organization – in that order

3. Remember it’s a team sport ! Your manager and a lot of other people have a vested interest in your success – the sooner you make use of help available, the better your odds of success . Heroics should be a last resort , not your leading card

4. Never lose perspective of business cycles and luck . If 80% of deals work as planned – you should be happy

5. Never burn bridges – enterprise is a small market and you will see the same people again . The most difficult CXO I ever sold to bought from me at three different companies – and I was within an inch of yelling at him for what he did the first time I met him 🙂

6. Never lose your moral compass. There is more to life than any one deal !

(Cross-posted @ Vijay's thoughts on all things big and small)

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

By on December 6, 2018

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)

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Should Your Startup Have a Quota Club? (And How Much to Spend on It.)

By on December 6, 2018

December is when most SaaS startups are closing out the year, trying to finalize next year’s operating plan (hint:  I know a software company that can help with that), starting to get a clear view on which salespeople are going to make their number, and thus beginning the process of figuring out who to invite to the annual “Quota Club” (a.k.a. President’s Club, Achiever’s Club, or Sales Club).

In this post, I’ll discuss why Quota Clubs are so controversial and how I learned to think about them after, frankly, way too much time spent in meetings discussing a topic that I view nearly as difficult as religion or politics.

Quota Club is always highly controversial:

  • It’s exclusionary.  Consider this quote my friend Lance Walter heard years ago (I think at Siebel): “the last thing I want at Quota Club is to be lying on a chaise lounge by the pool, roll over, and see some effing marketing guy next to me.”  Moreover, the sales personality tends not to blend well with other departments, so a well-intentioned attempt to send the top documentation writer on a trip with 30 sales people is as likely to be perceived as punishment as it is reward.
  • It’s expensive.  The bill can easily run in the hundreds of thousands of dollars for companies in the tens of millions of annual recurring revenue (ARR) and in the millions for those above that.  That doesn’t help your customer acquisition cost (CAC) ratio.
  • Even the basics of qualification are somehow complicated.  Now, on the face of it, you might that “making quota” would be sufficient to qualify for quota club, but in some people’s minds it’s not:  “no, at this company we expect people to make quota, so Quota Club should only be for those at 120% of quota.”  (The idea that maybe quotas are set too low doesn’t seem to occur to these people.)  That’s not to mention minimum attainment rules required to avoid accidents with ramped quotas (e.g., a new rep who sells $400K on a $200K quota.)  Or the intractable problem in decentralized organizations where Country A runs large number of junior reps at low quotas while Country B runs small numbers of senior reps at high quotas — so someone who sells $1.25M in Country A attends club while someone who sells $1.75M in Country B does not.
  • Invitations beyond quota-carrying reps (QCRs) are always controversial.  Do consultants who hit their utilization target get invited?  (No.)  Do sales development reps (SDRs) who hit their opportunity goals? (No.)  On what basis do sales consultants (SCs) get invited?  (Depends on SC model.)  Do CSMs who hit their renewals goals?  (Maybe, depends on your customer success model and how much selling they do.)  What about the executive staff?  What about a regional VP or CRO when he/she didn’t make their number?  Who presents the awards to their people?  And this isn’t to mention companies that want to inclusionary and invite some hand-picked top performers from other departments.
  • Guest policies can be surprisingly tricky.  Normally this is simple — each qualifier gets to invite a spouse or partner, with the implication that the company wants to reward the chosen guest for the sacrifices they made while the qualifier was working long hours on the big deal and doing extended travel. What if the guest is a friend as opposed to spouse or partner?  (Well, that’s OK if not quite the intent.)  But what if that friend is coworker?  (Hum, less so.)  What if that friend is another quota-carrying rep who failed to make their number?  (Even harder.)  Or, changing angles, what if their spouse is a sales rep at your top competitor?  What if they run competitive intelligence at your top competitor?
  • Opinions diverge on family policy.   Should qualifiers be encouraged to bring their children?  How about Grandpa to watch them?  Are these family members invited to any events or activities?  Can their pay their own way on the snorkeling cruise if they want to?  Is babysitting covered?  Is the reward for spending too much time away from your family a mandatory vacation away from your family?
  • The business meeting can be a religious issue.  Many sales VPs think Club should be a 100% reward — a complete vacation with no work.  If so, the CFO will take an income tax withholding from each qualifier:  thus was born the business meeting that keeps Club a business affair and off the W-2s of the attendees.  Some sales VPs thus think:  do the absolute minimum to stave off the tax man.   More enlightened folks think:  what a great opportunity to meet with our top performers to talk about the business.
  • People can’t even agree on the dress code.  Should the awards dinner be California Casual, Summer Soiree, Creative Black Tie, Brooklyn Formal, or just a regular Black Tie Affair.  (And where do they get these names?)
  • Picking the location is is difficult.   The Caribbean isn’t exotic for East Coasters and Hawaii isn’t exotic for West Coasters.  Some people think Clubs should always have a beach location, some think European cities are more exotic.  (By the way, try to find a reliably warm beach location in February or April.)  Should you invest your money in flights to a relatively inexpensive place or get cheaper flights to a more popular and presumably expensive place?  And this isn’t to mention any debates about hotel brands and their significance.
  • In-room gifts can jack up the price.  Club planners seem to love to include special gifts each night.  A welcome bottle of champagne the first night, a beach kit the second, a Tumi backpack the third, and a farewell mini-Margarita kit can quickly add up to $500 in extra cost per qualifier.
  • Planning is intrinsically difficult.   It’s inherently hard to plan when you have 30 QCRs and you’re not sure if 10, 20, or 30 are going to qualify — this is particularly difficult when you plan sales-only Clubs because you have less to fudge in terms of non-QCR attendees.  What do you do mid-year when you’ve planned for 20 and forecast that only 10 are going to make it?  Devalue Club by dropping the qualification bar for some reps or (the same act seen through a diametrically opposed lens) preserve the incentive value of Club by making it a realistic goal for the reps who otherwise had no realistic hope?

Holy Cow, just making this list gets my blood pressure up.  Are we sure we want to do this?  My answer remains yes.

Most startups, once you’re beyond $5M to $10M in ARR, should have some sort of Quota Club.  Here is my advice on how to do it:

  • Define it as the CEO’s club.  You can call it Quota Club or President’s Club, but make it clear to everyone that it’s the CEO’s event.  It’s a big expense (with a huge opportunity to waste a lot of money on top) and it’s full of decisions that are both subjective and polarizing.  Listen to what your current sales VP wants, but make those decisions yourself.
  • Start small.  At MarkLogic our first Quota Club was something like 10-15 people for two nights at the Bellagio in Vegas.
  • Leave room to make it incrementally better each year.  This is what I call Narva’s Rule, after my friend Josh Narva who came up with it.  (By the way, had we better applied his rule, we’d have held the first MarkLogic Club at Caesar’s Palace, saving the Bellagio for the following year — but at least we got the two days part right, leaving room to later expand to three.)  Don’t cover every bite or drink that goes in someone’s mouth in the early years:  folks can get a breakfast croissant at Starbucks or a drink by pool on their own nickel. You don’t need a group breakfast and a pool party to cover it.
  • Be inclusive of other functions.  This lets you recognize a few folks outside of non-quota-carrying sales each year.  (It also makes planning a little easier.)  Don’t be so inclusive that QCR/QCM attendance is less than 50%.  But take all your qualifying QCRs and quota-carrying managers (QCMs).  Add your selected SCs.  Add your qualifying CSMs (according to whatever rules you establish).  Then perhaps add a few folks — based on their helpfulness to sales — maybe from consulting, marketing, product, or salesops.  Helpful e-staff are also good candidates and can benefit from the direct feedback they will get.  Think:  I’d rather run a bit less luxurious event and invite a few more folks from across the company than the converse.
  • Do it at a beach in April, alternating East and West coasts.  Or, if you have a strong ski contingent, alternate between a ski resort in February and a beach in April.  Beware the sales VP will gripe about too much first-quarter time in meetings with a January kickoff and February Club.  But who says you can’t still ski in April?
  • Be family-friendly.  Be clear that kids and family are welcome at the event (at the attendee’s cost) and at most, but not all, activities.  If you have two dinners, make one a bring-the-clan affair and make the awards dinner spouse/guest only.  Let family opt-in to an any easily inclusive activities like snorkel trips. Help folks find and/or pool babysitting.
  • Take the business meeting seriously.  Run the meeting on the morning of day 2.  I like doing attendee surveys in advance (e.g,. via SurveyMonkey) and then doing a detailed review of the results to drive discussion.  This sets the tone that the event is for both fun and business and that the company isn’t going to miss the chance to have a great conversation with its top performers.  Discussing business at Club isn’t a party foul.  It’s part of why you have Club.
  • Stay aligned with event planner, particularly in the early days when you are trying to run a discount event as they will, by default, try to run a standard one.  Skip the bells and whistles like custom event logos, fancy signage, custom beach bags and towels, in-room gifts, and all-meals coverage. Define what your program is going to be and deliver against that expectation.  Then make it better next year.
  • Make and hold to a sensible budget.  Know, top of mind, the total event cost and cost/attendee — and remember that cost/qualifier is about double the cost/attendee, since each qualifier invites a guest.  As part of Narva’s Rule, increase that cost every year. Because I like to make things concrete, I think cost/attendee should range from $2.5K to $5.0K as a function of your typical salesperson’s on-target earnings (OTE) and your company’s lifecycle.  This means the “prize value” of the Quota Club invitation is $5K to $10K, equivalent to a roughly 2-4% bonus against typical OTEs.  On this sort of budget, you can offer a very nice, high-quality event, but you won’t be doing the truly unique, memorable, over-the-top stuff that some CROs like.
  • If you want to have an ultra-club do what we did at BusinessObjects.  While during most of my tenure at BusinessObjects we ran in nice-but-not-crazy mode, towards the end of my tenure there was a movement to make Club truly exceptional and unique.  That first led to discussions on how to trim down Club in order to increase the spend/qualifier, including potentially increasing the attainment bar from 100% to 125% and ending our inclusive philosophy.  I’m glad we didn’t do that.  Instead, we ended up creating an intimate ultra-club as a few days tacked on to the end of Quota Club.  It provided some niche cachet when the attendees were whisked off onto their continuation trip.  It allowed “the movement” to do some truly exceptional things for a small number of people.  Most of all, I think we correctly figured out who the “right people” were — not the one-hit wonder reps who had one big year, but instead the consistent reps around which you truly build a company.  I believe we set 5 years of consecutive Quota Club attainment as the criteria for an invitation to the ultra-club.  I’d invest extra in those people any day of the week.

 

(Cross-posted @ Kellblog)

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Sympathy for the DevRel

By on December 6, 2018

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)

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Where Cloud Computing Jobs Will Be In 2019

By on December 5, 2018

  • $146,350 is the median salary for cloud computing professionals in 2018.
  • There are 50,248 cloud computing positions available in the U.S. today available from 3,701 employers and 101,913 open positions worldwide today.
  • Oracle (NYSE: ORCL), Deloitte and Amazon (NASDAQ: AMZN) have the most open cloud computing jobs today.
  • Java, Linux, Amazon Web Services (AWS), Software Development, DevOps, Docker and Infrastructure as a Service (IaaS) are the most in-demand skills.
  • Washington DC, Arlington-Alexandria, VA, San Francisco-Oakland-Hayward, CA, New York-Newark-Jersey City, NY, San Jose-Sunnyvale-Santa Clara, CA, Chicago-Naperville-Elgin, IL, are the top five cities where cloud computing jobs are today and will be in 2019.

Demand for cloud computing expertise continues to increase exponentially and will accelerate in 2019. To better understand the current and future direction of cloud computing hiring trends, I utilized Gartner TalentNeuron. Gartner TalentNeuron is an online talent market intelligence portal with real-time labor market insights, including custom role analytics and executive-ready dashboards and presentations. Gartner TalentNeuron also supports a range of strategic initiatives covering talent, location, and competitive intelligence.

Gartner TalentNeuron maintains a database of more than one billion unique job listings and is collecting hiring trend data from more than 150 countries across six continents, resulting in 143GB of raw data being acquired daily. In response to many Forbes readers’ requests for recommendations on where to find a job in cloud computing, I contacted Gartner to gain access to TalentNeuron.

Key takeaways include the following:

  • $146,350 is the median salary for cloud computing professionals in 2018.  Cloud computing salaries have soared in the last two years, with 2016’s median salary being $124,300 a jump of $22,050. The following graphic shows the distribution of salaries for 50,248 cloud computing jobs currently available in the U.S. alone. Please click on the graphic to expand for easier reading.

  • The Hiring Scale is 78 for jobs that require cloud computing skill sets, with the average job post staying open 46 days. The higher the Hiring Scale score, the more difficult it is for employers to find the right applicants for open positions. Nationally an average job posting for an IT professional with cloud computing expertise is open 46 days. Please click on the graphic to expand for easier reading.

  • Washington, DC – Arlington-Alexandria, VA leads the top twenty metro areas that have the most open positions for cloud computing professionals today. Mapping the distribution of job volume, salary range, candidate supply, posting period and hiring scale by Metropolitan Statistical Area (MSA) or states and counties are supported by Gartner TalentNeuron.  The following graphic is showing the distribution of talent or candidate supply.  These are the markets with the highest supply of talent with cloud computing skills.

  • Oracle (NYSE: ORCL), Deloitte and Amazon (NASDAQ: AMZN) have the most open cloud computing jobs today. IBM, VMWare, Capital One, Microsoft, KPMG, Salesforce, PricewaterhouseCoopers, U.S. Bank, and Booz Allen Hamilton, Raytheon Corporation, SAP, Capgemini, Google, Leidos and Nutanix all have over 100 open cloud computing positions today.

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

By on December 5, 2018

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)

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