Cashing in on the Future

A financial incentive is a key driver for new business initiatives and investments. Future of Work technologies have a lot of potential to reduce costs, but they also have many additional benefits, called non-cashable benefits. Despite their name, it is possible to drive additional revenues through these to maximize the gross profit.

RPA vs Outsourcing

Today, Robotic Processing Automation (RPA) is one of the most mature Future of Work technologies. It is great for managing rules-based, data-heavy and repetitive tasks and integrates with existing systems – even legacy applications, without the need for a drastic overhaul. A conservative industry estimate is that an RPA robot costs one-third of an offshore employee and one-ninth of an in-house employee. RPA can also process many routine tasks faster and more accurately than most humans, allowing for significant reductions in overhead/FTE costs.

We have found that the typical ROI for RPA is between 300-800% over 5 years, with an average payback point achieved in under 18 months. The savings are not always back-loaded – in some cases of drastic process optimization, we have seen returns within as little as four months.

While BPO has helped many businesses achieve savings in the past decades, there are several underlying flaws with the offshoring model. For instance, it moves work to cheaper locations across the globe, resulting in work that is disconnected from the in-house processes (as discussed in the previous blog). Aside from this potential inefficiency, there is a high rate of attrition (offshoring roles traditionally have high growth and low salary), as the offshore labor market has become saturated over the years. So, while it may have started off as a cheap solution for most businesses, it tends to not remain economical due to increasing operational tensions, competitive labor markets and high inflation rates.

Who is Losing Out?

When there are larger costs for your resourcing solutions, it is the customer and employees who ultimately take the biggest loss. While labor arbitrage through offshoring may generate net savings, customers tend to suffer from lower-quality or less responsive services. This is an inherent trade-off to moving work hundreds of miles away. Additionally, employees tend to encounter issues with miscommunication, which can lower the quality of employment satisfaction. This is where a comparison between existing outsourcing models and RPA comes into the spotlight. Labor arbitrage through RPA tends to generate even larger savings, but without the bulk of the downsides listed above. In fact, RPA is increasingly recognized for the non-cashable opportunities it provides to a business.

Cashing in on the Non-Cashable Benefits

We like to represent the benefits of RPA under Mary Lacity’s “Triple Win” model. Automating processes that consist of routine, manual tasks can benefit not only the company, but also the employees and customers. The following are some examples of the common benefits we’ve seen among our clients:


  • The economics of RPA means that you are guaranteed to get hours back to your business. Time spent doing repetitive tasks can now be shaved off or dedicated to more valuable work.
  • Processes done by robots are always more accurate and faster than those done by humans. This provides a competitive advantage and improved output quantity and quality.


  • RPA takes the robot out of the human, so employees can look forward to more enjoyable and purposeful work
  • Employees learn new skills such as administrating robot workforces or working alongside assisted automation


  • Optimized workflows translate to faster turnaround times and improved service quality for customers. We often see improved customer satisfaction levels from RPA
  • Customers also enjoy service consistency and round-the-clock availability


High cash benefits are possible not only through the large ROI of Future of Work technologies like RPA, but also indirectly through non-cashable benefits. The benefits far outweigh those seen in traditional outsourcing models and provide promising solutions for both the business and the customer. These non-cashable benefits may be more difficult to quantify, but can ultimately make a significant business impact.

Source: -Cashing in on the Future

The ITIL 2018 update better catch up to modern IT

ITIL, once known as the Information Technology Infrastructure Library, was last updated in 2011. A lot has happened since then, and for an ITIL 2018 release to regain the relevance that the IT service management framework has lost, it must accommodate the drive toward DevOps.


IT projects are moving away from Waterfall’s cascade style of deployment, with large initial releases followed by interim patches and functional upgrades at six- or 12-month intervals. Instead, services and applications have become continuous delivery projects with new functionality updates that release in monthly, weekly or shorter time sequences. Containerization begets new methods to manage software packaging, release and management. The move to a microservices-based, composite application model has enhanced distributed applications.

These changes to IT delivery forced many of the organizations that adopted ITIL to either massively adapt it to retain IT service management (ITSM) efficacy or drop it in favor of other frameworks that better meet their needs.

Google Trends

Figure 1. Interest in DevOps, as illustrated by this Google Trends search data, picked up around the same time ITIL was updated in 2011 and has shot up steadily through the end of 2017. An ITIL 2018 update could steal IT organizations’ attentions again.

Few organizations have adopted ITIL fully, but have rather integrated pieces of it with other, internal service management processes that function better than what is nominally best practice.

This piecemeal adoption has hindered outsourcing companies that want to apply a full ITIL framework to a customer’s environment, as well as service management software vendors that sell their products as ITIL-compatible.

Figure 2. ITIL started in U.K. government projects in the 1980s. Yet, it only gained traction when U.S. companies started to use it, and the idea later crossed back over the Atlantic.

The ITIL 2018 update has more than internal or DevOps-based methodologies to take into account. It also must maintain commonality with the ISO standard on service management, ISO 20000, which is currently in its 2011 version. ISO 20000 Part 11 maps out how ITIL and ISO 20000 interact and intersect with each other.

ITIL 2018 expectations

Axelos, a joint venture company set up by the U.K. government’s Cabinet Office and private entity Capita plc, is the group that oversees ITIL and that will control ITIL 2018’s upcoming release, which is based on research conducted with the global service management community. This update to ITIL should be good news for organizations with ITIL processes in place, but as always, the devil is in the detail.

If everyone offers ideas about what would make the fastest, best racehorse in the world, what comes out the other end is a camel.

If it is to survive the rapid rate of change in modern IT, ITIL 2018 will need to fully embrace continuous development driven by DevOps, as well as work harder to break down the silos of control in the development, test and operations environments. Users must also see ITIL 2018 as a path to more streamlined and effective processes, rather than as a constraint to task completion.

Axelos reported that it spent 18 months communicating with constituents of the service management chain, and that, as a community-driven initiative, ITIL changes will reflect the needs of real-life organizations and individuals. While community insights are important, too much input can confuse and suppress progress. If everyone offers ideas about what would make the fastest, best racehorse in the world, what comes out the other end is a camel. Unless those who provide input already face the pressing issues of moves to hybrid cloud, continuous development, containerization and DevOps, their goal could just be to optimize old-style ITIL processes to make cascade projects move faster, rather than to bring the ITIL 2018 release in line with modern operations. For example, in U.K. government IT projects, Agile and DevOps are far less common than they are in the private sector. Both groups are ITSM constituents that ITIL 2018 aims to please.

ITIL’s value

ITIL, particularly in large organizations, creates common processes to deal with many issues that arise across IT infrastructures. ITIL-structured IT organizations should more easily identify recurring issues and eradicate root causes. ITIL allows large IT deployments to continue to scale by automating many functions that would otherwise be carried out as standalone tasks by individual IT operations admins.



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However, ITIL implementation also risks becoming unwieldy and expensive. It has been criticized for cementing many of existing IT environments’ problems, such as siloed development, test and operations groups, and for working poorly with other ITSM and business approaches, such as Agile and Six Sigma.

The ITIL 2018 release must demonstrate awareness of these problems and dedication to get ahead of them. With its strong focus on process, ITIL 2018 must better cover hybrid IT environments, relatively static platforms and highly dynamic ones. ITIL 2018 must also become far more flexible internally — seven years between updates does not demonstrate ongoing relevance.

ITIL 2018 must address where it sits in the wider scheme of things. It cannot fall further into a perception of it being the center of the universe. It must work alongside many other tools and approaches that businesses use.

There is little detail available about the content of the ITIL 2018 update, which Axelos announced at the IT Service Management Forum USA Fusion event in Orlando, Fla., in the fall of 2017. This is where the devil might lie in wait: Organizations that seek to adopt or continue using ITIL must read all the details as they come out to ensure that any investment in ITIL 2018 is worthwhile and ready for future developments. It is not enough to take on such an involved ITSM framework as a short-term, tactical solution.

Source: ITIL 2018 update better catch up to modern IT

IT service automation: A global CIO learns from a millennial

This is the first installment in a series on IT service automation by Pink Elephant expert Jan-Willem Middelburg. The series follows the journey of a fictional global CIO as he realizes that his well-regarded IT organization must radically change the way it delivers IT services. The first chapter below, “Dear CIO, are you ready for the self-service generation?” describes the encounter that sets the CIO on his quest.

It is 9 p.m. and you are staring out the boardroom window across the millions of lights of the city. You are looking back on a day packed with meetings… again. In the morning, you met with the IT steering committee, the risk auditors and the CFO. Your afternoon was filled with your deputy CIOs, each fighting to receive a portion of next year’s budget. It is that time of year again.

As you pack your briefcase and start toward the elevators, you notice the intern still working away. A typical millennial with the latest headphones and a million devices scattered across his desk. The guy was “lucky” to have been chosen out of hundreds of applicants for the summer internship at the CIO Office and, so far, he has been a tremendous asset to your team. The speed and agility with which he can complete complex analyses has frequently surprised you, and you have already decided that you will probably hire him after the summer. You look at your watch and decide it is time to send him home.

You walk over to his office and slowly tap against his screen. The intern lowers his headphones and immediately sits up straight, realizing the global CIO is addressing him. “Tomorrow’s a new day, time to go home,” you hear yourself mutter and the intern immediately looks at his watch, which lights up as he turns his wrist. The intern presses some last buttons on his machine and accompanies you to the elevators.

As you ride the elevator, the silence is uncomfortable, and you start some small talk: “Having a late dinner with your girlfriend tonight?” The intern quickly looks at his phone and replies with a smile: “Dinner should be at my friend’s house in 28 minutes,” he answers. “My girlfriend is staying at her parents this week to finish the paper for her online degree, so we decided it’s better to Airbnb our place for the week.” When the elevator door slides open, you keep wondering what the guy next to you just said.

Right there, at the parking lot in the pouring rain, you realize that you need to make your enterprise ready for the self-service generation. Not just for the young intern who grew up with technology, but for your customers who will also expect the services of your company to be available immediately and with the push of one button.

When you reach the main entrance, you see that it is raining cats and dogs. Your car is parked in one of the executive parking spaces only a few yards away, but you see that even the small distance will get your suit soaked. At the same moment, a small car pulls up at the entrance and the intern opens the door to the back seat. You suddenly realize that the guy already booked an Uber while he was closing his computer upstairs. There’s no thunder as you run for your car, but you feel like you’ve been struck by lightning.

As the intern steps into the Uber, you ask one more question: “Do you still ever call anyone?”

The intern replies: “Just my parents; they are very traditional. Have a great night, boss!”

Right there, at the parking lot in the pouring rain, you realize that you need to make your enterprise ready for the self-service generation. Not just for the young intern who grew up with technology, but for your customers who will also expect the services of your company to be available immediately and with the push of one button.

From service management to IT service automation

The next morning you wake up energized. You order an Uber to take you to work, and whilst you are in the backseat of the car, you reflect on the situation with the intern from last night. Everywhere in the world, new service providers are popping out of the ground with “disruptive” business models. Spotify, Uber, and Netflix are some of the main examples that everybody is talking about. They are able to attract massive groups of users and — like the intern — many people like to use these services, because they are instantly available with the click of one app or similar interface.

As you think about this a little more, you wonder what would happen if you could make the services in your organization available in a similar way with IT service automation. What if your employees could select their IT services by themselves and order them as easily as booking a rideshare service? Is provisioning a test server really so much different from booking a driver?

For years, you have worked really hard to achieve operational excellence of all global IT services. Your service catalogue is well-defined and you have consistently managed to reach the targets of your service level agreements (you became CIO for a reason…). You have a very effective and efficient Service Desk that delivers services all over the globe with high satisfaction levels. So, what is the difference between your organization’s services and the services your intern likes to use?

In a traditional service model, the user interacts with the service provider at every step, from request and proposal through paying the invoice and sending feedback. In the automated service provider model, the self-service portal — a technology layer — automates many or all of the steps.

As your Uber drives into the parking lot of your office, and your driver swipes that he has completed his ride, you suddenly realize the difference: The services your organization offers are control-oriented and frequently include manual steps. The services Spotify, Uber, and Netflix are offering are user-oriented and completely automated.

Source: service automation: A global CIO learns from a millennial

Legal, ethical issues could slow adoption of AI for finance

The short answer to the longstanding question about whether the use of artificial intelligence in business could create intractable ethical, legal and regulatory issues just might be: It depends on the humans.

Businesses have started using AI for finance applications that are powered by machine learning processes, but their embrace of AI technology begs the question of whether computers will follow the same standards and principles that humans are expected to follow. For some in finance and technology, the answer does indeed boil down to how people program and oversee AI tools.

“One key aspect of artificial intelligence is that it is just technology, which is an extension of human practices and thinking,” said Lex Sokolin, global director of fintech strategy at Autonomous Research in London. “It may automate and make faster certain decision-making — for example, requiring one person [assisted] by AI to do the work of 50 people previously — but that decision-making will reflect all the biases and mistakes of human society.”

Using AI for finance holds the potential of having the computer find valuable information that will allow companies to see previously unseen patterns and use that insight to make more informed decisions and pursue bold strategies. Using algorithms and machine learning, AI-powered tools “learn” from “experience” what their human programmers want them to learn.


For businesses that need help making sense of trends affected by the requirements of financial regulations, using AI for finance could be a boon, allowing computers to do the work of scores of employees. But the flip side of relying on AI for finance regulations is that the technology could fail to discern legal or ethical issues that a human would otherwise recognize. This means for now, with mainstream AI technology still at a foundational level, human programmers will need to stay on top of how a machine is learning to ensure that boundaries aren’t crossed.

“The European data privacy rules, GDPR, speak about the ‘right to an explanation,'” said Gartner research fellow Frank Buytendijk, who specializes in digital ethics. “But with some types of machine learning, using stacks of neural networks, it is not always easy or even possible to fully retrieve where a decision comes from. We just need to trust it works in a way. This will need some attention figuring it out.”

Need for human oversight puts limits on using AI for finance

Referring to autonomous vehicles and systems, Buytendijk said that for the first time in the digital era, the potential ethical repercussions of a new technology are being discussed openly before widescale implementation.

“It is a bit of a paradox that one would want to be careful with exposing AI to the real world until you get it right, but that AI needs to be in the real world for all the data [it needs] to learn. This problem hasn’t been solved yet.” He added: “As we figure out how machine learning works, maybe as consumers we should be more tolerant as well, and give the market some time and space in order to get it right.”

Frederic Laluyaux, CEO of the business intelligence software company Aera, believes that for the foreseeable future, AI technologies will help with black-and-white decisions, but “the shades of gray will be decided by humans.”

The need for a human touch could complicate AI’s promise to autonomously analyze unstructured data that is interwoven with complex laws and regulations. To ensure that companies stay above board, a human should be kept in the loop, said Sokolin of Autonomous Research. “That means automated technology should have a human copilot that can course-correct what the algorithm is doing,” he said.

Businesses should rely on vendors for guidance on how to use AI for finance processes, but they also need to learn on their own how to audit the software to understand how it works and see the implications of its decisions, Sokolin said.

“AI needs parental oversight,” said Gartner’s Buytendijk. “We don’t have to be afraid that the robots will wipe us all out, or big dystopian visions like that. But we need to make sure that applications that use AI techniques have some kind of rollback or override as part of the reinforcement learning process. And we need to make sure we arrange accountability and responsibility — and ownership of the data and the algorithms. In short, just get our digital governance right.”

Source:, ethical issues could slow adoption of AI for finance

The Robots are Coming – Should You Fear or Welcome Them

How does your enterprise compare with peers?

A few weeks back, we opened our Robotic Process Automation (RPA) Pinnacle Model study to enterprises to compare their RPA adoption performances head-to-head. Everest Group Pinnacle ModelTM assessments are unique in that they correlate quantified outcomes and capabilities with a special spotlight on the Pinnacle Enterprises that are outperforming their peers. As part of the study process, we also interview select participants to gather qualitative information about these same enterprises.

Having completed a number of these interviews and looking at some of the early tabulations from those have completed the RPA adoption survey, I’m sharing some of my early thoughts below.

Four thoughts on our RPA Pinnacle Enterprise survey results

  • The robots are truly coming, but the fears about the impact on jobs is way overblown – it is clear from our conversations that RPA is going to have an impact in many different parts of the organization, including both front office and back office, but the number of jobs being impacted is not going to be the primary value proposition. Yes, cost take out will be part of the equation, but it is highly likely it will impact slices of jobs and/or departments that will allow for those employees to be transitioned to higher-value tasks.
  • Improving the job for employees – One of the clear messages that we have heard so far is that employees are embracing RPA. In fact, the branding of these initiatives is about getting rid of the worst tasks of their current jobs and includes names like “Smart Automation” and “We Innovate.” In fact, many of these employees are already implementing their own home automations like Nest, Alexa, Google, Rachio, etc. and are becoming quite comfortable with these quality of life improvements automations. One of the enterprises we spoke with actually talked about seeing improvements in their employee retention rates when they were included in these initiatives and allowed to improve their own jobs. However, change management has not been “easy,” and companies have adopted various ways to create awareness about the benefits of RPA and how employees can use it to be more effective in their jobs. Some of the examples of approaches include workshops, training programs, newsletters, project of the year, and hackathons.
  • The real skirmish is between the business units and IT for ownership – one of the interesting aspects of this analysis is to see where the study participants reside in their organizations. In the conversations, it becomes apparent the business is the one driving the conversation and IT has been the reluctant partner. But I got the sense this was changing pretty quickly, and IT was beginning to see the light that they have to be part of these implementations for a variety of reasons. Also, organizations have internally gone through a debate as to whether to approach this is an IT project or a business process redesign. We will be interested in hearing how your organization is thinking about this. Participate in the study.
  • We are just getting started – we can see it in the data and with our conversations, enterprises are running multiple RPA initiatives and projects are spread across RPA implementation stages. At least 65% of respondents are in the process of scaling up their RPA efforts or running steady-state automations. However, the majority of enterprises are still in their rookie year when it comes to setting up RPA CoEs (or expanding existing automation CoEs). The implications is that the initial proof of concepts projects are seeing enough promise that formal teams are being stood up to begin the scaling process.

Source: Robots are Coming – Should You Fear or Welcome Them

Why the full-time job will never be so precious, as the gig economy crumbles and judgment work is digitized

The new “rules” of the workplace are being defined as computers are frantically being programmed to take the lead in the workplace, when it comes to judgment and intuition. We humans need to be the idea generators, the motivators, the negotiators, and the trouble-shooters to fix computer errors, if we want to govern our emerging digital environments. In short, we need to get closer to our firms, be more tightly integrated and intimate with work performance than ever before… which means the role and tenure of the much-derided middle-manager in the Dilbert Cartoons could be taking on a whole new potential twist – and a whole new (potential) level of relevance.

I would go as far as declaring 2018 as a new beginning of the value of the full-time employee – where alignment with the mission, spirit, culture, energy and context of an organization has never been so important. We are seeing the value of contract work diminish as so much “outsource-able” work is so much easier to automate and global labor drives down the cost of getting things done quickly and easily. Business success is more about investing in the core than ever – and that core includes the people who are the true pieces of human middleware to hold everything together.

The onus is circling back to the value of being a full-time employee, who needs to value the fruits of having a predictable income and adapt to the changing balance of how humans need to work with computers.

Remember when the rise of the gig worker was supposed to revamp how so many of us worked, as we escaped the shackles of the “evil employer”?

Almost two decades ago, the internet was creating the independent worker, as exemplified in Dan Pink’s timeless book “Free Agent Nation: How America’s New Independent Workers are Transforming the Way We Live” became the seminal guide for what is now known as the “gig worker”.

Furthermore, unless recent research from McKinsey of 8000 workers can now be categorized as fake news, 162 million people in Europe and the United States—or 20 to 30 percent of the working-age population—engage in some form of independent work today. And a recent study from freelance site Upwork (which undoubtedly wants to hype the impact of gig world) cranks up the numbers even further, claiming that a staggering 50% of US millennials are already freelancing, before declaring the freelance sector will comprise the majority of the US workforcewithin a decade. Wow.

So are the days of being gainfully employed really disintegrating before our very eyes? Or is the gig hype beginning to atrophy for many people?

The gig economy is becoming a tough place to craft a living if many of the new reports are to be believed. And it’s not just about driving Ubers, delivering food orders and contracting for logistics firms – i.e., working for businesses that exploit the gig economy to drive down labor costs and improve services. It’s the freelance gig economy where people forge a living writing code, supporting content development, delivering consulting work on-demand etc. Even that lovely Upwork research admits: “While finances are a challenge for all, freelancers experience a unique concern — income predictability. The study found that, with the ebbs and flows of freelancing, full-time freelancers dip into savings more often (63 percent at least once per month versus 20 percent of full-time non-freelancers)”. So even if the most biased of sources admits most gig workers can’t cover their living costs, we can conclude that those “Free Agents”, which McKinsey describes as the gig worker sector using gig work as its primary income, are not in a sustainable earning situation.

Today, it’s a buyer’s market for gig work

You only need to spend a little time on LinkedIn to observe just how many people are now marketing their wares as solo free agents, or as part of a company bearing their name. It’s abundantly clear that so many people have decided to set themselves up as independents, that the market for gig talent is saturated and it’s become a “buyers’ market” for gig work. Whether I want to commission a crack consultant to validate some RPA software, hire an analyst to endorse my product, commission a writer to produce a white-label assessment of an emerging market, produce a go-to-market strategy for my business, redesign my website, my logo, or just have someone support my business on a part-time basis… today, I am spoiled for choice. I barely need to hire fulltime employees these days, unless they are truly core to keeping my business ticking along – and I can create real competition to get the work done for much lower costs than a few short years ago.

On top of the risks of commoditizing gig work, we have to contend with the impact of automation and Machine Learning to stay relevant and worthy of earning a paycheck

We’re not in a world rejecting human work, but a world where work is rapidly changing – and the skills of the dynamic middle manager has never been so important. In short, the increasing availability of computing power to crunch massive amounts of data, coupled with advancing tools to tag and label data and workflow clusters with breakthrough programming in languages such as Python for syntax and R for data visualization, are the game-changers that will increasingly impact how we get work done, as we develop continually smarter algorithms to keep teaching computers to do the work of the human brain.

What’s more, the rapid development of Machine Learning (ML) environments such as Google’s TensorFlow, the Microsoft’s Azure Machine Learning Workbench, Amazon’s Sagemaker, Caffe and Alibaba’s Aliyun are becoming the new environments driving armies of coders and developers to align themselves with ML value – desperate to stay relevant (and well paid) against the headwinds of commoditization of legacy coding and app development.

As ML takes over judgment and (eventually) intuition, the human-value onus moves to interaction, agenda-setting, problem defining and idea generation

In short, the disruptive ML techniques are teaching computers to do what comes naturally to humans: to learn by example. Today’s emerging ML tools use massive amounts of data and computing power to simulate neural networks that imitate the human brain’s connectivity, classifying data sets and finding patterns and correlations between them.

Net-net, pattern-matching jobs are increasingly being affected by ML – vocations such as radiologists, pathologists, financial advisors, lawyers, procurement executives, accountants etc. are all being challenged as judgment work is (gradually) being replaced by smart algorithms. However, as elements of these types of jobs are being affected, other job elements become even more important, namely interacting with other humans, creating, setting the agenda, defining and finding the problems to go after. They motivate, they persuade, they negotiate, they coordinate. They are the dynamic conduits of driving information and ideas in an organization and will be increasingly in the driving seat as Machine Learning advancements increasingly take hold. The digital middle manager who can bring a team together and lead people in the right direction does not exist and likely never will…. I’d be amazed if we saw one emerge soon.

Fulltime employment is now becoming a premium situation

Having predictability of income, healthcare costs covered, guaranteed paid vacation time – and a constant supply of work to do – is fast becoming the dream scenario for the disgruntled gig worker. So here’s a thought – go get a JOB. Or if you’re in a job and wanted to try the gig work thing… spare a thought for what your ideal situation looks like, because last time I looked, most firms are doing everything they can to avoid hiring well-paid staff… especially if they can get the work done much cheaper from desperate gig workers.

The Bottom-Line: Five steps to keeping your job:

i) Become the conduit of ideas and information that is irreplaceable right across your organization. So we’ve now come full circle, where the value of having people really close to the business is becoming more important than ever, as computers perform more and more of the routine and judgement based tasks. To the point, the value of the full-time employee goes both ways: companies need people who really understand their institutional processes, their quirks and ways of getting things done… who are onhand to troubleshoot mistakes, but also there to keep the ideas flowing to keep the business ahead of its competition and close to its customers. “Human middleware” is becomimg the real OneOffice glue to break down those siloes and help govern a slick business operation from front to back office.

ii) Develop a positive attitude by finding aspects of your job you do like.Your full time job is likely the best gig-work you will probably ever get, so even if you hate your boss and most of your colleagues, ask yourself if you’d prefer scrapping around for the boring work other companies prefer to outsource. Focus on the interesting stuff you can do and keep reminding yourself that the grass is rarely greener elsewhere. Unless you are a whizz at Python development, the chances are your job-hopping days are numbered and you need to figure out how to stay put and make it better for yourself.

iii) Motivate yourself and become a real motivator. Being motivated – and helping to motivate others – is probably the least computerizable trait of all. If you aren’t motivated, you are placing yourself at risk when your leadership assess which of their team then want to take them forward into the future. If you really can’t get yourself excited about what you do, or your company just demotivates you in such a way you can’t dig yourself out of your rut, then you may need to take that Python course and brush up your resume…

iv) Let the computers take the lead and become the controller to fix mistakes double checking, intervening when the computers do something dumb. Humans and computers make different kinds of mistakes, so we really need to bring humans and computers together intelligently to cancel out each other’s mistakes. Fighting automation and ML is a lost cause, especially when your firm is completely bought in to the concept and it rolling out bots and working on developing smart algorithms. Just let these things take the lead and them figure out how to make them functional and monitor their errors, ad computers will always keep making them. You can’t fight innovation, but you can nurture it, manage it and troubleshoot it.

v) Find your pareto balance and stop whining. Nothing in life including your current or prospective employer will be perfect. Focus on the 80% that is right, versus making yourself (and others around you) miserable by the other 20%. There is rarely a perfect fit where workers only get to focus 100% on all the things they love to do… there has to be this 80/20 compromise, or you will be forever hopping around trying to find a workplace nirvana that doesn’t exist. And it today’s social world your reputation follows you around like never before… and employers are steering clear of the whiners at all costs.

Source: the full-time job will never be so precious, as the gig economy crumbles and judgment work is digitized