Wrong Hires- A problem bigger than we think

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In a recent survey conducted more than 90% respondents said their company has at least one wrong hire every year.

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Isn’t that a good indication of the extent of the problem? Careerbuilder survey showed that 3 out 4 companies are affected by bad hires.

IMHO all the companies have multiple cases of wrong hires every year.I suspect the respondents are a bit biased.

Here’s a list of reasons why I think so

  • No one likes to admit that s/he hired a wrong candidate
  • Wrong hires try and hiring managers help them to meet at least the minimum requirements allowing a lot of “settling in” time
  • Every one’s reputation is at stake- so all are hoping that things will eventually work out
  • The hiring manager himself/herself could be a “wrong hire”
  • There is no metric such as suitability score to clearly differentiate “right hires” from “wrong hires”
  • Everyone likes to believe that the company hires only “smart and versatile” professionals who can successfully meet any challenge

I reiterate that the problem is much bigger than we think.

Why do we end up hiring wrong people?

The top-most reason as per the respondents (88% agree) is because the hiring was done under time pressure. Following are the reasons why we find ourselves up against tight deadlines –

  • We don’t have a healthy pipeline. We hire only when the requirement comes up. In this HBR article Patty McCord advises that we should never stop hiring.
  • Many companies hire to a target which measure the number of positions filled – we tend to value quantity over quality
  • In the growing companies the high performing managers are busy and the task of interviewing and evaluating is often delegated to others

Second prominent reason is the lack of a strong process (63% respondents) that can prevent “gaming” by the smart candidates. The process should be geared to produce high quality measured by a well understood metric. Here are some typical issues with the selection process-

  • Interviewer bias introduced by different interviewers. Scheduling challenges end up in the candidate being interviewed by whoever is available.
  • Many selection parameters are subjective- making it hard to have a metric to measure suitability.E.g. attitude, cultural fit, communication skills, willingness to learn etc.
  • Any attempt to “standardise” the process by conducting technical quizzes, psychometric tests etc. are “gamed” by the candidates. Answers to many such tests conducted by large companies are published on the web.
  • Candidates often hype up their resumes. Boolean search on popular portals results in lot of resumes which are very similar. It takes efforts to separate “hype” from “reality”.
  • The prescribed process is often compromised under time pressure.

The third common reason is because the job requirements change over time (44% respondents) for reasons unknown at the time of hiring. Here are some common reasons why job requirements change

  • Enough attention is not paid while writing the “job description” initially. Some one from the HR department prepares a draft which might be based on or copy pasted from an earlier “JD”.
  • Concerned managers are not sure about the role which the selected candidate is likely to play nor are they sure about the project in which the candidate will be deployed.
  • By the time the process concludes and the candidate joins , it is too late. The job requirement might have ceased to exist by then.

Here are some other reasons –

  • Too much trust placed in labels like previous employers, candidate’s prestigious college etc
  • Political influence and vested interests building strong fiefdoms to protect self interests and power base.
  • Some times companys economise on compensation and end up hiring those with low expectations.
  • Inconsistent personal interview format that leaves a room for personal biases to play a role

Typical Symptoms of a Wrong Hire

Here are some typical symptoms which you can notice if you closely observe the newly appointed professional –

  • Gets stuck and blames others or the company’s processes for his inability to make progress (94% respondents). Not being able to do what you are supposed to do is a certain indicator of lack of skills.
  • Is inflexible, not ready to learn new skills. Such a person often complains that she is being asked to do what she wasn’t hired for. (88% respondents) . This clearly points to an expectation mismatch. A person who is very good at something might not want to be a rookie in a new area. Inertia is a sign of unwillingness.
  • Stays away from work for many reasons- comes late, takes leaves etc. (75% respondents) Unwillingness to be at the place of work certainly indicates lack of engagement.
  • Stays aloof, doesn’t communicate much . Such a person might be nervous or emotionally unsettled. (50% respondents) . It’s hard to tell if the silence comes out of fear, diffidence or habit.

How can you prevent wrong hires

Here are some ways in which you can avoid committing the mistake-

  • By hiring multi-skilled, flexible, versatile candidates who are ready to learn new skills (100% respondents). This is easier said than done. Candidates who have achieved “mastery” after years of hard work in a particular area would find it hard to start all over in a new area.
  • By ensuring “culture fit” in the interview process (94% respondents). This is very subjective. Often “interviewer bias” makes it hard to compare suitability on the basis of this attribute.
  • By having a fool proof selection process that can’t be “gamed” by some “smart” candidates. ( 88% respondents) This is doable with the latest advances in NLP and ML.
  • By having a better understanding of the job requirement in the beginning (80% respondents). This is the easiest and the lowest hanging fruit.

Negative Impact of Wrong Hires

 Cost of making wrong hiring decisions could be astronomical. Here are some negative impacts of wrong hiring decisions –

  • Highest impact is when wrong hires represent your company in front of customers or investors. Your company’s reputation can get impacted. It takes years of hard work to build a company’s reputation. But it takes only one mis-step by a wrong hire to destroy it.
  • Wrong hires can hire other wrong hires to consolidate their position. This can spread mediocrity like an infectious disease.
  • Cost of hiring, onboarding, training and cost of hiring a replacement
  • Cost of delay in the project or work assignment
  • Wrong hires impact the motivation level of others working in the team. At their worst, these misfits can trigger attrition and eventually drag the team down to become dysfunctional and unstable.

 

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Build ontologies, let your chatbots socialize!

It’s 2018 and companies need to improve their understanding of customers, vendors, employees and investors. Companies need many more ears and mouths to absorb and disseminate knowledge. Machines engaging with humans in productive conversations using Natural Laguage Processing is no more a dream as well demonstrated by Siri, Alexa and Google-Home! Why is this so important? Obviously because it would empower companies to do what they need to do this year. But before we get to it , let us pull back and take a look at what makes knowledge transfer hard.

Tacit and Explicit knowledge

Tacit knowledge is what we have internalized and that is what we really use when we make a number of decisions in our personal and professional life. A pilot uses his tacit knowledge and has no use for explicit knowledge such as an instruction manual while flying a large passenger aircraft. Doctors, lawyers, architects and soldiers owe their tacit knowledge to years of training alongside their seniors. This tacit knowledge transfer happens while socializing as per Alex Pentland in his famous book Social Physics

Explicit knowledge is what we externalize and express in English or some other language in articles, books, videos, podcasts, databases and spreadsheets etc.

Nonaka and Takeuchi have developed the SECI model for organizations to grow their knowledge-base by cyclically going through the following 4 steps that looks like an ever expanding spiral

  1. Socialization ( Tacit to Tacit)
  2. Externalization (Tacit to Explicit)
  3. Combination (Explicit to Explicit)
  4. Internalization (Explicit to Tacit)

 

  • SECI

    SECI Model of knowledge transfer

    Tacit knowledge is hard to externalize. No one can communicate all he/she knows by writing it down. On the contrary socialization is easy. It happens easily because of the social setting which provides the context. Human brain snacks on information that is in context. On the contrary most of the communication directed at you on the internet is sheer noise due to lack of context. Wouldn’t it be wonderful if chatbots storing a lot of explicit knowledge could use it in a virtually social setting to engage in conversations with us?

    Let chatbots do the hard work of externalization

    Imagine having a friend by your side who has the patience to answer all your questions that you need to be answered to understand and participate in a conversation. All of us were very inquisitive as children. We stopped asking questions when we saw adults around us losing patience.

    Chatbots can simultaneously engage in thousands of conversations. Socializing with chatbots makes it easy for us to weave bits and pieces of knowledge in the right context. This knowledge is provided accurately in the right context by a chatbot that is lurking the background while we are going about our daily routine of using our enterprise software or our favorite app. Explicit knowledge is internalized by a chatbot in a few seconds. But for chatbots to learn new pieces of knowledge , they need to ask questions and plug incoming pieces of information in the right place. Ontologies provide the underlying infrastructure for chatbots to organize knowledge – somewhat parallel to the way humans learn by organizing related facts in the same part of the brain.

    Building an ontology made easy!

    An ontology is a little more than a taxonomy which is a tree like structure that organizes entities in classes, subclasses and instances. In addition it also establishes connections between leaves of the tree by specifying one way, two way and transient relationships. Its best represented in a graph database such as neo4j. Organizing the knowledge specific to your company’s business domain in a graph database is a daunting task. Many companies have spent years building ontologies. No doubt that it’s an effort that more than pays for itself- but the sheer volume of work deters companies from investing in this project.

    Synerzip has developed a semi-automated way of building ontologies. The system takes documents relevant to your company’s domain – which may be catalogues, brochures, articles, emails, spreadsheets, resumes or handbooks and puts them in a corpus. Top few hundred the frequently occurring terms in the corpus are manually organized by an subject matter expert to form the seed ontology. This might take a few days or weeks at the most.

    Automation kicks in once you have a reasonably well populated seed ontology. The program computes similarity scores based on the context in which terms appear. Terms having high similarity are put in the same class. There are still some places where a human expert is consulted to resolve contention. But it makes the project more feasible and viable for companies planning to build their ontologies from scratch. For companies who already have built their ontologies , this system could be a good way to update and improve utility.

    Building chatbots has been made easy by providers like Facebook messenger, Slack and Skype. By the end of this year , we will see chatbots being preferred over mobile interfaces by many applications.

The Startup Way- Take-aways from the Lean Startup Week 2017

The main highlight of the conference was the new book- “The Startup Way” authored by Eric Ries. It was released in the week preceding the conference and I was pleasantly surprised to be able to buy a copy from a bookshop in Hong Kong airport. I finished reading the book on the long flight to San Francisco and was all set to hear more about it from Eric himself. I was in for yet another surprise when as a part of the conference kit I got a hard bound copy of the book which many of us got signed by Eric.

The book significantly captures how a passionately motivated team trying to solve a problem under conditions of extreme uncertainty can apply the principles of “The Lean Startup” – even when the team is inside an institution such as a large corporation , Government or a social enterprise. The common idea is that any hypothesis can be validated by conducting a controlled experiment. These are low cost short iterations of few weeks each ending with success or failure. In either case the end result is that the team acquires some validated learning at the end of each iteration.

The Startup Way introduces some new concepts. Innovation boards are senior stake holders within the institution who act like VCs for the internal startups. They decide whether a proposed startup is worth investing in. Once the decision is made they hold the intrapreneurs accountable by reviewing progress on a regular basis in Pivot or Persevere meetings. The innovation board may decide to release the next round of funding or to kill the startup based on the value of validated learning by the startup. The board gives complete freedom to the startup in the way the funds are utilized. But the startup has the onus to use the funds judiciously so as to achieve demonstrable validated learning so that they can go back to the board for the next round. This way of funding is called as metered funding.

As one can see “accountability” is the foundation. The process is designed so that it builds the culture needed to motivate and empower people. All the words in italics have more detailed explanation in the book. The pyramid below shows the order of priority and significance associated with the four concepts.

Accountability

Accountability is the foundation

In this earlier article I had discussed the problems with hierarchies and various options being evaluated. The discussion was inconclusive and we remained admitting that no single structure can replace the hierarchies. “Horses for courses” as suggested by Tathagat Varma was the summary of our conclusion.

The startup way seems to have taken this thought one step further by suggesting a perpetually transforming organization that spins up startups to deal with the challenges as they come up. Some startups will succeed and others will pivot or die. This dynamic structure behaves more like a tree and less like a tower.The process of transformation itself is a series of lean startup style experiments . The diagram below describes the org structure in more detail.

TheStartupWay

Organization Structure for the Startup Way

Startups can be nurtured within the project teams with ambitious and passionate individuals hiring team members and “moonlighting” their way to launch. At some point this team would get blessed by the “innovation board” with a round of metered funding. This means the money comes with strings attached. Innovation board would ensure survival of the fittest by continuing to fund only the few deserving startups. Finally the successful startup would become an integral part of the organization by becoming a revenue earning , profit making project team or business unit.

The entrepreneurship function is just like any other staff function such as marketing or finance. The functional head is in charge of a knowledgebase of validated learning gathered from failed experiments. She will also ensure that the innovation boards do their job of guiding and nurturing the intrapreneurs.

Everything that is written in the book is supported by actual work done in several large conglomerates like GE and Citibank. Its very hard to judge the level of success given the size of these organizations. Most of the evidence is anecdotal. “The Startup Way” brings hope to the large hierarchical dinosaurs – may be they would metamorphose into nimble and vastly more intelligent forms.

The biggest take-away from the book is the accountability that lies at the foundation of the transformation. It brings some sense of control to the whole process of experimentation which is likely to run amok in endless iterations. Measuring validated learning by monitoring a leading metric which serves as a proxy for the net present value is at the center of innovation accounting. The proxy metric also needs to be corrected by accounting for the probability of it actually resulting into a certain net present value. This makes it easy for the innovation board to do apples to apples comparison between various opportunities competing for the next round of metered funding.

Execution Behavior change Customer Impact Financial Impact
Did we do what we said we were going to do? Are our people working differently? Do customers (internal and external) recognize an improvement? Are we unlocking new sources of growth as a company?
Project Team Is the founder and the team committed and stable? Has the team spent enough moonlighting hours on baking the idea? Does the idea pass the test of basic commonsense? Are they having a well defined business plan canvas? Do they know their LOFA? Do they hold P & P meetings? Do they run their experiments properly? Is there a recorded impact in terms of customer satisfaction? Shorter cycle time? Monetary savings? Customer referrals? NPV of business model. Productivity gain? Savings?
Business Unit What is the % of employees in startups? % of startup founders in this BU? What are the number of successful projects in the BU? Does the BU have mechanism of celebrating failures and capturing the learning? Employee morale? Per project cost? Average time taken by projects before first customer demo? Average cost incurred before first customer demo? Growth of BU revenue? Billable Head Count? Total Valuation of all projects incubated by the the BU
Company What is the % of employees in startups? % of startup founders in the company? Success rate? Is everyone talking about LOFA , Experiments and NPV? Number of pitches to the innovation board? Is the process simple? Are we able to attract better talent? Is it impacting Net promoter score? Are the number of red days zero? % of deep green accounts? New products launched? Value of phantom stock, Growth in revnue , billable head count and EBITDA, % of resources on bench

The cells in the table suggest some leading metrics for each stage from execution to behavior change to customer impact to financial impact.

In the end I would like to leave the readers think about a quote from Jack Welch. “ “If the rate of change on the outside exceeds the rate of change on the inside, then the end is near.”

 

Is Automation Eating Jobs or Creating New Ones

-based on Atul Jadhav’s views in a tweetchat hosted by Rezoomex

Historically technology has always created new jobs by automating repetitive/mundane work with predictable expected results. Human brains evolve as they learn new skills to solve challenging problems. This leads to creation of new job types which are more evolved yet challenging . e.g. Delivery by drones may take up delivery jobs however this automation itself opens up a number of specialised jobs with higher expertise. E.g. GIS experts, geospatial data analyst/scientist, algorithm designer/ developers, control centre operators, security experts etc.

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But hundreds of delivery boys can’t learn these skills – they would lose their jobs unless they evolve. Change is hard especially for the unprepared. But we need to learn from experience. We have come a long way from postcards to emails.

We often come across cases of individuals like a manual tester known to the author. This tester has spent all the 10 years of his career doing manual testing but now he is unable to learn automated testing-as a result he is losing his job. Evolve or perish, that’s the mantra! Some people can’t evolve- either because of inaptitude or inertia.

Not all jobs will have a clear evolution path like typists who evolved by learning word processing as typewriters were being replaced by personal computers. It can be concluded that automation will impact low skilled jobs and create new jobs that use human intelligence and creativity. Upskilling is the way forward for those affected.

Another angle to consider is that we need not work as much as we used to because automation will give us more free time to undertake hobbies or creative and innovative pursuits. But looking at 140 years of data automation has always created more work.

Here are some points to ponder-

1) Examples of jobs that have been made obsolete by automation

In manufacturing robots have replaced workers with significant increase in productivity. Bank teller job is automated, check out clerks in the supermarkets are being replaced by automated point of sales machines. Telephone operators, travel agents , timekeepers, typists, film projectionists, packing labour, stockroom/warehouse managers, farmers, data entry operators, call center operators, weavers, knitters the list can go on.

2) Jobs that are not yet obsolete but are at risk

Research says by 2021,four out of ten jobs will be lost to automation. Tasks needing low skills that are highly transactional can be automated using AI & ML. (Artificial Intelligence and Machine Learning)

Like delivery boys losing their jobs to the drones, taxi and truck drivers too will follow suit if Daimler, Tesla & Google succeed in their plans to make vehicles driverless. The list can go on- nursing and health care, insurance underwriters, geographers, nuclear technicians, financial advisors, news reporters, loan officers, accountants and auditors. Many of these jobs will have their next version, different set of OKRs to serve much more evolved customer demand and business models. About 40% of the jobs will go away in the next 5 to 10 years. We can’t say for sure if your job is one of those. Hence its safer to assume that all jobs will become obsolete in the next 10 years.

3) New IT jobs that are recently created by automation

Technology is evolving faster than ever,IBM Watson, Google’s Deep Neural Network (Tensorflow) and several other AI & ML platforms require humans with skills to implement various business models and use cases on those platforms. New breed of systems integrators called Robotic Process Automation (RPA) implementers are building systems with optimised work flow. Skills like DevOps are high in demand due to the need to further automate the service offerings via CI & CD (Continuous Integration and Continuous Delivery). CD results in effective use of infrastructure resources by reducing the “Time To Market” for new products or services in addition to improving the efficiency and reducing the cost. Also in demand is the skill to convert old monolith applications to containerised microservices. Containerised microservices with container orchestration are taking the discipline of DevOps to the next level.

4) Industry verticals most likely to be Impacted by automation

Businesses today are striving for enhancing their customer experience. They are building products and services which will help them to achieve the same results more quickly and effectively. At the same time businesses – both B2C and B2B – are collecting lot of data about behaviour, productivity and performance issues.

E.g. your car goes to refuel itself from the nearest gas station while you sit in your office focusing on work or spending time with family without worrying much about gas level or spending that extra 10 minutes at the gas station. The data required is sent to the service station 24/7. When the car goes to get the servicing done by a fully automated service robot, it already has the data about all the vitals of your car along with exact issues. And this scenario isn’t as far fetched as it sounds and the day isn’t as far as is commonly believed.

Almost all industries are going to leverage the power of AI & ML. To name a few – healthcare, agriculture, manufacturing, transportation, customer service, finance and defence are going to be benefit from these technologies.

As a result new jobs will come into existence. Some of the tasks for almost all the existing jobs will need new skills. All of us have to keep learning new skills. We are all running on a treadmill. Running hard to stay where we are! And the speed is increasing continuously. e.g. some years back DevOps skills were in demand . But today developers are writing containerised microservices. They can both build and deploy without any help from DevOps. This way of working called NoOps is making DevOps skills redundant.

5) Opportunities for IT Professionals in Non-IT Sectors

IT departments in non-IT organizations will benefit from the advances in technology. Presently every company does have a technology department however they will be needing serious updates to the latest technology which will be a challenge in terms of costs to be incurred and new skills to be acquired. Consequently technology would become central to the future strategies of these companies.

To build the smart systems mentioned in point 4 above, we will require skilled IT professionals who are not only capable of developing solutions based on intelligent platforms but are also quick learners. As Non-IT sectors start being heavily driven by IT more techies will be needed to maintain existing systems, to cater to new business cases, to alter existing business cases while ensuring the data security. Governmental rules and regulations will evolve based on various learnings resulting from automation initiatives such as driverless vehicles. This will open up altogether new careers for IT professionals in law enforcement departments.

Right now every company is a  software company. Ford sells computers-on-wheels. McKinsey hawks consulting-in-a-box. Fedex boasts a developer skunkworks. IT is employed in every industry today, from manufacturing to agriculture, from logistics to education.

The new breed of millennials joining the workforce will be focusing on analytics and research, data interpretation and insights, intelligent assembly lines, IoT platforms for process automation, CAE & CED , 3 D printing for building prototypes, streaming data collection and ingestion, cloud based machine learning algorithms.

6) Evolution of Non-IT Jobs Complemented by ML,Chatbots & AI

Product Designers, Data Scientists, Digital Marketers, Actuaries, offline auditors and validators, e-tailors, health care professionals, legal advisors. Almost every job in existence today will undergo the next level of evolution. Automation technologies like AI and ML will bring actionable facts or research applicable to a situation or an individual. This information will be leveraged for doing relevant cross-sale / up-sale of products or services. Customer support/ service jobs will be assisted by intelligent chatbots delivering a delightfully engaging and constructively interactive end user experience.  In many cases final decisions still have to be made by humans. But these will be very well informed decisions.

These are the non-IT jobs that involve creative skills, emotional and social intelligence, smart networking that will get complemented by automation. Recruitment and selection process too will change with automated parsing, evaluation and shortlisting of resumes based on priorities.

7) How an Individual Should Evolve

There are ample resources available on the internet,  free and paid, in the form of video, text, guides which can be leveraged by an individual to re-skill or upskill. No doubts are left about learning opportunities open to anyone when you see a thirteen year old delivering a key note address on IBM Watson and actively contributing to AI & ML. Professionals need to start being more curious about the purpose of what they are doing and how it is impacting the businesses they are working for.Anything that is happening anywhere in the world that is likely to bring about the next level of disruption in their jobs. Use this awarenesss to chart the course of skill acquisition well before the disruption happens. It is not very different when we do financial investment to expect certain amount of returns in future. Investment in oneself has to be a continuous process. People have to work towards building creative skills and being versatile. Those having T-shaped skills would find it easier to learn neighbouring skills. Above all thinking creatively about your future is important. Try making the change instead of coping with it. Finally one has to overcome inertia which is the biggest problem. Unlearning old habits is difficult. Old habits die hard!

8) Jobs or Roles that will Prosper

Roles having more cognitively demanding tasks will be valued more than ever. Here’s an example to put it in perspective. Traditional tellers have almost been replaced by ATMs. The individuals who used to be Tellers are now more focused on customer relationship selling products which are relevant to retail/corporate banking customers. This needs more time to be spent to understand the intricacies of businesses that are served by the banks. The new role is to provide designer solutions to cater to the business needs.

This does increase customer satisfaction and business for the banks. This ultimately results in the increase in number of branches and subsequently in the number of jobs.

This logic of automation creating new jobs applies for many roles which are under threat due to automation (Construction, Healthcare, Services etc..). Improvement in tools and automation, increases the effectiveness of emotional and social skills, creativity and judgments. Consider the example of the automated voice response (AVR) system used for handling lost baggage complaints by airlines. If used without any human intevention; it can put anyone off- it simply lacks the empathy an aggrieved passenger needs.

Having said this, let’s not ignore the fast pace evolution going on in AI and ML and cognitive capabilities been built around them making them more and more self conscious. Synerzip was involved in building an iPad app in which an animated nurse could actually recognize facial expressions. We had to use hundreds of thousands of images of happy, sad, painful and smiling faces to train it. It just goes to show how difficult it is to build an emotionally and socially intelligent bot.

9) Management Help for Employees to Evolve with Automation

If automation is a challenge for employees then it is definitely a challenge for businesses who are employing them. Management has to invest on fostering the culture of multi skill acquisition by their employees. Businesses should proactively introduce the disruptive technologies into work environment and provide training to their employees. Recently Webonise got into the business of implementing Robotic Process Automation(RPA) and IoT solutions. This put demands on the employees to acquire new skills required to develop, implement and maintain these solutions. Thus creating learning opportunities for the employees of Webonise.  It was uncertain times for everyone involved. In these times of uncertainty management should develop an atmosphere of fearlessness- do gap analysis and find training needs. Once the training needs are ascertained; they should conduct classroom training followed by hands on reskilling programs. Its an opportunity for the company to develop a learning culture and to motivate every employee to perpetually keep learning and upgrading their skills.

As a part of strategic planning management should tell every employee what his/her next job will be like. Spare no one. There is no one whose job is future-proof. Fearlessness is a pre-requisite for creating a learning atmosphere. Management should measure performance and allow employees to do course correction by providing learning goals and performance objectives. Management should avoid replacing current employees by hiring laterally. Dynamic re-teaming and refitment of jobs would allow cross pollination of ideas and better learning across teams. Finally any good management should set the right employee expectations by making every one aware of the risk of obsolescence and impress the need to continually assess the way automation is impacting our work.

 

Blockchain and Smart Contracts Will Change the Game From Big Upfront to Lean Experiments.

We often make big upfront commitments , decisions, investments and assume the risk of not achieving the desired outcome.

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We hire a software developer or a team of developers at the beginning of the project. Often we are required to define the technology stack and expected level of complexity up front. We wouldn’t be able to hire the team without doing so. Our choice of technology may turn out to be a bad one. Or we might end up committing to a higher budget by hiring a team with higher than the needed level of seniority. But we can’t go back on our commitment and switch to a better team of developers with appropriate level of seniority and expertise in a new technology.

Welcome to the world of smart contracts and blockchains. In not too distant a future it might be possible to enter into a short 2 week contract with the team. (Please read my earlier post) Each developer gets automatically paid in cryptocurrency as their code passes all the automated tests and is deployed in production. At the end of the sprint we have the freedom to suitably modify the team composition by entering into a new set of smart contracts. This gives us the freedom to experiment and evaluation a few options.

What changed is our ability to enter into smaller , more frequent transactions without the need of a central aggregator such as a Bank or the Corporate HR department. Aggregators can’t economically work with shorter contracts and smaller payments because there is a fixed overhead associated with each contract and payment.

The table below shows a few more examples where such a change can occur.

Big Upfront Lean Experimental
Buying a car Sharing a ride- No aggregator such as Uber or credit card needed.
Buying Servers Virtual servers on the cloud can be rented on pay as you use basis- going forward there will be no need to use credit cards. Cloud provider will automatically receive their payment in cryptocurrency.
Buying heavy machinery Renting made possible by more details about usage, wear and tear and damage if any will get communicated by data streamed by IOT sensors
Launching a company to enter a new business with all the associated overheads Assembling a team loosely bound by smart contracts. Blockchain ensures compliance without the protection of corporate law
Group health insurance- healthy members pay more and those susceptible to health issues pay less. Claiming insurance is fraught with legalese embedded in fine print. Wearable devices communicate the current health status frequently enough that makes it possible for a healthcare provider to directly enter into a smart contract with the individual .

This new way of working has become even more possible because of recent advances in IOT which will enable digitalization of lot of information about the physical world. Broader acceptance of “shared economy” is pushing individuals to “rent & try” instead of “buy & regret”. All this works very well for those who think and work in the lean and agile way. It gives them the freedom to conduct experiments to validate their hypotheses before making a large commitment. It lowers the barrier to entry into businesses which were a territory exclusively reserved for the big corporations.