Leaders in an Agile Enterprise

Can leadership style be at dissonance with an organization’s incentive/rewards scheme? And what is its correlation to motivation?

An individuals motivation can be intrinsic or extrinsic. Intrinsic is when we act, not for external rewards, but for the simple pleasure or satisfaction of achieving something. Performing one of these activities is reinforcing in-and-of itself.

Laissez-faire leadership can be effective in situations where group members are highly skilled, motivated, and capable of working on their own, with very little guidance. Laissez-faire style is used in situations where followers have a high-level of passion and intrinsic motivation for their work.

So what happens when laissez-faire leadership meets people who are not good at setting their own deadlines, managing their own projects, and solving problems on their own, skills essential for an agile organization? How can leaders adapt?

Laissez-faire leaders are often seen as uninvolved and withdrawn, which can lead to lack of cohesiveness within the group. In such a situation, leaders need to change their style – easier said than done – and get more proactive, assertive and hands-on with the task or process needed to accomplish the task.

Eventually, as followers acquire more expertise, leaders might switch back to a more delegative approach that gives group members more freedom to work independently.

A major dissonance occurs when laissez-faire leaders implement rewards schemes, which in reality is a switch to a benevolent authoritative style. “Meet your targets and you will be suitably rewarded!”. The followers, even those who are intrinsically motivated, now must adopt performance to maximize rewards.

In this situation, the leader now uses rewards to encourage appropriate performance and listens more to concerns lower down in the organization, though what they hear is often rose-tinted, being limited to what their subordinates think the boss wants to hear.

And if the span of control is one to many (as is often the case), the leaders time has to be split across multiple stakeholders. Overtime, the leader chooses to listen more to those who think like they do (and is what they would like to believe), thereby reducing the space for dissenting voices.

Thereafter is a rapid spiral downward into a crisis.

In the final analysis, the source of the most insurmountable hurdle in achieving the goal of strategic agility is the organization’s leaders.

Software WIP

So you have established a software development process. Now what?

This question always comes up at a point when development teams have settled into their initial process and each team member understands the work flows, its tools and its artifacts.
It sometimes becomes difficult to explain why the processes that have been just implemented are just “initial” processes and that they now need to get down to the tasks of evolving these processes.

So why do you need to evolve an established process that everyone appears comfortable with? The answer, I believe, lies in determining the end objectives of any software development process, and those are to (1) maximize throughput (or minimize time to market) and (2) reduce work in process inventories.

While the first is easily understood and justifiable, the second is a bit elusive and as some may say, nebulous and theoretical. So let’s try and understand who is saying it and why they are saying it.

To many organizations, caught up as it is, with a routine and a host of day to day distractions, the thought of reducing work in process inventories probably never even crosses the mind.

I mean, is that important or can I just “get on with my job” is the attitude that flows back at you, especially if you are engaged as an external consultant. Here is a very apt graphic that captures the tone of the engagement at this point in time:

So now what? You have seen the problem and have wheels to offer, but your client says, “No thanks. We are too busy”. Or, “This is too theoretical! People are getting bored.”

Firstly, and let’s just get this out of the way – What the hell is “work in process inventory” for a software development organization? For software development organizations, “work-in-process inventory” is any software artifact, the creation of which has consumed some effort, and the said artifact has not been deployed to production yet. That includes all code files, test or database scripts, or documentation that is being worked on.

To really get to grip with this, you got  to take a look at costs. Assuming an average blended rate of $1 per hour per seat at a development centre in India. Now say, the TL of Team A has spent 2 hours reading the specification, an hour in discussion with the architect, and another hour in a discussion with the creative team on the user interfaces.

Together, the total time spent by the TL for this one requirement is 2+1+1 = 4 hours, which at the rate of $1 per hour works out to $4.

Total effort including the architect and creative designer works out to 4+1+1 = 6 hours, or $6.

Now suppose the TL switches context to another requirement and spends 2 hours in a discussion with the business analyst to develop, say, design options. Total effort by the TL and BA put together is 2+2 = 4 hours or $4, taking the total “work in process inventory” cost to $6 + $4 = $10. Both these requirements are a long way from production.

Now consider a centre with 100 developers. This amounts to 100*8=800 hours of capacity per day. Assume a cycle of 2 weeks, i.e., 800*10 = 8000 hours. Take a scenario where 35% of the effort (not too bad for many organizations) is spent in each cycle on artifacts that never move into production or one reason or the other (low priority, resource constraints, technology hiccup, upgrade needed, etc). That is 35% * 8000 hours = 2800 hours, which in cost terms is $2800 per cycle, given the same $1 per hour rate (this rate is difficult to achieve with just a 100 person team and is likely to be in the range of $2.35 to $3.00 per hour) .

Over 26 cycles (theoretical maximum per annum for this team), that amounts to $ 72,800.
Given a blended average annual CTC of $13,500 per person, the figure derived here works out to 5.4% of salary cost (COR). In other words it is like having an additional, hidden bench of 5% throughout the year. If you take the more likely cost per hour figure for this size of development centre, the bench works out to between 14-20%.

Now what? The solution lies in identifying who is really affected by increasing work in process inventories. In other words, who owns the cost metrics. This will, in most cases be either the portfolio or service delivery head, or the head of engineering, usually someone who owns the project management function within the organization. 

Engineering, as we saw earlier, has the two objectives:

1. To maximize throughput (by reducing time to market), and
2. To minimize WIP inventory

Incidentally, another dimension to WIP inventory is Technical Debt, but measuring that is even more nebulous, so we will cover that in another article. What is the solution? Meaning, how do you go about reducing WIP inventories? That’s precisely what Kanban is all about and the subject of yet another article. For flow process, this is fairly straightforward as long as there is a common definition for work prioritization. For batch processes, it is a more involved process and organization’s will get there over time through staged process evolution (Process Roadmap).

Does it make sense?

On the Sharing Economy

In what is called collaborative consumption, the sharing economy or the peer economy, owners rent out something they are not using, such as a car, house or bicycle to a stranger using these peer-to-peer services. The company typically has an eBay-style rating or review system so people on both sides of the transaction can trust the other. With the popularity of these services, many people don’t need to buy when they can rent from others.

Airbnb, the San Francisco startup is the poster child of the collaborative consumption or peer economy. Here, travellers can rent a room or a whole home or a British castle on Airbnb. Dog owners can leave their dog with a host who will take care of the dog on DogVacay. On Google Ventures-backed RelayRides, people can borrow cars from neighbours. They can rent the cars by the hour or by the day. If the car has a service like OnStar, users can open the car automatically through a mobile app.

TaskRabbit is a mobile marketplace for people to hire people to do jobs and tasks, from delivery, to handyman to office help. Founded in 2008, the site has 4000 TaskRabbits on the service nationwide who bid to do tasks that are posted by people looking for a service. All the “rabbits” are interviewed and have their backgrounds checked before going on the system.

Peer-to-peer car sharing company Getaround lets people borrow cars from others. Owners who are out of town can also leave their car with Getaround, which will rent out the car, clean it and take care of it. Getaround cars are covered by a $1 million insurance policy from Berkshire Hathaway.

Liquid, a rent a bike from a neighbour was formerly known as Spinlister. Zaarly is a peer-to-peer marketplace for people to provide services to others. Compared to other services, Zaarly focuses on creating “stores” for sellers to market their services, from home repair to iPhone repairs, buy a home made pie or hire a cobbler.

Lyft is a ride sharing service for people to find rides from “regular” people who have a car. The service, created by Zimride, only takes “donations” because it is not a taxi service.

Lending Club’s peer-to-peer network can be used to get cold hard cash. Lending Club is cheaper than credit cards for borrowers and provides better interest rates than savings accounts for investors. Since July 2007, Lending Club investors have invested over $1 billion in loans and received more than $85 million in interest payments.

Startup Fon enables people to share some of their home Wi-Fi network in exchange for getting free Wi-fi from anyone of the 7 million people in Fon’s network.

SideCar, a ride sharing startup, now available in San Francisco and Seattle, allows “regular” drivers to pick up people who want a ride. Drivers also accept a donation, but don’t charge a fee.

People buy or sell their clothing via Poshmark’s mobile app. They can also display their virtual closets and find friends who have similar styles.

And NeighborGoods is yet another community service where you can save money and resources by sharing stuff with your friends. With Americans spending $22 billion a year on self-storage, that’s a lot of unused stuff. So need a ladder? Borrow it from your neighbour. Have a bike collecting dust in your closet? Lend it out and make a new friend. And what more, borrowing and lending items on NeighborGoods is free of charge though members may charge a deposit or a rental fee for the use of their items. However, NeighborGoods does allow members to upgrade their accounts for $9.99 for access to more items.

The “sharing economy” has attracted a great deal of attention in recent months. Platforms such as Airbnb and Uber are experiencing explosive growth, which, in turn, has led to regulatory and political battles. Boosters claim the new technologies will yield utopian outcomes—empowerment of ordinary people, efficiency, and even lower carbon footprints. Critics denounce them for being about economic self-interest rather than sharing, and for being predatory and exploitative. Not surprisingly, the reality is more complex.

Sharing economy activities fall into four broad categories: recirculation of goods, increased utilization of durable assets, exchange of services, and sharing of productive assets.

The origins of the first date to 1995 with the founding of eBay and Craigslist, two marketplaces for recirculation of goods that are now firmly part of the mainstream consumer experience.

These sites were propelled by nearly two decades of heavy acquisition of cheap imports that led to a proliferation of unwanted items. In addition, sophisticated software reduced the traditionally high transaction costs of secondary markets, and at eBay, reputational information on sellers was crowdsourced from buyers, thereby reducing the risks of transacting with strangers.

By 2010, many similar sites had launched, including ThredUp and Threadflip for apparel, free exchange sites like Freecycle and Yerdle, and barter sites such as Swapstyle.com. Online exchange now includes “thick,” or dense markets in apparel, books, and toys, as well as thinner markets for sporting equipment, furniture, and home goods.

The second type of platform facilitates using durable goods and other assets more intensively.

In wealthy nations, households purchase products or hold property that is not used to capacity (e.g., spare rooms and lawn mowers). Here, the innovator was Zipcar, a company that placed vehicles in convenient urban locations and offered hourly rentals.

After the 2009 recession, renting assets became more economically attractive, and similar initiatives proliferated. In transportation, these include car rental sites (Relay Rides), ride sharing (Zimride), ride services (Uber, UberX, Lyft), and bicycle sharing (Boston’s Hubway or Chicago’s Divvy Bikes).

In the lodging sector, the innovator was Couchsurfing, which began pairing travelers with people who offered rooms or couches without payment back in 1999. Couchsurfing led to Airbnb, which has reported more than 40 million stays in over 190 countries.

This type of service includes non-monetized assets such as tools, which are more immediate neighbourhood based, providing people with low-cost access to goods and space and some opportunities to earn money.

The third practice is service exchange which began in the 1980s as a means to provide opportunities to the unemployed. These Time banks are community-based, non-profit multilateral barter sites in which services are traded on the basis of time spent. However, these have been less popular than monetized service exchanges such as TaskRabbit and Zaarly, which pair users who need tasks done with people who do them.

The fourth category focuses on sharing assets or space in order to enable production, rather than consumption. These include hackerspaces for computer professionals, makerspaces which shared tools and co-working spaces that served as offices. Other production sites include educational platforms that aim to supplant traditional educational institutions by democratizing access to skills and knowledge and promoting peer instruction.

These new technologies of peer-to-peer economic activity are potentially powerful tools for building a social movement centered on genuine practices of sharing and cooperation in the production and consumption of goods and services. But achieving that potential will require democratizing the ownership and governance of the platforms.

The trend has not been without its naysayers. “How are we going to harness the sharing economy to spread the wealth?” The Airbnbs of the world and their venture capitalist backers are siphoning off too much value, some argue. Discussions of labor exploitation, race to the bottom dynamics, perverse economic impacts, unequal access for low-income and minority communities, and the status of regulation and taxation are some of the negatives that have engaged industry watchers for a while.

And while the politics of these sharing efforts differ across the globe, what is common is the desire among participants to create fairer, more sustainable, and more socially connected societies.

REFERENCES

1. Airbnb is Inc’s 2014 Company of the Year; Dec 2014; Burt Helm [inc.com]
2. The Rise of the Sharing Economy; [triplepundit.com]
3. Debating the Sharing Economy; Oct 2014; Juliet Schor [greattransition.org]
4. Today’s smart Choice: Don’t Own. Share; Mar 2011; Bryan Walsh [time.com]
5. Consumer intelligence Series “The Sharing Economy”; Apr 2015;

Examining Complexity Management

It was Oliver Wendell Homes Jr. who said:

I would not give a fig for the simplicity on this side of complexity, but I would give my right arm for the simplicity on the far side of complexity.

Traditional management thinking evolved in the Industrial Age dominated as it were by machines, the linear mechanistic principle of cause and effect, and cast into management literature by the likes of Frederick Winslow Taylor in his treatise on Scientific Management. Management in this age was essentially reductionist in approach, that of decomposing complex problems into its elements and analyzing them in order to make sense of the whole.

However Complex Systems and organizations in the Knowledge Economy are non-linear, highly interconnected and interdependent demonstrating what is referred to as “emergent behaviour“, that is, the behaviour of a system that does not depend on its individual parts, but on their relationship to one another.

Emergent behaviour cannot be predicted by examination of a system’s individual parts (traditional reductionist approach). It can only be predicted, managed, or controlled by understanding the parts and their relationships – “the whole is greater that a sum of the parts.”

Individual parts of a complex system are arranged into a structure, which then determines the behaviour of the system. Systems analysis is thus a matter of identifying the relevant structure of the system and its most important parts.

Examples of parts are atoms, the parts in a machine, in people, and in nations.

Examples of structure are the social contract that people enter into to form a government or an organization, the molecular structure of a chemical compound like amino acids, or the way in which individual cells in the body are organized into a myriad of organs.

Examples of emergent behaviour then is, human life, the perfidies of the global financial system, dysfunction of communities (in demonstrating undesirable or criminal behaviour), the quirks of the economic models of nation states, as well as the impact of the environmental sustainability problem on the bottom lines of some large commercial organizations in sectors like Oil & Gas, Minerals, Metals, Mining, Fisheries, Energy, or even, Infrastructure.

The new prescription is that the more you step back and embrace complexity, the better chance you have of finding simple answers.

For example, in most organizations, sales is a frequent focus of financial objectives and concerns. When targets aren’t being met, and a business isn’t growing at the rate you hoped it would be, it’s tempting to assume that you simply need to drive more sales and for your sales people to work harder.

Their targets have been set, and they must achieve them. We even design incentive systems to reward those who simply achieve their target without reference to any other dimension of their work.

However, this can be a simplistic and naive outlook. For there may be a whole host of other factors that might be influencing this outcome.

It may be that your target market is declining or your market has shifted such that your product or service no longer meets the needsof your audience. A new competitor or a substitute product/service may have appeared in your marketplace making it difficult for you to compete.

Your marketing may be failing to generate an adequate number of leads or to encourage trust and loyalty to your brand. Your branding and positioning may have become outdated or may no longer be relevant to your market. Or it could be that your sales targets are in fact inaccurate.

And that is just a handful of the factors that could be involved. By focusing only on the sales and finance ‘nodes’ in this situation, you risk missing the fundamental root cause that is in reality your key to success.

Managing Complexity - The Demonetization Case Study

On 08 Oct at 8 PM, the Prime Minister of India, Mr Narendra Modi, in one of the most significant and dramatic policy changes in the history of independent India, announced to a shocked nation that high denomination notes of Rs 500 and 1000 would cease to be legal tender at the stroke of midnight.

For a majority of Indians, accustomed to the existence of the parallel economy for decades, it represented a bold and sweeping new landscape. It bought with it a glimmer of hope for a bright future, for this was the kind of change that India had expected after having elected Modi to power in May 2014.

For businesses, it is useful to follow developments as a case study in managing complexity using new sense-making frameworks such as the Cynefin.

Our first attempt at understanding the decisions and events following the announcement gave us this dynamic:

We attempted to explain the dynamics as under:
1-2: The long decent into chaos due to years of inaction and neglect by successive governments
2-3: The recovery through a series of modifying actions, post the policy announcement
2-4: Attempts by delinquent elements in the system which are attempting to disrupt the change though propaganda and incitement. Unfortunately, the media in India has been almost completely bought out and hence cannot be trusted to be objective or helpful.

There were a couple of problems with this assessment.

Firstly, did the decent into chaos happen gradually from the complex domain into the chaotic domain (which is a natural transition) or was there some sudden revelation (from the data available to the government) that needed immediate and drastic course correction?

Secondly, was the plunge into crisis the result of the announcement, or was the announcement the start of the recovery process? If the former, it is a controlled shallow dive into chaos for the purposes of driving innovation and behaviour changes among all stakeholders, including the general population. And if the latter, and besides some visibility of its negative effects - propagating terror, secessionism, naxalism, election fraud, endemic corruption, hawala & round tripping, fake currency (FICN) and such like - why wasn't the crisis more visible?

Does the "boiling frog" analogy hold here?

What does this have to do with boiling a frog?  There is a fascinating 19th century science experiment.  As the story goes, researchers found that when they put a frog in a pan of boiling water, the frog just quickly jumped out.  On the other hand, when they put a frog in cold water and put the water to boil over time, the frog just boiled to death.  The hypothesis is that the change in temperature is so gradual, the frog does not realize it’s boiling to death. While the results of the experiment are in question it is a good metaphor for organization cultures[5].

We know that the transition from complex to chaotic domains is a transition for controlled experimentation. And we also know that the transition from Simple to Chaotic is akin to falling off a cliff. This boundary, between Simple and Chaotic, is the Zone of Complacency. Organizations arrive here through neglect of changing circumstances, though believing in one's own myths.

So we revised our dynamic to arrive at this:

Here we moved the 1-2 dynamic to the right so that it first drifts from Complicated (where monetary policy formulation structures and systems design is anchored) to the Simple domain of "best practices" and onward to the Zone of Complacency, the boundary between Simple and Chaotic, until it topples over into a full-blown crisis.

The recovery dynamic was split into two.

The 2-3 dynamic is the recovery through a series of urgent policy modifying actionsto stabilize the situation and subsequently, once stable, the 3-4 dynamic brings the situation back to normal through a series of probing experiments that seeks to establish a coorelation between minor policy change actions and its consequential impact on the monetary system.

The 2-5 dynamic is of course, the actions of the delinquent elements within the system.

We have also linked 5 and 1 with an arrow to indicate the different designs of executive policy formulation processes and monetary policy formulation processes.

Now, there are two ways of arriving in the Chaotic domain - deliberately or accidentally.

• If we enter Chaos deliberately, its primary purpose is innovation and inducing behavior changes.
• If we enter it accidentally, then we need to act swiftly to stabilize the situation.

To further revise the picture, we need to know whether the crisis was induced deliberately or accidentally. 

Past government policies leading up to the demonetization announcement clearly indicate that a fair amount of preparatory steps lead up to it - from announcing Digital India, the opening of Jan Dhan accounts for the marginalized in support of the Direct Benefits Transfer scheme that brought access to basic banking services to a majority of the population, the series of bilateral agreements with numerous countries on money laundering and anti round-tripping, and subsequently the Voluntary Income Disclosure scheme (VIDS 2016) that ended on 30 Sep 2016.

Surely, anyone able to connect to dots would have been able to foresee the next step in this sequence or at the very least, get some inkling of what was to come. That fact that the government managed to keep such a dramatic shift in policy confidential worked well. There was no other choice. There was no way, "better preparation" could have been afforded, if the outcomes were to be realized and the cost of change justified.

It will also be great, if we can get to the real reason why the previous federal reserve (RBI) governor, Mr Raghuram Rajan was not given the extension to which he was clearly amenable. Maybe it will come out at some point in time, probably in an autobiographical narrative. For now, it is just one more dot in the pattern of government actions in the lead up to demonetization.

So we will have to revise the dynamic again, in view of the fact that, the policy was a bold and deliberate move by the government to "rock the boat" at a huge political risk. From all indications, the dynamic that most fits the bill is a deliberate and "deep dive into chaotic domain".

The reasons are obvious.

1. There is no way to bring about a dramatic change in behaviour in India that was unitedly in favour of both accountability and transparency in financial transactions, unless the level of use of physical currency in the country was reduced to a bare minimum, that too for small transactions.
2. There was no way to bring about systemic changes in the monetary and its associated systems unless it was jolted into a crisis. For example, even large public sector banks such as the State Bank of India, had consistently refused to participate in the RBI/IBA collaborative ventures such as the National Payments Corporation of India (NPCI), whose charter, among other things, was to create a standardized interface for all retail payment systems in India. 
3. There was a crying need to choke hawala channels, round-tripping and other money laundering mechanisms.
4. Curbing terrorism, naxalism, secessionist movements in the North East, the underworld, and political muscle, all being funded by the parallel economy had become a priority national security issue.
5. Lobbying, election funding, government contracts, defence procurement et al. needed transparency and reform.
6. Major step-by-step reforms were needed to prevent money laundering in the jewelry, gold and precious metals, real estate, metals & mining markets.
7. Rampant funding of NGOs and religious bodies by foreign interests, use of monetary inducements for proselytizing, blocking of development activities through contrived environmental concerns, misuse of official and legal machinery though front organization, all needed to be stopped.

So, assuming that from all indications, the policy was introduced as a deliberate "deep dive into chaos", here is the revised graphic.

The 4-1 dynamic or return to normalcy - the new normal - will take some time. For the moment, the control is very much with the political executive until the end-objectives are achieved (4).

REFERENCES

1. The Cynefin Framework; Cognitive Edge; [YouTube;11 Jul 2010]
2. Cynefin Framework; Cognitive Edge website; [Cognitive Edge ]
3. A leaders framework for decision making; David Snowden & Mary Boone; Harvard Business Review; Nov 2007 Issue
4. Understanding the Cynefin Framework - a basic intro; Julia Webster; Sep 29, 2013; Everyday Kanban website
5. Leadership and the Boiling Frog Experiment; Henna Inam; Aug 28, 2013; Forbes

Checking for creative accounting

When their operational parameters worsen, and the stress starts showing in the financial numbers, a lot of businesses try to be a little creative with their accounts so that investors don't take to their heels.
Can this creativity be detected through analysing the financial results that are released? Not with certainty, but as a probability, yes. What we have here is a probabilistic score that measures a company's resemblance to other companies where the accounting has been creative.

The Modified C-score tells the probability of financial manipulations based on the quantitative method. This has been developed by modifying James Montier's C-score using his six basic checks and further improving it by adding three more checks.

Score the firm based on these nine points by giving them zero or one in for each points based on qualification. Higher a C-score, the higher is the probability of financial manipulations.

Montier's C-Score is made up of six red signals.These are scored in a simple way, with a 1 for yes and a 0 for no. These are then totaled to give a final C-score ranging from 0 (no evidence of earnings manipulation) to 6.

The individual tests are:

1. Is there a growing divergence between net income and operating cash-flow? This is based on the simple observation that earnings can be inflated, but cash flows are hard to manipulate.
2. Are Days Sales Outstanding (DSO) increasing? When a company stuffs the dealer pipeline, this number increases.
3. Are days sales of inventory (DSI) increasing? A sign of slowing sales.
4. Are other current assets increasing vs revenues? Since managements know that DSO and/or DSI can be closely watched, they may use this to hide something.
5. Are there declines in depreciation relative to gross property plant and equipment? Companies may alter their estimate of useful asset life to enhance reported earnings.
6. Is total asset growth high? It has often been observed that high asset growth firms under-perform.

Additional checks

1. Are debtors as per cent to revenue increasing? This means that the company is selling more on credit and realising not cash.
2. Is assets quality improving or declining? Asset quality is measured as the ratio of non-current assets other than plant, property and equipment to total assets. Which means the company may have high non productive assets.
3. Is accrual ratio high or low? Total accruals calculated as the change in working capital accounts other than cash less depreciation. Accrual ratio gives the difference between the accrual accounting and cash actually made out of it. A high ratio means that there is high difference between the cash realised and earnings reported.

Flat-lined Agile

So you have implemented that shiny new Atlassian JIRA+Agile software on a shiny new cloud infrastructure, the system and processes are stable, the levels of enthusiasm from six months ago have ebbed, and it is back to business as usual.

Or is it?

Why have none of the HR policies changed?
Why are there no changed job descriptions?
Where are the promised new organizational roles?
Why is the organizational bureaucracy still in place?
Why are the micronarratives still the same?
Why is it doubly harder to get things done now?
Why have the number of meetings remained the same?
Why is there no significant jump in revenues?

Many organizations implement Agile like they would an ERP. That is, a core group trains on the selected software and run a pilot, the executive approves the spend and the system gets rolled out. At the end of the period of implementation everyone is "using" the software and all is well with the world.

Sounds familiar?

If the answers to any of the questions above leaves you blinking, your Agile initiative has flat-lined. Now is the time to get someone "on the outside" who can do an audit and make recommendations. It shouldn't take more than an hour for this outsider to let you know what went wrong.

The Agile Journey

Agile initiatives are long and exciting "journeys", that pass through multiple fascinating way points and changing landscapes, much like a trekking expedition through the high mountains.

You start at a low altitude.

You pass through the mid-altitudes

And you finally arrive at your destination peak

Does it end there?

Of course not. That's just the mid point.

You need to get back to civilization, tired, sun burnt, a few pounds lighter and yet with a new found confidence and camaraderie.

You are not done till it is done!

 The graphic above is indicative of that half way point - your objective. We start the journey at way point #1, passing through way points 2, 3, 4 etc until we reach our objective at #6. At #6, we are at "constraints maximum" on our Agile journey. All gaps, issues and changes needed would have been highlighted by the time the organization reaches this stage.

Then we have to get back!

It is a lot quicker in terms of the level of effort, but longer in time horizon. This is where most of the organizational learning happens. 

Unfortunately, most organizations are quite happy to sit at 6 and assume the implementation program is complete.

No!

That is just the half way mark.

You still need to find your way back.

Not the whole team, just the living core of the organization.

And the difference is that while your way point #1 was like being aboard a ship adrift, way point #9 is as precise as a street address. It is the new centre of gravity of the organization, designed to ensure the organization remains strategically poised to roll in any direction.

Confused? That's probably because of common perception that Agile resides in the system you finally implement.

Agile does not reside in the system you implement, it resides in nuances within the culture of the organization. Herein lies the crux of the challenge.