Automation & Artificial Intelligence: Job Encroachment or Job Enrichment (2/2)

In my previous article, I analyzed the level of jobs in an enterprises and proposed the depth of encroachment due to automation on those levels. (You can read it here)

Now, in this post, I shall try to look at what people working on those three levels should do in order to have a steady professional career.

Case 1: Nine to five computer jobs

It’s time for people falling under this category & doing mundane routine tasks to start upskilling themselves. These people can undergo training on new skills (data analytics, digital marketing, or higher formal education etc.). From an ITIL incident management role, an L1 or Service Desk agent may try upskill to a L2 skill level etc.

Case 2: Mid Management Level

Though the knowledge & decision making skills required at this level, keeps people working at this level safe. But since many service providers are toiling hard to make AI available at this level, hence, for the people at this level, the easiest way to stay relevant is to get their senior management buy-in on their skillset (though they too should try to upgrade). Cross-functional thinking, dependency building & taking end-to-end responsibility of execution may be the initial steps to show their skill-sets to buy management trust.

Case 3: Senior Management Level

Though automation & AI has little or no prevalence here, as of today, the people at this level are the ones who make decisions on adoption of automation in their enterprises. Hence, the challenge for the people at this level is to adopt automation responsibly in their enterprises, investing not only in automation, but also in training, educating & re-skilling their employees.

From job encroachment to job enrichment

For the people at the top level, they should inculcate the thought in the minds of their employees that automation is not going to cause job encroachment but job enrichment!

It’s about a thought process in solving problems in which the two approaches (human & machine) work together by complementing each other to use the advantages that each one has, resulting in Jobs Enrichment!

The leaders at the top management should not bogged down by claims of journalists and experts, but rather think logically upon the automation of process where the two approaches can work together, rather than machines substituting humans – This is what I call the enrichment strategy.

In my opinion, we should work to change the mindset, of employees & employers to ensure a different line of thought: automation is not job encroachment, but job augmentation.

This mindset has the power to change the upcoming time!

Automation, RPA & Artificial Intelligence: Job Encroachment or Job Enrichment (1/2)

In one of the sessions where I was explaining automation and the benefits it can bring to enterprises, I was asked an interesting question, “Considering all the hard & soft benefits which you mentioned, any CXO would be trying to get the bots into their environment, is my job SAFE? Or will I shown a pink slip in near future? What should I do?”

This question made me realize that though at the top level - automation, RPA & Artificial Intelligence - is gaining traction & creating buzz for innumerable reasons: cost optimization, improving process efficiencies & enhancing user experience; at the ground level, the level at which probably L0/L1 works, certain basics are missing resulting in fear in the minds of employees.

In this post, I shall share my two cents on effect of automation & job security at different levels in an enterprise & in the next post (the 2/2), what should a common person do.

As mentioned, for the job security part, I shall try to analyze the situation from three perspectives: from a person in a nine to five computer job, a person from a mid-management level & then from a person in a senior management level job. 

Case 1: Nine to five computer jobs

A typical automation (RPA) process works in bottom-to-top manner. It starts with analyzing what normal people do on the ground in a job, and then removes their intervention, step-by-step. It annuls any manual tasks which are either SOP (Standard Operating Procedures) driven or have a standard way of execution. Adding to this notion is that we have many players in the industry which have built bots that can automate standard routine tasks. So, if you feel that you fall under this category, then maybe you should start thinking on adding more knowledge flavors to your job to be safe.

Case 2: Mid Management Level

At mid management level, people perform certain knowledge work which required understanding language, serial decision making, tasks execution based upon that decisions etc.

Though not completely, AI is the disruption at this level. Here, the same players have tried to build data models and search algorithms to enable machines to perform less challenging cognitive work. But, as of today, many tasks which executives perform will get automated.

Case 3: Senior Management Level

The day-to-day tasks defined at this level are highly obscure, which requires analyzing a lot of conditions, contexts, situations, risks & many other unforeseeable parameters. Given the complexity of decision making and impact these decisions have, the amount of jobs encroachment by automation (RPA) & AI, at this level, as of today, is minimal or negligible.

So, for the people falling under case 1 & 2, the situation may seems dire. And that’s logical as well. But from an economy perspective, if we don’t create jobs at the same pace as encroached by automation, the number of jobless people will grow, and the social & psychological repercussions of unemployed population will be dire!

Invest in Innovation, Not in Technology

After creating a lot of noise and making hulk-like promises, innovators & early-adopters are finally beginning to reap the benefits of AI. With the advancements in computing, algorithms and AI data models are becoming more revolutionary. In addition, the world is generating unimaginable quantum of data to power AI.

These advancements have contributed to three times more investment in 2016 – between USD 26 billion to USD 39 billion- than it did three years earlier. So, when CXOs face the daunting decisions on the next wave of investment, a look at where the investment is happening in the market becomes important. Here is an important piece of information should for consideration – Invest in Innovation and Not in Technology.

What does investment in Technology means?

For example, intelligent automation has created a hype in the IT market and companies across verticals are looking for use cases to adopt it. The only challenge, I see, in this approach is that mostly adoption of intelligent automation is seen as an IT initiative, implying that the problem statement it is used to solve is around improving IT efficiency and effectiveness. However, given the current digital disruptions & VUCA environment, almost 25%-30% of overall revenue is going to come from new business lines, & this requires innovation.

Investment in technology is equivalent to investment in solving current point (as opposed to end-to-end) challenges & issues (reducing tickets or automating the resolution process), faced on a day-to-day basis, and these are incremental in nature. For example, you have implemented a RPA solution to automate restarting of failed batch jobs. The RPA solution only does a restart of required services when all its dependencies are met. This RPA solution does not perform a RCA (Root Cause Analysis) and deter the jobs from failing in the first place or looks at the complete process holistically and takes care of end-to-end automation.

Hence, the mandate of CXOs should be to lower the investment in such technological advancements and invest the savings in disruptive business models and innovations

What does investment in Innovation means?

Many people (including CXOs) believe in the power of innovation. The challenge comes when companies, while trying to be innovative, try to define the complete value of innovation. It is evident that the value of innovation is hazy in their minds, leading to scheduling delays, poor investment strategies & non-alignment of leadership.

Investment in innovation means investment in building companies for the next 20-30 years (long-term focus) rather than the next 5-10 years (short-term focus). This involves focus on people, acquisitions, vision etc. For example, PayPal acquired Braintree focusing upon next gen commerce startups. This investment calls for a greater tolerance for short-term risks & failures in order to pursue the longer-term objectives.

To continue our IT processes as example, it is important that investment in intelligent automation is looked upon as a business process improvement. The focus now shifts from automation to elimination, i.e. how can we focus on making robust systems leveraging intelligent automation where the system can itself self-heal & no ticket gets raised.

What I understand is that CXOs are approaching intelligent automation as a source of not just IT productivity but also as a source of innovation in terms of doing things/getting things done & in a faster way. The only challenge to this approach, I feel, is the typical “budget constraint” & investment priorities not reflecting that investment (in innovation) portfolios. Unless, companies take that step, where they focus upon using technology as a fuel for innovation; innovators, early-adopters & new entrants are going to drive them out of business.

4 Best Ways to Educate your Customers about RPA & Intelligent Automation

Nearly any task or process is impacted by RPA & intelligent automation in one way or another across industries today. By leveraging intelligent automation, zero manual intervention of entire processes, routine and workflows - data collection, analysis, understanding context & finally making decisions- can be achieved.

Until recent times, robotics had more applications in the primary sector- automating & eliminating the human involvement from the production value chain. Now, tertiary sector esp. the financial services industry (also High Tech/Telecom, see below image) has majorly started applying use-case driven RPAs & Intelligent Automation to automate and eliminate low (/no) value-adding activities performed by humans. There is a complete article on the state of Machine Learning & AI, 2017 by McKinsey. You can read it here

Apart from the industries mentioned above, the adoption of RPA & intelligent automation is still in nascent stages in many other industries. The chief reason as per my opinion is that there is a lot of variation in the way RPA & intelligent automation is positioned or marketed. Vendors & Start-ups do not talk much about the risk, compliance, governance, metering etc. of intelligent automation. Therefore, the overall eco-system of how/what/by when/disaster recovery of using RPAs or intelligent automation is not visualized coherently by the CXOs & the decision-makers, leading to low adoption rate of these new technologies.

In my opinion, the four best ways in which start-ups & vendors can build Trust & Authenticity are:

1. Analyst Speak:

There are many independent third party analyst in the IT industry who benchmark the various products in the field of RPA & Intelligent Automation. These analysts compare & categorize the various service providers based upon certain parameters and dimensions, and then, normalize the result to create a framework where potential clients can compare one provider with another.

On a side note, if you are a CXO who is exploring the adoption of RPA & intelligent automation in your enterprise, the best way to proceed is to seek advice from this community and get your uncertainties answered, possibilities of risk mitigation & feasible intelligent automation solution finalized for deployment.

So if you are looking to educate your customers & the market, educate the analyst community first.

2. Seminars, Symposium, Events

This point is for the service-providers. Attend all the major events, get a booth and prepare yourself to show the best version of your product to the people walking in. Try to capture their response, reaction and feedback. This, in my opinion, is one of the best strategies for lead generation for product.

3. Get your hands dirty: Perform Pilots & Proof of Concepts

Simple business rule is- New Customer Acquisition is tougher (both monetary & effort wise) than Existing Customer Retention. Therefore, you can focus upon adding the flavor of RPA & intelligent automation into their already existing managed services account.

Then, to get the initial buy-in from customers, communication & formulation of problem statement & relevant solution is very necessary. The easiest way to start is by doing a proof of concept or pilot for a problem statement, which is simple and can be easily scaled, but at the same time, is a major bottleneck to customers. In addition, what needs to be mutually agreed upon is the success criteria for that proposal (pilot or POC).

The advantage of such proposal is that you will be using the success criteria of that proposal to make the customers comfortable.

Once the POC or pilot exercise becomes successful, deploy the RPA or intelligent automation solution to the complete enterprise.

4. Get Customer Testimonial

Word of mouth has always been and shall be the best way to create Trust & Authenticity in a B2B environment. So, the more you can get your customers to share a dais with you, release a video etc. the better stand you have in the market to gain new customers.

Sweating today to get visibility & becoming an industry leader should be the approach to educate the market and customers. A positive market wave favoring your solutions can give you a first mover’s advantage in this field.

Adopting Intelligent Automation: From Stakeholder Management to Change Management

Changes occur in businesses in two distinct ways: External (Merger & Acquisitions, regulatory compliance etc.) & Internal (organizational restructuring, new leadership, adopting new technology etc.). An enterprise level change has always been difficult and innumerable researches show that they continue to fail. In the case of adoption of intelligent automation, the change involves so much stress (jobs uncertainty for employees) & risks (ROIs) that it becomes very important to focus upon stakeholders & the change management processes.

To adopt intelligent automation in their IT estate, business leaders have to agree upon the direction and the end objectives to be achieved. My opinion is that it’s a four way process to adopt intelligent automation:

Building consensus among stakeholders

Digital, intelligent automation, analytics and the changing nature of workforce have created myriad opportunities and challenges for the stakeholders. For the same set of data, various stakeholders may draw parallel, non-concurrent conclusions which can indefinitely pause the implementation plan of intelligent automation into their environment. To illustrate, the business managers, looking at their current inflow of password resets tickets, may go for some intelligent automation solution. But then, the end users, who are going to use the system may not align with the intelligent automation and may continue to use the earlier way of getting password resets done (tapping the shoulders of agents or sending emails etc.). So, even though there is intelligent automation in place, there is no reduction in the volume of tickets and hence no ROI for business to justify investment in further intelligent automation.

So, considering our case of adopting intelligent automation, building consensus among the various stakeholders becomes the first & the foremost step towards ensuring smooth adaptation and transition for intelligent automation.

There are many frameworks & processes on stakeholder management, but I personally go with Segmenting & Positioning (no Targeting, as everyone is a target for IT); segmenting the stakeholders and passing on the customized message which intelligent automation is going to deliver it to them.

To do this, it becomes important that you prioritize the segments which are going to be the early touch points for intelligent automation & narrate them the ease factor (“hard” & “soft” benefits) which it will bring in their way of doing jobs. Probably for the CIO: cost effectiveness, IT managers & business managers: reduced TAT & improved efficiencies & the end users: simpler processes are some e.g. RACI matrix, too, can come handy in this process.

Involvement of IT community

Believe it or not, a lot of risk is mitigated if the IT community is engaged from the beginning. Specifically, the innovators and early adopters. This group can speed-up the adoption rate of intelligent automation in at least two ways:

a.     Help with the beta test of intelligent automation use-cases

b.    Act as word of mouth for intelligent automation benefits

These are the people who require least effort to understand that intelligent automation is really valuable to them and to the enterprise as whole. In my opinion, if this group is convinced, a lot of push-backs from this complete group gets controlled.

Communication is the key: Counter Culture push-backs

Now, you have the go-ahead from the business & IT leaders, innovators & early adapters, but then the major chunk of people who will be using the intelligent automation have still not entered the picture: end-users & agents. They may still be skeptic about intelligent automation and the way in which it can make lives easier for them.

To roll-out intelligent automation into the enterprise effectively, we should communicate the “soft” changes that needs to accompany the “hard” changes in the IT estate to both these group. For e.g. proper communication explaining the rationale to go for intelligent automation in first place can bring a lot of acceptance without spending much efforts. In addition, training, workshops etc. can be used to make them understand the benefits to-be derived from intelligent automation.

Tessa Basford & Bill Schaninger from Mckinsey have captured the complete mindset & behavior change management here.

Support from the governance

Intelligent automation may take some time to show benefits or the people using it may find it a bit challenging to turn over a new leaf. Many processes may be re-engineered for intelligent automation, which can emit a lot of distress signal in the organization; such testing water situations demands patience from management and a sturdy focus on the woods & not the trees.

For many big enterprises, hiring an external consultant may be a good option as stakeholders within these enterprises have been used to doing things in a certain way. A third party angle to look at the current processes can help in re-designing it so that the implementation steps becomes easier & faster  

Involvement, communication, change management and ultimately alignment amongst all the stakeholders to adopt intelligent automation is what is going to keep the health check of enterprises in place in the future. Adopt it today to reap benefits tomorrow.

Why Automation is a Necessary Evil for your Enterprises!

One of the focus areas of CXOs today is speedy adoption of new technologies (RPA, AI, Machine Learning, intelligent automation etc.). On one side, they have to Invest in these technologies, and at the same time, they have to focus upon cost-reduction. Adding to the challenge is that few software vendors can truly promise that AI will give them X% benefits within the first quarter of deployment. In many cases, they do not have either the processes or the systems in place to quickly demand  (ROI)returns from their investments in these fields. Hence, comes the element of RISK appetite for the people in the leadership.

Why is Automation Necessary?

Creating Competitive Workforce

There is a lot of mundane, repetitive work in enterprises, which is currently carried out by humans. For e.g. cleaning of disks space, eye-ball monitoring of screens for issues, installing common software in desktops/laptops etc. These activities do not require any intelligence & personally, I feel that doing this work is a very restricted use of the education, which these people have received. In addition, I find that, after interaction with some of my friends in such situation, they mostly talk about switching company because of no career progression.

Therefore, in this case, automating such processes, which are typically L0/L1, is only going to benefit every stakeholder. For e.g. now my “correctly” complaining friends can use their time in learning new skills and controlling their career progression.

With the acquisition of new skills-set, now, they can at least think of rising quickly in the hierarchy (than their earlier case). So now, we have a workforce, which is skilled, solves challenging problems and mentally satisfied (finally they are utilizing their time). Many people will not be able to align themselves with this idea and will find themselves redundant and irrelevant. These people form the basis for my third point.

Improving Efficiency of Processes

I think we all agree on this point. Intelligent software do not sleep, do not question-follow rules, do not take break, give consistent resolution, are faster etc. If we have to compare the performance of machines and humans for the same process, ceteris paribus, the efficiency of intelligent software is better. The soft benefits are of using intelligent software is immense.

Cost Optimization

In my opinion, employees should be given freedom to experiment with ideas & concepts & encouraged to suggest & design new ways to solve problems in order for the enterprises to grow in today’s VUCA world. This necessitates providing continuous learning opportunities to employees by the enterprises. The more the employees are able to up-skills themselves, the better it is for the whole enterprise.

However, the sad truth is that, many organizations are still conservative in their approach towards adopting new things. Employees are skeptical on taking risks and bringing change in their day-to-day approach to tackle problems. They still find the one way, which worked once, to be the golden rule to make that thing work repeatedly. Hence, enterprise stubbornness becomes norm & professional growth starts to stagnate.

This brings me back to my first point. In my opinion, it is better to let go of people who are not adding value to the enterprise, than to bleed by taking care of them.

Additional benefits, from a vendor or a service provider stand point, of letting go of these people is that the operating margins starts increasing. The logic for this is simple. Vendors are still getting paid for the outsourced activities they were carrying (with human employees), but now, instead of humans, they have intelligent software doing it. So, all the salary which was to be paid, gets added to the bottom line of the vendor.

Answering the Ethical question

This is where automation becomes a Necessary Evil. Given the pros, any CXOs, who fall in the innovators group, will be willing to adopt automation (thereby downsizing) to cut costs. However, the cos is that the very people who worked hard for the enterprise to grow, who stood with the enterprise during recession and during other market uncertain situations, may be shown the pink slips. Hence, it is important to give the employees chance to either up-skill or cross-skill so that they continue to add value to the enterprise.

Employees should remember that there are No Free Lunches, but, at the same time, enterprises should remember that, the employees who helped it to reach today’s heights are its Biggest Assets.

Why Human like Chat bot is still a thing of the future?

I would start this article by giving 2 examples of chatbot:

1.     Microsoft Tay:

The “Think About You” or Tay was released on Twitter on Mar 23, 2016. But only after 16 hours of its launch, it was shut down as it began to post misogynist, racist & sexually explicit messages.

2.     The Loebner Prize, is one of the most famous & tested method to test which computer programs are most human-like. It is based upon Turing test. In 2013 & 2016, it was won by Mitsuku developed by Steve Worswick. But based upon my experiment with it (dated Jun 18, 2017), though human-like, it still gives me incorrect responses to publically available information (President of India as Pratibha Patil & President of the United States as Barack Obama)

Both these instances reveal that though significant progress has been made in chatbots field, there is a long way to go for chatbots to become fully operational as humans.

Training Artificial Intelligence

Artificial Intelligence is very good at parsing input text – for e.g. Skype’s translator or Google translator. But they are still very distant to understand the semantics of the language – deciphering the meaning of the sentences. Adding to that, there are other issues like human languages are very complex: meaning is spread across levels, from alphabets, to words, to phrases, to sentences; adding to developing a data model to converse like humans. All languages, in my opinion, are abstract-words & are simple ways to explain emotions which machines can’t experience.

The data used to train the bots defines how the bots are going to perform. The Garbage-In-Garbage-Out principle is always valid when we try to curate a bot.

Challenges

In my opinion there are two barriers which are hindering the development of human like chatbot

1.     AI scientist and programmers are less focused upon developing conversational bots, but rather intelligent systems which can provide end-to-end solutions to people’s problems: digital assistant to anticipating & catering to their owner’s wants

2.     Lack of training data, or creating environment where language simulation can be done. For e.g. you may think that internet is an abundant source & can be used to teach chatbots & improve their maturity. But the challenge is that what stands for what is NOT available in a machine-understandable and digestible format.

Way Forward

Chatbots can be a very potent means to resolve problems where expertise requirement is limited & hence a great source for cost cuttings. Hence, enterprises should work progressively to integrate chatbots into their environment.

There has been a great advancement in Deep Learning (see my article on deep learning here). Using Deep Learning along with Reinforcement Learning (I am soon going to write about it) is going to be the easiest way to make human like chatbots which are effective in every environment.

Serving Customer Experience on Platter: Data Mining

In today’s world, customer’s data is getting generated from multiple sources. Companies are looking for making sense of this data for micro-marketing (for a target group), enhancing seamless customer experience, and reducing attrition of customers or increasing customer retention.

With this current explosion of data, it becomes important to understand what can be achieved by using this data to gain insights & how to dig deeper to provide a personalized experience. Data mining can help you achieve all this.

In simpler terms, data mining is an analytical process used to extract important knowledge from a large mass of data. Some ways via which data mining can help in this field is:

1.Generating a 360 degree profile of customers:

Customer data is generated from multiple sources. Enterprises are unclear as to how to integrate & synthesize data from social networks, mobile applications, in-store POS etc. Understanding this data & integrating it with a Customer Management Platform is the key to generate a 360 degree profile of the customers. Mapping these profiles & behaviors (for e.g. a vegetarian) to specific product recommendations (e.g. food ordering app only shows vegetarian restaurants in recommended places) will eventually lead to stronger brand building and high loyalty amongst customers.

2.Understanding the Sales Life-cycle of customer

The buying patterns & behaviors of different customers are different and hence when you have created a 360 degree profile of customers, it becomes important that you map them to where they fall in the sales life-cycle. For e.g. if you are an IT service provider and you have got a lead on a customer. Now, if the customer is looking for a solution next year, then your approach towards him will be different from a customer who is looking to buy your services in the next few months. Hence, knowing the current position of your customers in the sales life-cycle becomes important.

3.Decoding the path of Least Resistance

Many a times you get a new visitor to your store (whether in brick & mortar or online) & many a times a customer does not know what she is looking for. Data mining can help us understand that. So, if we can deliver it to her with minimum clicks (efforts), then we have won that customer. Hence, Data mining can help us to determine that optimum & convenient position (whether in brick & mortar or online) to place a product which increases the probability of making a sale

4.Determining cross-selling opportunities

This is the classic case of what we call association in data mining. To understand this in a real scenario, let me give an example. Father’s Day 2017 in India shall be celebrated on June 18; to gift my dad, I go to Amazon.in, and there I find that though I am looking for Aviator sunglasses, it tells me under “Frequently bought together”, that a medium case cover pouch is also available. There is a complete TechTarget article on association, you can read it here

The opportunities provided by data mining is immense. From figuring out new opportunities & new markets (remember Ansoff matrix), to increasing customer retention, building genuine loyalty and enhancing the overall customer experience, data mining can help businesses drive growth in innovative & myriad ways.

Why Enterprises Should Not Believe In Data Analytics

Big data algorithms, machine learning & reasoning has become the heart of almost all applications today. These smart applications are solving crucial business problems and helping decision makers in quickly reaching a business critical decision in a matter of minutes. These techniques are defining the norms by also using statistical analysis & predictive modelling.

But, all that glitters is not gold and we have to understand that it’s NOT always the case that all insights that spawns out of such models is CORRECT. Business leaders have to understand the inflexion point where data starts to control them rather than other way round. If they are thinking that insights coming out from machines will be always Right & Correct, then it’s a Mistake!

In this post, I shall be explaining the various issues which comes with using the data analytics as it is

Simpson’s Paradox

The best way to understand this statistical paradox is – the groups have averages that point in one direction whereas the overall averages points in other direction.

Let’s understand this with 2 real world example:

Take #1:

In tennis, if the loser of the match has actually won more games than the winner, then we have an example of “Simpson’s paradox”. For example, though not very possible, if the final score is 0-6, 7-5, 7-5; then the loser has won more points in the game (16) than the winner (14).

A real game example is Isner–Mahut match at the 2010 Wimbledon Championships. If you see the Records section, the last but one point explains it. Mahut won 502 points in the match as compared to Isner’s 478 (difference of 24). But we all know that Isner won the match 6-3, 3-6, 6-7, 7-6, 70-68.

Take #2:

Suppose your enterprise has two business application towers: A & B. Now let us analyze the overall tickets generated from those applications:

If you look at the analytics reports & dashboards created at the Business Applications level, you shall see that the predicted tickets matches with the Actual tickets. So,

Inferences:

1.    the maturity of the model is very high

2.    resulting to say that we can scale it up to new towers.

However, if you deep-dive into the two towers, you can see that this inference is Incorrect. This is one of the most important challenges in the reporting & dash-boarding world. It is easy to think that we are meeting our numbers, when in reality; the case might be completely different.

Idiosyncrasies in the data

In many businesses, important decisions are made based upon the statistical inferences using the historical references and experiences. A major caveat here is that if the sample size in use is small, then few outliers can skew the understandings/inferences a lot.

Many predictive models use historical data to make predictions on the future. Hence, if the past data and its data model upon which it is based relies heavily on past incidents, and then it may not accurately give predictions on the future.

Believing numbers blindly

Too often, we are so driven by numbers that we forget that there are biases, which creep into the system, possibly during the initial requirement validation phases, designing the data model phase etc. Such biases, though very small, constraints the way with which we look upon the end-results (in the form of dashboards & reports). In addition, it is also important to continuously normalize & check the data for inconsistencies and do a ground check verification before any major decisions can be taken. To give an example, it may be the case that business/operations leaders may be seeing a high inflow of tickets, though at a ground level, those tickets always existed in the system; only thing is that they were NEVER being tracked!

Though these challenges exists, the Solution to these challenges are domain expertise, tacit business knowledge, common sense & above all, Critical Thinking, which can help business manager, escape such caveats.