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.