Data Governance, Ethics, And Trust.


We have a basic understanding of the data we have access to, where we could get it, and what we can do with it once we have it, we need to consider how to prevent it from becoming a liability. 

Data is more and more likely to rank among a company's most valuable assets. 

However, we must never lose sight of the fact that it has two sides to it. 

If you handle it appropriately, it will help you get the necessary insights. 

However, if you don't handle it respectfully, you may easily cut yourself! 

Nearly as many negative effects result from bad data management and planning as there are possible advantages. 

They include things like higher expenses and overhead, a decline in consumer confidence, the development of a negative public image about your firm's ethics or environmental impact, as well as very harsh penalties that might put your company out of business. 

Here we'll examine the critical elements that must be taken into account to make sure your data policy doesn't put you at odds with your clients, the law, or the line between right and wrong. 

We will discuss some of the risks and problems that must be avoided, such as privacy, prejudice, and the effects of AI and technology on the environment. 

  1. Understanding Evolving AI Ethics
  2. The Significance of "Clean" Data And Bias. 
  3. Keeping Data Within The Bounds Of The Law. 
  4. Implementing Effective Data Governance.

Additionally, we'll examine specific circumstances in which failing to act might result in a violation of our moral and ethical duties to society and our clients. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

References And Further Reading

  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, (archived at
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, (archived at
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, (archived at
  4. The Royal Society (2019) Explainable AI: the basics, November, (archived at
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, (archived at
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, (archived at
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, (archived at
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, (archived at
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, (archived at
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, (archived at

Implementing Effective Data Governance.

As you've probably inferred from reading the material we've discussed so far, the most important lesson to learn from this is the need of a complete and comprehensive data governance policy. 

We've addressed all the important topics that must be taken into consideration while developing your plan, including data bias and quality, legal and regulatory issues, data security, and, of course, the ethical issues you must address. 

Your strategy will help you arrive at a set of rules that you can use to manage and maintain your data and analytics technology infrastructure overall by taking all of these factors into consideration. 

Data governance refers to accepting responsibility for the moral and legal obligations you have as an individual engaged in the process of releasing the immense power buried inside information. 

It's important to ensure that you are following all applicable laws, have the appropriate permissions and security in place, and have a clear knowledge of who is in charge of maintaining the security, reliability, and correctness of your data. 

Much of this depends on how well you implement a data culture inside your business; given how crucial excellent data governance is to preserving consumer trust, it should be taught in everyone engaged as a fundamental value. 

It should be clear to everyone how important it is to the business and how carefully it should be handled. 

Naturally, your data governance plan should include the steps you will take to make sure you are in compliance with all applicable laws, including frequent audits of these processes that are owned by a designated individual. 

It also explains how permission to use personal data is requested, where these records are kept, and how they can be kept up to date in the event that permission is revoked or new requirements for data use arise that fall outside the scope of previously granted permissions. 

  • If you deploy CCTV cameras, there should be signs in place informing people that recordings are being produced and explaining why. 
  • If you use Bluetooth or RFID to collect information from consumers' mobile devices while they are on or near your property, you must ensure that contracts are in place that clearly state how the information will be used. 
  • Because you are now accountable for it, if you purchase data from third-party vendors, you must ensure that your uses are consistent with the rights granted when the source gathered the data. 

Data governance is really about managing data as the important company asset that it is. 

The same should be true for your data, just as you have procedures and systems in place to make managing your personnel easier. 

A successful and secure usage of data may be achieved by putting a solid data governance structure in place as part of a larger overall data strategy.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

References And Further Reading

  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, (archived at
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, (archived at
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, (archived at
  4. The Royal Society (2019) Explainable AI: the basics, November, (archived at
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, (archived at
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, (archived at
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, (archived at
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, (archived at
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, (archived at
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, (archived at

The Significance of "Clean" Data And Bias.

Algorithms for machine learning are only as good as the data used to train them.

Making sure the data you are using is as clean as possible is a crucial aspect of governance. 

We typically refer to data as being "clean" when it is both of high quality and bias-free. 

Data Integrity.

Quality refers to a set of measures that may be used to judge whether data is appropriate for the purpose for which you want it. 

To make sure you're meeting all the requirements for data quality, each of these factors should be examined since they are all equally significant. 

Consistency is the first one. This indicates that a dataset's data was all collected and recorded in the same manner. 

For instance, if a record has numerous fields, each record should have each field filled in. 

In order to always be able to utilize the data together, fields should be used consistently across all records and if we "know" anything about one item of information in the dataset, we should also "know" it about all other pieces of information. 

On to accuracy. This only indicates that the data is devoid of errors. 

The tools or sensors that have been used to gather or input the observations and measurements must be audited and shown to be operating properly for the observations and measurements to be considered accurate. 

We must make sure that stakeholders who are responsible for data entry are taught and aware of all the governance standards since mistakes may still happen when data is entered by humans. 

Another crucial measure is uniqueness, which simply refers to the absence of duplicate entries. 

When processing data, it's highly probable that your database may start to become inaccurate if the same piece of information is stored more than once in different entries. 

Validity is a metric for determining whether each record or piece of information in a database is appropriate for the use for which it is intended. 

For instance, are dates kept consistently and are all numbers stored consistently—as integers or rounded up or down to a certain decimal place? Timeliness evaluates how probable it is for your data to be relevant given the period it was obtained. 

Some processes, like the movement of glaciers, may be monitored and understood with just sporadic measurements. 

Others need measurements to the millionth of a second, such the locations of protons and electrons in a subatomic structure. 

Measurements must be collected and recorded with a delay that is as near to zero as is possible for procedures that call for real-time datasets. 

Last but not least, completeness measures how much of the entire amount of data that is available on a topic is represented in your dataset. 

Make sure every item you carry in your inventory is reflected in the database if you're utilizing a database of your items and pricing to determine which are the most popular. 

It would not be possible to capture the whole information for other uses, such as tracking animal migration routes, thus a sample would be selected for tracking and analysis. 

However, your insights will be more realistically based the more full your collection is. 

Data governance requires auditing your data using metrics that monitor these parameters in order to confirm that you are dealing with high-quality data. 

Biased Data.

Bias is the second component of "clean data." Data that is biased is not accurately reflective of the data topic. 

Typically, this is caused by elements innate to the method of data collection. 

For instance, your data would be intrinsically skewed if you use feedback forms to gauge customer happiness and only distribute them to clients who have given favorable evaluations. 

Bias may always come in because of the enormous and intricate datasets utilized in AI and machine learning projects. 

The fact that skewed data implies your insights won't be based on objective truth is a significant hurdle for many data endeavors. 

In fact, if the promise of AI is to be fulfilled, experts in the area of AI believe that one of the main problems society will have to solve is eliminating prejudice (or at least decreasing the harm it may bring). 

As mentioned above, biased data may be the consequence of poor data quality, but bias can also appear when your data is of high quality overall. 

This is due to the fact that bias might exist even whether the data is correct, distinctive, reliable, or current. 

It indicates that you aren't casting your net far enough to collect data from a range of sources or perspectives. 

As a consequence, the simulations and models you create won't accurately reflect reality. 

Data bias has some extremely dangerous consequences. 

When face recognition technologies used by police forces to locate criminals in crowds in the US were audited, it was shown that young, female, black citizens were considerably more likely to be incorrectly identified than those of any other age category. 

The algorithm's accuracy rate was determined to be 34% lower for this population when used in comparison to other groups. 

This may undoubtedly result in more erroneous arrests, stops, or searches of persons in this demographic if allowed uncontrolled. 

Recruitment is another instance where data bias might really be problematic. 

The datasets utilized by recruiting algorithms that Noel Sharkey has investigated are so rife with bias that they simply shouldn't be used unless they can be controlled and reviewed with the same level of rigors as is required for data used in pharmaceutical trials, according to Sharkey. 

After realizing that a machine learning algorithm used by Amazon to evaluate job applications was essentially sexist, the company ceased using it in 2018. 

The dataset that the algorithm used was found to discriminate against women, passing them over for opportunities for no reason other than the fact that it did not have enough data on female applicants for these roles because far fewer women than men had applied to work for the company over the previous 10 years. 

The fact that it may sometimes be appropriate to purposely add bias into a system in order to make up for societal elements that tend towards injustice or intolerance complicates matters even more. 

Microsoft and IBM both introduced AI-powered chatbots during the previous decade, but they eventually needed to be modified (or, in Microsoft's case, deleted) to prevent them from behaving in an offensive and bigoted way. 

This was due to the fact that they were developing their communication skills based on social media conversations, which are of course sometimes racist or hostile in nature. 

This entailed informing the bot that it shouldn't be learning from racist or abusive material, which necessitated injecting a deliberate bias aspect into the system. 

Of course, this obviously results in less realistic data being used to train the bots. 

There weren't many options, but it's obviously inappropriate for a computer speaking for a business like IBM to use racial slurs and quote Hitler. 

Another crucial step in the governance process is to balance the damage that might result from using biased data against the harm from omitting it. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

References And Further Reading

  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, (archived at
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, (archived at
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, (archived at
  4. The Royal Society (2019) Explainable AI: the basics, November, (archived at
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, (archived at
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, (archived at
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, (archived at
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, (archived at
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, (archived at
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, (archived at

Understanding Evolving AI Ethics

AI really has the power to transform business and larger society in ways that are now unthinkable. 

The basic shift we're seeing is that rather than only following predetermined, programmed instructions, robots are now able to make their own judgments. 

Computers will increasingly be utilized for activities that involve decision-making, rather than just doing repetitive chores like applying formulae to numbers in a spreadsheet (which they currently excel at). 

This will inevitably contain choices affecting people's lives. 

The fact that Amazon is known to have fired human workers using AI algorithms. 

In such instance, it was discovered that robots handled the whole process of overseeing and firing employees. 

Beyond that, we are aware that AI has been used to the creation of weapons to produce tools that can decide whether to murder people (currently the UN states that it is unacceptable for them to be allowed to do this ). 

What about circumstances in which robots are forced to kill? 

How should a self-driving vehicle behave while deciding whether to collide with a pedestrian or a brick wall is a typical scenario used when debating AI ethics (potentially injuring its own driver). 

These are extreme instances, but they are pertinent to any ethical decisions you would need to make if you're in charge of deploying AI-powered, automated decision making at any firm. 

The most promising AI technologies all have extremely strong potential benefits, but if they are handled improperly, they might also have very strong potential drawbacks. 

In medical imaging, machine vision can identify malignant growths, but it may also be employed by authoritarian governments to monitor their population. 

It is now simpler than ever to communicate with robots, and natural language processing can scan social media postings for indications that someone could be suicidal or depressed. 

By pretending to be someone else, it may also be utilized to plan frauds and phishing assaults. 

And although we may use intelligent robots to investigate the ocean below or the universe, aid the disabled, and clean up the environment, they are also being created for use as lethal weapons. 

It's crucial that we consider where our own implementations of AI fall on this spectrum. 

Can we be certain that a concept like, for instance, the data-driven customer segmentation in our ice cream parlor would lie on the correct side of the moral divide? 

Hopefully no one reading this book is intending on using it to harm anybody. 

In such case, concerns about permission and privacy would be the main issues. 

It follows that it is unethical to gather, utilize, or distribute anyone's personal data without their consent if we recognize that individuals have a right to privacy. 

Therefore, getting agreement for any work we undertake with clients' private data is a crucial first step. 

Of course, this is now a legal necessity in many areas.

However, when it comes to the governance of your data or AI initiatives, ethics and law must both be handled separately. 

For instance, it's unlikely that Amazon's automatic termination of workers is against the law. 

On the other hand, it's pretty simple to construct a compelling case for it being immoral since it includes giving robots the authority to make decisions that may have a significant influence on people's lives. 

Furthermore, it's far from certain that the people involved ever agreed to give the robots this level of control over their lives. 

Even if you are just using computers to execute processes more effectively rather than to make such life-altering choices, caution must be taken. 

In 2018, IT contractor Ibrahim Diallo discovered he couldn't enter his Los Angeles workplace because his security permit had been suspended. 

After gaining entry, he was unable to enter into any of the systems he needed to use for his profession, and soon after that, security personnel showed up to take him out of the facility. 

Also terminated was his salary. 

His boss and any other senior employees were unaware of what had occurred, but after being compelled to work from home for three weeks, they discovered that an HR error had incorrectly indicated that he had been fired. 

At that moment, automatic systems started running and there was no way to manually override them. 

He had to quit the organization and look for employment elsewhere because of the stress and his supervisors' inability to take any action. 

What happens if workers are dismissed but not because of AI? 

According to some sources, IBM's staff headcount decreased by 25% between 2012 and 2019, and many of the tasks performed by individuals who departed were replaced by machines. 

There are undoubtedly compelling arguments that AI creates jobs rather than eliminates them for human workers. 

In addition, the types of jobs that it creates, such as those for engineers, scientists, data storytellers, and translators, are probably more satisfying than the jobs that are lost. 

According to the World Economic Forum, 85 million human jobs would have been replaced by automation by 2025. 

On the other hand, 97 million new jobs will be created within the same time period as a result of the rise in AI and other sophisticated, automated technologies. 

Does it imply that, in terms of replacing human labor, automation is not ethically problematic? Well, not really, I'd say! 

This implies that you must carefully examine how your own AI, automation, and data projects will affect your human workforce. 

  • Is there a possibility that someone may develop redundancy? 
  • If so, might they be given additional jobs in accordance with their talents and responsibilities, but with an emphasis on using the human traits that computers still lack, such as creativity, empathy, and communication? 
  • Most importantly, is there a chance that they may lose importance regardless of whether you use your initiative? 
  • If you didn't implement it, for instance, would it affect your company's ability to compete in the market, making it impossible for you to continue paying them? 

Further reason for worry comes from an AI technique known as a generative adversarial network (GAN), which enables the creation of uncannily realistic representations of actual individuals as well as text and language. 

This is what causes the "deepfake" pictures that have proliferated online. 

This has the potential to cause a great deal of harm to both individuals and society. 

For instance, it is very easy to find fake pornographic images of just about any celebrity online. 

It has also been used for political purposes to create images of politicians and world leaders saying or acting in ways that they would never do in real life. 

This may exacerbate the issues brought on by false information and the simplicity with which false or misleading information may be disseminated online, thereby undermining the democratic process. 

The generation of "synthetic data," and text-to-image systems, which essentially allow us to make pictures by describing what they look like, are just two of the many valid and totally ethical reasons a corporation would wish to employ GAN technology. 

The ethical risk is that they may also be used to produce anything that aids in the dissemination of incorrect information or results in the misrepresentation of specific persons or groups of people. 

Once again, if you use this technology, you need to be aware of the hazards and know where your beliefs fall in terms of ethics. 

From an ethical perspective, recommendation technology could appear like a secure use of AI, but there are also risks. 

We are given additional stuff to look at that is comparable to the content we have already looked at through systems that provide content, like Facebook's algorithms. 

This might result in "filter bubbles"; for instance, if you start reading the many conspiracy theory websites discussing topics like whether 9/11 was a "inside job" or the more current QAnon conspiracy, you will soon notice that similar items are starting to appear in your news feed. 

The same holds true whether you lean heavily to the left or right and often read news articles that support their viewpoints. 

The information you have accessible to support your own thoughts and beliefs may eventually become imbalanced if you only see tales that match that certain narrative. 

Again, this might have extremely negative effects on the democratic process by creating the echo-chamber effect. 

The need of transparency is still another problem. 

If you're going to place humans in a situation where robots control their lives, you should at the very least be able to describe how the machines make decisions. 

We've previously discussed the "black box" issue with AI, which arises from the fact that the algorithms may get so complicated that it is extremely challenging for humans to fully comprehend why they function in a certain way. 

This is made worse by the fact that some AI is purposefully opaque in order to deter easy duplication by those who sell it. 

Ethics are a personal matter that each individual must consider for himself, but you could discover that you agree with the majority of people who believe that actions that have an influence on people's lives should be explicable. 

It's interesting to note that some people think AR and VR might be useful in this situation by enabling us to study and question algorithmic operations in ways that aren't achievable by merely looking at computer code on a screen or even 2D representations of the operations. 

The OECD's Principles for AI include transparency as one of its primary needs (more on this below). 

Ethical issues are also brought up by AI's effects on the environment. 

According to estimates, the amount of carbon produced by training certain NLP machine learning models is equivalent to the yearly carbon emissions of 17 Americans. 

All that computing power uses quite a lot of energy. 

This is equivalent to around 283.048 kg (626,000 pounds) of CO2. 

Due to their offsetting methods, some AI providers, like Google, claim that they are carbon neutral, while others, like Microsoft, won't be until years or decades from now. 

Of course, AI has the ability to increase operational efficiency in many sectors, which may reduce the effect on the environment. 

For instance, both the network-wide strategies utilized by power and utility companies and the smart home thermostats are focused on utilizing energy more effectively and reducing emissions. 

There is no clear-cut answer to the question "Is AI good or bad for the environment?" because it depends on individual applications, which means that each application needs to be evaluated on its own merit for environmental impact. 

This is similar to the ethics of particular AI capabilities like facial recognition and natural language, and the implications on jobs. 

Given all the worries, it shouldn't be shocking that the AI business and larger society organizations are beginning to take the question of advanced technology ethics seriously. 

The Organization for Economic Co-operation and Development (OECD) has released its AI Principles, which include the following: 

AI development should be done in a way that respects the law, human rights, democracy, and diversity; it should be transparent to allow it to be understood and challenged; and organizations should be held responsible for the results of their AI initiatives. 

Google, too, has put forth its own standards of ethical AI usage. 

These include demands that AI apps be ethical, impartial, trustworthy, and considerate of users' privacy. 

Additionally, the business has said that it would seek to prevent hazardous or abusive implementations of AI. 

Additionally, Microsoft has a program called AI for Good that encourages the use of AI to address issues related to the environment, society, healthcare, and humanitarianism. 

The underuse of AI is my last point for this section, which I really believe needs thorough study. 

Simply said, there are numerous circumstances when avoiding AI is a bad idea from an ethical standpoint. 

  • Doesn't that imply we have an ethical obligation to employ AI if there are problems in our business, society, or the world at large that might be solved with it but we choose not to - possibly because of other factors we've covered here, such the effect on the environment or transparency concerns? 
  • Is it appropriate, for instance, to use facial recognition technology to locate someone who may be in danger even if they haven't given their consent to be followed in this way? 

Police have used it to locate persons who have gone missing in China and the UK. 

My suggestion is that any company intending to use AI take the step of establishing some kind of "ethical committee," similar to what Google and Microsoft have done. 

Of course, depending on the size of the company, your council's scope, size, and resources will vary. 

However, it's crucial to have someone in charge of taking into account all the concerns brought up here and how they affect our activities. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

References And Further Reading

  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, (archived at
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, (archived at
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, (archived at
  4. The Royal Society (2019) Explainable AI: the basics, November, (archived at
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, (archived at
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, (archived at
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, (archived at
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, (archived at
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, (archived at
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, (archived at

Keeping Data Within The Bounds Of The Law.

Legislation is gradually catching up to the rapid speed of technical advancement, which has made a great deal of previously unthinkable things conceivable. 

As a result of the General Data Protection Regulation's adoption in Europe, privacy laws are becoming more stringent globally. 

Despite the absence of a similarly broad US counterpart, several states are seeking to enact their own laws, such as the California Consumer Privacy Act (CCPA), rather than waiting for changes to occur at the federal level. 

China is also working to implement the Personal Information Protection Law (PIPL), which will restrict what businesses can do with personal data they don't have permission to process. 

People in the West frequently have the misconception that data privacy is not regarded as a protected asset in China. 

From the perspective of governance, the main issues revolve on ensuring that we have authorization to use the data we wish to gather and compliance with any broad rules like GDPR, CCPA, or PIPL that are in existence in the territories we're operating in. 

Courts and legislators are becoming more conscious of the onerous needs of reading and comprehending reams of documents linked to whatever services we choose to use, so businesses can no longer hide behind T&Cs. 

Simply stated, they know that no one will read them, therefore the fact that you have waivers and disclaimers buried deep inside the tiny print is less and less likely to provide you a strong enough defense if someone thinks you are using their data for a purpose for which you are not authorized. 

It is no longer sufficient that data subjects have not yet exercised their right to opt out of having their personal information processed for certain purposes in order to be in compliance with various regulations, including GDPR. 

Hello Barbie, a range of natural-language-powered talking Barbie dolls manufactured by Mattel, had to be discontinued because of concerns that children's data was being processed and stored without their legal parents' permission. 

The question of IP ownership and rights must also be taken into account. 

Whether you are using algorithms with a license or because you created and own them completely, you must be certain that you have the right to do so. 

You should also be mindful of the legal standing of any output that comes from your AI and machine learning. 

As an example, algorithms exist that can produce creative pieces of art, poetry, and journalism, as we've seen before. 

The algorithms behind this were developed after researching millions of other works of literature, poetry, and journalism. 

  • Can we think of the AI as merely drawing inspiration from its training data, much as Van Gogh did when drawing inspiration from his contemporaries and other great artists? 
  • Or is it more likely that the AI is dissecting already-created pieces of art and utilizing them to make new ones? 

This hasn't yet, as far as I know, been put to the test in a court of law, but it's definitely something to keep in mind as a potential obstacle down the road. 

Safeguarding your Data. 

The criteria to keep your data secure from unintentional loss or malicious data breach are at the center of data protection. 

Both of these may have legal repercussions under regulations like the GDPR, but they are also crucial factors in any data governance strategy that deals with personal data.

Companies that handle personal data have the most governance requirements. 

This is likely to refer to any businesses who are serious about their data strategy, since this is often the most valuable data. 

But there are methods to lessen your responsibilities, and one of the best is to use a data reduction technique. 

In the past, it would have been considered strategically sound to gather every single item that is possible to gather in case we discover a purpose for it (Amazon's Jeff Bezos is credited as saying, "We never throw anything away"). 

Those times are long gone as a result of the enormous number of data nowadays as well as the increasing quantity of law and regulation. 

According to the GDPR, gathering personal information shall only be "minimum essential" to carry out the tasks for which you have been granted permission. 

Steps may be taken to break the connection, producing data that is still relevant for analysis but (ideally) doesn't meet the definition of "personal data," which is any data that can be connected to a live person. 

I use the word "ideally" since it has repeatedly been shown that many of the methods used to anonymize or de-identify data aren't entirely reliable. 

Even if you exclude all names from your records, a someone with the necessary skills and resources could be able to link your data to particular people. 

For instance, if you have information on every resident of a street but no names, you may be able to pinpoint a specific person based on their age range, work status, or marital status - especially if it's a very small street. 

Your non-personal information becomes personal information when it is in this manner associated with a living person, for whom you are accountable. 

Breach of Data.

The danger of data breaches to company is becoming more and more common. 

Businesses that experienced data breaches in 2019 paid an average cost of $8.19 million. 

But in addition to the significant financial cost, there may be reputational effects that may quickly put an end to a corporation. 

However, there are some indications that firms are becoming better at avoiding them, as the number somewhat decreased between 2018 and 2020. 

They are growing larger and more frequent. Nevertheless, they pose a significant risk, and any governance approach should include measures to reduce the risks. 

Authorization is a key concept to include in your approach. 

This is a definite permissioning system that establishes who has access to each specific collection of data. 

Only those who need access to it in order to complete the task for which the data is needed should have access to it. 

The usage of encryption is another. If data is encrypted, somebody attempting to steal it would find it much less valuable. 

As with any security measures, there will always be some friction when used for proper reasons, but there are now several systems that efficiently manage encryption and decryption on the fly, in a manner that is imperceptible to the data customer. 

Both the HTTPS protocol for transmitting online pages to your browser and the automated encryption employed by messaging services like WhatsApp are excellent examples of this. 

Your data may be encrypted just while it is being held in cold storage, encrypted only when it is being transported, encrypted constantly, or encrypted continuously and decrypted only when it is being utilized. 

Homomorphic encryption is another another choice that may be taken into account. 

Even the analytical algorithms cannot "see" the unencrypted data since the data is encrypted in this case so that it may be analyzed while staying in its encrypted form. 

People with the necessary rights may even change the data in the cloud without exposing the unencrypted data to the cloud servers. 

Homomorphic encryption is somewhat constrained by the computing power you have at your disposal. 

Partially homomorphic encryption and relatively homomorphic encryption are the two types of homomorphic encryption now in use, and both of them restrict the ability to alter the encrypted data. 

Although "fully homomorphic encryption" is not constrained in this manner, it does demand a lot of processing power and will unavoidably take longer. 

There is a trade-off between speed and security in any encryption. 

Masking and tokenization are two further methods that may be used to de-identify data. 

Masking is the process of obscuring sensitive data from the unencrypted data with similar-type data while maintaining the integrity of other parts. 

This may include, for example, changing every city field's data to reflect a different city. 

The data is only visible to those with the proper permissions, yet it is nevertheless helpful for many applications. 

Tokenization is similar, except it substitutes randomized, anonymous tokens for important or delicate portions of the dataset. 

Contrary to encryption, there is no mathematical method to reconstruct the original data from the concealed data (which is possible with huge amounts of computing power with many forms of encryption). 

This is due to the fact that the tokens are chosen at random rather than being calculated from the initial data. 

Additionally, although tokenization (and masking) are frequently performed to certain fields inside the record, encryption is typically applied to the whole record. 

In conclusion, it's important to keep in mind that data security is a highly specialized topic, thus you should definitely seek the advice of specialists when creating your data plan. 

Threats posed by IoT.

Hackers now have more options for attack due to the ever-growing number of linked gadgets and "things" we've let into our personal and professional life. 

A recent study found that 10 million IoT devices contain security flaws that might be used to get unauthorized access to data. 

Consider each networked linked device as a "door" into your business that has to be maintained closed and protected from intruders, just like any other door. 

Even your grandma definitely knows the value of firewalls and virus scanners on her home computer, but it's unlikely she's aware of the possible risk presented by her microwave, refrigerator, or smart toothbrush at the moment. 

Simply put, more gadgets equal more potential points of entry for hackers after your data. 

Even while it may not be immediately clear why a hacker would want to get access to your smart refrigerator, the basic plan is to exploit it to gain access to other devices where the actual jackpot would be located. 

Attacks might seem as fictitious faults or requests for patches or upgrades, which provide a back door for the attacker into your network. 

Sometimes they'll just try to get you to phone a "customer service" number so they can try to scam you out of money! It has been shown that connected toys, automobiles, and even medical equipment are all attackable, and new flaws are discovered daily, as rapidly as manufacturers can fix them. 

Given this, it should be obvious that any business working with IoT-related devices must take security seriously. 

As many IoT devices are often exploited in this manner, changing any default passwords or login credentials is a crucial practical first step. 

Make sure your customers are encouraged to do the same if you provide IoT devices. 

It's another another situation in which adopting a "minimization" philosophy might be beneficial. 

Do you really need your equipment to be able to connect and interface with a large number of other devices when there may not be a clear benefit to the user in having them talk to each other? Of course, most devices need to interface with a smartphone or computer app so they can be controlled by the user. 

Another essential element of any data governance policy is an audit of all IoT and connected devices. 

Make sure you know precisely "what is talking to what" and "what they are talking about" on your network. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

References And Further Reading

  1. Roff, HM and Moyes, R (2016) Meaningful Human Control, Artificial Intelligence and Autonomous Weapons, Briefing paper prepared for the Informal Meeting of Experts on Lethal Autonomous Weapons Systems, UN Convention on Certain Conventional Weapons, April, (archived at
  2. Wakefield, J (2018) The man who was fired by a machine, BBC, 21 June, (archived at
  3. Kande, M and Sönmez, M (2020) Don’t fear AI. It will lead to long-term job growth, WEF, 26 October, (archived at
  4. The Royal Society (2019) Explainable AI: the basics, November, (archived at
  5. Hao, K (2019) Training a single AI model can emit as much carbon as five cars in their lifetimes, MIT Technology Review, 6 June, (archived at
  6. Najibi, A (2020) Racial discrimination in face recognition technology, SITN Harvard University, 24 October, (archived at
  7. McDonald, H (2019) AI expert calls for end to UK use of ‘racially biased’ algorithms, Guardian, 12 December, (archived at
  8. Dastin, J (2018) Amazon scraps secret AI recruiting tool that showed bias against women, Reuters, 11 October, (archived at
  9. Johnson, J (2021) Cyber crime: number of breaches and records exposed 2005–2020, Statista, 3 March, (archived at
  10. Palmer, D (2021) These new vulnerabilities put millions of IoT devices at risk, so patch now, ZDNet, 13 April, (archived at

Role Of Artificial Intelligence In Ecommerce.

    Context - AI In Ecommerce.

    Information and communication technologies, machine learning, digitalization, robotics, and artificial intelligence, according to researchers, will fuel the fourth industrial revolution (AI). 

    Decisions will be made by machines, which will have a significant influence on corporate marketing strategies and society (Syam and Sharma 2018; Dwivedi et al. 2019). 

    The AI revolution will have a larger effect than the industrial and digital revolutions combined in the next 20 years (Makridakis 2017). 

    According to studies, the advent of intelligent goods and services is not only a fad, but they have the potential to revolutionize the world (Sonia et al. 2020). 

    Researchers are confident in AI's origins, which have previously been through two "hype cycles," namely the first in 1950–1983, a revival in 1983–2010, and the second in 2011–2017. 

    Artificial intelligence (AI) will now be the future of brains, minds, and machines (2018–2035). (Aggarwal et al., 2018; Simon et al., 2019). 

    According to Frey and Osborne (2017), AI will have a big influence on sales, marketing, and customer service, and 47 percent of US occupations might be automated by 2033. 

    Experts expect that AI will have a significant impact on three industries: retail, education, and health care (Ostrom et al. 2018). 

    With a large share of human effort and poor profit margins, the retail business, particularly e-commerce, is a perfect match for AI applications (Weber and Schütte 2019). 

    AI is often used by e-commerce companies to personalize webpages and provide product suggestions (Netflix). and other tech and retail giants are aggressively investing in research and development to advance AI applications such as Alexa, voice-powered assistants (Echo), and the Prime Air drone effort, among others. 

    In reality, Amazon's cloud platform (AWS) provides AI and machine learning capabilities to other businesses (Weber and Schütte 2019). 

    This paper will discuss AI and its applications in different business processes involved in an e-commerce firm to better understand the function and value of AI in e-commerce. 

    But, before we begin, we must first grasp the notion of AI, which is covered in depth in the next section. 


    Before considering AI, it's important to define "intelligence," which is defined as a person's capacity to learn, comprehend, or cope with new circumstances; think abstractly; and influence one's environment using knowledge ( 2020). 

    Intelligence is defined as the ability to acquire and apply memory, knowledge, experience, reasoning, imagination, judgment, opinions, facts, skills, calculations, information, and language in order to calculate, classify, generalize, and perceive relationships; solve problems; plan and think abstractly; comprehend complex ideas; learn quickly; overcome obstacles; and adapt efficiently to new situations, either by changing oneself or by changing one's environment (Legg and Hutter 2006; Paschen et al. 2019). 

    How Does AI Mimic Human Intelligence?

    Machine learning is used in artificial intelligence to replicate human intellect. 

    • The computer must learn how to react to certain activities, so it creates a propensity model using algorithms and historical data. 
    • After that, propensity models will begin to make predictions (like scoring leads or something).

    The origins of AI may be traced back to questions about how far a computer can partly or entirely replace humans in work performance (Weber and Schütte 2019). 

    As a result, AI is defined in terms of human intelligence in marketing literature. 

    • Researchers, for example, describe AI as a system that demonstrates features of human intelligence (Huang and Rust 2018), mimics intelligent human behavior (Syam and Sharma 2018), or mimics nonbiological intelligence (Syam and Sharma 2018). (Tegmark 2017). 
    • According to McCarthy (2007), AI is "the science and engineering of creating intelligent devices, particularly clever computer programs." 
    • AI is akin to the goal of utilizing computers to comprehend human intellect, but it does not have to be limited to physiologically observable ways." 
    • Because of these definitions, AI is reliant on human intellect (Bock et al. 2020). 

    Can AI Supersede Human Intelligence?

    Because of constrained rationality deriving from limited knowledge, cognitive capacities, and short time to make judgments, people sometimes engage in actions that may not lead to the optimal end outcome (Kahneman and Tversky 1979). (Dawid 1999). 

    According to some scientists, robots may demonstrate human-like intelligence in two ways: behaving intelligently (performing activities such as remembering, learning, reasoning, perceiving, and problem-solving toward a goal-directed action) and rationality (doing the "correct thing" under ambiguity) (Paschen et al. 2019). 

    AI can discern inclinations, intentions, and patterns that are beyond the intellectual capabilities of a human brain using Big Data and deep learning. 

    • The human brain can only evaluate and draw conclusions from limited data; robots, on the other hand, can comprehend billions of data points. 
    • AI has progressed through "four intelligence stages" (i.e., from analytical to emotional) to gain sophisticated skills such as reasoning, planning, conceptual learning, creativity, common sense, cross-domain thinking, and even self-awareness (Huang and Rust 2018). (Bock et al. 2020). 
    • In this case, Kaplan and Haenlein's (2019) definition of AI, which defines AI as "a system's capacity to properly read external input, to learn from such data, and to employ those learnings to fulfill specified objectives and tasks via flexible adaptation," seems more relevant. 



    Apart from product suggestions, online merchants are using artificial intelligence in the Ecommerce business to provide chatbot services, analyze client feedback, and provide tailored services to online customers.

    Companies participating in e-commerce undertake a variety of business procedures such as marketing, purchasing, selling, and servicing items and services . 

    • To carry out marketing, discovery, transaction processing, and product and customer services, these enterprises totally rely on e-commerce apps and internet-based technology. 
    • E-commerce websites use the Internet to conduct interactive marketing, ordering, payment, and customer service activities. 

    E-commerce also includes processes related to e-business, such as suppliers and customers using extranets to access inventory databases (transaction processing), sales and customer service representatives using the internet to access customer relationship management (CRM) systems (service and support), and customers participating in product development via email and social media (marketing/discovery) (O'Brien and Marakas 2011). 

    • AI, according to researchers, may boost corporate performance since AI solutions are quicker, cheaper, and less prone to human errors (Huang and Rust 2018; Canhoto and Clear 2020). 
    • As a result, we will attempt to comprehend how AI contributes to different e-commerce business operations in the following parts. 

    How Is Artificial Intelligence Used In Marketing?

    • Marketers may use AI to better understand their consumers and enhance their experiences. Marketers may use AI-powered marketing to generate a predictive customer analysis and construct a more focused and individually designed customer journey, effectively increasing ROI on each customer encounter. 
    • AI is often utilized in marketing campaigns when speed is critical. AI systems learn how to effectively engage with consumers based on data and customer profiles, then give them personalised messages at the perfect moment without the need for marketing staff involvement, guaranteeing optimal productivity.

    How is Artificial Intelligence Used In Market Research?

    Marketing Businesses exclusively employed information technology for data processing and transmission until the third industrial revolution. 

    The fourth industrial revolution, on the other hand, will enable computers to make suitable and trustworthy judgments (Syam and Sharma 2018). 

    Large organizations that fail to use cutting-edge technologies such as AI, according to researchers, will be washed away in the face of competition (Stone et al. 2020). 

    The basic goal of market research is to identify the most accurate client groupings. 

    To provide items to consumers via an acceptable distribution plan, selected segments are targeted with appropriate products, supplied at appropriate pricing, and supported with reasonable promotional and communication tactics (Syam and Sharma 2018). 

    Previously, segmentation was based on either "classic" approaches (such as cluster analysis (for clustering) or "more contemporary" techniques (such as hidden Markov models, support vector machines, artificial neural networks (ANN), classification and regression trees (CART), and genetic algorithms). 

    The new century's machine learning capabilities have improved the efficiency of these segmenting algorithms. 

    For example, AI-enabled ANN models may help B2B e-commerce enterprises solve marketing difficulties (Wilson and Bettis-outland 2020). 

    Marketers have access to vast amounts of data, which they may use to segment large amounts of unstructured data (Big Data) using unsupervised neural networks (Hruschka and Natter 1999). 

    Profitability or customer lifetime value (CLV) segments may now be found using decision trees driven by machine learning (Florez-lopez and Ramon-jeronimo 2009). 

    AI can sift through a massive volume of written and unwritten user-generated information on social media sites to uncover user requirements, preferences, attitudes, and behaviors. 

    For instance, IBM Watson AI can recognize psychographic features represented in text to provide marketers with important insights for new product development or innovation (IBM 2020). 

    To understand the user experience, AI can discover themes and patterns in users' postings, and the knowledge may be utilized to create strategies to improve the user experience. 

    It also aids in the collection, sorting, and analysis of external market information, or intelligence about external market factors and players that may impact consumer preferences and actions. 

    For example, AI systems driven by machine learning and natural language processing algorithms can detect false news from a large number of blog posts, social media posts, and other sources (Berthon and Pitt 2018). 

    Similarly, competitive intelligence may be derived from unstructured data (news, social media, website content, etc.) by recognizing themes or keywords. (2019, Paschen et al.) 

    Artificial Intelligence For Market Stimulation.

    Market stimulation happens at the same time as marketing, which is defined as "the activity, collection of institutions, and procedures for producing, conveying, delivering, and exchanging offers that have value for consumers, clients, partners, and society as a whole" (American Marketing Association 2017). 

    It usually consists of four distinct but interconnected components:

    1. product, 
    2. pricing, 
    3. location, and 
    4. marketing (McCarthy 1960). 

    However, the concept of marketing has evolved to the point where technological adaptation is required, and the impact of AI on each marketing mix component, namely, product (hyper-personalization, new product development, automatic recommendations, etc. ), price (price management, personalized dynamic pricing, etc. ), place (convenience, speed, simple sales process, 24/7 chatbot support, etc. ), and promotion (personalized communication, unique user identifiers, etc). (Jarek et al. 2019; Dumitriu and Popescu 2020). 

    The first three AI categories (picture recognition, text recognition, decision-making, speech recognition, and autonomous robots and vehicles) are heavily employed in marketing. 

    Because of the expense and unpredictability associated with AI technologies, marketers are hesitant to deploy them. 

    Large IT giants such as Google, Amazon, Microsoft, and Apple, on the other hand, are investing in AI areas such as speech recognition and self-driving robots and vehicles (Jarek et al. 2019). 

    As a result, AI has had a significant impact on modern marketing practices; for example, routine, time-consuming, and repeatable jobs (data collection, analysis, image search, processing) have been automated; strategic and creative activities to build a competitive advantage are emphasized; and business enterprises are designing innovative ways to deliver customer value. 

    It has also created a marketing environment in which companies that provide AI solutions are in high demand (Jarek et al. 2019). 

    How Is Artificial Intelligence Being Used For Transaction Processing?

    How Is Artificial Intelligence Being Used For Terms Negotiation?

    Negotiation is an art (rather than a science) of getting what you want via bargaining or a one-on-one contact. 

    In an e-commerce transaction, the majority of interactions are electronic, such as through email, social media, text chat, or phone. 

    AI may be used as a functional science in this situation to offer a competitive edge in the negotiating process (Mckendrick 2019). 

    • Negotiation is similar to a persuasive debate in which a proponent and an opponent iteratively provide a sequence of reasons, and both give counter arguments to disprove the other's point (Huang and Lin 2005). 
    • Scientists are teaching their chatbots and virtual assistants to anticipate many steps ahead and analyze how saying certain things may influence the result of the negotiation based on the components of negotiations (i.e., negotiation set, protocol, collection of tactics, and rule of deal) (Reynolds 2017). 
    • These AI negotiation bots may work on behalf of an e-retailer 24 hours a day, 7 days a week to seek consumers and automatically negotiate the best terms based on the administrator's requirements or even market circumstances (Krasadakis 2017). 

    It is very difficult for internet retailers to recruit, service, and keep consumers by communicating via graphical user interfaces (websites). 

    Customers, meanwhile, have no opportunity to bargain for a better value (Huang and Lin 2005). 

    Natural language interfaces for human–computer interaction, on the other hand, may successfully tackle this challenge (Jusoh 2018). 

    E-commerce is a fast-paced sector where prices fluctuate often. 

    E-commerce enterprises are utilizing AI to dynamically price their products and services, modifying prices in real time based on market circumstances (demand–supply) (Kephart et al. 2000). 


    How Is Artificial Intelligence Being Used For Order Prioritization and Order Selection?

    Alibaba has released Fashion AI technology in order to enhance sales. 

    • Customers may submit photos of products they want to purchase to the Taobao e-commerce site, and the system will automatically look for comparable items for sale (Simon 2019). 
    • Similarly, AI may gather real-time data by following a customer's online behavior on their or a competitor's website to determine whether to provide a price reduction or search the company's database to see whether earlier product suggestions were rejected or accepted (Canhoto and Clear 2020). 

    Resupply optimization is another key AI application. 

    • By calculating the best time and amount for placing an order with the central warehouse and suppliers, AI can cut inventory expenses (Stone et al. 2020). 
    • This may help with a variety of concerns, including reducing the quantity or amount of unsold items, optimizing warehouse shelf space, and increasing cash flow. 
    • Individual order and delivery (personalization) may be optimized using AI algorithms (Zanker et al. 2019), and complicated jobs like same-day delivery can be simplified (Kawa et al. 2018). 

    How Is Artificial Intelligence Being Used For Order Receipt? 

    AI can use predictive systems to evaluate prospects (customers) based on their propensity to buy (high-quality leads) (Järvinen and Taiminen 2016), use emotional AI to answer common questions and overcome customer objections (Paschen et al. 2019), and automate and speed up the checkout process (Paschen et al. 2019). 

    Amazon and other leading e-commerce businesses have launched language-assisted ordering (Amazon Echo) (Holmqvist et al. 2017). 

    For sales forecasting (Dwivedi et al. 2019), shop assortments (Shankar 2018), and customizing searches, suggestions, pricing, and promotions, complex AI models are deployed (Montgomery and Smith 2009). 

    On a range of devices, AI can automate service interactions and provide individualized and relevant information (Bock et al. 2020). 

    How Is Artificial Intelligence Being Used For Order Billing/Payment Management?

    Invoicing, payment optimization, and fraud detection are three areas where AI can assist e-commerce businesses (Mejia 2019). 

    The most difficult and time-consuming jobs in company are billing and invoice processing. 

    AI applications, on the other hand, may assist firms in matching client invoices to payments received (Dwivedi et al. 2019). 

    • Manual and semi-automated billing procedures cannot manage a large number of customer payments, but AI-enabled billing systems can handle a large volume of data and eliminate abnormalities, inconsistencies, and discrepancies in the bills (Bajpai 2020). 
    • One of the most convenient methods to make a payment using a computer and the internet is to conduct an online transaction. 
    • Customers may use their e-wallets to make purchases (Kalia 2016; Kalia et al. 2017a). 
    • Regardless of the security precautions in place, every online transaction has the risk of fraud (Papadopoulos and Brooks 2011). 

    By activating the autonomous decision-oriented and advanced fraud detection system, AI-powered billing software may prevent fraud from happening in the first place (Khattri and Singh 2018). 

    Fraudster, for example, detects payment fraud using past data connected to transactions, billing, shipping addresses, and IP connection (Canhoto and Clear 2020). 


    How Is Artificial Intelligence Being Used For Support and Service?



    How Is Artificial Intelligence Being Used For Order Fulfillment/Order Scheduling?

    Order scheduling and fulfillment include responsibilities such as picking up or delivering items and services in the correct amount and quality at the proper time and location (Weber and Schütte 2019). 

    The e-commerce sector need exceptionally efficient fulfillment operations due to erratic order patterns, limited order processing time, and short-term delivery schedules (Leung et al. 2018). 

    Here, AI systems may actively monitor and improve these operations by taking into account elements such as order demand and product characteristics to automate the ideal logistics plan (Lam et al. 2015; Paschen et al. 2019). 

    • According to researchers, sustainable supply chains and reverse logistics are the most important subjects for current and future AI research (Dhamija and Bag 2019). 
    • As a result, top e-commerce corporations such as are investing in robots and space-age fulfillment technology (order delivery through drones) (Dirican 2015). 

    Reverse logistics, in addition to fulfillment, is a difficulty. 

    • Products are often returned without their original packaging, as well as seasonal collections, and product similarities may make the procedure more difficult. 
    • These returns may be compared to catalogue photographs to categorize the goods using AI-powered automated image recognition (Kumar et al. 2014). 


    How Is Artificial Intelligence Being Used For Support and Customer Service? 

    Service quality may be a game changer in the e-commerce market (Kalia et al. 2016, 2017b; Kalia 2017a, b). 

    • Customer service and support may benefit from AI by boosting satisfaction, building relationships, customizing help, and providing recovery in the event of service failures. 
    • It's known as "service AI," and it's defined as "the configuration of technology to provide value in the internal and external service environments through flexible adaptation enabled by sensing, learning, decision-making, and actions," according to the researchers (Bock et al. 2020). 
    • As a result, service AI is more than just making preprogrammed decisions; it also has the ability to learn (Makridakis 2017). 

    Because AI-based services are more dependable, of high quality, consistent, continuously available (24/7), and less susceptible to human errors arising from fatigue and bounded rationality, they can affect customer satisfaction (Huang and Rust 2018). 

    Similarly, it is simpler for e-commerce businesses to engage in marketing operations in order to build, grow, and retain client connections (Lo and Campos 2018). 

    • Virtual assistants, for example, can send out notifications to millions of customers, analyze their purchases, returns, and loyalty card information, and deliver services that are beyond human capability. 
    • Using structured and unstructured data related to their psychographic, demographic, and webographic characteristics, as well as online buying behavior (frequency, recency, type, and size of past purchases), AI-enabled systems can create comprehensive profiles of current or potential customers, which can then be processed using machine learning and predictive algorithms to strengthen customer relationship efforts and prospecting of potential customers (Lo and Campos 2018; Paschen et al. 2019). 

    By learning to talk in different languages, recognizing consumers' emotional states, and retrieving information for them, AI can give a high-quality tailored experience to customers. 

    • A disgruntled consumer might disseminate unfavorable information. 
    • Firms, on the other hand, may use the high-quality consumer feedback to design service recovery plans (Lo and Campos 2018). 

    Ending Remarks.

    In many segments/processes of e-commerce business companies, AI plays a significant and important role. 

    The role of AI in e-commerce business processes such as market research, market stimulation, terms negotiation, order receipt, order selection and priority, order billing/payment management, order scheduling/fulfillment delivery, and customer service and support has been detailed in order to achieve this goal. 

    We discovered that AI has a wide range of potential applications in marketing, transaction processing, and e-commerce service and support. 

    At the present rate of technological advancement, AI will go from a data and information processing tool (weak AI) to a self-contained system capable of making human judgments (strong AI).

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.


    How is artificial intelligence (AI) affecting the e-commerce industry? 

    eCommerce sites may build customized online experiences and propose items that are tailored to each buyer using AI. 

    Companies like Amazon and Netflix, who were early users of AI, have had success with personalized marketing utilizing the technology. 

    What are some instances of artificial intelligence in e-commerce applications? 

    The Top AI Applications in E-Commerce and Retail Purchasing Recommendations Shopping assistants that respond to voice commands. 

    E-commerce purchasing experiences that are tailored to you. Warehouse "pickers" using artificial intelligence For ads, image and video recognition is used.

    Methods of payment based on facial recognition. 

    What are the advantages of artificial intelligence in the field of e-commerce? 

    1. Voice Commerce
    2. Virtual Assistants 
    3. Personalization in e-commerce. 
    4. eCommerce Automation using Searchandising (Smart Search). 
    5. Prospective Customers are being retargeted. CRM allows for a more efficient sales process. 

    Which is the most popular/trending AI application in eCommerce? 


    One of the most promising applications of AI technology in eCommerce is assisting customers in finding items more quickly. 

    It can be done using chatbots or by making textual search more meaningful, but visual search with picture recognition is one of the most promising technologies. 

    In e-commerce, how does AI affect consumer satisfaction? 

    Customer support for ecommerce shops is improved by artificial intelligence in the form of chatbots. 

    Chatbots answer quickly and may deliver replies that are tailored to the individual consumer. 

    As a result, the user experience in the shop is smooth. 

    What influence does Artificial Intelligence have in the internet business world? 

    By automating and optimizing common procedures and activities, you may save time and money. 

    Boost productivity and efficiency in your operations. create quicker business choices based on cognitive technology outputs 

    How can artificial intelligence (AI) enhance the consumer experience? 

    You can use AI to boost consumer engagement, foster brand loyalty, and increase retention. 

    While technology isn't a substitute for real humans, it may assist enhance productivity and remove low-hanging fruit off your customer support employees' plates, such as addressing commonly requested inquiries. 

    What role does artificial intelligence play in increasing client loyalty? 

    The most significant advantage of AI is that it enables organizations to process massive volumes of data in real time. 

    Brands can take use of its capacity to make good use of this data for loyalty marketing and to favorably influence consumer behavior. 

    What impact does AI have on how companies and organizations connect with their customers? 

    Conversational AI has made the communication space more fluid for both customers and companies, providing quick results for customers and making it easier for enterprises to pay attention to everyone who needs it. 

    What impact will artificial intelligence have on the corporate world in the future? 

    Artificial intelligence enables company owners to provide their clients a more tailored experience. 

    AI is much more efficient at analyzing large amounts of data. 

    It can swiftly spot patterns in data, such as previous purchase history, preferences, credit ratings, and other similar threads.


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