All that Automates is not AI – Conversational Commerce Strategy

All that Automates is not AI – Conversational Commerce Strategy

We live in a fast-paced world, and many organizations are trying to explore artificial intelligence-related technology to improve business processes, upgrade products and services, and enhance customer purchase experience. Everyone is tempted to choose solutions claiming to use AI. Maybe it’s because we have a skewed idea of what AI can do based on a few Hollywood sci-fi movies we have seen. We feel we’re far away from an army of robots taking over the world.

In one of the shocking reports by Verge, 40% of ‘AI startups’ in Europe don’t actually use AI. Companies just want to take advantage of the AI hype. There have been many such reports popping up all around the world. Hence we felt the need to clear a few basic concepts and make our readers more aware of this.

Let us first learn the difference between rule-based outcomes coded into systems versus artificial intelligence. The term Artificial Intelligence is used quite loosely.

Difference between Rule-based Outcomes Hard-coded into a System vs. Artificial Intelligence

Numerous approaches can implement AI. It is divided into two groups: rule-based versus learning systems. First, a computer system achieves AI using rules in a rule-based system. On the other hand, a learning system becomes AI-enriched with machine learning.

Monica Anderson, an American computer scientist, profoundly inferred that any technology that is non-learning is not AI. Some claim that rule-based systems simulate intelligence without any learning ability. Let’s explore both these systems in detail.

A rule-based system, such as a production system, uses rules for representing knowledge through composite statements. The core idea in these systems is to gather human knowledge and encode it in a computer system as rules.

Therefore, in this case, we can say that rule-based systems fake intelligence due to their lack of learning capability and simulate intelligence in limits through its underlying knowledge base or rule base. One can even regard this as a type of AI. Thus, rule-based systems are simplified forms of AI and implement lesser AI.

If rule-based systems get stuck, they will be unable to solve or even understand a problem.

In rule-based systems, it is difficult, to sum up rules to a vast knowledge base. For that reason, their maintenance becomes tedious and costly too. Thus, these systems are ineffective for solving complex problems. In some instances, such as cancer detection, it is impossible to define rules in a program using the systems based on rules.

Learning systems are way ahead of the ones that are rule-based. They implement only universal AI with their learning capability. Further, the existing knowledge can be changed and new knowledge is obtained adaptively. 

In a nutshell, rule-based systems precisely depend on static models of a domain while learning systems build their own models.

Whether you want to go for a rule-based or learning system depends on the problem you need to address. Both the above methods represent a set of various techniques and are implemented by concrete algorithms.

Industry practices of Falsified Claims about using AI

AI is gaining prominence across the world. Enterprises implement AI systems, and technology companies also offer AI-based solutions. The technical AI- and data-based complexities challenge companies to deliver at par, making them choose AI so as not to scale back but using humans to do the tasks that AI systems would do. In the AI industry, the concept of humans pretending to be machines doing human work is called “pseudo-AI” in technical terms. They are simply faking it.

Most companies claim to use AI to automate some parts of their services, such as transcription, scheduling appointments, and other personal assistant work. The reality is that these companies have been outsourcing their work to humans through marketplaces.

The pseudo-AI approach is one of the reasons behind the privacy and confidentiality breaches. A computer that processes information in isolation can safeguard data to various predetermined extents, but putting random humans in the loop is a recipe for potential data privacy breaches. In regulated industries, such as healthcare, finance, or government systems, AI solutions processing information can be compromised by humans who should not be allowed to access or view private or confidential information.

Why does the need arise to use buzzwords like AI and ML? 

Think big when thinking of AI! Every organization dreams up the most complex problems possible and attempts to solve them with AI. However, the other side of the coin is not knowing what to do with AI and not using it to solve real business problems. A report by McKinsey states that only 20% of surveyed executives use AI technology in their business. The question remains, is the market too competitive, and do you need something like AI to stand out from the crowd?

There are several business use cases where AI has proven to be the apt solution. It is because these companies ensure they have the data ecosystem for AI to do their work. With suitable business cases and data, AI can guarantee time and cost savings, and deliver valuable insights to improve businesses.

Implementing AI can be a big decision. However, if you take an incremental approach, you will be able to leverage its time and cost efficiencies to stay competitive in the market, both now and in the future. AI and ML are the buzzwords across the industries and have their perks in implementing and automating business processes. Hence, every business wants to make the optimum use of these technologies to stand out from the crowd. Still, this does not mean companies should capitalize by pretending to use AI and ML and offering solutions they are unequipped to provide.

A few examples of the problems AI can tackle 

No doubt, AI has a wide range of use cases. In this section, we will look at some of the critical business problems and how AI can solve them.

  1. Predicting Customer Churn Rate

Companies that offer customer experience management software to contact their clients and call centers consider churn reduction a crucial KPI. They do so by using demographics and the history of data transactions. However, this approach fails to capture the real-time and dynamic customer data over the phone in the form of records taken by call center workers.

The issue can be solved by integrating AI to analyze post-call comments and categorize them as per the topic. Then, AI can flag sentiment scores that indicate customer dissatisfaction and customer churn. Moreover, the company’s clients now get customer motivation insights, concerns, and reasons for calling, and they can use this data to spot and address the churn rate.

  1. Creating Surveys

Survey software companies can create and publish digital surveys in minutes. Their systems can crunch three million responses every day. AI can help leverage data of the consumer and employee responses database.

It can be done by tapping into past survey results and analyzing them. In this way, businesses can create highly performing surveys with excellent completion rates. Further, the system can help in delivering real-time recommendations with the data received from survey-takers. AI can also help organizations map customer feedback using sentiment analysis.

  1. Reading And Managing Online Reviews

Online reviews sites have reviews scattered across the websites, in which the guests, travelers, and diners rate and report their experiences. AI can help to obtain a comprehensive snapshot of unstructured, text-based reviews and the multilingual hospitality industry.

AI can also be used to monitor an organization’s competitor reviews to determine the guest’s needs. It can parse, summarize, and contextualize reviews. The data from this endeavor can allow gaining insights, planning the next steps, and maintaining a competitive advantage.

  1. Creating Messaging Resonating With Users

It is impossible to analyze, collate, and source data on a large scale without taking the help of technology. AI can help get insights and in-depth analyses for improving the companies’ messaging and communication in several industries, including pharmaceuticals, e-commerce, and travel and hospitality. It allows companies to analyze and categorize online discussions around specific products. Moreover, it can enable the clients to communicate more effectively with the providers.

Can business problems be solved using Automation and not AI? 

Yes, of course! Business problems can certainly be solved without AI-enriched technology. Even though we perceive the impact of AI in our everyday lives, right from healthcare and cybersecurity systems to e-mail translation apps there are flaws to adapting AI as well. Very simply said you don’t always need a truck to deliver a pizza. Inherently there are certain business problems that can be solved by complex rule-based automation.

Here are 3 questions to help you get better clarity! Just ask these questions to those claiming to be using AI. If they say yes to the below questions, it’s safe to assume it’s namesake AI.

  • Do you use defined set of rules?
  • Are the outcomes certain?
  • Will your results stay the same even after long duration of inputs?

The belief is that AI will deliver solutions if you collect enough data and prevent implementing ineffective solutions. AI is undoubtedly a powerful tool, but we must remember it is not a solution in itself. 

Using AI to provide personalized solutions can tackle this problem. For example, we provide tailor-made AI solutions as per the use case to enhance customer relationships. Being a leader in the omnichannel conversational commerce space for large-scale direct-to-consumer brands, we have to keep innovating. By focusing on customer needs and gathering consumer insights, our solutions constantly endeavor to solve your business problems. They allow you to do the following.

  • Keep yourself available to your customers and attend to them anytime, anywhere using messaging apps on your website, blogs, social posts, and advertisements.
  • Deploy AI conversational bots, assign sales associates, or amalgamate both to handle any volume of customer conversations.
  • Process transactions and use instant messaging to shop, take orders, collect payments, book appointments, and much more.
  • Discover meaningful insights about customers shopping patterns and notify them with relevant product suggestions.

Therefore, conversational commerce implemented with or without AI technology can help solve business problems, turning conversations into conversion by directly integrating with your business tools.

Here’s a quick overview of some of the features of the products offered by

  • Automated notifications
  • Automated responses
  • Chatbot builder
  • Commerce engine
  • Integrations
  • Multi-channel live chat
  • Social CRM

We would like to stress over here that there’s a structured process to dabble in AI. We have broken it down into 3 basic steps which every brand would have to follow before they can kickstart using AI for their business problems. Unfortunately, AI is not something you can just add to your cart and start seeing fantastic results right away.

  1. Data Gathering Phase

It all starts with Data! Your AI model is only as good as the data you feed it. There are so many considerations one has to account for in terms of feeding the data. There’s a whole lot of human intelligence required in first identifying which are the essential data points one needs to feed the system. Tracking good and authentic data sources is a mammoth of a task. The best and most trustworthy would be first-party data that your company collects itself directly from its existing or potential customers. We helped Ben and Jerry’s do that which then helped us to create personalized campaigns.

  1. Training Period of AI Models

On average, 40% of companies report that it takes more than a month to deploy an ML model during production, 28% state that it takes between eight to 30 days, while only 14% take seven days or less.

Training AI models is when the developers fit the best combination of weights and bias to minimize a loss function. Its primary purpose is to build the best mathematical relationship representation between data features and a target label. Data scientists use different loss functions based on the objective, type of data, and algorithm.

Model training results in a validated, tested and deployed model, and it is the primary ML step of AI. The model performance determines the quality of the applications built using it. Note that the quality of training data and algorithms are both crucial during the model training phase.

  1. Testing and Implementation Phase

The final stage involves testing the model against the validated data with its inputs and targets and using it to run the program. In this stage, we look at the results and evaluate them. Accordingly, the model can be trained with new variables, adjusting and improving the algorithm.

Once the model completes the validation process, it can be tested against data. If the model does well, it is adopted for the purpose for which it was developed.

Some AI use cases for Conversational Commerce

Conversational commerce offers customers a medium of communicating with a brand, with the ability to make online purchases using conversational interfaces such as chatbots, messaging apps, and voice assistants. The result is an engaging purchase experience that stands out compared to transacting on a website or app.

AI technology known as Natural Language Understanding (NLU) has brought virtual shopping assistants into existence. They understand customers’ preferences, make personalized product recommendations, and nudge them toward the product purchase. 

In this way, conversational commerce has begun to transform the way businesses engage, inform, and sell to their customers. An AI virtual shopping assistant is the perfect solution for the e-commerce and retail sector, but it can be leveraged by other verticals as well.

Here, we will discuss the use cases of conversational commerce for three industries.

  • E-commerce and Retail Industry

Conversational commerce is a step ahead of e-commerce. E-commerce enhances the customer shopping experience but lacks in offering a personal touch. Customers may not be able to decide what to buy, research information, compare options, or read user reviews. The best way to address these issues is to examine customers’ shopping trends in offline retail and see how they use personalized service to enhance the overall shopping experience.

The virtual assistant comprehends customer requirements and preferences by engaging customers in conversation. It also has relevant product recommendations, based on their current requirements and their past purchases. Further, it can display user reviews to convince the customer about a product.

A report from Microsoft’s Voice states that 54% of customers will use digital assistants for making retail purchases in the next five years.

Travel planning is an intensive process. AI enables processing travel and hospitality brands as swiftly as possible to enhance their customer experience. 

Travelers cannot rely solely on reviews and ratings. Their interests, travel companions, places they want to visit, the accommodation they’d like to reserve, and activities they’d like to engage in are all taken care of while making recommendations. 

A virtual travel agent grasps the traveler’s requirements and provides suggestions. It also guides hotels and flights, and also checks the availability in them. It also plans trips and finds tourist attractions at their destinations. Moreover, it narrows down the best holiday packages and deals matching customer requirements and facilitates the transactions.

  • Financial Services and Insurance (FSI)

FSI organizations need to engage digitally savvy customers to inform them about their product offerings. A study found that 92% of millennials prefer banks that offer digital services. Therefore, financial sectors always seek digital transformations of which conversational commerce is a part. 

An AI assistant can engage a customer in conversation, ask questions to know the customer requirements, and recommend the most suitable financial offerings. The assistant can effectively serve as an aid to customers taking loans, booking insurance, availing credit cards, getting stock recommendations, and much more.

Conversational AI enables them to deliver exceptional customer experiences on the digital platforms of their choice. 


AI is not something that can make companies fully automated from the very beginning. Organizations need to prepare for it first. In other words, AI bridges the gap between being a possibility and a chosen solution.

While the organizations constantly look for automating their processes, they sometimes fake using AI due to tremendous market competition for state-of-the-art offerings, while in reality, most work is manual. Nevertheless, when a personalized, carefully curated AI solution is implemented, it can vastly benefit numerous industries, from FinTech and retail to healthcare and hospitality. 

Against this background, it is evident that a tailor-made AI solution can change the face of your business. As the market leaders in the space of conversational commerce, we offer both automation and AI. Set up a call to discuss your goals and requirements with us and determine what works the best for your business! Connect with us to ensure the best conversational commerce experience for your customers!

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