How Business Automation Is Shaping Digital Transformation for Retail Industry

Business automation is usually a function of how fast a company is growing or maturing. Customers are moving from old desktop/server apps with limited connectivity into more modern apps with APIs enabled.

Now it’s more a “must have” rather than “nice to have.” APIs are built for improving automation and user experience, and it’s already happening. To give an example, Salesforce gets more API transactional traffic than from Browser or mobile UI.

Manual updating is at its peril: Scale-up

Manual updating and reconciling between systems will not sustain long-term growth. Think Facebook – if they had to approve or set up each person joining Facebook, where would they be? You get the point. If businesses want to move fast, they need to get manual steps out of the process.

Scaling up is the best way to compete.  It requires focusing the team’s efforts on growth, not maintaining current operations.  Also, the more there are links in the chain, the more it is prone to break, and this is even truer when humans are involved.
Businesses must take their team and let them spend that time creatively, figuring out how to take the company to the next level.  

A market tendency towards SaaS or Cloud-based applications

Software implementation helps to improve processes, not merely map current processes into the new software. It takes a little more time but is worth it.  We do see trends that clients who focus on getting the processes in place scale much faster.

The growth of Cloud is undoubtedly tremendous.
The tendency towards cloud-based applications came mostly from the years of heartache caused by on-site servers.

We also see the growth of eCommerce as integral. Earlier, warehouse data was used for internal purposes only. But now when a scan happens, that data is automatically reflected publicly to the whole world.

Software as a Service has inherently built-in demand for increasingly complex integration. If you have two SaaS apps, you have two integration points. If you have three SaaS apps, you have six integration points.

Evolution of Business Automation

Automation is evolving.

First, it was getting an application to expose APIs, then came orchestration of business processes.

The third phase would be intelligent automation, where business processes learn and adapt over some time. Much work has been done, and there is a lot to be done.

To improve process automation, it takes planning, implementation, and usually a bit of back and forth.  As technologies evolve, time to execute process automation tends to decrease and will continue to do so.  

Let’s compare an EDI setup to API integration.  API integration takes a fraction of the time, breaks less often, and is much faster to fix. It will continue to improve and speed up.  It only means that the time and cost to execute will become trivial.

All of our time and efforts can be spent on the planning stage, substantially increasing and speeding up our iterations, therefore speeding up our improvements, growth, and success.

Industry trends towards Business Automation

Two of the most talked about trends are – Artificial Intelligence (AI) and Blockchain.

While Blockchain is still trying to find a foothold and would take some time, AI is getting embedded in all parts of our lives, many of what we don’t even notice.

Businesses should start actively looking at AI on all aspects of what they do, including business process automation and often reducing manual labor. In a recent article, it was mentioned that there is a shortage of candidates, and many jobs are going unfilled.

With current immigration policies, it is to get worse, and this is where we feel businesses need to start thinking about removing manual work.
Digital integration and AI to play an active role in most software we build.

In the retail industry, retailers are moving towards private labels versus reselling models.
This is happening as the MAP becomes more prevalent, and brands themselves are going direct.  

With this, we see a lot of blurring of the traditional supply chain lines and an increase in 3PL as retailers compete with Amazon’s standards.
As the supply chain consolidates, EDI will die out. We can have fast, easy, stable, and inexpensive integrations that achieve standards with APIs instead.

Best Practices for organizations to implement Business Automation

Disciplined approach with Data: Flourish or perish

Focusing on business automation will enable organizations to compete in the digital age.  Companies that start down this path now are the ones that will survive over the next 5-10 years, but also are the ones who will thrive.

One of the things we notice with our most successful clients is that they pay attention to the data and use it to gain critical insights.
Companies beat out their competition over time with data insights. Getting the ideas and having the systems to execute them with pig-headed discipline is the key.

Transform error-prone manual approach into automation

Companies should look at each of their processes and manual touchpoints. Then to figure out which touchpoints are causing the most pain or error.
They can use Six Sigma or other methods to identify errors within business processes.

Take the one that is most impactful and automate it and then move to the next. I don’t mind going slow as long as organizations are tackling those process hiccups first that cause the most pain.

Why businesses need to work towards Digital Transformation and Integration

Businesses that are thinking digital today are the ones going to succeed in the future.
Many traditional retailers have already started their digital transformation, and this is just the tip of the iceberg.

It is an absolute necessity to have their systems work together seamlessly along with their suppliers’ and clients’ system. Not to mention capturing the data and getting the insights are also needed to decimate the competition.

There are two camps of thought.

Purchase an all-inclusive package to run your business end to end

Or,

Get the best of breed application.

While most “all inclusive” ERP packages are well integrated, companies have started to look for the best of the breed.
With this type of integration, they can maximize productivity for their businesses and minimize risks with vendor lock-in.
For example, Salesforce is mostly chosen for CRM,  even if the businesses have SAP, Oracle, or Microsoft. When organizations are looking at such business architectures, they need to be digitally integrated and provide their end customers a seamlessly integrated experience.

How automation has changed the way of work

Our client’s focus shift from maintaining the business to working on growing the business. When you have your systems and processes set up, then you have the framework or scaleup.  As you scale up, your systems can hopefully adapt to the change, but you will need to either update processes within your system as you grow or upgrade to ones that can scale with you.
It’s like hiring. Hire for the size you are now and will realistically be soon, but you always want some room for growth. The lucky part is that the right systems could take you through more business phases than a person usually can.   

Conclusion

From a software vendor standpoint, our goal is to “delight” our customers.
Our vision focuses around improving visibility and efficiencies in the supply chain. There are still lots of inefficiencies that we see in day-to-day activities that we will be working hard to eliminate.  Companies that take the customer feedback loop to heart will partner with similar companies, and these alliances will improve the experience of end consumers exponentially.

All-in-one software systems will struggle as best of breed becomes the norm again.  Throughout the past, the software has seen a few cycles from the best of a kind to the all-in-one.

Today, the ease of system integrations matched with stability dominates. We will see all-in-one systems that are hard to use, expensive, and impossible to set up, slowly die off even for large enterprises.

Describing Enterprise Artificial Intelligence: Platform as a Service With Current AI Infrastructure

If I will talk about the Enterprise AI, then it is hard to think of an application that doesn’t use a database. If you see from mobile to web to the desktop, every modern application relies on some of a database. Some apps use flat files while others rely on memory or NoSQL database.

If I will talk about the traditional enterprise applications, then they interact with large database clusters running Microsoft SQL, Oracle etc. The fact is that every application needs it.

Like databases, Artificial Intelligence (AI) is moving towards becoming a core component of modern applications. In the coming months, almost every application that we use will depend on some form of AI.

Enterprise AI simulates the cognitive functions of the human mind — learning, reasoning, perception, planning, problem solving and self-correction — with computer systems. Enterprise AI is part of a range of business applications, such as expert systems, speech recognition and image recognition.

Figure 1

1. Start Consuming Artificial Intelligence APIs

This approach is the least disruptive way of getting started with AI. Many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, natural language processing, and video search API.

Let’s look at a concrete example of analysing the customer sentiment in a customer product requirement demo session. Almost all the Customer calls to the service team are recorded for random sampling.

A supervisor routinely listens to the calls to assess the quality and the overall satisfaction level of customers. But this analysis is done only on a small subset of all the calls received by the customers to the service team. This use case is an excellent candidate for AI APIs. Each recorded call can be first converted into text, which is then sent to a sentiment analysis API, which will ultimately return a score that directly represents the customer satisfaction level.

The best thing is that the process only takes a few seconds for analysing each call, which means that the supervisor now has visibility into the quality of all the calls in near real-time. This approach enables the company to quickly escalate incidents to tackle unhappy customers and rude customer service agents.From CRM to finance to manufacturing domains, customers will tremendously benefit from the integration of AI. There are multiple AI platforms and API providers like (With link):

• Amazon AI Services (https://docs.aws.amazon.com/aws-technical-content/latest/aws-overview/artificial-intelligence-services.html)
• Google Cloud ML Services (https://cloud.google.com/ml-engine/reference/)
• IBM Watson Services (https://console.bluemix.net/catalog/?category=ai)
• Microsoft Cognitive Services (https://docs.microsoft.com/en-us/azure/cognitive-services/)
• Clarifai (https://clarifai.com/developer/guide/)

2. Build and Deploy custom AI models in the Cloud

While consuming APIs is a great start for AI, it is often limiting for enterprises.

We have seen the benefits of Integrating Artificial Intelligence with applications, customers will be ready to take it to the next level.

This step includes acquiring data from a variety of existing sources and implementing a custom machine learning model. It requires creating data processing pipelines, identifying the right algorithms, training and testing machine learning models and finally deploying them in production.

Similar to Platform as a Service that takes the code and scales it in the production environment, Machine learning as a service offerings take the data and expose the final model as an API endpoint. The benefit of this deployment pattern lies in making use of the cloud infrastructure for training and testing the models. Customers will be able to spin up infrastructure powered by advanced hardware configuration based on GPUs and FPGAs.

Platforms that offers Machine Learning as a Service:

• Amazon ML (https://docs.aws.amazon.com/machine-learning/index.html#lang/en_us)
• Azure ML Studio (https://docs.microsoft.com/en-us/azure/machine-learning/)
• Google Cloud ML Engine (https://cloud.google.com/ml-engine/docs/)
• Bonsai AppNexus (https://wiki.appnexus.com/display/api/Welcome)
• BigML (https://bigml.com/api)

3. Run Open Source AI Platforms On-Premises

The final step in AI-enabling applications is to invest in the infrastructure and teams required to generate and run the models locally. This is for enterprise applications with a high degree of customization and for those customers who need to comply with policies related to data confidentiality.

If ML as a Service (MLaaS) is similar to PaaS, and running AI infrastructure locally then it is comparable to a Private Cloud. Customers need to invest in modern hardware based on SSDs and GPUs designed for parallel processing of data. They also need expert data scientists who can build highly customized models based on open source frameworks. The biggest advantage of this approach is that everything runs in-house. From data acquisition to real-time analytics, the entire pipeline stays close to the applications. But the flipside is in the OPEX and the need for experienced data scientists.

Customers implementing the AI infrastructure use one of the below open source platforms for Machine Learning and Deep Learning:

• MXNet (https://mxnet.incubator.apache.org/api/python/index.html)
• Microsoft Cognitive Toolkit (https://docs.microsoft.com/en-us/cognitive-toolkit/)
• Tensorflow (https://www.tensorflow.org/api_docs/)
• Theano (http://deeplearning.net/software/theano/library/index.html)

If you want to get started with AI, explore the APIs first before moving to the next step. For developers, the hosted MLaaS offerings may be a good start.Artificial Intelligence is evolving to become a core building block of contemporary applications. AI is all set to become as common as databases. It’s time for organizations to create the roadmap for building intelligent applications.

AI Data evolutions like Data Processing and Neural Networks.

Now in present time we are feeding loads of data to the computer, so the computer will learn about Deep learning technologies and the reason for behind this to take AI Initiative.

Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem. Neural networks learn by example to solve complex signal processing and pattern recognition problems, including speech-to-text transcription, handwriting recognition and facial recognition.

Data processing is, generally, “the collection and manipulation of items of data to produce meaningful information.” In this sense it can be considered a subset of information processing, “the change (processing) of information in any manner detectable by an observer.”

AI data processing is the need for high-quality data. While data quality has always been important, it’s arguably more vital than ever with AI initiatives.

In 2017, research firm Gartner described a few of the myths related to AI. One is that AI is a single entity that companies can buy. In reality, it’s a collection of technologies used in applications, systems and solutions to add specific functional capabilities, and it requires a robust AI infrastructure.

Another myth is that every organization needs an AI strategy or a chief AI officer. The fact is, although enterprise AI technologies will become pervasive and increase in capabilities in the near future, companies should focus on business results that these technologies can enhance.

Conclusion

In, the Conclusion I would say everyone has to use the Artificial Intelligence Applications and as we found that many existing applications can turn intelligent through the integration with language understanding, image pattern recognition, text to speech, speech to text, NLP and video search API.

As we have seen earlier a supervisor can do the Random Sampling at the time of Customer Product Requirement demo session. So, what supervisor wants to listen in the last about the customer satisfaction and how better we can use the AI APIs to convert the call into text for the sentiment analysis. As, I have mentioned above in this use case we can definitely find out the score directly what is the satisfaction level of customer.

So, with the help of AI APIs we can easily understand the customer problems and give a better product to them for their use.

As, I have mentioned above for the same that we have to build and deploy the custom AI models in the cloud and why it is more useful and beneficial for the customers.

So, if I will define why is it more useful and beneficial as we have seen above the AI APIs will take to the customer to the next level. AI APIs will acquires data from variety of sources and help in to create data processing pipelines and identifying the right algorithm and deploy in production environment.

So, in the last I would say use the AI APIs applications to reduce the pain of customer and help them to reduce the complexity and make the process more effective with the help of AI APIs.