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World Is Moving Towards Automation, Where Are You?

Digital adoption has been accelerating across the globe ever since the pandemic. IT roles are becoming more prominent in organizations and routine tasks are permanently automated.

According to Gartner, by 2025 half of the world population will participate in at least one IoBs (Internet of Behaviours) programme formulated by private, commercial or government organisations.

Within the next year, 40% of all enterprise workloads will be managed by cloud infrastructure and platform services. Also, 40% of Product and platform teams will be using automated change risk analysis with the help of technology pipelines like DevOps to reduce downtime and risk mitigation.

By the year 2025, half of the total enterprises will have artificial intelligence (AI) orchestration platforms at their disposal.

New terms like IoB etc. are the best example of how technology is advancing into new fields and how automation is becoming a normal part of life. IoB programmes make use of big data and IoT to influence customer behaviour. Such programs can help retailers and manufacturers to find the right price for their products and increase sales.

Adoption of automation is not limited by industry. However, the IT, service, retail, marketing, and manufacturing industry are the major sectors that are adopting automation at a faster rate. In fact, by 2025, the majority of the workforce in the manufacturing industry will be replaced by machines, resulting in 20% of all products produced not being touched by any human being before they enter the market. It is estimated that such transformation in the operational process will help organizations to lower their operation cost by 30%.

If all these data points to one thing, it is that the future of businesses depends on automation. Today, digital transformation is not a convenience. It has become a necessity for businesses that want to survive the fearsome competition and changing market.

By 2025, 50% of enterprises will have artificial intelligence orchestration platforms at their disposal.

Should you adopt automation?

Automation helps you reduce manual and routine processes. As a business, it will help you save time and cost along with improving operational efficiency. The application of automation is not limited to cutting down costs and time.

Since technology is so advanced today, you can use automation to collect data on a large scale, analyse them and make real-time decisions. Such opportunities bring tremendous advantages to a business that is competitive and analytical with its long-term goals. If used in the right way, such strategies will help a business to understand their customers better and reach the right audience to promote their products and services.

Because it is handled by technology, the opportunity to scale with automation is limitless. Manual processes will not only slow you down, but they are also prone to trivial errors. Also, the security of data becomes a bigger concern if there are more people involved.

Automation can be adopted by any business since customer support, invoice generation, payroll management, documentation, record-keeping and tracking, reporting etc. are common to all businesses and there are already stacks and tools available in the market.

What can you automate?

While customer support, invoice generation, payroll management, documentation, record-keeping and tracking, reporting etc. are the most common tasks that gave way to automation, there are other opportunities that vary depending on the objective and the nature of the business.

For example, here is a list of automation opportunities available to different businesses.

Automation opportunities

Batch Scheduling

A batch is a collection of tasks or jobs that are grouped together for execution. Since they are resource-intensive, batch processes are pre-planned and scheduled overnight when the number of other processes is very less. Ever since the technology advanced enough to have more powerful resources in hand, these batch processes are executed frequently by organizations.

The best possible examples are the banking industry and the stock market. Prior to automation, all the documentation and transactions were handled manually and “closing” was scheduled weekly, monthly or even quarterly. As the technology advanced, the interval between each schedule became shorter and nowadays the closing is executed almost daily.

Automating batch scheduling helps organisations to reduce workload and human interaction. It also helps organisations to ensure that the process is executed on time.

Examples of batch process automation tools include IBM workload automation, VisualCron, VisualCron and Redwood RunMyJob.

Business Process Automation (BPA)

Business Process Automation mainly deals with sales, marketing, finance etc. BPA tools are essential for competitive businesses since they help with scaling and growth through digital transformation. Since enterprises deal with file transfer, report generation and distribution, employee onboarding, document approval etc. at a large scale on a day-to-day basis, BPA has become essential in this day and age.

Robotic Process Automation (RPA)

Robotic Process Automation should not be confused with BPA. RPA executes predefined tasks via user interfaces. Each RPA program will have certain rules that it follows to complete the task. Usually, RPA software is trained by a human being to execute the tasks. When ready, such programs can completely replace a human being; hence they are implemented mostly to reduce workload. Recent developments in Machine learning and Artificial Intelligence have enhanced RPA capabilities. Website scraping, screen scraping etc. are the best examples of RPA.

Big Data Automation

Big data automation is implemented to collect, organize, and process large chunks of data in a noticeably short time. It is immensely helpful in dealing with anomaly detection and fraud detection as the data collected can be used to discover patterns. Big data automation along with other technologies like artificial intelligence and machine learning can be used to detect fraud in big casinos and the banking industry.

Digital Process Automation (DPA)

Digital process automation aims to facilities end to end flow of information in a business ecosystem. It integrates applications, business operations, workforce and customers of a business to ensure that data is available in real-time to all the parties involved and as a result customers have a better experience. Digital process automation is implemented knowingly or unknowingly by businesses when they undergo digital transformation. However, without due focus, the advantages of DPA will be limited in scope. The customer support portals offered by most businesses are the best examples of DPA tools.

Enterprise Automation

Business Process Automation and Robotic Process Automation etc. can be limited to a single department or workflow. However, enterprise automation aims to create a single, enterprise-wide automated environment. Enterprise automation is the integration of different automation workflows implemented within an organisation. When they work in tandem, the overall efficiency and productivity of the enterprise is improved.

IT Process Automation

IT Process Automation aims to replace manual IT processes with the help of automated platforms and orchestration. It helps IT staffs to deploy, run, manage, and monitor processes using a single platform. For example, a business may implement Business Process Automation to augment its digital transformation initiative. The required IT infrastructure and process can be automated using IT Process Automation.

Infrastructure & Operations Automation

Infrastructure & Operations Automation mainly deals with the hardware and middleware of a business environment. There is a need to regularly update operating systems, tweaks configuration, and maintain optimal resource usage to avoid downtime and ensure security. An automated platform can be used for this purpose.

Cloud Automation

Businesses are moving to cloud environments faster than ever before. However, operating and managing cloud resources can still be a challenge for businesses. They must maintain a team who understands the technology to regularly monitor and configure the cloud resources as the demand and traffic can change at any time. Cloud automation aims to automate these tasks and offer a dynamic cloud environment where any changes in demand will be handled in real-time without the need for human interaction. PaaS services like AWS Elastic Beanstalk can help you with cloud automation.

Top 10 Programming Languages for AI Development

Recent studies show that businesses are investing more in artificial intelligence.

According to studies conducted by DataProt, 37% of businesses employ AI in their operations. The industry will be earning $126 billion by the year 2025 and the market value is expected to cross $267 billion by 2027.

Another study, conducted by Oberlo, states that 91% of top businesses have already invested in Artificial Intelligence. Also according to them, around 62% of customers are ready to share data if it improves their experiences with a business.

As artificial intelligence and related development are becoming more popular, the programming languages used for developing such software are also becoming popular.

If you are someone who has an interest in developing AI solutions, understanding the programming languages used for AI development will be compelling to you.

Best programming languages for AI development

When it comes to AI development, there are several programming languages you can choose from. Among them, here are major 10 programming languages that are used extensively in AI and machine learning development in 2022.

1. Python

Python is a high level, general-purpose programming language. It uses significant indentation to improve its code readability. Using language constructs and an object-oriented approach Python helps with developing clear and logical code for small to large scale AI projects.

Python was released in the year 1991 by Guido van Rossum. Ever since its inception, the language has been used in desktop apps, web apps, networking apps, scientific computing, machine learning apps and data science applications.

The libraries offered by Python such as Tensorflow, Keras, PyTorch, Scikit-learn, PyBrain and MXNet etc. make it one of the popular choices for AI development. Since Python offers rich text processing tools and uses modular architecture for scripting, it has also become a popular choice for Natural Language Processing (NLP).

A few of the leading companies that use Python include Google, Amazon, NASA, Reddit, Instagram, Intel, IBM, Facebook, Netflix, JP Morgan Chase and so on.

2. R programming language

R is a very popular programming language for statistical programming, especially data analysis and statistical computing. The language was created by statisticians Ross Ihaka and Robert Gentleman in 1993. As of March 2022, R is ranked at 11th position in the TIOBE index.

R is primarily written in C, Fortran and R itself. It comes with a command-line interface and offers support for multiple third-party user interfaces like RStudio and Jupyter.

R’s S heritage enabled it to have best-in-the class object-oriented programming facilities. R supports procedural programming with the use of functions and object-oriented programming with generic functions.

Due to its advantages, R is considered the primary programming language for statistical computations in domains such as biology, sociology, finance and medicine. The advantages of R can be extended through user-created packages that offer statistical techniques, graphical devices, import/export, reporting etc. The packaging system allows researchers to organize data, code and files in a systematic way for sharing and archiving. It is the ease of using such packages that drives the popularity of R as the best programming language for data science.

The alternatives to R programming language are SPSS, Stata and SAS, However, they are commercial statistical packages while R is a free software under the GNU General Public License.

Renjin and FastR used in the Java Virtual machines is a Java implementation of R programming language. The runtime engine “TERR” that is part of “Spotfire” is developed in R.

R is used by most of the leading companies including Facebook, Twitter, Google, Microsoft, Uber, Airbnb etc.

3. Java

Java is one of those programming languages that everyone has heard of.

This high-level, class-based, object-oriented programming language was designed in the year 1995 by James Gosling and became popular in the industry due to its write once, run anywhere (WORA) principle. WORA simply means that a compiled Java code can be run on all platforms that support Java without recompiling.

Java is one of the most used programming languages for client-server web applications. Even though it shares similarities with C and C++ in terms of the syntax used, Java has fewer low-level facilities than both.

Apart from web applications, Java is also used in Android apps, Artificial Intelligence and machine learning applications, search algorithms, server-side programming, neural networks and multi-robot systems. Its scalability, low dependencies, platform independence and support for Java Virtual Machines have made Java a popular general-purpose programming language.

When it comes to AI development, Java offers several libraries and frameworks such as Apache OpenNLP, Java Machine Learning Library, Neuroph, Deep Java Library, MLlib and so on.

Java is used by several companies including the noteworthy ones such as Google, Uber, Netflix, Airbnb, Instagram, Amazon, Spotify, Slack etc.

4. Rust

Designed by Graydon Hoare in 2010, Rust is multi-paradigm, a general-purpose programming language designed for performance and safety. Even though it is syntactically similar to C++, Rust guarantees memory safety unlike the former. Another benefit of Rust is that it offers memory safety without garbage collection and reference counting is only optional.

Rust offers low-level memory management as well as high-level features such as functional programming. Since it offers speed, performance and safety, Rust is gaining increased popularity day by day. It has been adopted and implemented by mainstream companies like Amazon, Dropbox, Facebook, Google, Microsoft and Discord. Google announced Rust as an alternative to C/C++ for their Android open-source project.

Here is a list of recent adoptions we have seen in the industry.

  • Microsoft Azure IoT Edge, a platform used to run Azure services and artificial intelligence on IoT devices, uses rust to create some of its components.
  • The blockchain platform Polkadot is written in Rust.
  • TerminusDB is written in Prolog and Rust
  • AWS’s Firecracker and Bottlerocket use Rust.
  • Google Fuchsia, an operating system by Google, uses Rust for its components.
  • Figma is written in Rust.
  • The Servo parallel browser engine developed by Mozilla in collaboration with Samsung is written in Rust.

5. Prolog

Prolog, which derived its name from “Programming in Logic”, is a logic programming language mainly used in artificial intelligence and computational linguistics. It was designed by Alain Colmerauer and Robert Kowalski in 1972.

Unlike many other programming languages, Prolog inherits first-order logic and is intended mainly as a declarative programming language. The logic is declared in the form of relations represented by facts and rules.

Even though Prolog was one of the first logic programming languages, hence one of the oldest, it still holds its position in the industry. There are several free and commercial adoption of Prolog such as Tabling (used in systems like B-Prolog, XSB, SWI-Prolog, YAP, and Ciao), hashing (used in WIN-PROLOG and SWI-Prolog) and Tail Call Optimization (TCO). 

Prolog has been mainly used as a primarily logic language for expert systems, term rewriting, type systems, automated planning, theorem proving and natural language processing. This programming language is best suited for an AI solution that features rule-based logical queries such as searching databases, voice control systems, and filling templates. IBM Watson is one such system. 

IBM Watson. Image credit:

6. C++

C++ is one of the well-known programming languages due to the popularity of C, the programming language it inherits from.

Designed by Bjarne Stroustrup as a general-purpose programming language in 1985, c++ has seen significant expansion over the years. Now it supports object-oriented, generic, and functional features besides low-level memory manipulation. 

The main advantage of C++ is its performance, efficiency, and flexibility as it was designed as a programming language for building resource-constrained software and large systems. The language is used extensively in building desktop applications, servers (mainly for e-commerce, web search and databases), video games and performance-critical applications such as telephone switches and space probes.

C++ offers several AI and ML libraries such as Caffee, Microsoft Cognitive Toolkit (CNTK), TensorFlow, DyNet, OpenNN, FANN, Shogun and mlpack library.

The popular companies that use C++ include Walmart, Google, Accenture, Twitch, Telegram and Lyft.

7. Lisp

Lisp, a name derived from “LISt Processor”, is the second-oldest high-level programming language still in use and is only one year younger than Fortran. Designed by John McCarthy in 1958, this family of programming languages has a long history with the presence of several distinctive dialects such as Racket, Scheme, Common Lisp and Clojure.

Designed primarily as practical mathematical notation for computer programs, Lisp later became the most favoured programming language for Artificial Intelligence. Several inventions in the field of programming are pioneered by Lisp and they include tree data structure, dynamic typing, conditionals, automatic storage management, recursion, self-hosting compiler, higher-order functions and read-eval-print loop. Apart from these, Lisp offers several features such as rapid prototyping, dynamic object creation, flexibility, garbage collection and information process capabilities.

CLML (Common Lisp Machine Learning Library), mgl, Antik and LLA are the popular AI and ML libraries offered by Lisp.

iRobot, The Mimix Company, NASA (in their PVS), Rigetti Quantum Computing, Grammarly, Mind AI, Carre Technologies, NuEcho, Kina knowledge, Emotiq and Anaphoric are a few examples of companies that are using Lisp in their products or operations.

Logo of Lisp programming language

8. Julia

Julia is a high-level, high-performance, dynamic programming language well suited for AI solutions that deal with numerical analysis and computational science.

Designed by Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral B. Shah in 2012, Julia supports concurrent, parallel and distributed computing.

Advantages of Julia include

  • It can direct call “C” and “Fortran” libraries without glue code.
  • Compiles all code by default to machine code before running it.
  • Automatic memory management/garbage collection
  • It uses easter evaluation
  • Offer libraries for floating-point calculations, random number generation, linear algebra, and regular expression matching.

Julia offers several packages for Artificial Intelligence and machine learning. Few of them are Flux.jl, Knet.jl, Mocha.jl, TensorFlow.jl, ScikitLearn.jl, TextAnalysis.jl, MXNet.jl, DecisionTree.jl, Merlin.jl, and LossFunctions.jl. Find the complete list here.

9. Haskell

Named after great logician Haskell Curry, Haskell is a general-purpose, statically-typed, purely functional programming language. Primarily designed for research, teaching and industrial application, Haskell boast of pioneering innovative features like type classes that enable type-safe operator overloading.

According to the number of Google searches conducted for tutorials, Haskell was the 28th most popular programming language in 2021.

The several features offered by Haskell include lazy evaluation, pattern matching, lambda expressions, list comprehension, type classes and type polymorphism. Since Haskell is purely a functional language, functions have no side effects.

Popular applications of Haskell include Agda (proof assistant), Cabal, Darcs (revision control system), Git-annex, Pandoc, TidalCycles, Cryptol, Facebook’s anti-spam programs and Cardano blockchain platform.

10. Smalltalk

This object-oriented programming language was specifically designed for constructionist learning. It is a dynamically typed reflective language which means that using Smalltalk, developers can create software programs that have the ability to examine, introspect and modify their own structure and behaviour.

Smalltalk’s reflective features help developers with advanced debugging in the most user-friendly way. In fact, Smalltalk ranked second in the list of “most loved programming languages” in the Stack Overflow Developer Survey in 2017.

Designed by Alan Kay, Dan Ingalls and Adele Goldberg in 1972, Smalltalk has influenced so many programming languages such as Python, Ruby, Java and Objective-C.

Even though it was created mainly for AI-related studies, Smalltalk lost its position in front of other popular AI programming languages such as Python and R. However, Smalltalk is picking up the pace by introducing more libraries for AI and ML development and natural language processing. For example, Pharo has a numerical package called PolyMath that is almost equal to NumPy of Python.

Due to its conciseness, object purity, simplicity and better OOP implementation, Smalltalk has started regaining the attention it always deserved as an AI language.


The adoption of artificial intelligence and machine learning is growing at a fast pace. There are several programming languages used in AI and ML development. However, languages like Python and R are the most popular. To meet the growing demand of the industry, there are several other programming languages that are expanding their capabilities to become the best AI programming language of tomorrow.