Adapting ‘I’ in ‘AI’: A diverse industry approach to Artificial Intelligence

In theory and definition, Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, perception, and natural language processing. It is based on the idea that a machine can be designed to imitate human intelligence in a way that is flexible, and adaptive.

Prateek Tokas

Nishit Vora
Vice President Digital
Ex Think9 Consumer Technologies Pvt Ltd

Artificial Intelligence: Inception and Current Landscape

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing’s seminal work, “Computing Machinery and Intelligence” which was published in 1950. In this paper, Turing, often referred to as the “father of computer science”, asks the following question, “Can machines think?” From there, he offers a test, now famously known as the “Turing Test”, where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its inception, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

AI today has various applications, ranging from automated customer service systems to self-driving cars, predictive analytics, medical diagnosis, fraud detection, image enhancing and many more.

There are different types of AI, including machine learning, natural language processing (NLP), deep learning, and computer vision, each with its own set of techniques and methods.

Application of AI across Industries

a. Financial Markets

AI trading companies use various tools in the AI wheelhouse — like machine learning, sentiment analysis and algorithmic predictions — to interpret the financial market, use data to calculate price changes, identify reasons behind price fluctuations, carry out sales and trades and monitor the ever-changing market.

There are several types of AI trading: quantitative trading, algorithmic trading, high-frequency trading and automated trading.

Quantitative trading, also called quant trading, uses quantitative modelling to analyse the price and volume of stocks and trades, identifying the best investment opportunities.

Algorithmic trading, also known as algo-trading, is when stock investors use a series of pre-set rules based on historical data to make trading decisions. (High-frequency trading is a type of algo-trading that is defined by large quantities of stocks and shares being bought and sold rapidly.) providing unprecedented scale & speed.

Check out the top few tools that stock traders use.

b. Creating content via Imaging & Photography

A few months ago, there was a wave of people across the world generating an AI version of themselves and showing it off to the world by sharing it on social media. That was all thanks to DALL·E2 from OpenAI that can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles.

AI-based facial recognition is also present in photo editing software, like Adobe Lightroom. For example, Adobe’s machine-learning face recognition feature, Sensei, can detect faces in images, automatically arrange the photos based on identified faces, and create albums according to people.

Like Apple’s portrait mode, the modern camera’s AI technologies make the face pop clearly, blurring the foreground and achieving the worldly loved – ‘bokeh effect’.

Moreover, Thanks to AI-powered software tools such as ‘Narrative Select’, the image culling process can be made 10x faster and more enjoyable when you have hundreds or thousands of photos to cull from.

‘Select’ allows photographers to important thousands of RAWs in seconds, then harnesses AI to identify if subjects are in focus and whether they have their eyes open. Not just this, cameras like Olympus OM DE M1X have inbuilt Deep Learning Technologies inbuilt to help trackability of the subject in focus.

These AI-powered tools can enhance the quality of video footage by reducing noise, removing artifacts, and improving colour accuracy.

In addition to the above, the post-production has also changed the game with –

  • Enabling personalized video content: AI algorithms can analyse user data and create personalized video content based on individual preferences, such as recommending content, creating targeted ads, or generating dynamic product videos.
  • Enhancing post-production capabilities: AI-powered post-production tools can be used to add special effects and CGI elements to videos, as well as manipulate video content in real-time to produce immersive experiences.
  • Streamlining the video production process: AI-powered project management tools can help video production teams manage their workflows more efficiently, reducing project timelines and costs.

c. Marketing

Brands can use AI to analyse customer data and create personalized content that is tailored to each individual customer. This can include personalized emails, social media posts, and even personalized product recommendations.

Artificial Intelligence (AI) has revolutionized the world of marketing by providing businesses with new tools and techniques to better understand and engage with their customers.

Personalized Marketing: AI enables marketers to analyse vast amounts of customer data and provide personalized recommendations to individual customers. This can include personalized content, product recommendations, and pricing strategies.

By noting & understanding consumer browsing pattern, internet giants like Meta, Google,
and many more target ads basis the usage, sites opened, clicks made etc.

Predictive Analytics: Analyse customer behavioural patterns and identify patterns, which can be used to predict future trends. This information can be used to optimize marketing campaigns and increase the effectiveness of advertising.

Customer Service: AI-powered chatbots can provide 24/7 customer support, handle customer inquiries, and provide personalized recommendations. This can improve customer satisfaction and reduce the workload on customer service teams.

Content Creation: Analyse customer data and create personalized content that is tailored to individual customers. This can include personalized emails, social media posts, and even personalized product recommendations.

Optimization of Ad Campaigns: Analyse ad campaign data and provide insights into which campaigns are most effective. This information can be used to optimize ad campaigns and increase customer engagement.

Lead Scoring: Understand customer data and score leads based on their likelihood to convert. This information can be used to prioritize leads and increase the effectiveness of sales teams.

Image and Voice Recognition: Recognize images and voices, which can be used to create personalized experiences for customers. For example, image recognition can be used to personalize product recommendations based on the customer’s preferences.

Automated content creation: Generate content automatically, including news articles, product descriptions, and social media posts. This can save time and effort for content creators and publishers, and can also improve efficiency in content production.

Personalization: Analyse user data to create personalized content, such as customized product recommendations, personalized news articles, or targeted advertising. This can improve user engagement and help content creators to reach their target audience more effectively.

Optimization: Leverage data on content performance, such as page views, click-through rates, and engagement metrics, to optimize content for better results. This can help content creators to refine their content strategy and improve their content quality over time.

Enhanced creativity: Help content creators to generate new ideas and insights by analysing large amounts of data, identifying patterns and trends, and making predictions based on historical data. This can help content creators to stay up-to-date with the latest trends and create more innovative content.

Wrapping up

Overall, AI has brought significant changes to the world of marketing by enabling businesses to better understand and engage with their customers. As AI technology continues to evolve, we can expect to see even more innovative uses of AI in marketing in the future.

About the author

Nishit Vora is currently the Vice President Digital at Ex Think9 Consumer Technologies Pvt Ltd. He has been a passionate marketing and advertising professional with over a decade of strong and diverse experience.

With a B.E Electronics background, his advertising career started with traditional media buying & planning at a legacy agency. With the advent of the Digital ecosystem growing up, he latched on to that bug a decade ago and has managed a diversified portfolio of top brands & products across FMCG, Retail, BFSI, Entertainment, and Consumer Durables sectors.

Over the years, he has been fortunate enough to have launched some of India’s iconic products to the tune of Tata Nano, Tata Photon, Aditya Birla Capital, Hershey’s Kisses, and to name a few apart from his last stint of leading Digital for Future Group’s Retail, Lifestyle, and D2C businesses.

Disclaimer: The views expressed in this feature article are of the author. This is not meant to be an advisory to purchase or invest in products, services or solutions of a particular type or, those promoted and sold by a particular company, their legal subsidiary in India or their channel partners. No warranty or any other liability is either expressed or implied.
Reproduction or Copying in part or whole is not permitted unless approved by author.


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