Generative AI has made impressive strides in the last one year, taking the technology world by storm. Thanks to consumer- facing tools like ChatGPT, Bard, Claud and the like, there has been a huge perceptional change toward Generative AI – a technology that has been existing ‘silently’ for years! 

In fact, Generative AI or GenAI adoption has been faster than that of smartphones and tablets, according to Insider Intelligence. 

In the enterprise world, this technology has gained massive interest and acceptance in short order. A Box-sponsored IDC survey shows that two-thirds of executives have already deployed generative AI in some areas at their organizations, which is further evidenced by an IBM survey. About 42% of enterprise-scale companies surveyed by IBM report having actively deployed AI in their business. 

But what really lies beyond the hype? Are enterprises reaping real benefits out of their GenAI investments? We asked a few CIOs at the Tech Saga 2024 organized by CIO Klub Pune Chapter. 

“Business leaders are enamoured by the success stories of these technologies that often they tend to ignore the failures and the basic requirements that are associated with GenAI’Business leaders are enamoured by the success stories of these technologies that often they tend to ignore the failures and the basic requirements that are associated with GenAI.”

Shobhana Lele, CIO of Bombay Dyeing

2024 – A year of GenAI in production? 

2023, which could be considered the watershed year for GenAI, was all about experimentation and pilots, indicate studies. In a recent Gartner poll, about 45% of the 1,400 executive leaders surveyed reported that they are in piloting mode with generative AI. An IBM research estimates this figure to be around 40%. Interestingly, about 10% of them have put generative AI solutions into production and this figure is steadily growing. 

A sizable number of the enterprises in piloting mode are expected to shift their focus to practical implementation in the next one year– making 2024 a turning point in GenAI adoption. “For the 40% of companies surveyed stuck in the sandbox, I am confident 2024 will be the year of tackling and overcoming barriers to entry like the skills gap and data complexity.” Rob Thomas, Senior Vice President, IBM Software.

Catapulted by the increasing investments, the GenAI market size is projected to top USD 66.62 billion in 2024 with an anticipated yearly growth rate of over 20% through 2030.

Indian CIOs see more GenAI use cases evolving

Tech leaders in India are bullish about the prospects of the technology. India is already considered a front-runner in the adoption of GenAI, with as high as 81% of Indian organizations already integrating the technology into their operations (Elastic report).

What kind of promising use cases will emerge in 2024? Which industries will get touched the most? 

“GenAI use cases will grow quite exponentially in the coming years,” says Shobhana Lele, CIO of Bombay Dyeing, who is a passionate advocate of AI technology. She believes that the healthcare sector is poised to hugely benefit from GenAI. 

“Of all the mature use cases that will emerge, I find the technology’s impact on the Healthcare sector quite significant and far-reaching. It’s one sector with massive amounts of data and AI can accelerate use cases that are critical for the whole humanity, helping us find solutions for some of the pressing problems that have been around for decades,” she highlights. 

A NASSCOM study from 2023 states that the use of AI and data in healthcare might increase India’s GDP by USD 25–30 billion by 2025. 

Vinod Deshmukh, GM – IT, Haldiram Foods is already exploring various use cases of GenAI at the FMCG firm and believes that the FMCG sector stands to benefit significantly from the technology. “There are strong business use cases already emerging, which help FMCG companies improve product quality, predict outcomes, and increase the overall efficiencies,” he adds.

Generative AI in the banking and financial services sector, which is expected to create a massive USD 12 Billion market in the next 10 years, is another great opportunity. 

“Strong business use cases of GenAI are already emerging, which help FMCG companies improve product quality, predict outcomes, and increase the overall efficiencies.”

Vinod Deshmukh, GM – IT, Haldiram Foods 

“This is another sector that holds huge amounts of personal and sensitive data. AI will be critical in effectively leveraging this data to help consumers get the most out of their assets and investments while devising innovative ways of protecting their critical data,” says Shobhana.

Gen AI is now catalyzing a significant shift in the financial services sector, reports EY, with 78% of the firms implementing or planning Gen AI integration. Around 61% of financial institutions anticipate GenAI to have a profound impact on their value chain, enhancing efficiency and responsiveness, states the EY report.

Banking institutions across the globe are leveraging this technology to improve customer experience, provide personalized marketing and improve collections & recovery. Underwriting copilots are another big use case in the banking sector. 

Noteworthy is the fact that sectors that have been slow in adopting artificial intelligence are moving to a more proactive approach when it comes to GenAI. 

“GenAI is encouraging many brands to push the envelope – even those who haven’t yet considered AI use cases are exploring the possibilities of GenAI, says Vinod. He cites the example of manufacturing industry. In the manufacturing sector, GenAI will soon be leveraged in quality analysis, decision making and other core areas.”

Less than 20% of manufacturers have begun to implement AI technologies, reports suggest. However, GenAI seems to be moving the needle up, with the global market for GenAI in manufacturing expected to reach around $7 billion by 2032. 

GenAI challenges for CIOs

There are several challenges that tech leaders must tackle before they deploy genAI across key business functions. Data privacy and transparency are greater concerns for sure. So are the growing AI ethical challenges which require collaborative work across various stakeholders in the industry. 

Limited availability of AI skills is perhaps the top concern for CIOs today, Vinod emphasizes. “Access to the right skillsets is a challenge, especially for businesses in tier-2 & 3 cities. Tech leaders need to develop the right resources and technology capabilities to take this journey ahead,” he adds. 

The recent IBM Global AI Index highlights that nearly 35% enterprises report lack of skills for implementation is a big inhibitor for GenAI implementation. One-in-five organizations do not have employees with the right skills in place to use new AI or automation tools and 16% cannot find new hires with the skills to address that gap, the same research shows. 

To fill this gap in the current scenario, CIO will be expected to lead the task of reskilling and upskilling the existing workforce – especially because skills in data science, machine learning and MLOps are hard to come by. 

All in all, many CIOs want to take a cautiously optimistic path when it comes to taking GenAI to the next level. And reasonably so. 

“In my observation, many CEOs are gung-ho about implementing AI or generative AI, whereas the CIOs are a little cautious and apprehensive. There is a reason for this apprehension – Business leaders are enamoured by the success stories of these technologies that often they tend to ignore the failures and the basic requirements that are associated with GenAI,” Shobhana says. 

She believes that this perception leads to a lot of pressure on CIOs to start using these technologies with the expectation that it’s going to be successful. “But, every CIO has the responsibility to make the organization aware of AI/GenAI’s basic requirement – the relevant data sets, skillsets and the support. So, it’s a tightrope walk for the CIO,” she sums up. 

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The rapid adoption of Generative AI (GenAI) technologies has led to a significant increase in sophisticated phishing campaigns. A recent study by Abnormal Security reveals that 80% of these campaigns now leverage GenAI tools, marking a critical turning point in the fight against digital fraud.

The integration of AI in phishing attacks has led to a dramatic 1265% increase in such incidents since 2022, as reported by InfoSecurity Magazine. The availability of free or trial-based AI tools, such as ChatGPT, has made it easier for cybercriminals to generate convincing phishing content, with the potential to create up to 30 templates per hour.

AI’s role in evolving phishing techniques

AI’s proficiency in generating high-quality content has significantly reduced the effectiveness of traditional phishing detection methods. AI-based proofreading tools can eliminate common phishing indicators, making attacks more challenging to identify.

The rapid response rates of AI models, like ChatGPT’s 15-20 seconds and the 3.5 Turbo Model API’s under 3 seconds, further enhance the efficiency of these attacks.

Lukas Junokas, Chief Technology Officer at Breezit, an event planning platform, shares a challenging encounter with a phishing email that closely imitated the writing style of a high-ranking executive, asking for confidential information. This email managed to evade standard detection filters because of its authenticity.

“Generative AI has undeniably transformed phishing, making attacks more personalized and harder to detect. The new challenge lies in the arms race between evolving AI capabilities in both creating and detecting sophisticated threats,” Lukas noted.

The emergence of malicious-AI-as-a-service

The concept of ‘Malicious-AI-as-a-Service’ is gaining traction, facilitating the automation and scaling of phishing operations. This development lowers the entry barrier for cybercrime, enabling even those with minimal technical skills to execute sophisticated attacks.

Valentin Rusu, Head of Malware Research and Analysis at Heimdal, highlights the potential dangers of Reinforced Learning in black-hat hacking.

“Imagine a hacker training an AI to break security systems through trial and error.” “This could lead to unprecedented cybersecurity challenges,” Rusu remarked.

Adelina Deaconu, Heimdal’s MXDR (SOC) Team Lead, adds that genAI has the potential to exploit personal vulnerabilities and advises people to step back if something seems suspicious.

“I’m especially worried about how generative AI can now analyze and exploit personal vulnerabilities and emotions, making the emails seem more convincing. I advise people to step back, verify information, and report any concerns. If something seems suspicious, it likely is,” says Adelina.

Brian David Crane, founder of CallerSmart, an app for investigating mystery phone numbers, believes that generative AI can scale up spear phishing and vishing attacks.

“With generative AI, cyberattacks can happen at scale, be relentless with malware code modification and generative chatbots using spear phishing & vishing attacks with an automated selection of targets based on publicly available data or information,” says David.

 Statistical insights: the growing AI threat

• 83% of companies prioritize AI over other technologies (Notta AI).
• 51% of businesses rely on AI for threat detection and remediation (EFT Sure).
• One in five people will open AI-generated phishing emails (SoSafe Awareness).
• 69% of organizations stated that they could not avert cyber-attacks without AI (CapGemini).

The way forward: awareness and vigilance

As AI continues to evolve, organizations and individuals must stay informed and exert caution when opening emails.

 “People should pay attention to strange email addresses, tone of the email, requests for sensitive information, signature and formatting, and should avoid clicking on URLs (hover over them first and see if the displayed URL matches the visible text),” says Adelina.

Understanding the capabilities and potential misuse of AI in phishing is the first step toward developing more effective countermeasures.

Source: Heimdal Security

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Udemy, an online skills marketplace and learning platform, released key insights ahead of the one-year anniversary of the launch of OpenAI’s ChatGPT, revealing how global organizations and professionals have embraced and implemented Generative AI over the past year, providing an in-depth look into the impact of emerging tech skills in the workplace.

“As we have observed, generative AI skills will lead to the rise of new roles and industries while simultaneously impacting overall corporate culture,” said Vinay Pradhan, Senior Director and Country Manager for India at Udemy. “Businesses will need to adapt to the new wave of flexible and effective skill development to make their workforce future-ready.”

Key findings from Udemy data collected between ChatGPT’s launch on November 30, 2022, through October 31, 2023, indicate how the technology has been leveraged globally over the last year:

• Nearly 800 instructors created and published more than 1,000 courses related to ChatGPT on the Udemy platform
• Udemy has more than 2.2 million enrollments in ChatGPT courses, with 557K+ enrollments in India.
• Udemy courses about ChatGPT have been developed in 25 languages with the most number of courses consumed in English amongst other languages.
• The highest number of enrollments in ChatGPT courses are from learners in India, the U.S., Japan, Mexico and the U.K.

Broader data trends on professionals learning generative AI reveal:

• The top-ten industries where organizations are empowering their employees with generative AI skills are: Professional Services, Consulting Services, Technology, Manufacturing, Retail, Financial Services, Entertainment & Media, Education, Government and Life Sciences.

• The most popular generative AI courses focus on Midjourney, AI art, Python, and ChatGPT prompts. The Q2 Global Workplace Learning Index also highlighted how AI learning is expanding into other functional fields such as marketing, HR, sales and project management.

The recent 2024 Global Learning & Skills Trends Report by Udemy highlighted that generative AI could help automate up to 30% of current work hours by 2030, contributing an estimated $15 trillion to the global economy. The Udemy platform witnessed a 60% increase in AI-related training over the past year and demand for ChatGPT courses surged 5,226% in Q1 2023 alone, demonstrating how organizations are prioritizing skills acquisition to optimize business outcomes.

“Globally, organizations are adopting generative AI in a purposeful and responsible manner, propelling workforce transformation and cultivating a culture that nurtures agility, resilience and competitiveness,” said Greg Brown, President and CEO at Udemy. “Cutting-edge solutions powered by generative AI have endless potential. At Udemy, we are excited to empower corporations with courses and curated learning paths on emerging skills and lead the transformation to a skills-based economy in this ever-evolving landscape.”

At Udemy’s recent FWD event, business leaders and practitioners from top global organizations gathered to share strategies for excelling in this transition to a generative AI era, including:

• Ensuring all employees have an understanding of generative AI. As companies continue to embrace new technologies and integrate them into transformation strategies, many jobs can be displaced and reshaped. The World Economic Forum predicts 97 million net-new roles are expected by 2025 – understanding the opportunity with generative AI will help organizations plan for the future and ensure they continue to be effective and efficient.

• Using AI as a catalyst for the transition to a skills-based economy. Managing talent is becoming less about degree requirements, and more about understanding where there are skill gaps in an organization and strategically closing those gaps, often with existing talent. AI can help do that at scale and can help upskill and reskill team members.

• Developing strong leaders at scale who possess the skill, resilience and agility to lead through transformational change, resulting in competitive advantage. Organizations must nurture leaders who are able to identify skill gaps and embrace emerging opportunities in the field of generative AI, and to effectively coach and lead teams through significant change. Leaders must ensure the fear of AI taking over roles does not build up and teams embrace the new technology versus reject it.

“AI is changing the way we work and learn, with our data highlighting efficiency and effectiveness as the top goals for organizations wanting to leverage generative AI tools,” said CEO and Founder, Jim Lundy of Aragon Research. “Organizations need to continue evaluating the right AI solutions that address accuracy, reliability, security and privacy concerns to expand their use cases beyond translations and media generation.”

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Nutanix, the hybrid multicloud computing, announced the Nutanix GPT-in-a-Box solution for customers looking to jump-start their artificial intelligence (AI) and machine learning (ML) innovation, while maintaining control over their data. The new offering is a software-defined AI-ready platform, along with services to help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of large language models (LLMs). It allows customers to easily procure AI-ready infrastructure to fine-tune and run generative pre-trained transformers (GPT), including LLMs at the edge or in their datacenter, said the company. 

Many enterprises are grappling with how to quickly, efficiently and securely take advantage of the power of generative AI and AI/ML applications, especially for use cases that cannot be run in the public cloud because of data sovereignty, governance and privacy concerns. From automated transcription of internal documents, to high-speed search of multimedia contents, and automated analysis, many organizations see the opportunity with AI but are struggling with growing concerns regarding intellectual property leakage, compliance and privacy. Additionally, organizations looking to build an AI-ready stack often struggle with how to best support ML administrators and data scientists, while the prospect of large AI investment costs has enterprises stalled in their AI and ML strategy.

“As customers look to design and deploy generative AI solutions, they find themselves struggling with balancing the deep expertise required to install, configure, and run these workloads with concerns around their data security and protecting company IP – all while controlling costs,” said Greg Macatee, Senior Research Analyst, Infrastructure Systems, Platforms and Technologies Group at IDC. “With GPT-in-a-Box, Nutanix offers customers a turnkey, easy-to-use solution for their AI use cases, offering enterprises struggling with generative AI adoption an easier on-ramp to deployment.”

The Nutanix GPT-in-a-Box solution delivers ready-to-use customer-controlled AI infrastructure for the edge or the core data center and allows customers to run and fine-tune AI and GPT models while maintaining control over their data. Nutanix provides a full complement of security and data protection offerings ideal for AI data protection.

This new solution includes:

• Nutanix Cloud Infrastructure platform, with the Nutanix Files Storage and Objects Storage solutions, the Nutanix AHV hypervisor and Kubernetes, along with NVIDIA GPU acceleration, which can be sized for large to small scale.
• Nutanix services to help customers size their cluster and deploy an opinionated stack with the leading open source deep learning and MLOps frameworks, inference server, and a curated set of large language models such as Llama2, Falcon and MPT.
• Ability for data scientists and ML administrators to immediately consume these models with their choice of applications, enhanced terminal UI, or standard CLI.
• The platform can also be leveraged to run other GPT models, as well as fine tune these models leveraging internal data, hosted on included Nutanix Files or Objects Storage services.

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According to various industry estimates, somewhere between 60-85 percent of AI projects fail to deliver value or make any business impact. At a time when technologies like Generative AI is set to transform the way we work, organizations need a reality check. 

We spoke to Anuj Gupta, an AI and machine learning expert with global reputation. As the Founder & CEO of Gradient Advisors, Anuj works closely with founders, CXOs & VCs to help them obtain business results out of their AI projects. He comes with over two decades of experience, spearheading end-to-end AI efforts at CXO level for startups and Fortune 50 companies.

He is also author of a popular book in AI endorsed by top AI leaders including those from CMU, UCSD, DeepMind, Google AI, Microsoft Research, Meta, Airbnb, Spotify to name a few.

Watch the full interview here:

Anuj believes that organizations must focus on getting the data piece right or risk AI projects failure. 

According to him, the reasons for the high rate of failure ranges from not having enough data in terms of either quality or quantity to having ill-formed problems. 

“Data is the foundation of any AI project. Every organization has petabytes of data, but very few of them have data sets which can be used for machine learning. One analogy that I often use is, if data is the crude oil, machine learning models are like the fighter jets. Unfortunately, fighter jets cannot run on crude oils. They need specialized fuel to fly. Organizations need to set up ‘refineries’ that can convert the ‘crude oil’ into different fuels, which can then be used in fighter jets,” he says. 

Wrong approaches and perspectives of the leadership team can sometimes backtrack an AI project. “Leadership teams often end up applying the people and project practices which they have developed for a traditional software development. But the ways in which AI models are developed differs from traditional software engineering. There is also this perception that deep learning models are comparable to human brains. This sets unrealistic expectations, especially for non-technical leaders.”

Teams that collect, curate and process the data systematically are the ones that are able to make much better progress, Anuj adds. 

The latest buzz word in the industry – Generative AI – will create more and more sustainable business models and revenue streams for businesses in the days to come.   

The more interesting aspect to watch out for however is the legal war around data ownership, he adds. 

“ChatGPT is trained on Internet data, which means it has data from sources like Reddit, Stack Overflow and many more. A lot of these companies own this data, and yet they have no share in the revenues of ChatGPT. The next legal war is going to be around data and its ownership,” Anuj sums up. 

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Artificial intelligence was the front and center of this year’s Microsoft Build, its annual flagship event for developers. In his opening keynote, Satya Nadella highlighted five biggest announcements of this year’s conference – all of which revolved around how AI will transform the way people use Microsoft products and platforms. 

The biggest of all was the integration of Bing with OpenAI’s ChatGPT. Bing thus became the default search engine for ChatGPT, enabling it to give more up-to-date and higher quality answers sourced from the internet. The feature is available to ChatGPT plus users for now and will be extended to free users soon. This marks another key step in the company’s partnership with and its multi-billion dollar investment in OpenAI. 

Nadella also announced that the company is bringing Copilot to the biggest canvas of all – the Windows.  “This is going to make every user a power use of Windows,” he added. With Windows Copilot, Windows 11 will be the first PC platform to provide centralized AI assistance to users, taking productivity to the next level. 

Nadella also introduced the Copilot stack making it available to developers to build their own copilot for their applications. “We will have everything from the AI infrastructure to the foundation model to the AI orchestration, all the way up to your copilot and its extensibility.”

This also means that there will be common extensibility across Bing Chat, Microsoft 365 Copilot, ChatGPT and Windows Copilot and the users’ own Copilots. Developers can now use one platform to build plugins that work across both consumer and business surfaces, including ChatGPT, Bing, Dynamics 365 Copilot (in preview) and Microsoft 365 Copilot (in preview).  “This is one of the most powerful things for a developer to be able to write a plug-in and have it reach billions of users,” Nadella said. 

At Build, Microsoft also announced Azure AI studio – a full lifecycle tool chain to be able to build, train and deploy intelligent apps and Copilots. This, according to Microsoft, will enable developers to ground AI models in just a few clicks using structured and unstructured data. 

It comes integrated with a Responsible AI dashboard that helps the developers during the testing phase to ensure that the models are safe. 

The last one in Nadella’s list, Microsoft Fabric, is an end-to-end analytics product, which the company says can address every aspect of an organization’s analytics needs. Fabric comes with capabilities including data movement, data lakes, data engineering, data integration, data science, real-time analytics, and business intelligence. 

Microsoft made 50+ announcements at this year’s Build conference.  

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Zoom announced a strategic partnership with and investment in Anthropic, an AI startup founded by former OpenAI employees, which focuses on constitutional AI methods. 

As part of the collaboration, Anthropic’s AI assistant, Claude, will be integrated with Zoom’s platform (which includes Team Chat, Meetings, Phone, Whiteboard, Zoom IQ), starting with Zoom Contact Center. 

“Anthropic’s Constitutional AI model is primed to provide safe and responsible integrations for our next-generation innovations, beginning with the Zoom Contact Center portfolio. With Claude guiding agents toward trustworthy resolutions and powering self-service for end-users, companies will be able to take customer relationships to another level,” said Smita Hashim, chief product officer for Zoom. 

The partnership, according to Zoom, is part of its federated approach to AI which leverages its own proprietary AI models, along with those from leading AI companies. 

As the next step in evolving the Zoom Contact Center portfolio, (Zoom Virtual Agent, Zoom Contact Center, Zoom Workforce Management), Zoom plans to incorporate Anthropic AI throughout its suite.

Zoom’s move comes soon after Microsoft’s announcement of the new version of Teams with Copilot-driven AI smarts. Google too announced a number of AI-based upgrades to Google Workspace. Recently, Salesforce introduced Slack GPT with generative-AI capabilities integrated into Slack.

Early this year, Google had made an investment of $300M in Anthropic, which is touted to be an OpenAI rival. 

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(Image: AI-generated artwork via Bing Image Creator, generated by Digital Science.)

While artificial intelligence (AI) appears scary and may threaten jobs, workers of the near future have less to fear from AI than they think if they are open to learning how to use AI as a tool that extends their capabilities. Becoming a proficient user of AI “magic” is going to be a key skill, according to Digital Science CEO DrDaniel Hook.

Digital Science is a technology company creating the infrastructure that supports the global research ecosystem of the future.

With AI forming a central part of the Fourth Industrial Revolution, Dr Hook says the workforce has much to gain if they embrace new opportunities such as those offered by ChatGPT and other emerging AI technologies.

As a result, using AI could become less of a specialized skill set and more natural for most people in their daily working lives. 

His comments have been published on Digital Science’s new blog site, TL;DR.

In his new post, Dr Hook says today’s best users of AI technology, known as “prompt engineers”, are a kind of “modern day programmer-cum-wizard who understands how to make an AI do their bidding”. He likens the skills required for getting an AI to produce a desired result as “something of a dark art”.

However, that is likely to change, he says, with AI interfaces having “the potential to fit more neatly into our daily lives and workflows than a programming interface”.

“We are living in a world where what AIs can achieve seems magical to us – one in which we are only limited by our own imaginations. The consequences are wondrous and terrifying in equal measure,” he says.

“This strange new world is one in which words have a new power that they didn’t have just a few months ago.

“Will prompt engineers be the social media superstars of tomorrow? Will they be the programmers that fuel future tools? Or, like using a word processor or search engine, will we all be required to learn some level of witchcraft?” Dr Hook asks.

He says with the emerging use of AI technology, humankind stands “in the midst of an exponential revolution” where technologies build upon and replace themselves in more rapid succession.

“While world domination by an AI is not likely in our imminent future, it is almost a certainty that jobs will change. However, if we view AI as the tool that it is, and think about how it can complement our work, we begin to position ourselves for the new world that is emerging,” he says.

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Hugging Face, the US-based AI startup that focuses on open-source machine learning, has launched an open-source alternative to ChatGPT, dubbed HuggingChat.

According to the company, “the goal of this app is to showcase that it is now (April 2023) possible to build an open source alternative to ChatGPT”. 

Clement Delangue, the CEO & Co-founder of Hugging Face, tweeted, “I believe we need open-source alternatives to ChatGPT for more transparency, inclusivity, accountability and distribution of power. Excited to introduce HuggingChat, an open-source early prototype interface, powered by OpenAssistant, a model that was released a few weeks ago.” 

He added that the current chatbot is a v0 with many limitations but the team is iterating quickly on the interface and safety mechanisms and intend to support the next rapidly improving open-source models. 

In this particular version of the chatbot, says the company, they only store messages to display them to the user, not for any other usage (including for research or model training purposes). 

“For now, it’s running OpenAssistant’s latest LLaMA based model (which is one of the current best open source chat models), but the plan in the longer-term is to expose all good-quality chat models from the Hub,” the company said. 

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ChatGPT is mind-blowing – for want of a better word. Since its launch in November end, this smart chatbot has triggered some vital discussions on what AI as a technology can potentially achieve. 

And, it has already made history. In just about five days after its release by OpenAI, an astounding 1 million users signed up for Chat GPT. Within a span of two months, ChatGPT became the fastest application to hit 100 million users, surpassing the records set by TikTok, Instagram and Facebook. 

“What makes ChatGPT so impressive is its ability to produce human-like responses. Another thing that differentiates ChatGPT is its ability to log context from users’ earlier messages in a thread and use it to form responses later in the conversation. So, this is very different from how computers used to behave before,” wrote Jijy Oommen, Chief Technology Officer & Digital enthusiast in her recent Linkedin post. 

In the revolutionary world of Generative AI

What it also did in the process is to push Generative AI into the broader mainstream discussions. If we borrow Bill Gates’ words, “Generative AI like ChatGPT will change our world!”. 

Generative AI is the branch of artificial intelligence that uses algorithms to ‘generate’ new content in the form of text, audio, images or videos. 

By now, we have seen numerous thrilled users across the globe taking to social media to showcase their experiences with this bot. 

While ChatGPT may be the most popular ‘Generative Pre-trained Transformer’, a number of players have already launched similar products in the market – such as Jasper, Anthropic, Bloom, Chinchilla or the new kid on the block – Google’s Bard. 

And yet, we have probably only seen the tip of the iceberg. As Microsoft CEO Satya Nadella rightly said at the World Economic Forum 2023, ‘the world is entering a golden age of Artificial Intelligence, and AI is just the beginning of the S-curve.’

The global generative AI market size is anticipated to reach USD 109.37 billion by 2030 growing at a CAGR of 34.6%, according to a new report by Grand View Research. 

Investments around generative AI has been soaring. According to Pitchbook, Venture Capitalists have increased investment in Generative AI by 425% since 2020 to $2.1bn. 

Business applications abound 

New business use-cases of this technology are expected to create far-reaching impact across industries. Early foundation models like ChatGPT focus on the ability of generative AI to augment creative work, but by 2025, we expect more than 30% — up from zero today — of new drugs and materials to be systematically discovered using generative AI techniques,” says Brian Burke, Research VP for Technology Innovation at Gartner.

 According to experts, here are a few of the emerging yet sophisticated applications of generative AI for businesses:  

Marketing & social media: From creating personalized content to driving customer experience, generative AI has broad range of use cases within marketing and social media. According to Gartner estimates 30% of outbound marketing messages from large organizations will be synthetically generated, by 2025. Currently, this number is just above 2%.

Product design & development: Smart algorithms have the power to redefine the process of product design and development. This will ensure faster product development and better products. 

Real-time &Data-driven decision making: Generative AI is expected to play a crucial role in decision optimization, by allowing decision makers to leverage huge amounts of real-time data. 

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