Tag: AI

Impact of ChatGPT and Similar Generative AI Solutions on the Talent Market | Blog

ChatGPT’s arrival has brought much hype and speculation that it could replace several human workforce areas. While ChatGPT shows great early potential, how will it impact the “future of work” and the overall talent landscape? Read the latest blog in our series to learn more about the impact of ChatGPT and other generative Artificial Intelligence (AI) solutions on the workforce.

Since its advent, ChatGPT has taken the internet by storm, reaching a million users in under a week. No wonder it is the most talked about subject in technology and innovation. While ChatGPT has generated a lot of curiosity among netizens, the big techs are not far from the spotlight.

Microsoft has already invested billions in the technology and even integrated it into its search engine Bing. Google has officially announced “Bard,” its ChatGPT rival based on an in-house language model that is undergoing testing before being released to the public.

Chinese search engine Baidu has announced the testing of a similar tool, “Ernie Bot,” while Alibaba also confirmed working on an AI tool. Worldwide, we are witnessing rapid innovation and updates in this field, and by the time you read this blog, we might expect some more new developments.

What does it mean for the talent and workforce industry?

While the utility of a generative AI like ChatGPT remains an area to explore, we expect HR and business leaders to leverage ChatGPT across various dimensions of work and talent management. The workforce industry has evolved over the past few decades, and with the advent of machine learning and AI, we can expect to see some major transformations in the coming few years.

While ChatGPT has the potential to impact talent management, it is still not a replacement for human recruiters. Instead, it can assist them by streamlining the process and making it cost-effective and efficient by automating routine tasks, improving the candidate experience, and enhancing the recruitment process.

Some functions like job screening, content development, and job pricing will see a greater impact than other roles, as illustrated below:

Current mapping of ChatGPT and similar AI across the talent management value chain

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Where can ChatGPT replace human involvement in the near and long term?

ChatGPT has already proven its capability to solve math, write code and content, create poetry and literature, converse with other AI tools, and assist with business problems. Soon, generative AI tools have the potential to replace most non-automated tasks such as targeting prospects, writing sales pitches, drafting reports, writing basic code, developing financial models, analyzing data, assessing candidates, optimizing operations, etc. Although the list has no definite bounds, the possibility exists for a single generative AI replacing jobs across multiple domains such as marketing, sales, finance, operations, etc.

The potential impact of ChatGPT and similar AI across workforce areas

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Key examples of generative AI adoption

Here are some of the applications for these tools in the industry:

  • Content creators at leading cloud services company VMware use the AI-based content creation toolJasper to generate original content for marketing – from email to product campaigns on social media
  • Morgan Stanley is working with OpenAI’s ChatGPT to fine-tune its training content on wealth management. Financial advisors are using it to search for existing content within the firm and design tailored content for its clients
  • Codeword, a leading tech marketing agency, has already hired the world’s first AI interns as an experiment to assist them with content writing, design, animation, and marketing

On similar themes, we have seen companies leveraging AI, such as Tesla building driverless cars and McDonald’s experimenting with employee-less eateries. In a few years, AI bots could replace various roles, such as customer service executives, recruiters, content writers, and even coders.

We might expect to see a single generative AI tool functioning across multiple domains (finance, HR, marketing, customer service, operations, etc.) within an organization, reducing the need for human intervention.

Blue-collar jobs were already at risk, and the success of ChatGPT further threatens several white-collar professions as well. In the long run, ChatGPT and similar AI tools can open doors to many new opportunities for AI integration, and any prediction we make has a higher risk of falling short of reality.

What challenges are associated with ChatGPT adoption?

We have already discussed the technical challenges of ChatGPT in our earlier blogs (see links at the end of this post.) Human interaction and empathetic judgment are the two major challenges for any AI tool to penetrate the talent management space. Also, limited capabilities in languages other than English and text-driven communication style restrict the use cases of generative AI in non-English speaking regions. Ethical and legal concerns also need to be addressed as the distinction between AI-generated and human-generated data blurs.

In addition, most short-term use cases of generative AI, such as chatbots, already have an alternative available in the market. It will take time for ChatGPT to further integrate into the talent market and move from an experimental basis to organization-wide implementation. Integrating a new system also requires additional investments and training that organizations need to explore.

Impact of ChatGPT on the future workforce

Amid all the hype and speculation, one thing is for sure: AI is here to stay. As humans, we need to embrace it and learn to co-exist with it. With the rise in AI adoption, the talent dynamics also are expected to change, and certain skills/roles associated with it will soar as we enter the age of AI.

Going ahead, we can expect to see higher demand for relevant technical skills. This also creates opportunities for several related skills, such as people with specific domain knowledge to train models and personnel, review content, ensure data reliability, and integrate systems based on industry needs.

Follow our next blog in the series to learn more about the type of skills/roles that will be affected and the new roles that will emerge in demand.

Below are some illustrations of the current capabilities and limitations of ChatGPT on talent-related queries. (The screenshots were taken on February 20, 2023, from India and the responses might be different for other users.)

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For our previous blogs on this topic, see ChatGPT – Can BFSI Benefit from an Intelligent Conversation Friend in the Long Term?, ChatGPT Trends – A Bot’s Perspective on How the Promising Technology will Impact BPS and ChatGPT – A New Dawn in the Application Development Process?

If you have questions about the latest trends in the talent landscape or would like to discuss developments in this space, reach out to [email protected] or [email protected].

You can also watch our webinar, Top Emerging Technology Trends: What Sourcing Needs to Know in 2023, to learn more about how organizations can implement new technologies into processes and operations.

ChatGPT – A New Dawn in the Application Development Process? | Blog

ChatGPT, the advanced Artificial Intelligence (AI) chatbot that’s taken the world by storm, can potentially accelerate various stages in the Software Development Lifecycle (SDLC), from gathering requirements to design and testing, and also enhance developers’ productivity, among other benefits. But it still has limitations. Read on to learn more.   

ChatGPT made headlines when it reached 1 million users in just five days after being unveiled in November 2022. Not only was the tech community awed, but it also has interested a wider audience, from students to industry veterans, and attracted more than 100 million users by the end of January 2023.

ChatGPT and other AI chatbots, such as DALL-E, are poised to radically disrupt multiple professions, including education and healthcare. In our ongoing coverage of this trending topic, we’ll explore how these recent developments may rapidly advance the application development process.

What is ChatGPT, and why is it creating major upheaval?

ChatGPT (Chat Generative Pre-Trained Transformer) is a chatbot built by AI firm OpenAI. It is based on Generative Pre-Trained Transformer (GPT-3) architecture, a neural network Machine Learning (ML) model that generates human-like responses to natural language text inputs. Its ability to converse like a human, answer follow-up queries, and reject inappropriate queries makes it more special than its predecessors. Its capabilities include language translation, text summarization, and text generation.

We tried our hands on ChatGPT and asked it to write a blog on itself, and the results amazed us. See the exhibit below for the blog that ChatGPT generated.

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Next, let’s explore in more detail how ChatGPT could be embedded in the Software Development Lifecycle (SDLC) to create applications and the associated benefits.

The avant-garde movement in application development

While low-code/no-code and AI-assisted application development made leaps and bounds in this field, ChatGPT has the potential to step up the game even further. This potent AI tool can be used to accelerate different processes at various phases of the SDLC, leading to faster development cycles, enhanced productivity of developers, and quicker value delivery to enterprises.

Here are the potential benefits of each phase:

Requirements gathering: ChatGPT can significantly simplify the requirements gathering phase by building quick prototypes of complex applications. It also can minimize the risks of miscommunication in the process since the analyst and customer can align on the prototype before proceeding to the build phase

Design: DALL-E, another deep learning model developed by OpenAI to generate digital images from natural language descriptions, can contribute to the design of applications. In addition to providing user interface (UI) templates for common use cases, it also may eventually be deployed to ensure that the design of a given application meets regulatory criteria such as accessibility

Build: ChatGPT has the capability to generate code in different languages. It could be used to supplement developers by writing small components of code, thus enhancing the productivity of developers and software quality. It even can enable citizen developers to write code without the knowledge of programming language

Test: ChatGPT has a major role in the testing phase. It can be used to generate various test cases and to test the application just by giving prompts in natural language. It can be leveraged to fix any vulnerabilities that could be identified through processes such as Dynamic Code Analysis (DCA) and perform chaos testing to simulate worst-case scenarios to test the integrity of the application in a faster and cost-effective way.

Maintenance: ChatGPT can significantly improve First Contact Resolution (FCR) by helping clients with basic queries. In the process, it ensures that issue resolution times are significantly reduced while also freeing up service personnel to focus their attention selectively on more complex cases.

While ChatGPT has an important role to play in automating more cognitive tasks in the SDLC, users must be aware that security and privacy concerns with the current version still need to be properly addressed.

Now let’s cover a few issues with the tool.

 Five possible roadblocks to ChatGPT adoption

  • Privacy and security – Privacy and security are concerns with the current tool. As it learns from each query, keying in any sensitive data would have drastic repercussions on enterprises. Amazon has reportedly warned employees to not put confidential data on ChatGPT, fearing security concerns
  • Limited knowledge – ChatGPT currently is not connected to the internet and has limited knowledge of the world and events after 2021, meaning the code it generates will not be in line with the latest security patches
  • Potential Bias – While OpenAI has added guardrails against bias in responses, users can occasionally get around this by rephrasing their questions or asking the program to ignore its guardrails
  • Inaccurate responses – ChatGPT responds to queries based on the patterns it learned from the training dataset and also can generate fictitious responses that cannot be verified for accuracy. Although the tool is still evolving, inaccuracy in responses can be a major hindrance to its adoption
  • Energy Consumption – As an advanced AI-based tool, ChatGPT takes a huge amount of computing power to process the information, leading to high energy consumption and carbon emissions. With environmental, social, and governance (ESG) becoming a key mandate across geographies, enterprises may be apprehensive about large-scale adoption

The way forward

ChatGPT is seeing rampant adoption among the developer community, and as it gains further traction, enterprises need to ensure suitable governance models are in place. Service providers need to collaborate with tech players like OpenAI and DeepMind to proactively shape the market and build capabilities for efficient application development.

As details unfold on how this technology will revolutionize the application development process, enterprises and service providers need to closely monitor this space and make proactive investments – clearly, the cost of missing out is too great.

For our other recent blogs on how ChatGPT will impact various industry sectors, see Can BFSI Benefit from an Intelligent Conversation Friend in the Long Term and ChatGPT Trends – A Bot’s Perspective on How the Promising Technology will Impact BPS.

We’ll investigate the implications of ChatGPT for the technology services industry in more detail in a follow-up blog.

To discuss how ChatGPT will impact the application development process, please reach out to [email protected], [email protected], or [email protected].

Digital Doppelgängers and Evil Twins: How Brands Can Guard against Identity Theft and Fraud in the Metaverse | In the News

Humans have a one-in-a-trillion chance of having a doppelgänger in the world—that is, someone who looks exactly like them down to their eyes, lips, and bone structure. But in an avatar-driven digital environment like the metaverse, another individual running around with your (digital) face is much more probable.

As reported by Everest Group in their “Taming the Hydra: Trust and Safety in the Metaverse” report, 55% of respondents in the US were concerned about the tracking and misuse of their personal data in the metaverse.

Read more in Fast Company.

ChatGPT – Can BFSI Benefit from an Intelligent Conversation Friend in the Long Term?

With the advent of chatbots reaching human-like sentience and mannerisms, and banks being at the forefront of adopting conversational Artificial Intelligence (AI), the question arises whether ChatGPT threatens the likes of Google, other AI platforms, and the non-critical workforce in the technology and services industries. While its promise remains high, will the banking, financial services, and insurance (BFSI) sector unearth ChatGPT’s full potential?  Read on to find out.

ChatGPT has taken the internet by storm and has become a trending sensation overnight. This AI-powered innovative chatbot has taken the world for a spin and is generating a big buzz among millions of professional users experimenting with it. Microsoft has also invested billions in the tool.

But what is ChatGPT? Developed by OpenAI, it is a generative language model that has been trained over large volumes of text to generate human-like responses. Like a search engine, it curates answers for queries but is designed to answer in a more conversational flow that goes beyond chat and delivers a richer experience with an intelligent chatbot. The AI engine generates solutions for all sorts of queries, including R, Python, and VBA codes.

Let’s explore ChatGPT’s potential to impact the future of AI and its usage in the technology and services industry, particularly by financial institutions, banks, and insurers.

What makes ChatGPT approachable and different to use?

  • The amount of data used to train the GPT model
  • Human-like interaction
  • Versatility and variety of responses
  • Low data input requirements
  • Highly scalable
  • Adjustable coherence and adaptability

What does it mean for banking and financial services?

Banks can use ChatGPT in several ways to enhance their operations and customer experience. Here are a few examples:

  1. Assistive chatbots: ChatGPT can be used to build natural language-based chatbots that can assist customers with common inquiries, such as account balances, transaction history, and bill payments. The chatbot also can guide customers through more complex processes like applying for a loan or a credit card. It also could help increase agent efficiency by aggregating requests by type to the appropriate departments
  2. Automation of simple and repetitive tasks: ChatGPT, along with other conversational AI models, can be used to automate simple and repetitive tasks, such as customer service interactions, order processing, and data entry. This can increase efficiency and lower costs for service providers and their clients
  3. Customer service: ChatGPT can assist the human agent in answering customer questions, improving efficiency and response time, and providing more accurate and detailed information. This can improve customer service and satisfaction and employee onboarding
  4. Marketing: Banks can use ChatGPT to analyze customer data and build personalized marketing campaigns that target specific customer segments. It also can generate personalized responses to customer inquiries by fine-tuning the model to a specific client, enabling it to generate tailored responses to their needs
  5. Decision Making: With the right database connections and integrations, ChatGPT can be used to analyze data to generate insights that can be used in decision making
  6. Learning and development: ChatGPT can be used as a learning and development tool. It can be trained with a company’s pre-existing data to create learning tools and modules and as an onboarding tool for new employees

Current mapping of ChatGPT to the BFS BPS value chain

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Current use cases of ChatGPT in banking and financial services (BFS) and business process services (BPS) operations are limited. Building capabilities around conversational AI and incorporating ChatGPT into offering portfolios can help BFS and BPS firms unlock innovation. Enterprises such as Microsoft, AWS, and Meta are developing their capabilities internally or through partnerships with conversation AI specialists.

Industries leading in innovation investments are becoming early adopters of ChatGPT. Microsoft is reportedly investing US$10 billion in OpenAI and plans to introduce it along with its Azure OpenAI service bundle in the Bing search engine. This furthers Microsoft’s stake in the market, where it already has a working partnership with OneReach.ai, one of the market’s leading conversation AI providers, since 2019.

Current capabilities still have hurdles to overcome

Although ChatGPT appears to have multiple uses and strengths, some limitations include:

  • Biased and inconsistent output: Content generated by ChatGPT depends on the trained data, making it prone to biases. It is difficult to achieve the same level of consistency in output generated. Cases requiring more context and complexities may lead to biased and inconsistent output. When training for complex operations such as trade reconciliation, exception management, and know your customer (KYC) remediation, the subject matter experts (SMEs) must be well-versed with minute details, which can’t be guaranteed when using ChatGPT
  • Standardized data requirement: ChatGPT cannot process different file types or extract information from them. A lot of consumer data is often received in varied file types and formats that require intelligent operations to skim through and sort, which is beyond ChatGPT’s current text-based data capabilities
  • Largely text driven: Its text-based generated content can fall short of expectations for the coming generation of users that desire more visual stimulation. Dashboards and descriptive analytics have become a basic requirement of all transaction-intensive industries that ChatGPT cannot fulfill
  • Limited ability to handle sensitive customer information: ChatGPT may not have the necessary security and privacy measures to handle sensitive customer information, such as account numbers or personal identification numbers. With the ever-evolving compliance norms varying across industries, it doesn’t yet have the capability or the secure framework to process, analyze, and interpret KYC or transaction data
  • Outdated information: ChatGPT’s information database is limited to data up until 2021 and can result in outdated opinions and facts. Deals, news, and updates in recent years aren’t recorded. For a constantly-evolving industry like BFS, where new deals and contracts dictate the capital markets, this makes the source of information unreliable
  • Ethical concerns: As artificial intelligence improves, the lack of proper credit for AI-generated content is becoming more widespread. The distinction between content created by AI and content created by humans is becoming less clear, causing confusion, mistrust, and ethical dilemmas
  • System Integration issues: Incorporating new technology with outdated systems can be difficult due to potential incompatibilities and differing protocols or data formats. This can decrease efficiency, add complexity, and impair interoperability

 Where will the future take ChatGPT?

While ChatGPT’s future looks promising, it is too early to say the product will revolutionize banking and financial services. Before it gets integrated into banking products, it needs to overcome several hurdles, including:

  • Responding to competition from rising financial technology (FinTech), regulatory technology (RegTechs), and other AI/Machine Learning (ML) service providers
  • Meeting regulatory, compliance, and cybersecurity requirements
  • Catering first to front-office requirements for low-critical queries and then for more complex queries and back-office operations that have not yet been explored
  • Maintaining high operational efficiency, accuracy, and customer satisfaction
  • Expanding variation in output categories
  • Overcoming the lack of recent factual data

Though ChatGPT use cases are promising, it is still a machine learning model that needs modifications to be used in real-world applications. The model would have to consume specific industry data to build domain depth and be programmed to manage contextual nuances for various tasks. Its ultimate success would depend on end customers’ user experiences.

While the road is being paved for innovation, ChatGPT still has a long way to go before making strides into banking and financial services.

To further illustrate the nature of results and drill down on the capacity of ChatGPT, below are some screenshots for financial crime and compliance queries (platforms, codes, advisory):

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If you have questions about banking and financial services trends or would like to discuss developments in this space, reach out to [email protected], [email protected], and [email protected].

Also, download our Navigating the Regulatory Tightrope via End-to-End Solutions – Financial Crime and Compliance (FCC) State of the Market 2022 report to explore key trends. Stay updated by following the latest research on Banking and Financial Business Process Services.

Top 5 Stories of the Week: Deloitte’s Cybersecurity Predictions, the True Cost of a Breach, AI’s New Diet | In the News

A new report released this week from Perception Point and Osterman Research found that, on average, companies pay $1,197 per employee each year to address cybersecurity incidents — which can add up quickly the larger an organization is.

Sandeep Pattathil, a Senior Analyst at the IT advisory firm, Everest Group, told VentureBeat that a major challenge still ahead will be quantum computing’s algorithmic advances — not speed.

Read more in VentureBeat

New Enterprise Learning Tools Put Employees in Control | In the News

Before the COVID-19 pandemic, online learning and a growing category of “learning experience platforms” were already taking off, augmenting and sometimes replacing stodgy learning management systems that had been around for decades.

Priyanka Mitra, Practice Director at research firm Everest Group, said AI plays a crucial role in shifting learning from a one-size-fits-all approach to a more personalized experience for each employee.

Read more in TechTarget

Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

Top Analytics and AI Services Specialists

Enterprises looking to adopt Artificial Intelligence (AI) initiatives are finding it difficult to implement them at scale due to data-related challenges, inability to acquire skilled talent, advanced IP, and lack of AI and cloud capabilities. Hence, they are turning to analytics and AI services specialists to serve their needs. In turn, these providers are improving their capabilities through investments in talent, products and platforms, partnerships, industry expertise, and AI-based solutions designed to serve specific client needs.

In this report, we present an assessment and detailed profiles of 22 analytics and AI services specialists featured on the analytics and AI services specialists PEAK Matrix®. Each provider profile presents a comprehensive picture of its service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. The assessment is based on Everest Group’s annual RFI process for the 2021 and 2022 calendar year H1 (January-June), interactions with leading analytics and AI services specialists, client reference checks, and an ongoing analysis of the analytics and AI services market.

DOWNLOAD THE FULL REPORT Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

What is in this PEAK Matrix® Report

This report provides a detailed analysis of 22 analytics and AI services specialists and includes:

  • Everest Group’s PEAK Matrix® evaluation of analytics and AI service providers and their categorization into Leaders, Major Contenders, and Aspirants
  • An overview of enterprise analytics and AI priorities and key challenges in scaling AI
  • Key analytics and AI services trends
  • A detailed assessment of the strengths and limitations of the providers in terms of their market impact and vision and capability

Scope:

  • Industry: data and analytics
  • Geography: global

LEARN MORE ABOUT Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

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What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

IMC 2022 Highlights: India Mobile Conference Focuses on 5G Business Opportunities | Blog

With the launch of 5G in India last month, the 2022 Indian Mobile Congress (IMC) demonstrated many exciting possibilities for the high-speed network to deliver innovative use cases in India. Beyond the technology benefits, 5G can be leveraged to solve efficiency and optimization challenges and enable future growth for enterprises. To learn more about 5G business opportunities, read on.  

India embarked on its “new digital universe” with the official unveiling of 5G technology by Prime Minister Narendra Modi at the sixth edition of the Indian Mobile Congress (IMC), Oct. 1-4 in Pragati Maidan in New Delhi. In this blog, we share some of our key takeaways from the event organized by the Cellular Operators Association of India (COAI) and India’s Department of Telecommunications (DoT).

The evolution of connectivity technologies with 5G as a platform for boosting productivity and innovation was among the key themes that emerged from this India mobile conference that drew an enthusiastic response from technology service and infrastructure providers, manufacturers, industry and government officials, academia, and the public.

Shifting narrative: from explaining technology to showcasing possibilities

While the 5G benefits of increased connectivity speed, low latency, and improved reliability are now well known, the India mobile conference highlighted several 5G-enabling technologies. These include carrier integrated 5G network (low- and mid-band); open-source technologies and architectures (O-RAN); network cloudification through Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Multi-Access Edge Computing (MEC); small cell 5G architecture, private 5G, network slicing, and Fixed Wireless Access (FWA).

An interesting highlight of the event was the increased emphasis on showcasing the applications of 5G. Among the possible use cases spotlighted were massive and critical Internet of Things (IoT), machine-to-machine communication, collaborative robotics, autonomous driving, vehicle edge computing, metaverse and Augmented Reality (AR) powered collaboration, predictive maintenance, remote surgery, real-time analytics and decision making, cloud-based gaming, smart cities solutions, intelligent supply chain and logistics, and smart retail.

With 5G resolving connectivity problems and other building blocks like cloud, Artificial Intelligence and Machine Learning (AI/ML), and IoT now mainstream, enterprises have all the needed elements to optimize and modernize their technology landscape and capture the next wave of growth opportunities.

5G for sustainability: an emerging conversation

While 5G network equipment and components are generally expected to consume more power than the previous generation, recent equipment and software innovations aim to make products as energy efficient as possible.

Some examples of the energy-efficient technology presented at IMC included lightweight massive Multiple-Input Multiple-Output (MIMO) radios and software solutions such as traffic-aware dynamic network management solutions for energy monitoring and management that provide 5G levels of expected network performance while consuming the same amount of energy as the traditional 4G network.

5G also is expected to power the next generation of sustainability applications around Greenhouse Gas (GHG) emissions monitoring and management, optimal resource management, smart transport, and other uses. Its higher bandwidth will make it possible to connect large numbers of IoT devices over the Internet and enable faster decisions through increased connectivity speeds and low latency.

Turning possibilities into practicalities: the need for building a contextualized business case

While 5G offers numerous benefits, from optimization and efficiency to unlocking new growth avenues, the strategic business value needs to be clearly communicated to enterprises.

Currently, the 5G ecosystem is a bit fragmented, with different types of players offering their own strengths. For example, OEMs are focusing on improving the equipment and hardware; communication service providers are focused on increased speed and low latency; and system integrators (SIs) bring data, AI/ML, IoT, and cloud expertise.

To move to the next level, industry players need to combine 5G’s benefits of connectivity, reliability, and low latency with AI/ML, IoT, and cloud to build business use cases that add value to enterprises beyond just showcasing the possibilities.

Ecosystem players need to help enterprises realize that 5G is not only an improved wireless network technology but also a solution to their long-standing efficiency and optimization challenges that can enable their next wave of growth.

To further discuss the India mobile conference and how to capture the most value from 5G business opportunities, please reach out to us at [email protected] and [email protected].

Watch our webinar, What’s Ahead After a Decade of Digital Transformation?, to hear our analysts share perspectives on what’s in store for the digital transformation industry in the next ten years.

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