Artificial Intelligence has ceased to be merely a familiar technological advancement of the future. Instead, AI has become the fundamental tool that enables vital changes within an organization, from the operators to the final decision-makers. More recently, sales leadership has been one of the areas gaining the most from AI advancements. As organizations continue grappling with the increasing complexity and competitiveness of the business environment, IT sales are being propelled by AI and Generative AI, which are solving problems and providing sales leaders with new technologies on how to interact with customers, run queues, and make management decisions based on metrics.

Initially, the main task of artificial intelligence was the mechanization of monotonous actions. At present, however, it has matured into providing actionable insights, forecasts, and suggestions. In a recent Salesforce report, 52% of IT sales leaders deploy AI technology for enhanced productivity, with a further 85% predicting that it will form a core part of their strategies within the next five years. Companies that apply AI to their business report an increase in conversions of 10 to 15% and a growth of 5 to 10% in the rate of client retention.

Managing complex sales pipelines and client contracts is a constant headache for IT sales leaders. Failing to remember a renewal date or a milestone shoots a company in the foot, resulting in incoming revenue loss and, in the future, an opportunity loss as well. AI-based applications like Salesforce Einstein, Microsoft Dynamics, and HubSpot bring in real-time alerts for such cases, along with reminders for renewals and milestones. They allow sales teams to pursue customers and improve chances for retention as well as upsells. A Salesforce global report showed that the participating companies that had AI integrated within the sales cycle saw their pipeline accuracy increase by 50%.

A case in point here is Schneider Electric, a global energy management and automation leader, that implemented Salesforceā€™s Sales Cloud and Service Cloud as part of its “One Schneider” strategy to unify its systems and create a 360-degree view of customers. The company enhanced its CRM with AI-powered CRM Analytics and Einstein Discovery, which analyze data from multiple sources, including IoT, to identify sales opportunities and predict conversion likelihood. This AI-driven approach helped Schneider Electric reduce its sales cycle time by 30%, ensuring that global sales teams focus on the most promising leads.

It is equally important to ensure active scanning of new customer data. The most sophisticated tools, such as Perplexity AI and TextCortex, are now crucial tools. Unlike conventional analytical resources, Perplexity and several other resources combine offline and online information to create a picture of the target audience’s behavioral activities and tendencies at the moment. This allows sales teams to access such data whenever there is a change in clienteleā€™s needs or interests, making interaction with such clients more helpful and more focused. This function is most effective with highly developing industries or highly dynamic clients.

Tools of Generative AI, such as ChatGPT, CoPilot, or Google Bard, have also been of great use in ensuring that communication is effective and, above all, personalized. Enterprise sales teams can use these tools to automate the generation of personalized proposals, along with any follow-up and marketing copy that will appeal to their clients in no time. This level of automation ensures that interactions with clients remain relevant and personal, with no substantial input of manual effort, thereby easing the burden of responding to the increasing demands of customers in terms of their expectations for the degree of personalization and speed. The application of such tools is seen globally in many different fields, such as industry and finance, where there is increasing pressure to provide clients with tailored services and fast feedback.

Another common challenge is the degradation of knowledge, particularly during team transitions or onboarding new hires. AI-powered tools like Gong.io and Chorus capture and analyze sales conversations, while IBM Watson organizes and stores insights from past client interactions. These platforms reduce reliance on ā€œtribal knowledge,ā€ aid new hires in getting up to speed quickly, and ensure that best practices are documented and shared across the team. With teams often dispersed across different regions, these tools are crucial for maintaining consistency and knowledge continuity in global sales operations.

For example, Iron Mountain, a global leader in storage and information management, used Gong to reduce new-hire ramp time by 3 months and improve sales effectiveness. By leveraging Gongā€™s AI-driven insights, the company provided data-backed coaching and visibility into sales calls, helping new reps quickly identify the best practices and improve their performance. As a result, 60% of new reps hit their key metrics within five months, compared to just 9% before Gong, leading to a 148% improvement in performance. Gongā€™s call tracking and coaching features enabled more efficient onboarding and faster ramp-up, even in a fully remote environment.

As we have seen, therefore, both AI and Generative AI are transforming the IT sales landscape as they allow leaders to more efficiently manage their pipelines, enhance their customer interactions, and protect crucial information. They enable sales leaders to tackle the challenges of modern, fast-paced, global markets effectively. They can remain in touch with customers, be aware of the latest trends and strategies, and be prepared for expansion. As AI evolves further, it has been predicted that AI will complement sales functions with increasing significance, and it is likely to become an important success factor for IT sales leaders globally.

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Author:

Harsh Agarwal,
Senior Manager – Business Development

If your business is caught up in endlessā€”and sometimes confusingā€”discussions around why, how, where, and when to adopt Generative AI, take heart. You are having the right discussions. Sooner rather than later, your organization will have taken major leaps with Generative AI. Gartner says that over 80 percent of enterprises will have used Generative AI APIs, models, and applications in production environments by 2026. A Gartner study also found that enterprises with pilots in Generative AI had risen from 15 percent in March-April 2023 to 45 percent by October 2023. Another 10 percent were already in production mode. The race to get ahead by infusing Generative AI into business and IT functions is well and truly on. Is there a way to make the race go well? There is: If the Generative AI pilots and in-production initiatives are aimed at automation, analytics, and applications, the investment will likely deliver visibly fast ROI. The three domainsā€”automation, analytics, and applicationsā€”form the 3A Axis around which most enterprises should place their initial bets.

Where and How to Apply Generative AI: The 3A Axis

Automation: The first mile in automation has been run by most enterprises. Simple, everyday tasks have been automated, eliminating manual labor and improving workforce productivity. These are tasks like employee onboarding, clearance, settlements, placing orders, generating reports, transferring data, using computer vision to scan products for quality assessment, etc. Generative AI is now being used to extend the automation footprint to knowledge automation. Some examples of knowledge automation include using large language models (LLMs) to contextualize and interpret enterprise knowledge, such as summarizing legal proceedings, an insurance claim, or customer feedback. It could even help create new knowledge or new content. For example, it could be used to provide new incident mitigation processes or turn customer reviews of a product into a story. The story could then be given a creative visual interpretation by Generative AI tools, in effect creating content that did not exist.

Analytics: A new paradigm in the consumption of analytics is arriving in the wake of Generative AI. Until now, traditional data warehouses have been trawled, and various toolsā€”regression analysis, Monte Carlo simulations, etc.ā€”have been applied to the data. The output is tables, graphs, charts, diagrams, histograms, maps, box plots, etc. Now imagine a marketing manager asking a simple question, in plain English, using Generative AI, ā€œWhat is the sales trend in EMEA?ā€ or a product manager asking, ā€œWhy do customers hate my product?ā€ and getting an answer in plain English. It changes how analytics is consumed and, more importantly, changes who can consume it by removing the technical literacy required to consume analytics. In truth, the powerful capabilities of analytics are unlocked by an ability to ask the right questions. In a Harvard Business Review article, researchers Hal Gregersen, Senior Lecturer in Leadership and Innovation at theĀ MIT Sloan School of Management, and Nicola Morini Bianzino, Global Chief Technology Officer of EY, say their studies showed that 94 percent of the time, AI-led respondents had questions that differed from non-AI-led respondents. This means Generative AI can help non-technical users in asking more abstract questions, shifting the focus of analysis from identification to ideation.

Applications: Over the last decade, AI has been steadily used to improve enterprise applications, ensuring a more consumer-style experience with intuitive and accelerated workflows. Generative AI has set this trend into turbo mode. SAP has Joule, a natural language processing (NLP) Generative AI copilot, embedded in its HR, finance, and supply chain applications. Joule lets users chat with the enterprise data and ask questions in plain English, such as, ā€œHow many sales orders were canceled last year?ā€ Salesforce has Einstein, a Generative AI assistant, for its CRM application. ServiceNow has Now Assist, which leverages Generative AI to speed up app delivery. The user describes the process flow and Now Assist authors the code.

The changes triggered by Generative AI in automation and analytics can be best harnessed through a CoE. Today, the basic foundation blocks of Generative AI, like data, analytics, knowledge models, LLMs, NLP, and neural networks, are housed with the data science team. Interestingly, we are seeing an integration of data, analytics, and automation CoEs.

Most large organizations have an enterprise-level data and analytics CoE to further the cause of data democratization and to unlock the value of analytics. These organizations have also created CoEs for automation. However, the boundary of automation has moved to knowledge-based domains.

Today, knowledge-based tasks are getting automated using Generative AI. A simple example is a Generative AI-based research assistant that lets legal experts converse with massive libraries of digitized legal information. Marketing teams can create text, audio, images, and video using text prompts with ChatGPT, Audiogen, Stable Diffusion, and Synthesia.

To stay on top of these developments and leverage them, organizations need to extend their traditional data, analytics, and automation CoEs without setting up a new one. They can do this because Generative AI uses the same tools and skills. These tools can be merged to create a Generative AI CoE.

The Challenges of Applying Generative AI

The advances in Generative AI are not without hiccups—some of which revolve around the ethical use of data, loss of fidelity, and nonsensical/hallucinatory outputs, and can be inordinately frustrating and challenging. Additionally, there are caveats related to cost and practicality. Large Language Models can be expensive, difficult to maintain, and lead to ethical conflicts. Therefore, in several instances, organizations will do well to train their Generative AI models on data owned by the organization instead of relying on publicly available data. Aside from eliminating anxieties around public data sets, using internal data also produces early success with Generative AI. Over the last six months, our experience has shown that when internal data is used, the fidelity of the results is high.

Our experience also shows that marketing agencies use Generative AI for images more effectively than IT service providers. Image generation tools are maturing rapidly. Marketing agencies find integrating these tools into their existing workflow and traditional toolsets easier than a BPO. By contrast, audio and video tools are not as mature and have inherent problems like deep fakes and IP violations.

From Pilots to Production, Securely

We know that Generative AI pilots have taken off, and experimentation has begun. However, few organizations have built a Generative AI CoE that can scale up their initiatives and ensure consistency of results in production. But organizations know they must put in place a strategy for Generative AI adoption so they can move from pilots to production and quickly scale their initiatives for broader adoption across the organization. The strategy must include two vital components: data privacy so the organization can enjoy public trust, and compliance requirements that governments are quickly implementing.

The reality is simpleā€”but daunting. The technology is evolving rapidly; the jump from, say, a ChatGPT 3.5 to ChatGPT 4.0 will not be incrementalā€”it will be steep, sharp, and demanding. The tools will be expensive. Identifying the right use cases to deliver ROI will be difficult. And the ethical challenges will be a headache. What organizations need at this point are frameworks and assessments to navigate around the technology and help make the right decisions. And it is the decisions that are taken wisely, with the insights and expertise of technology partners, that will help create the lightning rods for Generative AI within the organization.

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Author:

Sandeep Kumar,
Sr. VP & Head Global Consulting

No one will argue with the fact that the technology that has created the most intense ripple effect across industries in recent times is Generative AI. The progressive penetration of this technology into the workforce of organisations is posing a threat to the companies which are still evaluating its potential applications. Traditional Artificial Intelligence (AI) has been predominant in multiple areas of business transformation for quite some time. However, introduction of generative AI has added a new dimension to the transformation process across industries like CPG. CPG industry has long been a prime user of AI technologies like Machine learning & Predictive analytics to resolve the mystery of consumer behaviours. In last few years this industry has seen manifold increase in adoption of digital technologies due to emergence of D2C (Direct to Customer) business model. The aspirational journey of D2C could be further accelerated by the features of generative AI through proper applications. Potential of this technology could generate significant value in areas like Sales & Marketing, Customer operations and Product R&D. As McKinsey report highlights that ~75% of total annual value from generative AI use cases are accounted for in above areas along with Software engineering. Functions like supply chain & logistics, which are at the core of D2C business model, are also not far behind from reaping benefits of this technology.

Following benefits are lying ahead for the CPG & D2C players who aspire to become game changers in the industry.

Marketing & Sales:

• Content creation: Generative AI is undoubtedly a useful tool for potential savings in time and effort for content creation. Primary use could be preliminary idea generation. Even this tool could be used to create content out of collective ideas generated by different team members.
• Consumer preference: Ability of generative AI to read through texts, videos and photos to retrieve useful information and analyse those to generate insights within short period could help D2C firms create vast number of individual consumer profiles with higher accuracy. Based on individual preferences this tool could be used to draft engaging campaigns and advertisements to increase the probability of conversions.
• Market research: This is the area that is receiving increasing focus in D2C firm because of the rising competition. Generative AI could be deployed to understand overlapping areas of feedbacks received from consumer researches, social media views, academic research outcomes and responses from online campaigns.
• Customised offerings: Customisation is at the core of D2C business model wherein firms have already deployed traditional AI tools and analytics models. Generative AI could prove effective in this space by its ability to convert text-to-image for visualisation of offerings through interplay of colour, textures, ingredients, tastes etc.
• Synthetic customers: Unique value proposition of generative AI is mimicking human actions based on analysis of responses. D2C firms could utilise this facility to analyse customer feedbacks and generate ā€˜Synthetic Customerā€™ which is a digital replica of actual customer exhibiting purchasing preferences. This could help companies revisit their existing strategies of getting closure to any customer and improve the chances of lead generation.

Products & Services:

• Product Innovation: Product conceptualisation might become easier with generative AI through analysis of market research data, consumer preferences, competitorsā€™ activities and consumersā€™ browsing history. Moreover, simulation of product formulations considering combinations of ingredients, their pricing and attributes could help create improved product variants with increased monetary savings.
• Packaging Design: An attractive and appealing packaging that provides relevant information within shorter viewing span is always preferable in CPG industry. Generative AI could add value to packaging design by processing & recognising consumer preferences and market needs from texts, photos, videos, research articles and social media views.
• Product portfolio & pricing: Generative AI could analyse the pricing of competitors products, sales data, market trends and consumer preferences to suggest optimised pricing. Sales trajectory, stock movement, consumer demands and market demands are useful information which could assist this tool identify low performing products that need replacement with a more effective solution.
• Customer service: Generative AI could improve the quality of interactions by engaging in more emotional dialogue through analysis of previous interactions. The tool could also be deployed for answering multiple customers at a time. Its ability to retrieve historical data about similar problems and suggest probable solutions could help human representatives in dealing with customers.

Supply chain & logistics:

• Inventory management: The advanced algorithms used in generative AI enable it to continuously learn through texts, images and videos and derive insightful patterns and trends out of consumer demands and market dynamics. Such a feature is crucial to recommend stock planning at factory outlet and warehouses within shorter time span as faster delivery has now become the driving force for the success of D2C business model.
• Demand forecasting: Accuracy of demand forecasting is a function of consumer buying behaviour which is the foundation for any D2C business. Firms that have higher accuracy in forecasting are set to win more than half the battle of customer acquisition. Generative AI with its advanced analytical engine could easily enhance the existing models with more accurate predictions made out of multiple factors.
• Route optimisation: By reviewing the probable routes from geographical map and analysing historical trends of traffic conditions along these routes, generative AI could recommend the fastest delivery route to consumers. Generative AI could also create convenience to customers by recommending customised schedule of delivery based on previous choices and take the firm one step closer towards a trusted relationship.

Although above benefits are going to make generative AI tempting in the long run, there is a note of caution while using this tool. Generative AI works primarily on the set of data fed into the model. Hence, relevance and authenticity of data is a concern for generating output from this technology. Moreover, there is always risk of plagiarism, copyright infringement, violation of property rights and erosion of brand value when contents are generated from publicly available information. Further, D2C firms must take note that rising adoption of generative AI will only provide a level playing field among competitors. Itā€™s the vision and strategy of any firm to find the right avenue of application of this technology so that it could convert itself as a mere facilitator to a generator of business in the long run. 91¶¶Ņõ being an experienced digital solution provider in CPG industry is geared up to help D2C firms accelerate their business with right applications of generative AI.

For more information, contact 91¶¶Ņõ.

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Author:

Debal Chakraborty,
Principal Consultant

Artificial Intelligence (AI) commands significant attention in boardrooms worldwide, with the buzz amplified by the swift proliferation of generative AI. Business leaders are swiftly acknowledging AI’s potential to automate tasks, extract valuable insights, and create new content, especially in performance management, where traditional processes often burden managers with data collection and interpretation.

Generative AI (Gen AI) is the latest in AI technology, offering expanded capabilities that can potentially transform the modern workplace and boost productivity significantly. As per McKinsey, generative AI has risen as a transformative power in the contemporary business landscape, with nearly 60% of organizations already adopting Gen AI. These advanced tools are mainly used in key business functions, which mirror the areas with the highest overall AI adoption: marketing and sales, product and service development, and service operations. This indicates that organizations are strategically adopting these new tools in areas where they can derive the highest value.
So, how can generative AI for business enhance employee effectiveness and productivity while facilitating informed decision-making, process streamlining, and cost reduction?

How Gen AI Acts as a Catalyst for Business Productivity

The benefits of Gen AI for business productivity extend far beyond content creation, impacting various sectors and elevating organizational productivity. Differing from traditional AI systems, which react to inputs based on predefined rules, Gen AI models possess the capability not only to react but also to generate information. This advanced ability enables them to contribute to data creation and analysis, offering a more dynamic and proactive approach to information processing.

Here are some ways that Gen AI can boost business productivity:

Customer Support ā€“ Many enterprises employ Gen AI to extract valuable insights from their knowledge management systems, FAQs, and guides. When the FAQ falls short of solving a customer’s issue, Gen AI takes the lead in addressing queries independently. It looks into intricate databases for technical data and offers solutions based on past cases, freeing human agents to focus on more complex customer inquiries, thereby elevating the overall quality of service.

Data-Driven Decisioning ā€“ Gen AI for business productivity can nurture a consistent, dependable data source, enhancing data depth and usability. Through LLM-based data augmentation, it enriches business datasets, elevating AI models’ intelligence. Gen AI adeptly handles structured, unstructured, and legacy data, uncovering previously unnoticed valuable data points. This shift transforms data from mere availability to profound accessibility and insights.

Streamlined HR Process Automation ā€“ With the help of Gen AI for business productivity, HR operations can be easily streamlined with automated tasks like resume screening, candidate matching, internal communication, and performance evaluation. This enables HR professionals to prioritize strategic initiatives, accelerates recruitment by swiftly identifying top candidates, simplifies internal communication through generated documents, and offers data-driven insights for employee development. Moreover, it boosts accuracy by automating payroll processing and attendance tracking, reducing errors, and ensuring seamless administrative operations.

Coding ā€“ Generative AI efficiently reduces the software development lifecycle (SDLC) by producing code and related tasks. Leveraging extensive code repositories, user requirements, and testing scenarios, it generates code snippets, creates user stories aligned with business needs, and devises comprehensive test cases. Additionally, it creates synthetic data, addressing privacy concerns and ensuring robust testing without the use of sensitive real-world data.

Products and Services Customization ā€“ Facilitating customer-centric customization through data analysis and advanced algorithms, generative AI enhances the customer experience in e-commerce, product design, and service offerings. The real-time adaptation based on customer interactions and feedback creates a personalized experience every time, thus fostering loyalty. Additionally, it aids businesses in creating diverse product variations that suit the varied needs of customers, aligning with customer expectations and brand identity.

Innovative Product Development: Gen AI transforms product development by broadening creative horizons and introducing fresh design possibilities. It leverages market trends, customer preferences, and existing data to inspire rapid, innovative concepts. Additionally, it streamlines the creation process with 3D models and virtual prototypes, reducing both time and costs. This AI also promotes iterative design with multiple variations and encourages cross-disciplinary collaboration to meet diverse requirements.

Integrating AI to Streamline Employee Workflows

A recent study conducted by Harvard Business School explores the impact of generative AI on highly skilled workers and finds that when artificial intelligence is used within its defined capabilities, it can enhance a worker’s performance by up to 40% compared to those who do not utilize it.

While the speed and ability of AI to stimulate rapid idea generation and produce persuasive text make it enticing for knowledge work, caution is advised for critical tasks. Some AI-generated responses may appear credible even when incorrect. As organizations integrate AI into employee workflows, they must proceed mindfully. The same study recommends implementing an onboarding phase to familiarize workers with where and how AI performs effectively, as well as areas where it may not be suitable. Roles need to be reconfigured by engaging employees from various roles in joint experimentation to optimize work structures.

Conclusion

Gen AI revolutionizes multiple functions, from marketing and legal to procurement, operations, R&D, and sales, prompting businesses to reassess traditional, siloed structures. It’s not about reducing staff numbers; it’s about reimagining team operations. By utilizing AI assistants to bridge disconnected workflows, applications, and knowledge bases, teams break free from silos, collaborate effectively, and deliver results.

With Gen AI’s growing problem-solving capabilities, employees must excel at problem identification, as they play a crucial role in triggering Gen AI to discover innovative solutions and opportunities.

Introduction

ø鱚³¦±š²Ō³ŁĢżĀ reports have suggested that by 2025, an estimated 30% of outbound marketing messages emanating from major corporations will be generated synthetically, representing a substantial increase from the meager 2% recorded in 2022, a significant impact of generative AI. Generative AI has captivated global imagination, democratizing its presence across society and business domains. Its impact spans from supply chains and IT operations to customer service and HR. Organizations face mounting challenges with the continuous growth of cybersecurity expenses and the escalation of regulation and governance demands.

As generative AI (gen AI) undergoes a transformative evolution, open-source platforms are making their way to the forefront of sales operations. Simultaneously, sales-tech players are making significant investments in gen AI innovations. These technologies are swiftly emerging as indispensable tools in today’s digitally driven landscape, characterized by increasing business complexity and speed.

Benefits of Gen-AI:

• AI-Generated PersonaĀ ā€“ Having a Gen-AI persona as a research assistant offers numerous benefits to organizations. It provides unparalleled efficiency by rapidly processing vast amounts of data and generating real-time insights. Its ability to learn and adapt enables it to consistently deliver accurate and relevant information. Additionally, a Gen-AI persona eliminates human biases and limitations, ensuring objective and unbiased research outcomes. It also enables seamless collaboration, allowing multiple users to access and benefit from its knowledge base simultaneously. With its 24/7 availability, organizations can leverage a Gen-AI persona as a reliable and unwavering research partner, enhancing productivity and driving informed decision-making.
• Improved Marketing and SalesĀ ā€“ Through the utilization of advanced algorithms, Gen AI possesses the remarkable capacity to harness patterns within customer and market data, thereby enabling the segmentation and targeting of pertinent audiences. Leveraging these capabilities, businesses gain the ability to conduct efficient analysis and identification of high-quality leads. This, in turn, paves the way for the implementation of exceedingly effective lead-activation campaigns and newsletters tailored precisely to meet the needs and preferences of the intended recipients. Gen AI’s innovative techniques revolutionize the lead-generation process, empowering businesses to optimize their marketing strategies and achieve unparalleled customer engagement and conversion levels.
• Risk MitigationĀ ā€“ By leveraging Gen-AI for predictive analytics and risk modeling, organizations can proactively identify and mitigate potential risks, enhancing overall risk management strategies.
• Continuous Learning and Adaptation ā€“ Gen-AI can continuously learn and adapt based on new data, improving its performance over time and keeping organizations at the forefront of advancements in their respective industries.

Challenges faced by organizations:

• Data Privacy and SecurityĀ ā€“ Gen-AI raises concerns regarding the privacy and security of sensitive data, necessitating robust measures to protect confidential information.
• Ethical ConsiderationsĀ ā€“ Critical ethical dilemmas can arise, including biases in data, the potential for misuse, and accountability for decisions made by AI systems.
• Transparency and InterpretabilityĀ ā€“ Understanding the decision-making process of Gen-AI algorithms can be challenging, making it crucial to ensure transparency and interpretability for regulatory compliance and user trust.
• Skill Gap and Workforce ImpactĀ ā€“ Developing and maintaining a skilled workforce proficient in Gen-AI technologies can be challenging, and organizations may need to adapt their workforce strategies accordingly.
• Fast-Evolving Tool SetĀ ā€“ The rapid evolution of the Gen AI tool sets presents an opportunity and a challenge. The technology advancements are marching far ahead of the regulatory and user adoption frameworks, thereby presenting a challenge to build Gen AI capabilities and get them institutionalized.
• Generative AI is a Unique Component of AI UniverseĀ ā€“ Gaining a comprehensive understanding of the scope and limitations of Generative AI is crucial, considering its widespread fascination and perceived omnipotence. It is imperative to discern the capabilities and, equally significant, the limitations of Generative AI to make informed decisions.

Conclusion

Gen-AI is pivotal in significantly augmenting companies’ return on investment (ROI) by optimizing efficiency, precision, and expediting business decision-making. By harnessing rapidly advancing NLP and LLM models, Gen-AI is poised to revolutionize process optimization, democratize AI accessibility, uncover untapped cost reduction opportunities, and streamline operations for greater agility. Its capacity to function as a digital assistant while amplifying human productivity and efficiency empowers organizations to operate at accelerated speeds and heightened efficiencies. Gen-AI is poised to reshape the work landscape, akin to the transformative impact of past innovations like the Internet or the mobile phone.

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Author:

Sandeep Kumar,
Sr. VP & Head Global Consulting