Catch Everest Group Partner, Ronak Doshi, as he talks about how generative AI can help enterprises overhaul customer experience. Ronak shared his personal journey and perspective around the new age of AI that is challenging marketers to deliver the consumer and business buyer experiences of the future.
The content ecosystem has evolved from text-based content in the 1990s to today’s short-form, engaging formats. Developing a consistent omnichannel content supply chain is crucial to meet consumer expectations. Read on to understand more on the challenges of developing a well-coordinated content supply chain, or get in touch.
The content ecosystem has evolved dramatically since the 1990s. Initially, the internet introduced digital content that was mostly text-based. Web 2.0 then brought dynamic, user-generated content, spurred by blogging platforms, smartphones, and social media.
The COVID-19 pandemic accelerated the shift toward the next generation of content preferences, favoring short-form, engaging, and easily consumable content. In this phase, which includes various formats such as text, videos, and AR/VR, the evolving content ecosystem continues to reshape consumption behaviors. As we move into a connected future, developing a consistent omnichannel content supply chain will not only determine which formats endure, but also drive the creation of new and more engaging content types.
In the ever-evolving digital world, content is the lifeblood of brand engagement and customer interaction. It drives awareness, consideration, search engine relevance, conversion, engagement, customer service, and even trains AI models. However, like everything else, content is governed by the laws of supply and demand. As content supply has exponentially increased on the internet and social media era, its value is diminishing, unless it is relevant and tailored to the consumer. While we do pay for some content with money, most of it is paid for with our time and attention—both of which are limited resources.
Is content still the king? Short answer, yes. Slightly longer answer: The right content for the right audience is.
In today’s world, personalization is crucial. Consumers now expect experiences tailored to their individual needs, preferences, and behaviors. To stay relevant and maintain a competitive edge, enterprises must create content that not only captures attention but also resonates deeply with their target audience.
However, enterprises face numerous challenges in developing a content supply chain, particularly in producing, distributing, and analyzing content performance to meet these expectations effectively.
These content management challenges pose significant issues for each stakeholder involved:
Content developers
Content developers often find themselves in a feedback vacuum. Without clear Key Performance Indicators (KPIs) to measure the effectiveness of their content, they struggle to understand what works and what does not. This lack of actionable insights prevents them from refining their efforts and aligning their work with broader business objectives.
Marketers
Marketers face hurdles in reaching and engaging their target audiences, due to a lack of relevant and precise content. The inability to tailor content to specific audience segments undermines the effectiveness of marketing campaigns. This not only reduces engagement rates, but also results in missed opportunities for driving conversions and building lasting customer relationships.
Enterprises
For enterprises, the inability to manage and scale content efficiently translates to untapped potential and lost opportunities. As the volume of content continues to grow, managing ithe content supply chain becomes increasingly challenging, often resulting in a chaotic and inefficient workflow. This disorganization limits the company’s ability to respond swiftly to market opportunities, optimize content for various channels, and ultimately drive business growth. Enterprises that cannot adapt their content strategy to scale risk falling behind more agile competitors.
Consumers
Consumers often bear the brunt of poor content management practices. When brands fail to deliver personalized and relevant content, consumers are inundated with irrelevant and annoying information. This not only leads to disengagement but also fosters a negative perception of the brand. Consumers today expect tailored experiences that cater to their individual needs and preferences. Brands that cannot meet these expectations risk losing customer trust and loyalty, which can have long-term negative effects on their reputation and bottom line.
The fragmented content landscape presents numerous challenges that hinder effective content management and impact various stakeholders. Addressing these inefficiencies requires a shift towards a seamless and interconnected content supply chain. Wondering how to make this happen? Stay tuned for part 2 of this blog series to understand the tools and technologies that need to come together to make this happen and fix the content supply chain.
If you have any queries on content supply chain, please reach out to Nisha Krishan or Prachi Rohira. You can also read our State of the Market report on revolutionizing the content lifecycle for further information.
Guidewire’s latest release, Kufri, showcases the company’s dedication to innovation, efficiency, and global reach in the insurance technology space. Emphasizing streamlined processes, advanced data analytics, and expanded global solutions, Kufri is set to enhance the competitive edge of insurers worldwide. Reach out to us to explore further.
In the rapidly evolving world of insurance technology, Guidewire continues to lead the charge with innovative solutions that cater to the industry’s growing needs. The latest “Kufri” release, the second of three planned releases for 2024, marks another significant milestone for the company. Named after the picturesque mountain town in Shimla, India, Kufri symbolizes Guidewire’s commitment to blending innovation with a global perspective. This release emphasizes process efficiency, accelerated time to market, and enhanced data analytics capabilities, all while expanding Guidewire’s reach beyond North America.
Guidewire’s strategic initiatives open up a multitude of opportunities for growth and market expansion:
As we look forward to Guidewire’s final release of the year, there are several areas where further advancements are anticipated:
Guidewire’s consulting and system integration (SI) services partner ecosystem plays a crucial role in supporting its global expansion and product development efforts. Here are some key opportunities for this ecosystem:
As Guidewire continues to innovate and expand, the Kufri release sets a strong foundation for future growth. The company’s focus on efficiency, data, and global expansion positions it well to meet the evolving needs of the insurance industry. With the final release of the year on the horizon, expectations are high for even more groundbreaking developments that will further solidify Guidewire’s leadership in the market.
Existing customers:
Based on current technology maturity and appetite for REQUIRED change, insurers must have a defined roadmap to maximize value from existing core investments, without making massive customizations. They should work with their SI partners to identify capability gaps and build future-proof playbooks to adopt plug-and-play solutions from the Guidewire marketplace.
New customers:
Insurers evaluating whether they should embrace modern core systems such as Guidewire must factor in off-the-shelf product capabilities and ecosystem-led scalability. They must bake in the integration effort involved in customizing the product to their business context and conducting a thorough cost-benefit analysis for the migration – for both the immediate term and long term. Enterprises must also adopt a “partner over vendor” mindset and encourage a two-way conversation, where the SI partners are incentivized to drive value additions and bring in best practices from other such engagements to drive on-time and on-budget implementations.
AI and cloud readiness
In line with the vision to scale data-driven decisioning capabilities, insurers should evaluate the potential of the Guidewire Data Platform and augmented data-sets for effective risk assessment and pricing capabilities. Insurers must gauge cloud-native and embedded AI capabilities and seek ongoing guidance from Guidewire and their SI services partners to ensure building a future-proof core tech estate.
Experience
Leverage Jutro’s off-the-shelf templates to accelerate the delivery of engaging digital experiences for policyholders as well as agents. Migrating to Guidewire Cloud allows insurers to access such updates quickly and adapt to evolving stakeholder needs, providing a more personalized and responsive user interface.
To learn more about Guidewire and the platform services market, please reach out to [email protected], [email protected], and [email protected].
Check out our webinar, Mid-market Digital Transformation: Insights and Outlook for 2025, for best-practice recommendations for adopting newer technologies, based on our analysts’ recent experiences.
In this blog, we explore how generative AI (Gen AI) is not only enhancing our personal and professional interactions but also ensuring that the essence of human connection remains central in our increasingly digital world. Reach out to us to explore further.
In a world where digital transformation is the norm, maintaining genuine human connections becomes our greatest challenge and opportunity. Gen AI isn’t just a futuristic concept – it’s a present-day reality reshaping how we interact on both personal and professional levels. Amidst the whirlwind of technological innovation, the essence of human connection remains a priority. Let’s delve into how gen AI is revolutionizing customer and employee experiences, ensuring that humanity stays at the forefront of our digital journey.
Gen AI is a technology that doesn’t just respond to your commands but anticipates your needs, understands your emotions, and adapts to your preferences. It’s a revolutionary leap beyond traditional AI, enhancing our interactions and deepening our connections. This isn’t about robots taking over; it’s about AI becoming a true partner, understanding us on a more profound level than ever before.
With its ability to sift through vast datasets and interpret the subtleties of human emotions, gen AI is transforming customer experiences into something remarkably intuitive and personalized. Picture your favorite brands offering customized solutions for your needs, with customer service that feels less like a transaction and more like a genuine interaction. Gen AI can improve operational efficiency in contact centers by 15-25% and create experiences that resonate on a deeply personal level, making every interaction feel uniquely tailored to you.
The impact of gen AI goes beyond revolutionizing customer interactions; it’s also redefining the workplace. Gen AI is transforming how we work, learn, and grow. Imagine walking into your office and having an AI-powered system that understands your work habits, suggests ways to enhance your productivity, and even recommends personalized professional development opportunities. This isn’t merely about efficiency – it’s about creating a fulfilling and engaging work environment.
By leveraging gen AI, companies can significantly reduce agent training time by 20-30%, enabling employees to reach proficiency more quickly. AI tools can help managers give personalized and timely feedback, making employees feel more connected and appreciated. AI-driven training platforms tailor learning experiences to each person, ensuring everyone gets the most relevant and useful training. This makes learning exciting, and employees more engaged with their roles. Additionally, AI can analyze how engaged employees are and suggest ways to boost morale and productivity. By understanding how we work and what we need, AI creates a supportive environment where everyone feels valued and empowered.
While diving into the digital deep end, we never lose sight of keeping it human. Gen AI might be smart, but it’s no substitute for real connection. So, how do we strike the balance? Simple: by putting empathy first and foremost in every interaction.
Think of AI as your trusty sidekick, not the hero of the story. By designing AI systems with empathy in mind, we ensure that technology enhances, not replaces, human interaction. For instance, customer service bots are programmed to recognize when a customer is frustrated or upset and respond with appropriate empathy and understanding and when to pass on the conversation to an agent. This isn’t about mimicking human emotions; it’s about creating a supportive and responsive environment.
Similarly, in the workplace, AI tools are designed to support employee well-being. For example, if an AI system notices a drop in an employee’s engagement scores or an increase in negative feedback, it can proactively suggest wellness resources or flag the issue to HR for a more personalized follow-up. These small, empathetic touches can make a big difference in creating a supportive and nurturing work environment.
Forget the man vs. machine showdown. It’s time to embrace collaboration. Gen AI isn’t here to steal our jobs; it’s here to supercharge them. By teaming up with gen AI, we can unlock new levels of creativity, productivity, and innovation. According to research by Everest Group, 65% of enterprises believe that gen AI will improve or transform their workflow, underscoring its potential to revolutionize the workplace.
AI can handle the repetitive, mundane tasks that often bog us down, freeing us up to focus on what we do best: creative problem-solving, strategic thinking, and building meaningful relationships. This collaboration between humans and AI can lead to more innovative solutions and a more dynamic and engaging work environment.
For example, in customer service, while gen AI can handle routine queries, human agents can step in for more complex issues, providing the empathy and nuanced understanding that only humans can offer. This not only improves efficiency but also enhances the overall customer experience, creating a win-win situation for both businesses and their customers.
As we gaze into the future, one thing’s for sure: gen AI is here to stay. But let’s not forget our roots. Let’s keep the human touch alive and kicking, even as we ride the digital wave into tomorrow.
The future of gen AI is bright, with endless possibilities for enhancing both customer and employee experiences. However, it’s crucial that we approach this future with a balanced perspective. While embracing the technological advancements that gen AI brings, we must also prioritize the human element, ensuring that empathy, connection, and understanding remain at the forefront.
In conclusion, maintaining human connections in the digital age isn’t just a matter of convenience – it’s a fundamental aspect of our humanity. As we continue to embrace the possibilities of gen AI, let us do so with a deep appreciation for the power of human connection. By harnessing the transformative potential of technology while preserving the essence of what makes us human, we can build a future where empathy, understanding, and compassion flourish in the digital realm.
Watch the webinar, Gen AI Unhyped: How It Is Evolving and How to Plan for Success, to gain insight into how to stay ahead using this emerging tech as a lever, and how to harness the full potential of gen AI.
In today’s competitive landscape, enhancing customer experience (CX) is crucial for brands to achieve superior growth and loyalty. This blog is the first in a series that will highlight tech providers and how integrated tech solutions can empower enterprises to meet CX challenges and seamlessly connect various tools for improved productivity and data flow. Reach out to us to discuss further or for questions.
Enterprises love talking about customer experience, or CX, and for good reason. Brands that prioritize CX (think Apple, Amazon, Disney, or JP Morgan) consistently outperform their peers in terms of topline growth and customer loyalty.
However, to get to this place of superior CX, enterprises need to invest in a strong backbone of integrated tech solutions that can meet the challenges of an increasingly interconnected operational environment that transcends channels and places. With generative AI-led innovation, digital CX solutions are becoming more sophisticated and impactful.
Enterprises require multiple technologies within the CX tech ecosystem, such as data platforms, including customer relationship management (CRM) and Customer Data Platforms (CDP), contact center solutions, including cloud, AI bots, and agent support, as well as analytics tools for customer and sales insights. Additionally, marketing solutions like content management systems and social media tools are crucial.
The whole ecosystem has had time to become mature and rich with multiple compelling offerings in the market. However, enterprises seeking to simplify their transformation journeys and reduce the burden on IT and procurement are increasingly looking at ecosystems or platforms that can act as a one-stop shop for their technology needs. This approach also helps create a CX ecosystem where different tools can seamlessly integrate and connect with each other for better efficiencies and information flow.
Sensing this opportunity, numerous tech providers are working to address this emerging need in the market. Players such as NICE, Sprinklr, Salesforce, and even tech giants such as Amazon, Google, and Microsoft are moving in this direction. We plan to cover some of them in our series of blogs, starting with Salesforce, which is in the spotlight today.
Salesforce offers a range of CX solutions to enterprises for their customer experience initiatives. The company has developed a suite of CX solutions designed to support enterprises in their customer experience initiatives. Let’s have a look at some of these integrated tech solutions and what they’re capable of across the processes they cater to below.
Salesforce Einstein 1 Platform: The Salesforce platform’s AI capabilities bolster sales, marketing, and support strategies by unifying organizational data. The Einstein 1 platform enables enterprises to offer connected customer experiences, develop targeted strategies, and track their effectiveness. It also includes Einstein Copilot, a customizable AI assistant.
Einstein 1 is a modular platform that can be tweaked as per the enterprise needs and comes with core solutions for sales, marketing, service, commerce, and data that are natively integrated into the platform, along with other solutions that can significantly enhance these capabilities, such as analytics and AI. Let’s look at the core offerings under the Einstein banner in detail below:
(Image source: Salesforce)
Service Cloud: Service Cloud integrates customer service and field service needs onto the Einstein 1 Platform, connecting business data and apps to provide a complete view of every customer. Service Cloud helps businesses across industries deliver service from first contact to final delivery on multiple channels.
It offers features such as omni-channel routing, case management, analytics and dashboard, telephony integration, knowledge management, AI integration, service console for agents, community collaboration, social media integration, and field service among others.
Sales Cloud: Sales Cloud is a comprehensive platform designed to support sales organizations of all sizes, industries, and regions. It offers a wide range of tools and capabilities tailored for different roles within a sales team, including sales leaders, representatives, and operations. These tools cover various aspects of the sales process, such as prospecting, engagement, team collaboration, analytics, sales programs, performance management, partner management, CPQ (Configure, Price, Quote), and billing. Built on the Einstein 1 platform, Sales Cloud integrates harmonized data and connects to the broader Customer 360 ecosystem through Data Cloud, utilizing reliable AI to enhance its functionality.
Marketing Cloud: As the name suggests, Marketing Cloud is a digital marketing platform aimed at helping organizations manage customer journeys. Marketing Cloud comprises five main core capabilities, each focusing on specific aspects of digital marketing. The key products within Marketing Cloud are Data Cloud for Marketing (customer data platform), Personalization (real-time next best actions), Engagement (email, mobile, advertising, journeys, and loyalty management), Account Engagement (marketing and sales alignment, lead generation, and ABM), and Intelligence (performance insights, analytics, and reporting). The platform is aimed at B2C customers. The product architecture sits on its own platform apart from the core Salesforce infrastructure, using connectors for integration.
Formerly called Pardot, the Marketing Cloud Account Engagement is aimed at B2B customers. It can automate and streamline marketing processes and multifunctional campaigns across channels. It includes functionalities to manage and nurture leads, run targeted campaigns, align with sales to close deals, and track the effectiveness of marketing efforts.
Commerce Cloud: Designed for brands to create and manage online stores and scale personalized ecommerce experiences from acquisition to conversion to loyalty, Commerce Cloud offers digital storefronts for business-to-consumer (B2C) and business-to-business (B2B) customers, along with full-scale order management and payments solutions that can be connected to the rest of the business. Commerce Cloud converts insights into actions, driving revenue and loyalty throughout the entire customer journey by leveraging unified data and trusted AI on the leading AI CRM.
Besides these, Einstein 1 also features other solutions inside and outside the core platform. These include solutions such as:
Salesforce Einstein’s integrated and holistic operations support enterprises in designing their customer experience. Features such as low-code development, integrated AI, and unified data management along with a platform-based approach to CX transformation are some of its key features. With recent investments, especially in AI-focused organizations, Salesforce has shown an intent to focus on upcoming AI solutions and add those to its core offerings.
Of course, it is one of the several compelling options out there. Stay tuned to this space as we explore other integrated tech solutions that can help you on your CX transformation journey. For questions or to discuss this topic further, reach out to Sharang Sharma at [email protected].
Watch the webinar, Elevating CX: Trends and Insights for a Unified CX Tech Strategy, to learn how integrating service, sales, and marketing capabilities through a platform approach can streamline operations, centralize customer data for better insights, improve collaboration across departments, and enhance the overall customer experience.
Lest you think I’m channeling Jean-Paul Sartre’s 1944 play Huis Clos (No Exit for those of us English speakers), rest assured I’m not reliving my school French classes. But the title’s been swirling around in my head as I see global business services (GBS) ponder the next steps in their careers. Some are GBS careerists, happy to leap to a role in another enterprise, with (hopefully) more prestige, scope, and pay, while others simply want to do something else. With mobility relatively high, jumping from job to job seems to be within our leaders’ gift; however, moving into a non-GBS business or functional role, not so much. Are there enterprise career paths for GBS leaders who move from job to job, or is it a case of No Exit?
“I’m fully qualified to take on the role of (COO) (CTO) (Chief Transformation Officer) (Chief Digital Officer) (Head of X business). Why is my company passing me over? I’m a perfect match for the role.
Amongst us chickens, we’d like to assume that a GBS leadership role is the pinnacle of enterprise purview. We see across functions and processes. We know how to work globally. We transform. Relative to the rest of the enterprise, we are early adopters of technology tools. We own the data and can derive insights (like no one else likely can if only we’d focus on it). We can juggle the interests of a myriad of stakeholders. We have our CXOs’ attention. Capabilities such as these allow me to perform well in a range of roles. What’s there not to like?
Despite these impressive credentials, empirically, a relatively few GBS leaders brought in from the outside move into progressively responsible roles elsewhere in the enterprises they serve, as opposed to those leaders that come from the inside. The former are branded GBS; the latter group are seen as loyalists who live and breathe the company.
Now, it’s not impossible to stay, but there are barriers as well as conditions that foster mobility. Based upon what I see, an internal move is less likely when the leader:
But moves into other enterprise positions are possible for bought-in GBS leadership. What needs to be true for those seeking internal mobility?
Now, you are probably thinking these barriers and conditions aren’t specific to the mobility challenges of GBS hires—and you’d be absolutely right. However, because we still have difficulty defining GBS capabilities and aligning them to those in other enterprise roles, the GBS leader fetched in from the outside, more often than not, has to move to a new company in the quest for career growth.
Are moves possible? Of course. Parting words for those leaders who have bought into their enterprises’ culture, align with the mission, and admire the leadership and want to stay put?
Good luck!
A top-notch customer experience (CX) can transform skeptical shoppers into loyal brand advocates. However, achieving this level of service can be challenging. With an ever-expanding stream of customer interaction channels available, AI can help enterprises manage these diverse touchpoints more consistently and coherently.
Modern customers, including GenZ and millennials, expect seamless experiences, whether voice, chat, or social media. However, many enterprises manage these channels separately, leading to disjointed customer experiences, fragmented data, and service inefficiencies.
For example, let’s say John adds a laptop to his cart on a retailer’s website but decides to buy it later. When he visits the store the next day, the sales associate has no information about his online cart. Frustrated, John calls customer service, but they also can’t access his cart details. Each channel – online, in-store, and phone – operates in silos, causing John frustration and ultimately leading him to abandon the purchase.
This fragmentation leads to delays and diminishes the customer’s trust and satisfaction. Additionally, valuable data gathered from these interactions remains isolated within each channel, limiting the ability to gain insights into customer needs and preferences. Such a fragmented approach can negatively impact CX, as seen below.
Source: Based on an Everest Group survey of over 600 consumers in Q3 2023
AI has emerged as a transformative force in integrating various customer interaction channels, breaking down organizational silos, and addressing the issues consumers are facing on both spoken and written channels. But how can AI in CX be the answer to solving customer issues?
AI revolutionizing data aggregation and analysis
Robust data integration and management practices are crucial for digital technologies to address the challenges of heterogeneity, volume, and velocity in customer data. AI in CX can revolutionize data aggregation, integration, and management in several ways. Automated data collection and aggregation through schema mapping, data normalization, deduplication, and automated Extract, Transform, and Load (ETL) tools ensure consistency across data sources. AI also modernizes data quality processes, enabling large-scale, accurate data annotation and labeling.
Further, generative AI (gen AI) creates bias-free, cost-effective synthetic data, enhancing AI adoption in sectors like retail, manufacturing, and autonomous vehicles. Traditional AI models also enhance data security and privacy by detecting threats in real time and automating data cleansing to improve reliability. AI-powered techniques revolutionize data analysis with descriptive, diagnostic, predictive, and prescriptive analytics, helping organizations interpret customer data and predict customer experiences.
However, challenges such as bias in AI models, the interpretability of black-box algorithms, and the need for robust data privacy safeguards must be addressed to fully leverage AI’s potential.
Reconciling customer lifecycle touchpoints through AI
Extending these traditional and gen AI tools to enhance the integration of data across customer lifecycle journey touchpoints – encompassing sales, support, and marketing – builds a comprehensive understanding of customers.
Traditional AI and ML algorithms unify disparate databases, integrating them into a central system, which allows seamless access to data from various departments. This provides customer service representatives with a 360-degree view of each customer. It enables organizations to monitor every interaction with their brand, integrating key information such as contact details, survey responses, purchase histories, and more.
On the other hand, gen AI leverages natural language processing (NLP) and deep learning models to personalize customer interactions. By analyzing vast amounts of data from various channels, such as social media, emails, and chat logs, gen AI creates detailed customer profiles. By integrating AI with customer relationship management (CRM) systems and customer data platforms (CDPs), organizations can deliver highly personalized and contextually relevant responses. This not only enhances customer satisfaction but also ensures a consistent and unified experience across all touchpoints.
AI-driven predictive analytics models analyze historical data to identify patterns and predict future customer behaviors. For instance, machine learning algorithms can detect early signs of customer dissatisfaction, allowing businesses to address them proactively. AI in CX can monitor network performance and automatically notify customers about outages, providing estimated resolution times and minimizing customer frustration.
Furthermore, AI-powered marketing platforms utilize automated data mining, real-time data processing, and advanced segmentation algorithms to target campaigns effectively. By analyzing past interactions, browsing history, and behavioral data, AI creates precise customer personas and segments. This enables businesses to deliver personalized marketing messages and offers at the optimal time.
Delivering personalized experiences at every stage
AI in CX not only streamlines data flow but also enables the delivery of personalized experiences at every stage of the customer lifecycle. Personalization is crucial. A 360-degree view of customers, enabled by AI, offers several benefits, including:
What does this mean to the customers?
Embracing AI in CXM is essential for businesses aiming to maintain a competitive edge. The ability to unify customer interactions across channels and deliver personalized experiences will be a differentiating factor.
By breaking down silos and integrating customer interaction channels, businesses can revolutionize their customer journeys and achieve long-term success.
AI is not just a tool but a strategic imperative for modern CXM. The future of customer experience is unified, personalized, and powered by AI—let’s embrace it.
If you have questions or would like to further discuss gen AI’s evolution, please reach out to Sharang Sharma or Joshua Victor.
Watch our webinar, Elevating CX: Trends and Insights for a Unified CX Tech Strategy, to discover how leveraging unified platforms and innovative technologies can help businesses scale, increase agility, and create seamless, personalized customer journeys.
Generative AI is rapidly transforming industries as it evolves. Read on to learn how generative AI developments are impacting functions, including personalized learning, content creation, and web search, and surfacing the need for responsible AI practices. Reach out to us to discuss this topic.
Generative AI is fast transforming various aspects of the technology landscape. Major updates and launches announced in the OpenAI Spring Update, Google I/O, and Microsoft Build event this year, show how rapidly this technology is evolving. At present, the artificial intelligence (AI) market is marked by technology companies looking to rapidly develop IP and shape eco-systems and standards and by providers and enterprises looking to evolve their business models to absorb generative AI.
Generative AI’s current functionalities and its rapidly evolving capabilities offer much in terms of potential benefits but also come with their fair share of uncertainties. Figure 1 gives an overview of the areas of generative AI impact that we will dive into.
Generative AI promises to personalize learning and make it interactive. It can empower teachers through AI assistants to offer more engaging and accessible learning. Examples of such context-aware AI innovations include OpenAI’s GPT-4o, Google’s LearnLM, and Microsoft’s Khanmigo. GPT-4o offers personalized and adaptive learning, identifying students’ strengths and weaknesses and providing solutions in their preferred learning styles, with multilingual support.
Recent generative AI updates have highlighted advancements in educational tools and platforms, showcasing new features and functionalities designed to enhance personalized learning experiences. Going forward, educators will likely be able to use generative AI tools to customize learning plans for students and understand their learning challenges through data and insights. Perhaps what is even more remarkable is the self-learning potential that generative AI offers. In a world where educators are largely overwhelmed, generative AI may be the force multiplier the education industry has been crying out for.
Generative AI-enabled learning tracks can help organizations thread the needle between scaling L&D initiatives and contextualizing them to different stakeholder needs. Generative AI may have the potential to not only provide 1-1 tutoring on emerging skillsets across a variety of languages but may also be leveraged to ideate and design the curriculum. At a time when the half-life of talent is becoming shorter, generative AI may be the answer to ensuring organizational L&D stays relevant and nimble.
While generative AI has a wide-ranging impact across the media and entertainment value chain, content generation is where the impact is most acutely felt (see figure 2).
One of generative AI’s most striking use cases has been the creation of hyper-realistic content that seems indistinguishable from artist or studio creations. Recent generative AI updates and advances like GPT-4o have made content generation easier. These technologies can recognize tone, multiple speakers, background noises and produce outputs with embedded emotion such as laughter and songs. Innovations like OpenAI’s Sora or Google’s Veo empower creators and professionals to generate high-quality videos across different cinematic and visual styles without requiring extensive filmmaking expertise.
Advances in content generation have sparked fears about the ongoing relevance and demand for creative roles. Stories like that of Hollywood filmmaker Tyler Perry putting the brakes on a planned US$800 million expansion of his Atlanta studio upon seeing Open AI Sora’s video generation capabilities do little to allay such concerns. While current concerns about AI taking over creative work are understandable, it is more likely that going forward, we will simply see creatives engaged in higher-order work while AI solves for more time-consuming tasks. Content may become more synthetic, i.e., generated as opposed to filmed, produced faster, and more personalized. We may even start tiptoeing towards real-time content generation.
Hyper-realistic generated content also opens the door for deepfakes. False images, videos, and sound clips mimicking public figures or enterprises can lead to public unrest and material damage. With multiple elections being held around the world this year, deepfakes can have a meaningful impact on political discourse. This has understandably led to increased government scrutiny toward generative AI companies. Beyond politics, deepfakes are increasingly being used to commit fraud. Related to this, an employee at a multi-national organization was duped into paying out millions of dollars to those the employee believed to be key stakeholders at the company.
Generated content also poses some interesting intellectual property (IP) questions. Who has rights to the IP of generated content? Is it the person who prompted the output? Is it the technology company whose algorithms are being used to generate the content? Do the individuals or organizations whose data was used to train the algorithm also have some stake in what the model produces? Apart from adherence to IP laws, those using generative AI to create content will also have to be mindful of possible algorithmic biases manifesting in the generated content. Increasing efforts around responsible AI and transparency are needed to ensure biases in training data don’t get reinforced through the usage of generated content to train/tune other models.
Generative AI is expected to have a massive impact on how web search takes place, and by extension, how online advertising plays out. Consequently, digital advertising, particularly SEO and SEM, are key areas being disrupted by generative AI (see figure 3).
The move from coursing through books at the library to typing out keywords in a search bar was one of the biggest shifts in how people looked for information. Similarly, the impending transition from typing out keywords to simply asking in natural language promises to be the next big shift.
Generative AI updates have introduced new features in search engines and voice assistants, transforming how users interact with these tools. Advertisers increasingly express concerns about bot traffic eating away at their ad dollars. How would they feel about bots being the norm? Imagine if search fundamentally shifts to an audio-visual interface, with those searching for information rarely scanning the website themselves. How might this affect existing advertising models? Here are some possibilities – advertisers may realize that the customer is no longer on the website and needs to be engaged elsewhere. This can lead to a shift in the advertising mix, with more audio ads being rolled into searched information. SERP1 ads may also become more expensive due to their proximity to the search interface. For publishers, the shift may be from using ads to monetize content to directly monetizing the content itself based on how it is consumed to answer questions. Ad exchanges may evolve to become a network for generative AI bots to find content at a given price point. While these are indeed speculations, what is clear is that we are on the brink of a fundamental shift in information search and, by extension, digital marketing. All stakeholders within advertising may have to reassess their role in the broader ecosystem – be it advertisers, publishers, or ad exchanges.
For now, the impact of generative AI on everyday information search is limited. We are starting to see the integration of generative AI tools into existing search engines. For example, Google has integrated generative AI into its search tool. Through this feature, Google can interpret complex visual questions, provide explanations, suggest next steps, and offer resources using an AI overview. Voice assistants like Siri are also getting an overhaul. Apple’s partnership with Open AI promises to provide Siri with generative AI capabilities. The search space had one undisputed king for a long time – generative AI looks to be one of those seismic events that has the potential to reshape this hierarchy.
Generative AI promises to have a wide-ranging impact across multiple sectors. Given the massive impact that generative AI can have, tech companies need to balance innovation with safety. Responsible AI (RAI) is fast becoming an area of focus for enterprises looking to invest in and scale generative AI. Figure 4 illustrates some key considerations that will shape emerging RAI policies.
Enterprises will increasingly look to collaborate with service providers and technology companies that prioritize data security and have effective governance setups to ensure responsible usage of AI. Implementing ethical guardrails is essential to unlocking the full potential of generative AI and ensuring its responsible usage. As user expectations and government oversight rise alongside AI’s evolution, companies that embrace RAI will be the ones leading the charge in this exciting new era.
If you have questions about this blog or would like to discuss recent developments in the generative AI space, please reach out to Abhishek Sengupta or Oishi Mazumder.
Watch the webinar, Gen AI and the Future of Cybersecurity: Advanced Strategies for Cyber Defense, for insights into new developments, emerging applications, challenges, and opportunities presented by generative AI in cybersecurity.
Global Business Services (GBS) must evolve and, as the iconic Queen song references, “break free,” from outdated practices. This blog explores how GBS organizations should move beyond their comfort zones when it comes to managing hybrid models, finding a balance for in-house and outsourced resources.
Since the inception of Global Business Services (GBS) almost two decades ago, enterprises have been experimenting with its resourcing model—whether to perform the work with their own resources, outsource the work, or create a hybrid resourcing model that is a strategic blend of both in-house and outsourced resources. In the early days of the model, enterprises leaned on business process outsourcers to get transactional delivery up and running. But, over time, as they mastered the art of location, hiring, and delivering processes, in-house delivery has increasingly become the option of choice when it comes to the delivery of work that is highly contextual and requires proximity to the stakeholder. As a result, GBS is often relegating third-party delivery for activities with standardized workloads, variable volume, and those that require specialized expertise and/or technology. Today, the norm is a hybrid of both resourcing models.
During the last six months, Everest Group interviewed eight GBS leaders across industries and asked them to shine a light on how they manage their GBS hybrid resourcing models. All aspire to put science and discipline into their operations, with some semblance of guiding principles and criteria that guide what is delivered in-house and what is delivered by third parties. However, in reality, GBS leaders’ biases—as opposed to data-driven decision criteria—have governed who does what, when, and how. No surprise—the leaders’ resourcing strategy is driven by a comfort factor and received industry wisdom. It’s usual to weigh past experience and beliefs about the value of contextual understanding, proximity, and the same name on the paycheck to stakeholders. As one of our study participants stated, “Proximity dictates the game—be it language, culture, or time zone,” while another said, “We find comfort in leveraging proven methods from our past successes because it minimizes risk.”
However, as scope increases, technology advances, and the level of partnership with the business changes, managing and governing hybrid models must evolve, ensuring that the right work is delivered by the right source in the right location, and that as conditions change, the model can flex to deliver to new business exigencies.
Why do GBS organizations fail to optimize their hybrid models?
There are inherent, real challenges in the way we manage our hybrid GBS models today.
The current chaos and the proposed unified model
As Everest Group has found, currently very few organizations have a focused hybrid management function with processes aligned to optimize operations. The function in and of itself is “hybrid;” it must connect the dots between a number of decisions that GBS organizations commonly make in a vacuum. Today, procurement or a dedicated GBS vendor management team manages compliance to a contract while GBS’s management team looks at other metrics, perhaps only those of in-house delivery. The strategy function is focused on locations and perhaps the business case for one source of labor or another in which location. HR has been taking a stab at workforce planning but failing in execution. And service delivery leaders usually focus only on in-house delivery without regard to how the outsourcer is performing. If we only took one step back, we would realize that each decision is connected to each other, and only when they are joined up can a GBS make optimum resourcing decisions. This disconnected approach means that important decisions are not well-coordinated, leading to suboptimal outcomes.
Now, consider a power trio working together seamlessly. At the top, the central strategy team guides where and how work should be done—onshore, offshore, nearshore, in-house, or outsourced—to balance cost, efficiency, and quality. On one side, HR handles workforce planning and talent management, ensuring the right skills are in place. On the other side, the unified service delivery team, an evolved form of the current GBS vendor management team, monitors performance, manages contracts, and ensures compliance to desired service levels.
To make this trio effective, two things become critical: investments in efficient tools and technology, and clear governance mechanisms to ensure all teams are on the same page. This interconnected approach is how GBS leaders can make hybrid resourcing not just functional but exceptional.
Why make the change now?
Let’s unpack the imperative and potential advantages of taking hybrid model management to the next level:
As the Queen song says, “I’ve got to break free.. I want to break free,” it hits home for GBS organizations, right? Most of them have recognized the need for change but are struggling to nail down the perfect formula for the trinity of right place, right work, right time. The fact is, there’s no one-size-fits-all solution here. GBS leaders are in for some trial and error, tinkering until they find that perfect mix. So, don’t fret if you’re still figuring it out. Keep experimenting, keep tweaking, and who knows, you might just stumble upon that magical hybrid balance that’s tailor-made for your tribe. Reach out to us to explore this topic further.
In just 78 minutes, a faulty update from CrowdStrike caused global chaos, grounding flights, disrupting hospitals, and halting banking services. This incident serves as a stark reminder of the urgent need for enterprises to bolster their resilience strategies. Read on to learn the essential steps enterprises must take to prepare for future disruptions. For more details, reach out to us to discuss this topic further.
CrowdStrike pushed a faulty sensor configuration update for Falcon that made the Windows devices crash; however, Linux and Mac devices weren’t impacted by this update. The update was pushed on July 19, 2024, at 4:09 UTC, and the remediation was provided on July 19, 2024, at 5:27 UTC – within 78 minutes, but these 78 minutes were enough to create waves that would result in major economic and societal impacts. CrowdStrike (or any other large software provider) can make kernel-level changes in Windows, and it was a kernel-level change that resulted in the Blue-Screen-of-Death (BSOD) error. This approach is very different from Mac, Apple revoked the kernel access to technology providers in 2020, but that resulted in a lot of technology providers having to re-write their entire software.
Microsoft confirmed that the number of Windows devices impacted was close to 8.5 million (around <1% of overall global Windows devices) in its recent press release, but we can’t ignore the severity of the impact.
Some of the major impacts were felt across the companies that directly dealt with end-consumers, including:
Microsoft called this outage a demonstration of the “interconnected nature of our broad ecosystem,” but this raises a lot of questions about how software updates are pushed, whether enterprises should trust all the updates, and what to do in such situations. In one interview, the Chair of the Federal Trade Commission said, “These incidents reveal how concentration can create fragile systems.”
This is not a one-off incident, and in no logical sense will this be the last either. Enterprises face several challenges in managing these kinds of incidents, but some of the biggest challenges are as follows:
Enterprises must prioritize building business resilience to address black swan events, such as the CrowdStrike update incident or the COVID-19 pandemic. Business resilience is the ability of an enterprise to quickly adapt to disruptions while maintaining continuous operations and safeguarding people, assets, and brand equity. This approach not only ensures long-term sustainability but also provides a competitive advantage, as demonstrated by airlines and banks that remained unaffected.
One of the core pillars of business resilience is cyber resilience, which is more about how to deal with zero-day attacks that can literally halt the business operations of a company. We have internally developed a cyber resilience framework called 5R. Our 5R framework can help enterprises remain cyber resilient in the face of such black swan events.
A parallel can be drawn for operational resilience, the other important half of business resilience, using the same framework – enterprises can look at these individual 5Rs of Ready, Respond, Recover, Reinforce, and Revamp from a business perspective. In CrowdStrike’s faulty update push case specifically, enterprises need to focus on Reinforcing their learnings and leverage supply chain best practices to make sure that the impact of black swan events can be minimized.
To summarize, here are some key actions enterprises should take for a long-term fix:
While talking to some enterprises over the “outage weekend,” we realized how the industry leaders are looking to build stronger OKRs around business resilience and tie them to SLAs. Some of the OKRs and corresponding SLAs that we discussed are added below:
| Objective | Key result | SLAs |
| Ensure operational continuity | Reduce system downtime by XX% | Maximum allowable downtime of XX hour per month |
| Enhance disaster recovery capabilities | Implement automated backup solutions across all systems | Data backup completed within XX hours of changes |
| Strengthen cybersecurity posture | Decrease security incidents by XX% | Incident response time of less than XX minutes |
| Improve supply chain resilience | Diversify suppliers for key components | XX% of key suppliers with alternative sourcing options |
| Boost employee readiness | Conduct quarterly business resilience training sessions | XX% employee participation in training sessions |
Enterprises should strategically identify and align with key service providers within their ecosystem to enhance business resilience, including preparation for black swan events. Service providers specializing in infrastructure management and cybersecurity services are ideal partners, as these areas are more crucial to overall business resilience. Opting for one or two partners enhances accountability and effectiveness in resilience efforts. Here are key recommendations for enterprises for choosing a strategic partner for business resilience:
The recent CrowdStrike update incident underscores the vital need for robust business resilience. To mitigate future disruptions, enterprises should invest in innovative resilience strategies, enhance cybersecurity measures, and collaborate with service providers to ensure continuous operations and safeguard their assets. To learn more about the 5R framework or for questions, reach out to Arjun Chauhan or Kumar Avijit.
Watch the webinar, Gen AI and the Future of Cybersecurity: Advanced Strategies for Cyber Defense, for insights into new developments, emerging applications, challenges, and opportunities presented by gen AI in cybersecurity.
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