Category: Blog

 Lipstick Doesn’t Look Good on Pigs | Blog

The global services industry adores shiny new toys. Every few years, the industry gloms onto something fashionable and pretty under the belief that if we have one of those, our GBS organization will be most admired. Remember analytics driving GBS value? That was circa 2011-2013. RPA anyone? Following close on the heels of analytics. Process mining? The 2016 era drug of choice. And then, perhaps with COVID pushing us to recreate the way our enterprises work, we pivoted to service experience as the new kid on the block. Don’t take it from me; according to SSON Analytics, over 60% of respondents to a recent poll say that they intend to focus on customer experience over the next few years.

The buzz around experience, almost as if it is a revelation, is somewhat of a headscratcher to me. Fundamentally, what is GBS all about—deliver whatever scope it’s been able to grab with excellence and get stakeholders to adopt and hopefully embrace new ways of working. Yet I see GBS organizations of all shapes and sizes focus on the latter as an afterthought, thinking buying a slick workflow technology is a way to get friction out of a service construct that frankly needs to be completely transformed through digitization. But leaving that aside, if accessing services and solutions is a headache, changing the experience for something the business doesn’t value or need is not worth doing and won’t stick. If a platform is built on suboptimal processes, how can it possibly deliver a seamless experience? And if the GBS team is not communicating a brand promise every day in every way, how can the implementation of a platform possibly solve world peace? You get the lipstick-on-a-pig analogy.

There’s a lot of podium time and tire-kicking devoted to the concept of service experience, usually as an umbrella term for the implementation of an easy-to-navigate workflow platform rather than the transformational, value-adding initiative it should be, moving to digital GBS. And GBS folk who hear the buzz are looking for best practices, use cases, sample business cases and gratis consulting from system integrators in the GBS know. With luck, better service experience has been successfully implemented by HR or IT, and there’s an approach to design and implementation that has been road tested to build on. But GBS is really a different animal; aggregating disparate processes and functions, metrics and underlying data, and juggling the needs of disparate customers is not for the faint of heart. There are preconditions for success that don’t allow for shortcuts. And, when the rules aren’t followed, no one—from the GBS team who provides service, the CXO who funds, to the customer who expects a certain experience—is happy.

Why doesn’t investment in service experience play out for many GBS organizations?

  1. “I want one of those, too” Someone presented a compelling use case at a conference, so GBS leaders, afraid of being caught out as not au courant or best-in-class, embark on buying a platform service experience without doing the ideation, planning, resourcing, alignment, and enablement that is a very heavy lift. We like to say no two enterprises and their GBS work the same way, yet we think that implementing service experience can be achieved with a cooky-cutter approach
  2. Underlying process is broken Like my lipstick-on-a-pig analogy? Designing and implementing a slick portal that orchestrates suboptimal processes is not only a waste of time, but it can also further denigrate the experience by frustrating the user. If underlying processes are not seamless and frictionless, no investment in experience engineering will cover it up
  3. Lack of linkage to change, stakeholder or business relationship management, and brand It’s a very holy trinity, and likely should be aligned programmatically if not organizationally. Service experience is not static; it will constantly evolve as business needs and GBS organizations’ mandate changes. A new experience without comprehensive change management is likely to fail. An approach to business relationship management that is not aligned with experience is suboptimal. And ultimately, experience is the creator of brand in the eyes of the stakeholder. If these are not approached as one, confusion will be the order of the day
  4. Selling an Amazon-like experience Nada, unless there’s a plan to fully digitize GBS operations. GBS is not selling shoes and paper clips that are easy to consume with exceptions few and policies uncomplicated. GBS does not and very likely will not have the funding to set up an all-singing, all-dancing consumer experience. So why set a bar that will never be reached, setting expectations that are entirely unreasonable?
  5. Wrong success measures For many GBS organizations, “put up and shut up” is the primary goal of an investment of experience, targeting no noise out of a transaction manifested by Tier 1 resolution. But experience is a driver of so many of our metrics. For example, a better experience can reduce the number of change requests. A good experience is the best insurance around the proliferation of shadow organizations. Experience is vanguard when it comes to growing GBS scope. Sure, turn-around time, first-call resolution, and the like are important metrics, but experience drives almost every aspect of GBS performance
  6. Prioritizing the wrong use cases It may seem sensible to hit the messy, complicated use case first in order to stop the noise, but other factors should be taken into account. Attacking which process renders a quick win that is a good proof of concept? Where are you likely to get the right resources? Which stakeholder will sing your praises from the rooftops when you take friction out of their delivery? The right starting point has strategic program implications
  7. No-impact reporting Friends, appointing a manager well-versed in implementing a platform to drive the change that experience represents reporting in the bowels of the GBS organization is a non-starter. If experience implementation and management isn’t a top-of-the-house initiative, it will be seen as a tool, not a brand, and not a new way of working. And senior GBS leaders won’t take it seriously
  8. Low patience Ok, we all know Rome wasn’t built in a day, but when it comes to improvement in service experience, GBS organizations often have unrealistic expectations. It takes time to move the dial on C-sat, and even more work to scale and keep the numbers within a reasonable range. Any GBS organization that expects that a sudden focus on service experience is a one-time Hail Mary pass is sadly mistaken
  9. Lack of training We’d like to think new ways of working can be made intuitive, but there are so many significant exceptions to the norm in GBS operations—such as regulation, localization, and unplanned events—that workflow will become complicated. Assuming that both the GBS team and users can easily figure it is a trap
  10. Not investing in easy-to-use, comprehensive, up-to-date knowledge repositories The first principle of experience is to avoid interaction by providing all the answers in an accessible, easy-to-navigate, topical repository (in English or the language of record if you please). Yet it’s a slog for most GBS operations to interrogate, model, align, record, update, and communicate how-tos. So they don’t. The result—too many transactions, frustrated users, overstretched GBS team

 

So, I’ve properly chastised the GBS industry for running to the next big thing. What’s the best antidote to service experience disappointment (besides making sure your processes are shipshape and Bristol fashion)? If you read my last year’s treatise

Service Experience: The Next Value Driver for Global Business Services – A Getting Started Guide, some of this may sound familiar. Here’s a quick list:

  1. Determine your brand, then align experience tenets So many GBS organizations haven’t a clue as to what their stakeholders actually think of them, nor do they know what their brand promise is. It’s not a logo or a tagline; rather, it’s service experience that is ultimately the manifestation of a GBS brand. If one of the tenets is responsiveness, how is it made real through a process and a tool? If alignment with the business is critical, how does GBS cater for both exceptions and high-value interactions? If going above and beyond is a brand hallmark, should there be a level of human intervention at certain points in the workflow? Building a brand from the bottom up—rather than starting with a logo first—is imperative
  2. Decide what you want your stakeholder to feel Is the output a frictionless transaction? An efficient escalation? Collaborative solutioning? Not all experiences should evoke the same reaction. Step back and shape experience not on metrics, but perception. Should stakeholders believe they are heard? That their time is valued? That they are important? Experience can be engineered if the GBS team takes the time
  3. Build a coalition of the willing. Don’t try to boil the ocean What’s the burning platform for a better service experience? Will IT and other functions buy into it, giving GBS some tailwinds? Have enterprise functions already started on a service experience journey providing coattails to align with? Are there stakeholders who can easily grasp the benefits and sign up for partnership? Forgo a big bang approach (but follow a roadmap), and thoughtfully work your way through implementation
  4. Create and ladder use cases that provide a return Make sure your underlying processes are not only ready for experience prime time, but that by staging implementation GBS can create a ladder of benefits that create an ROI…and ultimately momentum for investment and change. Don’t forget the previous point—select partners who see the big picture and will work collaboratively with you
  5. Invest in the team Successful service experience implementation is both a high art and an exact science. Getting the right talent in place with a pan-GBS mandate for change is critical. And, at the same time, delivering a sustainable, programmatic experience means everyone on the GBS team must get onboard. Help your team to understand not only the imperative for GBS sustainability, but also the reality—today, service experience depends on both technology and human intervention. The trick is to know what and when

GBS service experience is a heavy lift. Making superficial or cosmetic changes—for example, the way a stakeholder interacts with GBS—in a futile effort to disguise its fundamental process failings—is a waste of time and money and can ultimately create more harm than good. Don’t put lipstick on a pig.

Comparing Large Language Models: Everest Group’s AI LLM Assessment Provides a Powerful Framework for Enterprise Decision-Making | Blog

Selecting the right Large Language Model (LLM) is critical for developing the best-suited generative AI (gen AI) solution. However, choosing an LLM on the number of parameters alone can be a costly mistake as larger size alone doesn’t always equate to better performance. Discover a comprehensive framework that evaluates and compares more than a dozen LLMs on 27 key parameters to enhance enterprise decision-making.  

Since the fervor surrounding gen AI started in November 2022, the explosion of LLMs is redefining language understanding and generation boundaries. As more models continue to emerge, evaluating them presents a significant challenge. A more structured and detailed approach is critically needed to evaluate these massive models that goes beyond assessing them solely on their sheer number of parameters.

Given the rising interest in gen AI across diverse applications, the lack of comprehensive research into LLM evaluation is striking. Relying solely on the parameter count when choosing an LLM can be misleading. It neglects crucial performance aspects, increases implementation costs, hinders enterprise readiness, enhances risk, and more. As these LLMs shape interactions and decision-making, an all-inclusive evaluation framework is essential to navigate their impact effectively.

Building on inaugural research in this area, Everest Group has assessed LLMs on multiple parameters and showcased how they rank against each other to help enterprises make the best and most informed decisions.

Introducing Everest Group’s AI LLM Assessment

Everest Group’s AI LLM Assessment presents a comprehensive framework, offering valuable guidance for stakeholders seeking to understand the various elements of LLMs. This assessment meticulously evaluates 13 leading LLMs across 27 distinct dimensions.

The framework evaluates LLMs’ unique capabilities, enabling a deeper understanding of their functionalities. Consequently, enterprises can determine which LLMs are fast, user-friendly, and capable of handling large amounts of input data for practical implementations.

The AI LLM Assessment evaluates various capabilities through such dimensions as the number of tokens they can process, the modalities supported, inference speed, training data quality, and overall market perception. These factors ultimately become differentiators, setting LLMs apart from peers and predecessors.

Below is a snapshot of the Everest Group AI LLM assessment matrix or explore the full framework.

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The hype of large language models – is bigger always better?

Recently, we have witnessed numerous technology providers developing their LLMs. Each model aims to outperform the others by emphasizing its larger size compared to its peers and previous iterations. However, it is rarely discussed whether having more parameters and larger datasets actually enhances the ability to deliver value across various use cases.

LLM advancements have highlighted a fascinating trend where smaller models like PaLM 2 have demonstrated superior performance despite being trained on fewer parameters than their predecessors. These compact models not only offer better performance but also deliver faster inference times and reduced processing costs. This underscores that larger models may not always be the only way to achieve the desired outcomes.

Choosing the right LLM

Deciding which LLM is the best fit for enterprise applications and use cases based on LLM capabilities and features is the most crucial step in developing a gen AI solution. After assessing a model for these variables, understanding how easily it can be integrated into enterprise operations is vitally important.

To address this need, the framework takes into account the feasibility of practical implementation, considering factors such as the average implementation cost based on usage and ecosystem readiness. The framework also examines the selected LLMs for potential risks that may hinder enterprise adoption.

By considering capability versus adoption ease, the framework offers enterprises a balanced approach for analyzing LLM attributes and functionalities while also accounting for the associated challenges and considerations to integration and utilization.

The path to enhanced LLM performance and adoption

This framework has the potential to help developers enhance their capabilities over peers in building new LLMs tailored for specific tasks or applications by better understanding competitors’ strengths and weaknesses.

While LLM evaluation is undeniably complex and continuously evolving, this framework provides a vital starting point. As Everest Group continues to track developments in the gen AI landscape, we welcome discussing potential use cases, risk and cost considerations, and the impact of gen AI across various industries.

Please reach out to Priya Bhalla, [email protected], Vishal Gupta, [email protected], Vaibhav Bansal, [email protected], Yukta Sharma, [email protected], or Vatsalya Singhal, [email protected] to discuss generative AI topics further.

Three Digital Laws Drive Investments and Change Management | Blog

Fundamentally, investing in digital technologies is the start of an unending journey with continuous change on not just the tech stack but also on business operations. Companies navigate the tech stack part of this journey through an ongoing series of sprints or projects. However, the business operations portion is often less well orchestrated; this slows progress, creates unnecessary friction, and reduces the progress toward the objectives and key results (OKRs) that the transformation aims to achieve.

Energizing the Distributed Hybrid IT Environment: Implications of HPE Acquiring Juniper Networks | Blog

Beyond consolidating the competitive networking market, Hewlett Packard Enterprise’s (HPE) planned acquisition of Juniper Networks can potentially unlock a distributed, hybrid, secure fabric to optimize today’s hybrid IT environment. Explore the projected benefits to both companies and market repercussions in this blog. 

Reach out to learn more on this topic.

HPE’s announcement earlier this month of its plans to acquire Juniper Networks for approximately US$14 billion has mainly focused on the benefits HPE can gain, such as Artificial Intelligence (AI) capabilities, new business lines, and margin improvements. However, the deal has far greater potential and can significantly impact the hybrid IT environment. Let’s delve into this further.

Hybrid IT: a distributed puzzle

Today’s businesses operate across a combination of on-premises infrastructure, public clouds, and edge computing resources. While this distributed hybrid model offers flexibility and scalability, it presents formidable challenges. Managing disparate technologies, ensuring seamless connectivity, and maintaining security across these diverse environments is a complex puzzle.

Cloud has already created a gamut of challenges for the networking and security industry, which both HPE and Juniper have struggled with. Yet, with the shift toward a distributed hybrid IT environment, a new growth story may emerge from these two infrastructure giants.

HPE + Juniper: a combined force for hybrid IT management

The HPE-Juniper merger holds the potential to simplify this landscape significantly. Bringing together HPE’s expertise in servers, storage, and edge computing with Juniper’s leadership in networking and security solutions can create a hybrid IT management powerhouse.

Here’s how this combined force could benefit both organizations:

  • As-a-service infrastructure stack: Imagine Greenlake’s as-a-service offering complemented by Juniper’s networking, security, and AI portfolios. This could culminate in an as-a-service infrastructure stack that can compete (theoretically) with the public clouds. Given the market’s search for alternative options amid rising cloud cost concerns, this can become an attractive option for enterprises and finally help HPE expand its edge-to-core strategy
  • Embedded security: Juniper’s security portfolio complements HPE’s existing offerings, providing a more comprehensive and integrated approach to securing hybrid environments. This can mitigate risks and ensure data protection across the hybrid ecosystem
  • Alternative network fabric options to Cisco: Despite being a long-time enterprise networking giant, Cisco’s innovation hasn’t scaled significantly to meet distributed computing model demands. With the combined scale of HPE and Juniper, enterprises finally will have meaningful alternatives for end-to-end networking requirements and, even better, an AI-enabled option through Juniper’s Mist capabilities
  • Consolidated automation capabilities: Juniper’s AI and automation capabilities through its Mist capabilities have been disrupting the data center and wireless markets. After HPE integrates these capabilities into its entire server, storage, cloud, and campus portfolios, enterprises stand to benefit from a more consolidated automation potential across the complete hybrid IT infrastructure stack

Challenges and considerations

While the potential benefits of the union are significant, there are also challenges to consider. Both companies have sizeable portfolios that overlap, especially in the networking segment. The strategy for prioritizing the network products between Juniper and HPE Aruba is unclear. Allocating the networking portfolio to Rami seems like a smart move, but how the internal sales and product will adapt to a combined market offering remains to be seen. Only time will tell if Antonio and Rami can make this merger work.

The road ahead: what the acquisition means

Competitors: With the consolidation, Cisco, Arista, and Extreme will have opportunities to target the existing Juniper and HPE accounts until the integration is fully operationalized. In the long term, price wars and another scaled competitor in the networking space could emerge. With the distributed world rising, competitors will need to enhance their offerings to provide an end-to-end intelligent, distributed, hybrid, and secure connectivity fabric.

Enterprises: Existing HPE and Juniper customers will benefit from the complementary portfolio. HPE clients can take advantage of the enhanced automation capabilities enabled by Mist offerings. For prospective clients, it also provides enterprises with increased bargaining power, having another significant provider in the market with comparable offerings.

Telecom service providers: If HPE continues to focus on its enterprise strategy, existing telecom customers of Juniper might lose out on possible innovations and receive less attention.

Channel partners: System integrators, resellers, and managed services providers may need to reexamine their strategies in light of the combined entity and prepare for contingencies if support is reduced.

Despite these challenges, the HPE-Juniper acquisition will significantly impact the market. If poorly executed, it might end up being a consolidation exercise. However, if well executed, it has the potential to ignite an often overlooked but mammoth market segment.

To discuss further, contact [email protected] and [email protected].

Look into our webinar, The Generative AI Odyssey: A Year in Review and What’s Ahead in 2024, to explore the hype vs. reality of generative AI, showcase actual production-level use cases, address challenges head-on, and provide a glimpse into the future of this transformative technology as we move into 2024.

The Generative AI Revolution: Transforming Customer Experience Management | Blog

Generative Artificial Intelligence (AI) is poised to revolutionize customer experience management (CXM) by creating personalized, empathetic, and more fulfilling experiences that drive brand loyalty and business growth. In this blog, explore examples of early generative AI adoption and learn about the benefits and challenges of this game-changing technology.

Learn more on this topic in the webinar, The Generative AI Advantage in Enterprise CXM Operations.

As contact centers shift their main focus from improving efficiency to creating impactful customer experience, generative AI is leading the charge in this new direction. Recognizing generative AI’s promise to enable the personalized, hyper-contextual interactions customers desire, enterprises are looking to invest and deploy solutions to leverage its transformative potential.

A recent Everest Group survey revealed nearly 60% of enterprises believe generative AI solutions have huge potential to disrupt the current contact center landscape. Additionally, another 37% perceive these solutions as beneficial in some areas.

Transforming the CXM landscape

By mimicking human creativity, generative AI can create nuanced and contextually relevant content. This opens a wide range of possibilities to reshape the way brands engage with customers across various touchpoints and provide the following benefits:

Enhanced customer service

  • Conversational AI: Supports intelligent virtual assistants for natural, contextual interactions, fostering deeper connections and loyalty
  • Personalization support: Analyzes vast customer data, tailoring experiences and providing real-time product support for heightened experience

Contextual marketing

  • Swift content creation: Crafts personalized content and product descriptions quickly, reducing production time and boosting conversions
  • Engaging storytelling: Creates compelling brand stories and personalized campaigns that resonate with specific audiences

Building stronger relationships

  • Personalized recommendations: Recommends products based on individual preferences, fostering trust and repeat business
  • Proactive engagement: Personalizes messages, contributing to lasting customer relationships

Enterprise generative AI adoption

With vast potential applications, enterprises across vertical markets are already reaping the rewards of early-generation AI adoption. Let’s explore some pioneering examples:

  • Virtual experience: A leading global furniture brand has built a generative AI chatbot to guide customers through the customization process, making furniture shopping more intuitive and natural while also offering 3D product configuration
  • Content enhancement: Prime Video has introduced Defensive Alerts, a generative AI feature that tracks the movements of defensive football players before the snap, reads their acceleration, and identifies “players of interest” likely to rush the quarterback. A red circle appears under the potential blitzer, giving fans a heads up, allowing them to place themselves in the coach’s seat and read developing plays
  • Customer support: Dave, a digital banking service, is implementing AI-powered chatbots that can hold natural conversations with customers, answer complex questions, and even resolve certain issues without human intervention
  • Content generation:com is testing its AI Trip Planner, which utilizes generative AI to create personalized offers and travel itineraries based on customer preferences and provide direct booking options to deliver an integrated travel planning experience
  • Itinerary planning and customization: Expedia has integrated ChatGPT into its app to help users make and save travel plans. Customers can ask the AI for recommendations on destinations, accommodations, and transportation as if it were a human travel agent. The app can also save locations so users can easily check availability and book travel
  • Student coaching: Language-learning platform Duolingo uses the technology underpinning ChatGPT-4 to help users practice language skills and understand when they make a mistake. It also uses the technology to allow learners to practice real-world conversation skills with the roleplay feature in the app
  • Dynamic promotion, pricing, and loyalty program: Levi Strauss & Co. has implemented generative AI to increase diversity on its website and expand its loyalty program by offering personalized benefits. This has significantly increased loyalty enrollments to 5 million members worldwide and boosted revenues and app registrations. Generative AI allows for tailored product recommendations, localized discounts, and customized store experiences based on consumer data and mobility insights. AI-driven analytics help optimize stock for various sales events, including mid-season, end-of-season, and Black Friday sales in the U.S. and Europe
  • Agent assist: Advisors at a multinational IT company that provides subscription-based technology support services worldwide access a secure generative AI-based model to easily answer customer queries

Addressing the challenges

While generative AI’s potential benefits are intriguing, addressing the inherent challenges that come with its implementation is critical. Enterprises have expressed a wide range of issues, from regulatory to accuracy, that could arise with generative AI. The top three enterprise concerns to generative AI adoption are:

  1. Data security and privacy: Robust security measures and transparent data usage policies are necessary to utilize customer data. The risk of data leakages during model training or deployment further intensifies the threat to data privacy. The implementation of generative AI exposes vulnerabilities to cyber threats and presents issues related to the secure handling of sensitive information for training the model
  2. Compliance issues: Enterprises are concerned about copyrights and ownership of intellectual property (IP) produced by generative AI while ensuring the solution doesn’t violate other organizations’ IP. With the diverse generative AI applications, sector-specific regulations are crucial. The technology’s evolving nature also calls for dedicated regulations addressing unique challenges and ethical considerations
  3. Accuracy: Organizations are wary of the risk of biased output stemming from training data biases, the potential for unethical responses requiring human oversight, and instances of “hallucinations” – all underscoring the pressing need to refine and enhance model accuracy

Future of CXM with generative AI

The changing landscape of generative AI in CXM is a testament to the transformative power of technology. The generative AI revolution is here, and it’s poised to significantly alter the way brands interact with their customers. By responsibly and strategically embracing this technology, CXM service providers can create personalized, empathetic, and, ultimately, more rewarding customer experiences, leading to stronger brand loyalty and increased business growth.

To discuss generative AI adoption trends in CXM, please contact Chhandak Biswas, [email protected] and Rishav Kumar [email protected].

Discover how enterprises are looking at generative AI-based solutions adoption to improve CX in the webinar, The Generative AI Advantage in Enterprise CXM Operations.

Beyond Filters: Exploring the Impact of Generative AI Influencers on the Marketing Landscape | Blog

By leveraging generative Artificial Intelligence (gen AI), brands can elevate influencer marketing to the next level by creating compelling content that connects more deeply with consumers. In this blog, discover how AI influencers are changing the influencer marketing market and key factors brands should consider.

In an increasingly digital world, consumers seek personalized connections and are drawn to influencers who embody relatable lifestyles and offer trustworthy recommendations. Brands recognize the potential of influencer marketing to enhance visibility, credibility, and engagement. Influencer marketing fosters genuine connections that resonate with today’s consumers and provides brands with a powerful platform to amplify their message in the crowded digital marketplace.

Let’s take a look at how consumers and brands perceive influencer marketing.

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Adding gen AI to the mix

Quick cut to the gen AI disruption – where cutting-edge technology meets influencer creativity. With its capability to produce creative text formats, images, and videos, gen AI brings a new opportunity to this market. It has the potential to empower influencers to craft compelling content at scale that uniquely resonates with followers.

Influencers now find themselves equipped with innovative means to captivate audiences, experiment with storytelling formats, and consistently produce engaging content that reflects the pulse of their followers.

Callout: “A survey of consumers across the UK and the US found a majority (60 percent) prefer creator content designed using gen AI. An additional poll of content creators found most (81 percent) reported more favorable audience engagement with content designed using AI technology.”

Let’s look deeper at how gen AI is being used in influencer marketing.

  • Influencers are crafting more personalized and authentic content, easier and quicker using gen AI
  • Gen AI is assisting influencers with audience engagement, based on data and insights from sentiment analysis
  • Influencers are using AI-generated prompts and ideas to spark creativity, ranging from unique storytelling angles to creative challenges
  • Gen AI-powered influencers are gaining popularity on social media platforms enabling conversations and human-like responses in comments and messages

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Rise of AI influencers

The use of AI influencers in marketing is a relatively recent development that has gained significant traction. High-profile brands such as Prada, Versace, Red Bull, and Tinder have all activated AI influencers for social media promotions. Although the results driven by AI influencers are similar to those of human creators, the key difference lies in creating a relatable brand presence in consumers’ minds.

In the graphic below, we compare the skill levels of human and AI influencers in important areas:

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With their complementing skills, virtual and human influencers can create engaging content at different ends of the same spectrum. As gen AI becomes more prevalent in the influencer industry, balancing authenticity and AI-generated content will be crucial to maintaining genuine connections.

 Key considerations for brands

 While the intersection of gen AI and influencer marketing presents a transformative landscape for brands to connect with their target audience, a few areas of concern still need to be addressed, including:

  • Identifying the right influencers from a crowd of “experts” with genuine followers
  • Managing controversial content or affiliations that conflict with the brand message
  • Safeguarding against the potential risk of copyright infringement due to inspired content from gen AI
  • Measuring the actual impact and return on investment (ROI) of influencer marketing

As influencer marketing evolves, the future is oriented toward adopting an omnichannel and full-funnel strategy. This entails brands leveraging influencer content across diverse marketing channels, from connected television (CTV) ads to opportunity-to-hear (OTH) display ads. By taking this approach, brands can establish influencer-led paid media and integrate it with commerce, removing steps in the customer journey and ultimately driving faster conversions.

To discuss the growing role of AI influencers in the influencer marketing market, contact [email protected] and [email protected].

Join our webinar, The Generative AI Advantage in Enterprise CXM Operations, to learn how enterprises are looking at generative AI-based solutions adoption to improve customer experiences.

 

MXDR: A Revolutionary and Comprehensive Solution Transforming Cybersecurity Detection and Response | Blog

Managed Extended Detection and Response (MXDR) has emerged as a game-changer in combating modern cybersecurity threats. Combining managed services with a technology platform, MXDR offers an encompassing, automated, scalable, and cost-effective solution incorporating real-time threat intelligence. Discover how MXDR compares to other cybersecurity offerings, its core components, and pricing models in this blog.

Request a complimentary price check on three cybersecurity roles across three countries.

In the ever-evolving cybersecurity landscape, organizations face the daunting task of safeguarding their digital assets against countless threats. With the increasing sophistication of cyber attacks, traditional security measures often fall short.

To counter this, various threat detection and response offerings have emerged over the years, including Endpoint Detection and Response (EDR), Network Detection and Response (NDR), Managed Detection and Response (MDR), Extended Detection and Response (XDR), and, most recently, Managed Extended Detection and Response or MXDR.

While these offerings are closely related, they differ in the following fundamental ways:

Offering

 

EDR NDR MDR XDR MXDR
Endpoint detection and response Network Detection and Response Managed Detection and Response Extended Detection and Response Managed Extended Detection and Response
Type Technology platform Technology platform Managed service Technology platform Managed service plus technology platform
Definition Protect endpoints and servers from malicious activity through continuous monitoring and behavioral analytics Analyze network traffic to stop network threats through machine learning and behavioral analytics Modern security operations center (SOC) capabilities to rapidly detect, analyze, investigate, and actively respond to threats Provides a holistic view of the threat landscape by analyzing telemetry from multiple sources such as endpoints, network devices, cloud workloads, third-party data, etc. Combines MDR and XDR

Although these cybersecurity solutions are effective, they are limited by being either a managed service or a specifically focused technology platform. This is where MXDR has emerged as a game-changer, offering a unique and holistic cybersecurity approach by integrating technology with managed services. As a result, MXDR currently stands out as the most comprehensive cybersecurity offering available.

Driving factors behind the evolution to MXDR

An MXDR solution always incorporates an XDR platform that integrates with a data lake to gather data from distinct sources. It employs Artificial Intelligence (AI)/Machine Learning (ML) and analytics to correlate the data and generate alerts that threat hunters subsequently investigate.

Given the threat landscape’s constant evolution and the expansion of attack surfaces, the industry is naturally transitioning from MDR to MXDR. Essentially, MXDR provides a “Managed XDR” solution, delivering around-the-clock threat management services.

Primary features that should define any MXDR solution include:

  • A modern, remotely delivered 24/7 SOC with around-the-clock monitoring capability
  • Threat hunting and analysis, which involves searching for undetected intrusions in an organization’s environment
  • Investigation of alerts and incidents generated by the XDR platform using telemetry gathered from various sources like endpoints, cloud workloads, networks, identities, etc.

While service providers or vendors may define their MXDR solutions in distinct ways, these solutions typically encompass the following core services and technological components:

Picture1 2

Some providers offer optional additional services in their MXDR solution, such as vulnerability scanning, onsite incident response and digital forensics, threat detection for OT environments, etc.

The MXDR vendor space is also quite diverse, ranging from global service integrators who partner with technology players to create MXDR offerings to specialized security providers who leverage deep cybersecurity expertise to develop MXDR offerings.

Let’s explore the different MXDR pricing models

While MXDR pricing models are still evolving, the following are the most frequently used:

  • Unit-based tiered pricing – Specialized security providers commonly bill customers according to specific units, such as the number of assets, endpoints, or IT users. Providers often establish distinct pricing tiers with varying unit prices. For example, they may set a per-unit price for environments with 2,000-5,000 assets and a different unit price for those with 10,000-15,000 assets
  • Fixed fee pricing – Global systems integrators (GSIs) typically follow this model that charges the MXDR fee based on the number of endpoints, servers, network devices, data processed, etc.

In a few cases, we also see hybrid pricing, such as per-unit pricing for some MXDR components and fixed fees for other elements.

While traditional detection and response solutions have played a crucial role in the cybersecurity landscape, the emergence of MXDR signifies a paradigm shift towards a more integrated, automated, and adaptive approach. Its holistic nature, automated capabilities, scalability, continuous monitoring, cost-efficiency, and integration of real-time threat intelligence position MXDR as a formidable response to today’s cyber threats.

As organizations strive to fortify their digital defenses and look to select an MXDR vendor, they should consider various factors like current needs, IT landscape, and existing technological investments.

For a more detailed analysis and assistance on MXDR services and pricing, please reach out to [email protected].

Or request a complimentary price check on three cybersecurity roles across three countries of your choice.

Navigating the Summit: Tech’s Role in Achieving the World Economic Forum’s 2024 Vision | Blog

Technology stands at the forefront in realizing Davos 2024’s vision of “Rebuilding Trust.” By providing innovative strategies and solutions, the technology sector can help address cybersecurity, job creation, artificial intelligence, and climate challenges. For a preview of the critical topics that world leaders will address at the annual event, read on.

As we stand on the precipice of a new year, the global community is gearing up for the World Economic Forum’s annual meeting in Davos. This pivotal event from Jan. 15-19 brings together leaders from across industries to collectively address the world’s most pressing challenges.

In 2024, the spotlight is on “Rebuilding Trust,” with a focus on restoring collective agency and reinforcing fundamental principles of transparency, consistency, and accountability among leaders. Let’s take a glimpse into the themes that will shape the conversations at Davos. Also, see this LinkedIn Live, Pressing Global Issues and Solutions in Tech: Reflections on WEF Davos ’24, for key takeaways from the WEF annual meeting and major trends in the climate and sustainability tech and services industry that are changing the marketplace in 2024.

The 2024 theme: Rebuilding trust

The overarching theme for Davos 2024 underscores the critical need to rebuild trust in a world marked by fractures and uncertainties. The summit aims to catalyze actionable solutions that transcend borders and industries, placing businesses at the forefront of global collaboration.

Against this backdrop, the role of the technology and technology services sectors takes center stage, offering innovative strategies and solutions to address the following four subtopics outlined for this year’s summit:

  1. Achieving security and cooperation in a fractured world

In an era of geopolitical complexities, achieving security and cooperation is paramount. The technology sector, with its expertise in cybersecurity and collaborative technologies, has a unique role to play. Beyond safeguarding digital assets, tech can foster global cooperation through secure communication platforms and advanced analytics for early threat detection.

  1. Creating growth and jobs for a new era

For the technology services sector, the second subtopic hits close to home. The emphasis on creating growth and jobs in a new era aligns with impact sourcing, an inclusive talent strategy that empowers marginalized communities by providing them with meaningful employment opportunities. As we navigate an ever-evolving workforce landscape, technology can be a driving force in fostering inclusive economic growth.

  1. Artificial intelligence as a driving force for the economy and society

Artificial Intelligence (AI) has already transformed the technology and technology services sectors, and its impact is only poised to expand further. From personalizing training programs to enhancing productivity and offering expanded service offerings, AI is a linchpin in shaping the future of work. Davos provides an unparalleled platform to discuss responsible AI practices that prioritize ethical considerations and human-centric approaches.

  1. A long-term strategy for climate, nature, and energy

The urgency of addressing climate change has never been more evident. The technology sector is pivotal in developing sustainable solutions and innovative approaches to mitigate climate change’s impact. From energy-efficient technologies to data-driven insights for environmental conservation, tech leaders at Davos can forge a path toward a greener, more sustainable future.

As Everest Group anticipates the discussions at Davos, we recognize the transformative role that the technology and technology services sectors can play. By aligning with the summit’s themes and subtopics, the tech industry has the potential to contribute significantly to rebuilding trust and shaping a more inclusive, sustainable, and prosperous global future. for a LinkedIn Live conversation on Feb. 7 with analysts Arpita Dwivedi and Rita N. Soni, and tech expert Marisa Zalabak as we navigate the summit and bring you insights into the pivotal role of technology in achieving Davos 2024’s vision.

Analyst Relations Quarterly Newsletter | Q4 2023

Hello AR colleagues!

I’ll keep this newsletter short and to the point – I know how busy everyone is preparing for the end of 2023 and the beginning of 2024. With that spirit in mind, this issue highlights two AR-focused events that you will find relevant.

On November 30, many of you joined our LinkedIn Live entitled How AR Teams Use Research to Support Sales and Position for Competitiveness. Thank you for being part of a lively discussion! Thank you also to my co-presenters, Molly Norton, Global Client Director at Everest Group, and Allan Racey, Global Talent Services Lead, Accenture. We took a deep dive into actual cases where AR brought research to bear on specific sales and marketing scenarios and helped drive outcomes. Please watch the replay and hopefully it will help you make more of an impact.

You can join me and two of our leading analysts, Rajesh Ranjan, Partner, and Alisha Mittal, Vice President, for an AR-focused webinar. We’ll first look at Everest Group’s 2024 Key Issues study, which lays out our predictions for the coming year. Then, the session will consider how AR teams can plan their strategy for 2024 according to these predictions. We held a similar event last year, which proved to be helpful to many. You can watch the event here.

With that, I wish you all a good wrap-up for 2023, and we’ll continue the discussion in the new year. Best wishes to all!

Best Regards

Katrina Menzigian
Vice President, Analyst Relations Engagement

Thought Leadership

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A leading quick service restaurant assess and validate the pricing, solution, and performance characteristics of cross-tower IT applications and infrastructure proposal, and

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READ ON 

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How AR Teams Use Research to Support Sales and Position for Competitiveness

Watch Accenture’s Allan Racey, Global Talent Services Lead, and Everest Group’s Katrina Menzigian and Molly Norton in a compelling LinkedIn Live session. The speakers explore the strategies that enable AR teams to bridge the gap between research and growth. This session is designed to equip AR professionals with the knowledge and tools they need to better utilize their existing research investments and identify key opportunities where custom research can effectively strengthen sales initiatives.

WATCH NOW

Upcoming Events

Key Issues 2024: Creating Accelerated Value in a Dynamic World | Webinar

Building a Sustainable Future: Reflections on COP28 and Insights for 2024 | LinkedIn Live

2024 Analyst Relations Strategy Planning: Aligning with Market Predictions to Create Impact | Webinar

The Generative AI Odyssey: A Year in Review and What’s Ahead in 2024 | Webinar

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Navigating the Challenges and Opportunities in Salesforce Industry Clouds: Service Partner Imperatives in 2024 | Blog

While industry cloud offerings are rising in popularity, they also present enterprise challenges and complexities. Service providers can help customers overcome obstacles by enhancing their capabilities with Salesforce Industry Clouds. Discover five essential strategies for providers to unlock the benefits of the Salesforce platform tailored to specific industries. 

Reach out to discuss cloud and the opportunities service providers can offer.

Present and future adoption of industry clouds

Industry clouds are computing platforms customized for specific industries, offering a mix of industry-specific applications, data models, and cloud services. Major enterprise platforms such as SAP, Salesforce, Oracle, and Microsoft Dynamics aim to improve industry cloud offerings in 2024 to provide benefits such as accelerated innovation, improved efficiency, and enhanced security and compliance.

The industry cloud is currently in the early adoption phase, where the core focus is to identify industry-specific whitespaces, establish business cases for industry-specific offerings, and improve enterprise adoption readiness. We expect a significant increase in these specialized solutions across the enterprise application portfolio in the next five years.

Salesforce industry cloud adoption and challenges

Salesforce stands out with the most robust portfolio, generating about 18% of its revenue from industry cloud offerings, surpassing other enterprise platforms in this domain. Based on market interactions from our recently concluded PEAK Matrix® Assessment and State of the Market Report, we expect this number to increase to 40% by 2025, establishing Salesforce as a significant player in this market.

Enterprises experience various challenges with Salesforce Industry Clouds. Let’s explore areas for improvement that Salesforce and its partners should address to increase customer satisfaction in 2024:

  • Integration – Salesforce has expanded over the years through numerous acquisitions of startups and industry-specific solutions, with MuleSoft, Tableau, and Slack among the larger deals. Integrating new offerings within and outside of the Salesforce ecosystem remains a top challenge for many enterprises
  • Customization – Enterprises often find Salesforce Industry Cloud offerings difficult to customize as it may require purchasing add-ons or custom integrations to meet specific needs, resulting in increased overall cost and implementation complexity
  • Product maturity – Recently launched industry clouds, such as communications, life sciences, media, and energy and utilities, are in the early maturity stage and lack strong proof points. Many industries also are not aware of the difference between Salesforce Industry Clouds and non-industry cloud offerings
  • Talent – Industry-specific expertise is in short supply. Only about 5% of the global Salesforce service partner ecosystem has the required in-depth knowledge of key Salesforce industry products to meet complex enterprise needs. Furthermore, talent possessing thought leadership in two or more industries is scarce
  • Change management – Organizations are not adequately prepared to adopt Salesforce Industry Cloud and service providers lack the expertise in handling change management related to implementation. This is primarily because service providers do not offer most of these activities or enterprises manage them internally

Service partner ecosystem imperatives in 2024

In response to enterprise demands, service providers must gradually increase their investments in building strong industry-specific service capabilities in these key areas:

  1. Identifying industry-specific whitespace – As Salesforce continues to focus on improving margins and driving profitable growth, product innovation becomes a partner ecosystem imperative. Service providers should invest in identifying use cases and building solutions intersecting Artificial Intelligence (AI), data, and customer relationship management (CRM) for specific industries since these are key areas where most existing Salesforce users are looking to invest
  2. Building talent – Service providers need to develop industry-specific tech talent expertise by strengthening internal learning and development (L&D) programs and initiating industry hiring because the Salesforce certification program has limitations in building industry-specific skills
  3. Optimizing cost – Enterprises want to add industry-specific capabilities and also optimize the overall licensing spend. Providers can alleviate enterprise concerns around industry cloud adoption by helping enterprises negotiate better software purchase agreements and improving license utilization through benchmarking against industry peers
  4. Developing thought leadership – Many enterprises today grapple with understanding the industry cloud’s future, and some also find it hard to differentiate industry cloud from non-industry cloud offerings. To help enterprises overcome these challenges, service providers should invest in building thought leadership
  5. Investing in change management – Service providers should consider broadening their focus on providing advisory services and effective change management services across the industry cloud. This is a potential growth area as user adoption continues to challenge the broader Salesforce services portfolio and enterprises are expected to make significant investments in these areas this year

Everest Group will continue tracking this market and analyzing its evolution. For the latest insights, watch for the next version of the Global Salesforce Services PEAK Matrix® Assessment, which will be released in May. To discuss Salesforce Industry Clouds, please reach out to [email protected] and [email protected].

Join the webinar, Adapting to Change: Boost Value in Outsourcing and Software Contracts When Uncertainty Persists, to learn current pricing trends and how enterprises can find greater value and lower costs in their outsourcing, Cloud, and SaaS contracts in the new year.

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