IT Services Archives - Page 2 Of 15

Unpacking The Potential of a Hybrid Copilot Strategy: A Roadmap for Success | Blog

March 7, 2024

“We are the Copilot company; we believe in a future where there will be a Copilot for everyone and everything you do.” – Satya Nadella, CEO Microsoft

A hybrid Copilot strategy delivers the benefits of purchasing ready-made Copilots and developing custom solutions. Discover a six-step roadmap for devising a successful hybrid Copilot strategy and compare buying versus building in part two of our blog series.

Read our previous blog to explore the emerging Copilot trend, M365 Copilot opportunities for service providers and enterprises, and why a hybrid strategy represents the future for Copilot. Reach out to us to discuss more in depth.

Hybrid Copilot is an innovative approach that seamlessly blends out-of-the-box offerings procured directly from vendors (Buy Copilots) and customized solutions built by the user with native tools or no-code/low-code platforms such as Microsoft Copilot Studio, Azure Open AI Service, Google Vertex AI, Open AI GPT Builder, AWS PartyRock, and others (Build Copilots). This synergy empowers enterprises to overcome the limitations of purchasing or developing, unlocking unprecedented potential within their environments.

While the traction for out-of-the-box Copilots like M365 Copilot is palpable, enterprises are increasingly recognizing the value of building Copilots. A shift is underway with more customers developing Copilots rather than defaulting to M365 Copilot.

The exhibit below summarizes the rising momentum for Build Copilots among enterprises in various sectors, signaling a growing demand for custom solutions tailored to unique requirements.

In two months, more than 10,000 organizations have used Copilot Studio to either tailor Copilot for Microsoft 365 or create their own custom Copilots, Microsoft announced in its second quarter earnings call.

Contrasting Buy versus Build Copilot

The exhibit below captures the differences between the two approaches:

In navigating the Copilot landscape, enterprises find themselves at a critical juncture where the choice between buying and building carries profound implications. Let’s look at the pros of each:

Buy Copilot: Offers the allure of rapid deployment, lower upfront investment, and access to a vast array of pre-packaged functionalities. Additionally, it requires minimal IT support, streamlining implementation processes, and reducing overhead costs
Build Copilot: Provides unparalleled customization capabilities. By developing Copilots internally, enterprises gain complete control over the development roadmap, enabling tailored solutions fine-tuned to address unique business requirements. Furthermore, building provides integration flexibility, allowing seamless alignment with existing business applications and workflows

Economies of scale also come into play. While the upfront investment might be higher, the total cost of ownership (TCO) decreases as the adoption of custom solutions scales. Conversely, pursuing a Buy Copilot strategy may lead to constant TCO increases due to ongoing licensing fees and dependencies on external vendors.

Roadmap for a hybrid Copilot strategy

In charting the course forward, enterprises must strike a delicate balance between buying and building Copilots.

The following six-step roadmap outlines a path to craft a successful Copilot strategy:

Assess enterprise needs: Begin by comprehensively evaluating enterprise requirements, including workforce dynamics, operational workflows, and strategic objectives

Identify use cases: Determine specific use cases and essential functions to maximize productivity and efficiency in the enterprise environment

Evaluate Copilot categories: Weigh factors such as the number of use cases needed, customization and integration requirements, and budget constraints to determine what option is better

Design and implement the hybrid strategy: Formulate a hybrid Copilot strategy that blends the strengths of buying and building by providing employees with either Buy or Build Copilots as required

Monitor and optimize: Continuously monitor Copilot performance, gather user feedback, and optimize the strategy iteratively to drive ongoing improvements and maximize value

Establish change management: Provide employees with training and adoption workshops to ease them into working with Copilots, helping boost productivity

The roadmap for a hybrid Copilot strategy empowers enterprises to leverage the best of both worlds, harnessing the rapid deployment and diverse functionalities of Buy Copilots while capitalizing on the customization and integration flexibility Build Copilots offer.

By strategically aligning with organizational needs and continuously optimizing, enterprises can confidently navigate the Copilot landscape, driving innovation, efficiency, and success.

Everest Group will continue to follow development in this space. To discuss, contact [email protected] and [email protected], or share your views at [email protected].

Watch the webinar, Global Services Lessons Learned in 2023 and Top Trends to Know for 2024, to learn the latest on delivery locations, sourcing strategies, deal trends, talent strategy, and cost optimization strategy.

Leveraging Strategic Partnerships to Unlock the Potential of Gen AI in Customer Experience Management | Blog

February 27, 2024

By strategically partnering with third-party providers, enterprises can fully harness the potential of gen AI in customer experience management. Learn insights from our latest survey on enterprise readiness for gen AI adoption and how collaborating with providers can help overcome the major obstacles.

Reach out to us for more information or to further discuss this topic.

Generative Artificial Intelligence (gen AI) is emerging as a game-changer in customer experience management (CXM) by offering the potential to personalize customer interactions, enhance operational efficiency, and provide a competitive edge. As enterprises adopt gen AI, third-party providers have an increasingly vital role. Let’s explore this further.

Current demand for gen AI

Demand for gen AI solutions is skyrocketing among enterprises across industries. According to a recent Everest Group survey of top executives and CXM leaders of 200 enterprises worldwide, nearly 75% believe gen AI will significantly impact their CXM strategies within the next two years.

Enterprises highlighted personalized interactions, cost reduction, and operational efficiency as the top three drivers for gen AI adoption.

Personalized customer interactions: Gen AI can produce customized content, product recommendations, dynamic pricing, and marketing campaigns tailored to individual customer preferences, enhancing customer satisfaction and loyalty
Increased efficiencies and automated CXM processes: Gen AI can empower customer support agents with intelligent tools such as agent assist, next-best-action recommendations, language translation, and accent neutralization. These tools enhance productivity, reduce response times, and enable agents to focus on more complex and value-added tasks. Using gen AI to automate routine tasks and repetitive processes can further boost operational efficiency
Reduced cost: By automating repetitive tasks, streamlining processes, and enabling advanced data and analytics capabilities, gen AI adoption can increase operational efficiency and productivity, optimizing cost

Current enterprise capabilities

Despite growing gen AI demand, the technology is still nascent. Most enterprises and service providers are investigating gen AI operations use cases or piloting solutions to test feasibility. Given the technology’s evolving nature, enterprises currently face or will likely encounter challenges in adopting and implementing it effectively.

Our survey asked enterprises about their readiness to adopt gen AI to identify significant areas where enterprises need specialized support. Below are the results of the preparedness of enterprises across industries and key parameters:

EXHIBIT 1, Enterprise readiness for gen AI by industry, Source: Everest Group (2023)

Challenges in gen AI implementation

Based on the readiness levels and significant challenges identified by enterprises in the survey, the following overarching issues for enterprises emerged:

55% of enterprises reported a shortage of the right talent pool, including AI/ML engineers, data scientists, and software developers needed to integrate gen AI with existing tools and create agent interfaces
56% of enterprises highlighted the scarcity of high-quality training data required for training and testing models
Between 45-50% of enterprises expressed concerns about computing power required for gen AI adoption and their ability to scale
Regulatory compliance concerning the fairness of the output, data security, and privacy, and misuse of the models also stood out as significant issues for enterprises

How can enterprises navigate these challenges?

The gaps in enterprise capabilities identified in our survey underscore the need for strategic partnerships to ensure successful implementation. While executing gen AI in-house offers greater control over the development and implementation process and allows enterprises to tailor solutions to specific needs, it requires significant internal expertise and resources that may be limited. Outsourcing gen AI implementation can provide access to specialized knowledge and resources, accelerating the implementation process and reducing the time to market for these solutions.

Our survey revealed that nearly 89% of the enterprises are looking to either outsource the implementation of gen AI in customer experience management to specialized AI companies or contact center providers or follow a hybrid approach involving in-house and outsourced development.

EXHIBIT 2, Implementation plan for Gen AI solutions within CXM, Source: Everest Group (2023)

The top three areas where enterprises are seeking service providers’ support are:

Building gen AI solutions and enhancing technical capabilities: Enterprises need partners who can help train gen AI models on enterprise data, customize foundational models to build tailored solutions, and set up necessary computational systems to run these solutions
Integrating gen AI solutions with existing technologies: Enterprises need partners who can create necessary bridges to integrate gen AI with existing business intelligence tools, providing a more comprehensive view of customer interactions
Supporting and complimenting in-house teams: Enterprises need partners who can support in-house technical teams, aid in maintenance, troubleshoot activities, and supervise the functioning of gen AI solutions

Future outlook

Gen AI has the potential to revolutionize CXM, enabling enterprises to deliver personalized, efficient, and innovative customer experiences. However, successfully adopting and implementing gen AI requires overcoming expertise, data quality, and change management challenges. Strategic alliances with third-party providers can bridge these gaps, providing enterprises with the necessary industry-specific knowledge, resources, and guidance to unlock the full potential of gen AI.

Third-party providers have a crucial role in helping enterprises translate the perceived potential of gen AI into practical applications. These partners can help enterprises identify specific CXM use cases where gen AI can deliver tangible benefits, develop gen AI strategies, design and implement solutions, and provide ongoing support to ensure successful gen AI adoption.

Read Everest Group’s Generative AI in CXM: Assessing Enterprise Readiness for this Disruptive Transformation to better understand gen AI in Customer Experience Management and how providers can help enterprises adopt gen AI. If you have questions or want to discuss digital CX strategies and solutions, contact Anubhav Das at [email protected] or Mohit Kumar at [email protected].

Don’t miss our LinkedIn Live, How Will Next-gen Technologies Be Financed in CXM Delivery?

Kickstart Your Hybrid Copilot Journey with M365 Copilot | Blog

February 22, 2024

M365 Copilot is rapidly becoming the go-to solution for enterprises to boost employee productivity. The future lies in adopting a hybrid approach, combining buying prebuilt solutions and building custom offerings. Delve into this transformative shift and potential opportunities for enterprises and service providers in this blog.

Reach out to discuss further.

“Our vision is pretty straightforward. We are the Copilot company.”

— Satya Nadella, CEO Microsoft

Microsoft’s recent rollout of its Artificial Intelligence (AI)-enabled digital assistant Copilot, coupled with Nadella’s above statement, has generated much buzz in the enterprise sphere, igniting discussions about its potential impact on the IT industry and the expected employee benefits.

In the ever-evolving IT landscape, enterprises have always prioritized productivity and aim to provide digital counterparts to assist and augment employees’ day-to-day tasks.

Everest Group research found enterprises ranked productivity as a top three expectation from service providers in 2024.

Copilot marks a significant leap towards this pursuit. Next-gen technologies such as ChatGPT and Copilots are frequently interlinked with productivity gains that replace humans and lead to job loss. The human-in-the-loop concept ensures governance and accountability, representing a crucial shift toward responsible AI implementation.

Consequently, Copilots and other generative AI tools are not intended to replace humans but are built to empower them. Inevitably, this will lead to a future where “Employees with Copilots” replace “Employees without Copilots.”

Deciphering the significance of M365 Copilot: Navigating the distinction between different Copilots

Having firmly established the importance of Copilot in the IT landscape, enterprises are now left with the pivotal question of which Copilot should spearhead their transformation journey.

Within the expansive realm of the Microsoft stack alone, many out-of-the-box Copilots with distinct capabilities abound, coupled with out-of-the-box Copilots from other vendors such as Google, AWS, Moveworks, Salesforce, and more. This crowded market makes decision-making daunting for enterprises.

Let’s take a closer look at some of the available options:

Even the Microsoft stack alone has a multitude of Copilots with distinct capabilities abound:

Microsoft 365 Copilot: Helps users streamline knowledge management and generate content based on data from Microsoft Graph (Word, Excel, PowerPoint, and Outlook)

Microsoft Security Copilot: Combines AI with cybersecurity to offer users more advanced protection against attacks

GitHub Copilot: AI tool that helps in code completions by turning natural language prompts into coding suggestions

Google Gemini: Helps users find information and generate answers from data all over the web

Moveworks’ AI Copilot: Helps employees automate tasks such as enterprise search and knowledge management

M365 Copilot offers the most extensive use cases to enhance employee productivity. It also integrates seamlessly with Microsoft’s productivity suite, including Office 365, which has unparalleled market dominance with 400 million paid users. For these reasons, M365 Copilot is emerging as the leading choice for enterprises to kickstart a Copilot journey.

In two months, adoption for M365 Copilot has been faster than E3 or E5 suites, Microsoft announced in its second quarter earnings call.

Evidence from early adopters such as Lumen Technologies, Goodyear, Chevron, and Avanade reinforces the preference for the M365 Copilot.

Hargreaves Lansdown reported the following benefits after the implementation of M365 Copilot:

Employees expect to complete tasks such as client documentation four times faster and save an estimated two to three hours weekly
96% of employees find Microsoft 365 Copilot useful in simplifying daily tasks

Early Copilot for Microsoft 365 users were 29% faster in a series of tasks like searching, writing, and summarizing, according to the company.

The potential for innovative service offerings is immense, presenting opportunities for enterprises and service providers alike to capitalize on the expanding Copilot market.

The exhibit below captures M365 Copilot value creation opportunities for enterprises and service providers:

For Enterprises	For Service Provider
Knowledge Managament	Data readiness
Document creation on Word and PPT	User readiness and training
Excel data analysis and visualization	Change management
Meeting summarization	Proof of concept
Personalized replies on Outlook	Extensibility

The way forward

While out-of-the-box Copilots like M365 Copilot hold significant value, they represent only one facet of the coin. Moving ahead, enterprises should exercise due diligence to check the alignment between the M365 Copilot offering and their employees’ needs.

Out-of-the-box Copilot such as M365 Copilot might not be a good fit for all employees due to the following reasons:

High price tag – The steep cost of an out-of-the-box Copilot such as M365 Copilot ($30 per user/month) may not be justifiable for every employee
Underutilization of Copilot offerings – While out-of-the-box Copilot, such as M365 Copilot, provides a vast array of use cases, many employees may not utilize them all. Instead, a tailor-made Copilot might be an ideal fit
Limited customization – Custom built Copilots offer more personalization
Limited integration with custom business apps – Integrating Copilots with custom business apps and data is restricted
Less control in Copilot roadmap development – Clients have less control over future development when buying than building a solution

Adopting a hybrid Copilot strategy that combines out-of-the-box (Buy) Copilot(s) and tailor-made (Build) Copilot(s) based on unique employee and business needs is the ideal solution.

While starting with Buy Copilot(s) is effective initially, this is a suboptimal strategy in the long run. Ideally, the future of enterprise Copilot strategy should move beyond buy versus build and, instead, toward a hybrid Copilot strategy.

The next blog in this series will explore the hybrid Copilot strategy in detail. To discuss your Copilot journey or for help on a Copilot strategy, please reach out to [email protected] and [email protected].

Watch the webinar, The Generative AI Odyssey: A Year in Review and What’s Ahead in 2024, to learn about actual production-level use cases and get a glimpse into the future of this transformative technology.

Navigating the New Landscape: How DORA Regulations Will Reshape the Future of Financial Services | Blog

February 22, 2024

With the deadline for the European Union’s Digital Operational Resilience Act (DORA) less than a year away, financial entities and service providers need to begin acting to reach compliance. Learn the steps organizations should take to prepare now and discover how the new DORA regulations will strengthen digital operational resilience.

Financial institutions’ reliance on information and communication technologies (ICT) for core operations brings immense opportunities in today’s digital world but also exposes banks, investment firms, insurers, and other financial entities to significant cyber threats and operational risks. To address these growing vulnerabilities, the EU has enacted DORA.

The DORA regulations are expected to significantly enhance the digital resiliency of the EU’s financial sector and foster greater stability, consumer protection, and trust. Financial institutions and authorities are working toward meeting the implementation deadline of January 17, 2025. Let’s explore this further.

DORA addresses two critical concerns:

Rising cyber threats: DORA mandates robust cybersecurity measures to protect financial systems from increasingly sophisticated and frequent cyberattacks that steal sensitive data, disrupt operations, and erode trust
Potential financial instability: DORA aims to prevent ICT incidents from cascading through the financial system, jeopardizing its stability and impacting consumers and businesses. The regulations ensure financial institutions can withstand, respond to, and recover from ICT-related incidents

Who will be impacted by DORA regulations?

DORA will impact all financial institutions and ICT third-party service providers. This includes banks and credit institutions, investment firms, trading platforms, and providers delivering critical services like cloud computing, data centers, credit ratings, and data analytics. It applies to over 22,000 financial entities in the EU and ICT infrastructure support outside the EU.

DORA framework

DORA establishes a comprehensive framework for managing digital operational resilience across the financial sector. Some key provisions include:

Enhanced ICT risk management: Financial institutions must implement robust ICT risk management practices, including threat identification, vulnerability assessments, and incident response plans
Mandatory incident reporting: Major ICT-related incidents and significant cyber threats must be reported to authorities, enabling faster response and improved threat intelligence sharing
Regular digital operational resilience testing: Financial institutions must conduct regular ICT systems testing to identify and address vulnerabilities
Strict oversight of ICT third-party providers: Financial institutions are accountable for the resilience of their third-party ICT service providers, with DORA outlining clear oversight and risk management requirements

DORA requires third-party providers to maintain robust cybersecurity measures and operational resilience capabilities to mitigate risks from potential vulnerabilities and disruptions. Moreover, financial institutions must ensure their current and future contracts with providers are compliant.

DORA focuses on five strategic pillars centered around data: risk management, third-party risk management, incident reporting, information sharing, and digital operational resilience testing. However, financial institutions still have many technology legacy systems that could create obstacles to data management.

How can financial institutions comply with DORA regulations?

Immediate next steps financial institutions should take to prepare for the January 2025 deadline include:

Conduct a gap analysis and develop an operational resilience framework, business continuity plans, and governance policies
Assess risks with third-party providers in the sourcing portfolio and review existing contracts that may be at risk of termination by authorities
Ensure risk and compliance leaders are represented on management boards, as the board will have full accountability for ICT risk management
Establish systems for managing, logging, and reporting ICT incidents to regulators

How can providers help financial institutions achieve compliance?

By leveraging their deep understanding of enterprise technology footprints, providers should proactively assist enterprises in meeting the regulatory deadline. We recommend providers take the following actions:

Develop a perspective on how DORA will impact financial institutions to ease clients’ worries and gain mindshare with new customers
Identify accounts needing support to determine current and future states, business continuity plans, risk management frameworks, etc.
Evaluate incumbency status and competitive landscape threats. Acknowledge financial institutions will need to reduce their reliance on a single or small group of providers and have open discussions with clients to ensure transparency and collaboration
Develop effective rules, procedures, mechanisms, and arrangements to manage ICT risks to financial entities
Review contracts and proactively identify clauses needing changes to incorporate DORA compliance
Prepare to undergo threat-led penetration testing with financial institutions if deemed critical by regulators

In the near term, we foresee the banking, financial services, and insurance (BFSI) industry in the EU being impacted in the following ways:

Spiked demand for security services as financial institutions run security services maturity assessments to review the current state of DORA compliance
Revamped sourcing portfolios as financial institutions assess concentration risk of functions deemed critical under DORA
Increased demand for a qualified talent pool to conduct vulnerability assessments, performance testing, penetration testing, etc.

With the deadline fast approaching, enterprises and providers cannot afford to wait for the regulatory process to conclude and must begin to take these recommended steps to reach compliance by 2025.

To learn more about the Digital Operational Resilience Act and how to achieve compliance with the DORA regulations, contact Kriti Gupta, [email protected], Pranati Dave, [email protected], and Laqshay Gupta, [email protected].

To learn about Global Services Lessons Learned in 2023 and Top Trends to Know for 2024, don’t miss this webinar.

Changes in Funding Gen AI and Other Technology in 2024 | Blog

February 16, 2024

I believe 2024 will be the year of focusing on business value. That applies to gen AI, but it goes further than that. Most companies are now more than ten years into their digital transformation journey. Having already picked the low-hanging fruit, they now want to get more value and return on those investments. However, there are fundamental sentiment shifts in the appetite to invest more to get more value.

Consulting Playbook: Organizational Readiness for Gen AI Adoption | Blog

February 6, 2024

While most enterprises today want to adopt generative AI (gen AI), they struggle to embrace it because of organizational unpreparedness. Consulting service providers can play a key role in this transition. Discover Everest Group’s Organizational Gen AI Readiness Framework, which includes critical strategy and operations components for successful gen AI adoption.

Reach out to us to further discuss gen AI.

The challenge of embracing change

Enterprises that have successfully embraced automation and digitization now face another technological challenge: gen AI. A recent Everest Group survey of over 200 enterprises reveals contrasting sentiments about gen AI adoption. Despite as many as 90% expressing a desire to adopt gen AI, only about 10% of respondents indicated they would significantly invest in the technology during 2024. Most enterprises plan to implement gen AI in small areas, run pilots, or begin building strategies this year.

Additionally, when asked about key challenges with gen AI adoption, more than half (57%) cited organizational readiness as the top obstacle, which was unsurprising. Here are the results:

Survey on gen AI adoption challenges

The survey findings underscore a broader enterprise sentiment where excitement for the future is tempered by the challenges of adopting gen AI into established business models. It reflects a blend of eagerness and trepidation, with significant concerns about organizational preparedness for this change.

In this transition, consulting service providers become crucial in bridging these gaps and ensuring smooth adoption into existing operation frameworks. Let’s delve into this further.

Everest Group Organizational Gen AI Readiness Framework:

Crafting a blueprint for a gen AI-driven future

The ease of using gen AI from an end-user’s perspective often belies the complexity of building and operating gen AI models and applications. Consulting service providers can play a critical role in assisting clients in comprehensively understanding their internal capabilities for gen AI.

Leading consulting organizations have developed unique gen AI readiness frameworks that can add significant client value. These frameworks are essential to help organizations analyze their current state in-depth, identify areas needing enhancement, and craft tailored strategies for effective gen AI deployment.

In guiding organizations through gen AI adoption, a consulting firm’s approach is anchored in two principal tenets:

Strategy involves shaping a visionary outlook, navigating market trends, managing risks, and identifying innovative opportunities
Operations focuses on refining processes, building a skilled team, enhancing technology infrastructure, and implementing effective performance metrics. Together, these elements form the backbone for successful gen AI adoption

Gen AI readiness framework

The strategy

Vision

For gen AI consulting providers, it’s vital to engage clients in a thought-provoking dialogue about their gen AI objectives. Instead of prescribing a set path, encourage clients to consider the following:

Clarify the end objective with gen AI: What do clients hope to achieve through gen AI? Guide them to ponder whether they see it as a strategic tool transforming their business model or merely as a means to fill specific capability gaps
Alignment with organizational goals: Assist clients in aligning gen AI initiatives with their broader organizational goals. Is gen AI a step toward future innovation, or is it addressing immediate operational needs?
Crafting an integration roadmap: Encourage clients to consider their gen AI integration roadmap. This should encompass not just the immediate technological requirements but also longer-term industry trends, potential disruptions, and opportunities for innovation.

Organization and culture

Culture as the cornerstone of gen AI adoption

Corporate culture can accelerate or impede gen AI adoption. The same cultural traits underpinning organizational success – such as adaptability, agility, and innovation – are now the bedrock for gen AI adoption. Creating an environment where transparency, connectivity, and continuous learning are not just buzzwords but the essence of the organizational ethos is crucial.

Cultural changes needed for gen AI adoption

Governance, security, and compliance

For gen AI consulting providers, navigating governance, security, and compliance is a multi-faceted challenge in a domain where innovation often outstrips regulation. Consultants must guide clients in developing robust governance strategies, enhancing security measures, and ensuring compliance to build trust and manage risks associated with gen AI.

Integrated framework for governance, security, and compliance in gen AI

Return on investment (ROI)

Understanding the economic impact of gen AI is crucial. Consulting firms should help clients evaluate the ROI of gen AI initiatives. This involves cost-benefit analyses and identifying areas where gen AI can drive revenue growth and efficiency gains. As part of the readiness assessment, enterprises must establish clear, measurable metrics for gen AI adoption that align with their strategic objectives, such as efficiency gains, revenue growth, customer satisfaction, and innovation. This can provide data-driven insights into the effectiveness of gen AI solutions.

Operations

Talent

The impact of gen AI spans all levels, from enhancing the employee experience to empowering managers, as well as requiring a new leadership paradigm focused on fostering gen AI adoption and integration. Consulting providers will become essential in helping enterprises adapt and evolve their talent strategies.

Talent strategy transformation needed for gen AI adoption

Impact category	Examples	Description
Roles that will become extinct	Traditional Data Analysts, Basic IT Support Technicians	These roles, primarily focused on repetitive tasks and manual testing, will likely become obsolete as gen AI automates these processes.
Roles that will emerge	Gen AI Ethics Officer, Gen AI Integration Specialist	New positions like Gen AI Ethics Officer will arise to address ethical considerations of gen AI use. Gen AI Integration Specialists will be needed to blend gen AI solutions effectively into existing tech infrastructures.
Roles that will evolve	Software Developers to Gen AI-enhanced Developers, HR Managers to AI-Driven Talent Strategists	Existing roles like Software Developers will evolve into Gen AI-enhanced Developers, focusing more on AI-driven solutions, while HR Managers will transition to AI-Driven Talent Strategists, leveraging gen AI for talent acquisition and management.

Tech

Consulting providers must have a nuanced understanding of the organization’s current gen AI capabilities. They should enable clients to discern where they stand on the gen AI integration spectrum – whether they are at the stage of creating gen AI solutions from scratch (Do It Yourself), using ready-made components (Do It With Me), or adopting fully developed gen AI products (Do It For Me).

Gen AI tech stack readiness categories

Data

For most enterprises, the immediate priority lies in addressing gen AI’s data-related risks. Confidentiality, data leakages, reliability, and plagiarism are the four horsemen of the gen AI apocalypse, each capable of causing financial, legal, and reputational damage. The solution? Getting the data house in order. This involves scalable data management, prioritizing discovery, acquisition, and curating data to effectively feed into the gen AI models.

Ecosystem

The ecosystem aspect is pivotal in preparing organizations for gen AI. Consulting service providers play a critical role in:

Strategic partnerships: Assisting clients in forming alliances with technology providers and industry experts to access essential gen AI resources and knowledge.

Seamless integration: Helping clients blend gen AI into their existing business and technological frameworks, ensuring that gen AI efforts align with broader organizational strategies.

Fostering innovation: Encouraging clients to engage with innovation networks supporting continuous gen AI development, idea sharing, and collaborative growth.

Conclusion

As the gen AI wave gains momentum, consulting providers are uniquely positioned to guide enterprises. By focusing on providing services around technological, cultural, and structural transformations, they can lead these organizations not just in adapting to gen AI but also in harnessing its full potential for innovation and growth.

Read more Everest Group Research on Consulting and gen AI in our reports:

If you would like to reach out to us to learn more, email Sandeep P, [email protected], Alisha Mittal, [email protected], and Parul Trivedi, [email protected].

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

January 25, 2024

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.

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

January 22, 2024

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

January 22, 2024

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.

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

January 12, 2024

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:

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
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
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
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
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.

Category: IT Services