As we embark on a new year, our team of analysts working within the Experience, Sustainability & Trust service line looks at the 5 key themes and trends that we can expect to drive the sustainability technology and IT Services market in 2025. 

Reach out to discuss this topic in depth. 

1.Green computing will become integral to decarbonization strategies 

As national and local decarbonization strategies move towards implementation, green and sustainable computing, as well as energy efficient hardware and software, will become integral to achieving those objectives. Enterprises will include green computing initiatives and transition to energy-efficient equipment as part of their broader decarbonization commitments. We expect green data centers that consume less electricity and edge computing models to become more pervasive, as decarbonization approaches embed green computing. 

Aiming to reduce 50% of its Scope 1, 2, and 3 emissions by 2030, Google has already taken measures to reduce emissions across its operations and power some of its offices and data centers using carbon-free energy*.  

Unilever, in its Climate Transition Action Plan, has also committed EUR150 million to decarbonize its manufacturing program, which includes improving its electrical efficiency by installing more efficient equipment and controls, and transitioning to sustainably sourced biofuels. 

2.Circular economy practices of waste reduction and reuse & recycle will become mainstream 

In an effort to reduce costs, appease scrutinizing investors and comply with local regulations, companies will increasingly adopt circular economy practices, emphasizing resource efficiency optimization and waste reduction through better management.  

Increased attention on designing and producing sustainable products with longer lifecycles, coupled with measures to reuse and recycle materials and components, will also reduce the environmental impact of products and services.  

Microsoft, for example, achieved 89.4% reuse and recycle rates of servers and components across all cloud hardware in FY2023.  

Adopting packaging innovation, Unilever has designed 72% of its plastic packaging portfolio to be easily recycled, such that it avoids emissions at end-of-life incineration. Furthermore, it is collaborating with the World Economic Forum and the Ellen MacArthur Foundation to identify refill-reuse solutions for consumers. 

3.Emerging technologies such as Artificial Intelligence (AI) will create new use cases for sustainability and climate action

Much has already been written and said about the potential of artificial intelligence (AI) across industries. It is, however, interesting to observe the emergence and adoption of AI across sustainability.  

From monitoring and identifying ways to reduce a company’s carbon footprint to optimizing energy consumption in smart cities, from enabling precision agriculture to increase yield to predicting climate disasters, AI is playing an instrumental role in advancing sustainability use cases.  

As an example, Sipremo, a Brazilian startup, has used AI to predict the location and timing of climate disasters, along with the type of disaster that will occur. This helps businesses, governments, and communities better prepare for impending adverse climate events.  

Another AI-led startup, Eugenie.ai, has embedded AI in its emissions-tracking platform that combines satellite imagery with data from machines and processes and helps companies track, trace, and reduce their emissions by 20-30%.  

In addition to these environmental use cases, we expect social ones to emerge as well. Unbiased Generative AI (gen AI) can promote diversity in hiring, reduce biases from seeping through, create more equitable workplace, and improve social outcomes. Key stakeholders in the gen AI ecosystem – technology providers, service providers, and enterprises – have a crucial role to play in influencing sustainability outcomes. 

4.The “skills gap” will drive demand for ESG professionals, particularly for regulatory compliance

We predict that the social impact of sustainability will be visible through the pronounced ‘skills gap’. As more regulators tighten the grip on companies to demonstrate their sustainability commitments, the talent gap for professionals with background and experience across these evolving frameworks will widen.  

This is already witnessed globally – the 11.6% growth in global demand for green talent is almost twice the 5.6% growth in talent supply. This trend is expected to continue until at least 2030, when one in five jobs is expected to lack the required talent.  

Current demand for ‘green’ skills is high in areas of pollution and waste prevention, renewable energy generation, sustainable finance, environmental audits, environmental policy, and sustainable procurement. Particularly in Europe, the changing regulations and the ongoing energy transition are driving up the demand for such professionals with skills pertaining to climate change and/or sustainability, notably in the UK, Ireland, Norway, and Switzerland. 

Diversity, Equity, Inclusion, and Belonging (DEIB) is another area that warrants more attention. This year, inclusive recruitment will be crucial, from a social standpoint, to address commonly observed biases in technology (gen AI) adoption. We expect increased regulatory interventions through policies and consultation papers to help stakeholders navigate the uncertainties currently engulfing this space. 

5.Climate tech investments will create financial asset classes and broaden capital allocation opportunities 

Driven by regulatory pressures and consumer demand for sustainable products and services, companies are investing in climate technologies. This is leading to the development of innovative solutions to tackle climate-related challenges and is also opening investment opportunities for sustainable finance. 

Continued investments in green hydrogen, battery technology, sustainable fuels, green built environments, and nature conservation, to name a few, are expected to push forward technology innovations and create opportunities for environmentally sustainable financial investments.  

IBM, for instance, uses AI-driven material discovery to identify molecular structures for carbon dioxide (CO2) separation membranes, leading to cost-effective CO2 emissions capture and storage.  

From an investment perspective, climate bonds** surpassed a cumulative volume of US$5.1 trillion in the first half of the year (H1) of 2024, suggesting growing volumes of capital allocated to climate technologies and environment-friendly objectives.  

This is already evident in the social impact sector – the orange asset class has emerged to advance gender equity for prosperity, peace, and planetary resilience. We expect this trend to become more pronounced, particularly as global events in 2024 such as COP16 (biodiversity) in Colombia and COP29 (climate change) in UAE emphasized the importance of climate finance and ‘finance for good’. 

What does this mean for us? 

What do these trends and predictions mean for you as a stakeholder in this space? What are the implications for enterprises and service providers? How can we be better prepared for 2025? 

As an enterprise: 

• Invest in building capabilities internally or hiring skilled professionals to help with accurate reporting on sustainability initiatives for regulatory compliance 
• Identify opportunities across your supply chain for emissions reduction, waste and pollution prevention, reuse and recycling, and technology adoption 
• Conduct competitive benchmarking exercises to map yourself against industry peers and identify opportunities for improvement 
• Collaborate with industry associations, development organizations, and government agencies on industry-level initiatives

As a service provider, software/technology provider, or consulting/advisory firm:

• Bridge the skills gap by providing enterprises with qualified skilled professionals with strong domain knowledge of evolving regulations (e.g., the Corporate Sustainability Reporting Directive (CSRD) in Europe) to help them report accurately on their sustainability initiatives. 
• Build competencies for internal initiatives and conduct pilots and Proofs of Concept (PoCs) within the organization. Leverage successful initiatives as case studies to demonstrate expertise to clients on specific sustainability programs. 
• Conduct research on sustainable Information Technology (IT) and energy efficiency initiatives to identify investment opportunities that cut across multiple facets of sustainability (e.g., circular economy principles to reduce e-waste) 

If you found this blog interesting, check out our blog focusing on Boosting Project Readiness In Technology Talent: A Comprehensive Framework For Developing Project-Ready Workforce | Blog – Everest Group, which delves deeper into another topic relating to this service line. 

To discuss these 2025 predictions and other insights from our research on current and future STS trends and evolutions, please reach out to Siddharth Muzumdar (siddharth.m.ext@everestgrp.com) and Arpita Dwivedi (arpita.dwivedi@everestgrp.com) 

Key: 

* Google’s definition of renewable energy sources, which includes carbon capture and storage technologies  

** Include green, social, sustainability, and sustainability-linked bonds 

The digital workplace began with a focus on service standardization, ensuring consistency. As it evolved, priorities shifted to employee productivity, leveraging automation for efficiency and stronger alignment with business outcomes. Eventually, the emphasis moved to employee-centric experiences, enhancing satisfaction, well-being, and engagement.

However, in the past 12–18 months, the focus on value realization amidst challenging macroeconomic conditions, coupled with innovations like generative AI and copilots, has reignited a focus on productivity, marking the resurgence of the productivity era.

Join us for an engaging LinkedIn Live session to explore how organizations can design impactful digital workplace strategies for the year ahead in this productivity-driven era. We’ll discuss key changes in the operating model, their implications for enterprises and providers, and the future of the workplace in 2025.

During this collaborative LinkedIn Live session, we explored:

• Key changes to expect in the digital workplace services operating model during the productivity era
• The implications of these changes for enterprises and providers
• What the future of the workplace will look like in 2025

Join us as we reveal findings from the annual “Mapping the Next: Key Priorities for 2025” study, offering an inside look into the top concerns, expectations, and goals of industry leaders for the coming year. This session will spotlight leaders’ core priorities, such as technology adoption, digital transformation, generative AI, customer experience, and sustainability.

Our experts will also discuss critical global services decisions, including build vs. buy strategies, in-house vs. outsourcing, onshore vs. offshore operations, and the balance between long-term and short-term contracts.

This is your chance to hear exclusive, high-impact insights and gain an edge so you can make more informed decisions that will improve organizational resilience, competitiveness, and chances of future success for 2025.

What questions will the webinar answer for the participants?

• What are the trends that will shape the global services market in 2025?
• What are the likely changes in sourcing spend, sourcing strategy (in-house vs. outsource), and locations?
• What is the enterprise outlook on tech and AI adoption in 2025?
• Which digital services and next-generation capabilities are expected to be in demand?
• How are outsourcing deals, enterprises’ leverage of service providers, and bill rates expected to change?

Who should attend?

• Enterprise/business leaders (CIOs, CDOs, CTOs, CFOs, CPOs)
• Global sourcing leaders
• GBS/shared services center heads
• Leaders at IT and BP providers

The Rising Enterprise Adoption of Agentic AI

Agentic AI has been capturing popular imagination in the past few months and has now started proliferating from the more intuitive consumer use cases to the more elaborate enterprise applications.

As the agentic AI ecosystem evolves, so will its potential to deliver greater benefits which in turn will increase utilization and reliance on the technology, leading to widespread permeation. But enterprises have already started realizing the rewards and extraordinary accomplishments this technology promises to yield, including increased productivity, optimized workflow and improved decision making.

The early adoption pattern of agentic AI reflects a strong preference for horizontal use cases (Exhibit 1) with functions such as sales & marketing, customer support and HR leading the pack, though the industry-specific processes are also expected to pick up soon. 

Reach out to discuss this topic in depth. 

Exhibit 1: Adoption of agentic AI by business function 

Source: Everest Group (2024) 

Emerging Agentic AI tech landscape 

The tech landscape for agentic AI is evolving rapidly. Everest Group’s Innovation watch assessment on agentic AI products (Exhibit 2) published in September 2024, mapped out the key agentic AI technology providers on their market performance and ecosystem drivers. While market performance is linked to the scale of operations and maturity of the product, ecosystem drivers include key partnerships and investments.  

Exhibit 2: Everest Group innovation watch assessment for agentic AI products 

Source: Everest Group (2024) 

As you can expect, the pace of change and innovation in this budding market is very high. In the short time frame since this assessment was launched, many providers have made announcements or have ventured into this space. Some notable ones are: 

1. Salesforce Agentforce: launched at Dreamforce 2024, Agentforce comes with multiple prebuilt agents such as sales development agents, sales coaches, personal shopping agents, service agents, and campaign agents. It also supports agent customization via Agent Builder, Model Builder, and Prompt Builder​ 
2. ServiceNow Xanadu: Agentic AI integration into the ServiceNow platform is expected to be available from November 2024 with Customer Service and information technology (IT) Service agents being the first in a set of agents that will continue to be added through 2025 
3. UiPath: Agent builder was launched at the Forward event in October 2024. UiPath is also expected to come up with a comprehensive agent orchestration platform with required security and agent ops 
4. Kore.ai: Kore.ai launched its platform named GALE to build, test, integrate and deploy AI agents and applications. The platform also comes with essential guardrails and analytics capabilities

With the slew of tech providers continuously entering this space, the market dynamics are bound to change. In the current landscape, our analysis (Exhibit 3) reveals four categories coming to the fore:  

1. Hyperscalers: hyperscalers are sizeable providers engaged in providing broader cloud, network, technology, and data services. Microsoft, Google, and AWS are the biggest names in this category, each with their own Agent frameworks, application programming interfaces (APIs) and even off the shelf agents in some cases. Hyperscalers have the financial clout and customer base to make significant advances and gain market share in Agentic AI 
2. Pureplay providers: pureplay providers operating in this field are mostly startups and AI native companies offering specialized capabilities for use cases, ranging from broad based to highly targeted ones. These players operate with an undivided focus on agentic AI and aim to continuously innovate in this category. Some notable names here are Ema, crew AI, Newo.ai, Relevance AI, Lyzr, Dust AI and so on 
3. Enterprise platforms: enterprise platforms are tech players that provide a centralized hub for a range of comprehensive software products/solutions such as customer relationship management (CRM), enterprise resource planning (ERP) and supply chain management (SCM). These are advanced software solutions that assist enterprises by integrating multiple functionalities into a cohesive system. As mentioned earlier, Salesforce and Service Now have already made announcements on their Agentic AI offerings on top of their existing platforms and others are now expected to follow suit  
4. Intelligent automation providers: these players are focused on streamlining business processes through a combination of rule-based automation and AI, using technologies such as robotic process automation (RPA), IDP, conversational AI, process orchestration and process intelligence. Some of these providers, such as Automation Anywhere, UiPath and Kore.ai have already launched their agentic platforms / capabilities, while others are on course to do the same in the near future

Apart from these tech providers, the ecosystem of tech services providers is also emerging where system integrators, managed services providers and consulting players are all expected to play a significant role in the agentic AI space through a range of advisory, solution development and operations services 

Exhibit 3: Emerging Agentic AI tech landscape 

Source: Everest Group (2024) 

How should enterprises go about selecting the right tech partner for Agentic AI?  

With the deluge of providers with diverse specialties operating in this space, the question being faced by enterprises is how to develop the right agentic AI ecosystem. Should they extend their relationships with their existing partners or explore new specialized partners focused on agentic AI?  

Identifying the right partner(s) begins with identifying what enterprises want to achieve in their agentic AI journey, and how the partner’s capabilities align with that vision. Here (Exhibit 4) is a framework that enterprises could use to evaluate their potential tech partner(s) for their agentic AI journey. 

Exhibit 4: Agentic AI tech provider evaluation framework 

Source: Everest Group (2024) 

As a part of this framework, enterprises can evaluate providers across 2 dimensions, ease of adoption and comprehensiveness of solution. Ease of adoption is defined by the ability of the provider to deploy a fit for purpose solution at a fast pace with minimum disruption. On the other hand, comprehensiveness of solution covers the exhaustiveness, robustness and flexibility of the solution.  

The framework can be used by the enterprises to identify the category of the tech provider (as defined earlier in this blog) that will suit their needs and/or to evaluate and choose among multiple tech providers in one or more categories. Let’s consider a couple of scenarios to understand how enterprises can benefit from this framework. 

Scenario 1:  

Situation: company A is one of the largest financial services companies in the world and deals with origination and servicing of personal and property loans. The company is required to process large amounts of data in a highly secure manner. It has already fostered a partnership with a hyperscaler for its data, cloud and AI needs.  

In recent times, the company has been seeing a decline in its business and there have been many unrecovered loans.  The company aims to increase the loan volume as well as strengthen its loan disbursement mechanism to avoid losses.

For this, it wishes to deploy an agentic AI system that reduces loan disbursement time and precisely identifies loan requests that are unlikely to be recovered. For this purpose, it is looking for an ideal tech provider that has the ability to deliver a scalable solution coupled with robust security measures. In the longer term, the company also plans to extend this solution to other standard as well as non-standard organizational processes. 

Choosing the right partner: from the perspective of comprehensiveness of solution, company A is looking for an agentic AI provider with the capacity to deliver a solution with progressively increasing levels of complexity as the magnitude of the work increases. It should also exhibit robust security guardrails and ease of integration into the existing ecosystem. From an ease of adoption standpoint, an ideal partner would be a provider capable of rapid agentic AI development and deployment. In this case, it may make sense for company A to partner with a hyperscaler, preferably the existing partner, that can develop a robust customized agentic AI solution on top of the existing infrastructure and scale the solutions later as per organizational needs 

Scenario 2: 

Situation: company B is a small-scale logistics services provider operating a fleet of trucks for road-based transport. The company is not very mature in terms of leveraging automation except aspects of the business that are very standardized or repetitive in nature with little to no exceptions or dynamic situations. This means there is heavy reliance on manual workforce and dependence on their decision-making skills which is often not backed by sufficient data. 

Unanticipated weather conditions or political situations frequently require a proactive rerouting of shipments to ensure timely delivery. However, in the current scenario, this is more reactive, costing company B significant time and money. It wants to deploy an agentic AI solution that can reroute shipments on its own with minimal manual intervention. Given the cost associated with each route and rerouting, there is an urgent need to deploy a solution with minimum build and rollout time. 

Choosing the right partner: as company B is a relatively small organization in the logistics market, it has limited resources available to invest in agentic AI. It is looking for a specialized solution with a low cost of investment. It should opt for a partner that is already servicing clients with its proven Agentic AI solutions and use cases in the logistics industry. In this case, it may make more sense for company B to partner with a pure play / niche agentic AI provider operating in the logistics space to launch a best-of-breed solution in a cost-effective manner.  This will help it focus on its core competencies without big investments in the underlying solution development 

Conclusion 

While the agentic AI partner evaluation framework is a good aid, there is no one size that fits all approach that can be used in terms of partner selection.

Ultimately, enterprises will have to make a choice between one or more players in the agentic AI tech landscape, keeping their needs, goals, and priorities in mind. For a big enterprise, a combination of multiple providers might work out as a better strategy, while for a small sized enterprise, being more focused might be a good strategy to begin with.  

Additionally, the agentic AI market is still very nascent and there are still a lot of unknowns. As every provider comes up with their own set of agents and solutions, we are likely to see an agent sprawl. This will necessitate the need for proper orchestration, integration and governance mechanisms within the agentic AI solutions and ultimately these could become the deciding factor over everything else.

With the number of start-ups burgeoning every day, we can also expect a significant consolidation in the months and years to come. Placing your bets on likely leaders and an ability to be agile can go a long way in emerging as a winner in the agentic AI journey! 

If you found this blog interesting, check out our blog on Agentic AI – Exploring Its Enterprise Potential | Blog – Everest Group, which delves deeper into the topic of agentic AI. 

If you have any questions, would like to gain expertise in artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Vaibhav Bansal (vaibhav.bansal@everestgrp.com) or Vershita Srivastava (vershita.srivastava@everestgrp.com)  

In today’s digital-first world, customer expectations have evolved rapidly…

Modern customers now expect fast, accurate, and personalized interactions from the brands they engage with. Furthermore, meeting these heightened expectations has become a challenge for businesses, driving the adoption of advanced technologies to enhance customer engagement.

At the forefront of these technologies is Conversational AI (CAI), an increasingly transformative solution reshaping how companies interact with their customers.

In this blog, we will explore how CAI technology is revolutionizing engagement across the entire customer journey, and how businesses should integrate CAI into their tech stack for providing an efficient, scalable, and personalized engagement to the modern customer.

The evolution of CAI:

CAI has been one of the biggest beneficiaries of the AI revolution over the past decade. Early solutions were rule-based, functioning on pre-programmed scripts that limited their ability to adapt to diverse inquiries or provide truly personalized service.

Today’s AI-powered bots can use sophisticated Machine Learning (ML) algorithms to understand context, intent, and sentiment, enabling more natural and engaging interactions across the plethora of channels that exist i.e. voice, chat, email, and social media.

Now with the addition of generative AI (gen AI) and the ability to effectively leverage customer data, CAI bots have grown more adept at handling complex queries, offering dynamic and customized responses, often with limited human intervention.

Supercharging the customer journey: A CAI-powered approach:

One of the most impactful aspects of CAI is in its true versatility i.e. its ability to assist customers at every stage of their journey, from initial engagement through to post-purchase support. From the moment potential customers discover a brand, CAI bots can engage with them in real time 24/7, as explained below.

1. Lead generation

Generating high-quality leads is one of the most crucial tasks for sales and marketing teams. CAI can enhance lead generation efforts by engaging potential customers on websites or social media channels in real time. Through outbound campaigns, they can gather essential data and seamlessly hand off qualified leads to sales teams

2. Product discovery

Instead of browsing through static menus or endless product categories, users can rely on conversational search to find what they’re looking for faster. CAI systems, especially when integrated with enterprise applications like customer relationship management (CRMs) and customer data platforms (CDPs), can analyze user preferences, behavior, and past interactions across various channels

3. Purchase support

CAI can provide insights on bundle deals, warranty options, and related products, helping customers make informed purchase decisions. If a customer hesitates at checkout, the chatbot can step in with timely offers or discounts to encourage completion of the purchase. Furthermore, these chatbots seamlessly integrate with payment gateways like PayPal and Apple Pay, allowing secure transactions directly within the chat interface, adhering to industry-standard security protocols

4. Post-purchase assistance

CAI can conveniently help customers with order confirmation, receipt generation, and next steps such as shipping details. It enables brands to check in with customers, asking about their experience and offering tips for maximizing product use. The chatbot can also assist customers with returns, refunds, and exchanges making the process hassle-free

5. Customer retention

CAI can schedule follow-up interactions with customers after they’ve left, sending personalized emails or messages highlighting new features, improvements, or exclusive return offers. Automating win-back efforts ensures the brand maintains a connection and demonstrates a commitment to addressing any previous issues.

To illustrate the comprehensive support CAI provides, the following exhibit showcases how a potential customer navigates a fictional e-commerce website, TechTrends, that has embraced CAI across the customer journey.

Best practices for implementing CAI solutions:

While CAI presents significant opportunities for businesses, successful implementation requires thoughtful planning and execution. The following best practices are recommended to successfully implement and harness the capabilities of CAI.

• Start small with careful planning: Before implementing any CAI solution, it’s essential to define clear objectives, as well as identifying small pilots that can deliver a quick return on investment (ROI). This approach allows organizations to test the CAI solution, gather feedback, and gradually expand into more complex areas as they gain confidence with the technology
• Customer-centric conversational flow: Conversational flows should be designed mindfully, ensuring they are intuitive and user-friendly. This includes incorporating fallback mechanisms, such as human handover options, to provide seamless transitions when the chatbot encounters complex queries or customer frustration
• Establish a robust data infrastructure and integrations: Enterprises should ensure all customer data sources, including CRM, past chat logs, and behavioral data, are unified and regularly updated as usage scales. There also must be a focus on building application programming interface (APIs) and middleware that allows context transfers across channels for omnichannel deployments
• Utilize modular architecture for scalability: Modular, microservices-based architectures allow for easy upgrades, testing, and scaling, making it possible to refine and scale specific parts of the CAI solution without affecting the entire system
• Prioritize AI transparency and governance: Besides complying with regulations, it is vital to implement AI explainability, especially in regulated industries such as finance and healthcare, to help agents and customers understand the basis of AI recommendations
• Embrace change: Transitioning to CAI also requires a cultural shift, emphasizing that it is a tool to assist, not replace, human roles. Providing training and fostering an open mindset will help customer facing teams to effectively leverage CAI

Conclusion:

CAI’s capabilities can transform what was once a series of disjointed transactions into a fluid, intuitive, and highly personalized customer journey.

This streamlined approach saves time for the customer, increases conversion rates for the business, and ultimately creates a more satisfying and efficient experience.

Looking ahead, the future of CAI is poised for remarkable advancements. CAI bots will evolve into agentic systems, becoming autonomous digital colleagues, capable of higher-order planning and independent decision-making.

Through the combination of deep learning and reinforcement learning, these systems will be able to process large amounts of data, recognize complex patterns, and learn from their actions and experiences in real-time environments.

The bottom line for enterprise leaders remains the same, conversational AI’s real impact is not just in introducing it in a siloed fashion, but embedding it deeply across the customer journey, into the core of business processes, where it can be of deliverable measurable value.

If you have any questions, would like to delve deeper into the Experience, Sustainability & Trust market, or would like to reach out to discuss these topics in more depth, please contact Simran Agrawal (simran.agrawal@everestgrp.com) and Anubhav Das (anubhav.das@everestgrp.com)

In our previous blog, we discussed how the advent of generative AI in our day-to-day lives has skyrocketed in the past few years, helping individuals and companies efficiently tackle tasks through automation and reduce the time taken to complete them. 

Furthermore, new applications of gen AI for business solutions are being developed at a breakneck pace across industries such as Retail And Consumer Packaged Goods (RCPG) Retail and Consumer Packaged Goods, Banking And Financial Services, Healthcare and Life Sciences, and Human Resources , among others.  

Additionally, companies are now expecting more tangible results from the implementation of gen AI to avoid losing market share. This is true for all the previously mentioned stakeholders: technology providers, service providers, and enterprises.  

At the same time, these stakeholders must be mindful of their critical role in fulfilling the DEIB (Diversity, Equity, Inclusion, and Belonging) mandate, which includes promoting inclusive and equitable practices in gen AI development and deployment. The absence of comprehensive DEIB measures in gen AI models can have detrimental effects both internally and externally. 

Furthermore, equitable artificial intelligence (AI) learning is essential. A survey conducted by a leading consulting firm, indicates that only 10-15% of businesses have established AI roles focused on fostering diverse perspectives within their teams.  

Professionals’ lived experiences provide critical insights for mitigating bias—a truth that all stakeholders must embrace. Before exploring potential solutions, it’s important to investigate the root causes of bias, the different types of biases present, and their implications, as our analysts have done below. 

Reach out to discuss this topic in depth. 

The Case for DEIB in Gen AI:

While technology offers substantial benefits, a significant DEIB challenge persists within current gen AI frameworks, leading to adverse effects for individuals and organizations. AI algorithms – a host of which are trained on existing framework models, lack diverse perspectives, and can mirror the biases of their creators, perpetuating inequalities and harming marginalized communities.

Cultural and social biases often infiltrate these systems, resulting in flawed outputs that do not accurately reflect varied experiences and knowledge.

Some benefits of unbiased gen AI Models include:

At the same time, adopting unbiased gen AI models can significantly benefit organizations by:

• Enhancing Decision-Making: Eliminating biases allows for more accurate, objective insights, improving decision-making across scenarios

• Improving Customer Insights: Objective data analysis helps businesses better understand customer needs, facilitating targeted marketing

• Promoting Diversity in Hiring: Unbiased AI can eliminate discrimination in recruitment, supporting diverse candidates, including neurodivergent individuals

• Streamlining Operations: Reducing bias in automated processes optimizes operations, enhancing overall efficiency and productivity

• Fostering Innovation: Bias-free AI models yield more diverse and creative ideas, propelling innovation across sectors

• Improving Risk Management: Unbiased AI provides clearer, balanced assessments, aiding organizations in identifying and managing risks effectively

• Ensuring Compliance with Ethical Standards: Utilizing unbiased AI aligns with ethical norms and best practices, fostering trust and accountability

• Creating a More Equitable Workplace: By promoting fairness, unbiased AI contributes to a more inclusive environment, driving organizational growth

A deep dive into the causes and types of the bias in terms of DEIB?

Gen AI models are statistical by nature and prone to errors, especially when lacking domain expertise. Currently, a small, homogeneous group often determines the data used for training these models. Many models are built on foundational frameworks such as BERT or RooBERTa, which can carry inherent biases if not addressed from the outset.

Types of DEIB bias include:

The social and business cost for business by utilizing a biased gen AI model:

Addressing these challenges is paramount for companies when accounting for the vast use cases of this technology across sectors. For example, 19% of organizations are leveraging AI to develop new products and services across the RCPG space, according to an Everest Group insight.  

Similarly, 40-45% of business leaders of mega enterprises (revenue exceeding US$ 1 billion) have reported successful implementation of gen AI across various operations in this Everest Group viewpoint. We expect this number to consistently increase in the coming years.  

If the models used for these products or services produce biased results or incorrect outcomes (an important component of ‘hallucinations’), it could negatively impact the companies’ reputations and their bottom lines. Thus, there are both direct and indirect costs associated with leveraging these models. The two key types of costs that businesses would suffer from are the following: 

Business Cost: The direct financial expenses incurred by a business, including production costs, operating expenses, and the costs of complying with regulations. These costs can be both internal and external to the business 

Social Cost: The total economic cost to society, including both direct costs borne by individuals and businesses, as well as indirect costs such as environmental damage, decreased quality of life, and social inequality 

While unbiased AI models are essential, their development and deployment can be costly. Collecting high-quality data for model training, designing and customizing AI models from scratch, and employing sophisticated techniques and specialized talent all contribute to the complexity.  

Additionally, scaling these models across large organizations or multiple geographies can introduce new biases due to variations in cultural, linguistic, and socioeconomic factors. Therefore, companies must be deliberate in identifying which products, services, or functions truly require such AI models.  

In response, some organizations have appointed Chief Diversity, Equity and Inclusion (DE&I) Officers, but this approach may be limited, as these officers typically focus on talent acquisition and retention.  

Effectively addressing AI’s DEIB impact requires input from multiple leaders, including the Chief Information Officer (CIO)/ Chief Technology Officer (CTO), Chief Product Procurement Officer (CPO), Chief DE&I Officer, and Chief Sustainability Officer, making it both resource- and cost-intensive. Furthermore, while algorithmic impact assessments are well-intentioned, they often fall short in fully capturing the broader social implications of AI models. 

To address this challenge, Everest Group has developed a framework that stakeholders can use to navigate these complexities effectively, with the overarching principle of the “Comprehensive Inclusion Framework” viewed from both an internal and external perspective. This principle is broken down into four key areas: 

• Inclusiveness emphasizes broad representation in the entire AI development lifecycle. It ensures that diverse perspectives, experiences, and needs are considered when designing, developing, and deploying AI systems 

• Impartiality ensures that AI decision-making processes are neutral, objective, and free from bias or unfair influence by continuously assessing the outputs of the model and checking for impartiality. Thus, blending in objective data driven insights 

• Equity, in the context of AI ensures that all user groups experience fair and just outcomes from AI systems, regardless of their background, demographics, or identity  

• Accessibility, focuses on making sure that AI technologies are usable and beneficial to all individuals, regardless of their socioeconomic status, disabilities, education, or geographic location 

The framework provides a comprehensive approach to integrating gen AI and DEIB policies within organizations across vertical and horizontal processes. It categorizes various policy combinations based on the level of emphasis placed on AI and DEIB and offers recommendations to achieve optimal alignment. The categories include: 

• Low DEIB Impact: DEIB efforts are not prioritized due to the lack of strong business or social cases 

• Medium DEIB Impact: DEIB efforts are focused on business and social benefits, with AI considered a tool to enhance these case 

• High DEIB impact: DEIB values are deeply integrated into organizational culture, using AI to drive inclusivity and equity throughout the business 

The current state of the market in terms of DEIB embodiment by stakeholders: 

As mentioned in our last blog post, across stakeholders, the current level of DEIB integration according to our ROLE framework is as follows: 

As gen AI increasingly influences business operations, stakeholders must prioritize DEIB in their AI development and deployment efforts.  

Tackling inherent biases and fostering fairness will not only mitigate risks but also enhance decision-making, customer insights, innovation, and workplace equity. By adopting frameworks such as Everest Group’s “Comprehensive Inclusion Framework”, organizations can effectively align their AI and DEIB strategies, ensuring long-term success and ethical compliance. 

We are actively tracking the evolution of artificial intelligence and its impact on the future of all sectors. To discuss the latest trends and their implications for brands, technology vendors, and service providers alike, feel free to reach out to Kanishka Chakraborty (kanishka.chakraborty@everestgrp.com), Meenakshi Narayanan (meenakshi.narayanan@everestgrp.com), Abhishek Sengupta (abhishek.sengupta@everestgrp.com), Abhishek Biswas (abhishek.biswas@everestgrp.com), Rita Soni (rita.soni@everestgrp.com) and Cecilia Van Cauwenberghe (cecilia.vancauwenberghe@everestgrp.com). 

If you found this blog interesting, check out our blog focusing on Building Purpose-Driven Generative AI (gen AI) – Why We All Have A Role To Play In The Future Success Of The Gen AI Ecosystem  | Blog – Everest Group, which delves deeper into the subject of gen AI. 

This is the first of a new series of blogs, with plenty more to come in 2024 and 2025! 

Life & Annuities (L&A)  and Property & Casualty (P&C)  insurance leaders are adopting cloud , data , and AI  technologies to drive operational efficiency, improve customer experiences, and solve the industry’s most pressing challenges.

Watch this interactive LinkedIn Live  session to discover the leading 50 insurance technology providers  and the key trends that set them apart. Learn about their ability to innovate rapidly , deliver tailored solutions , and demonstrate measurable value  through transformative technology as we introduce the Leading 50 Property & Casualty (P&C) Technology Providers 2024 report and results.

Attendees will gain actionable insights  into leveraging these providers to deploy leading-edge technology solutions that boost efficiency , streamline operations , and enhance customer experience . 

During this engaging event, our speakers  offered valuable insights for both insurers and technology providers looking to stay competitive in an evolving market .

During this collaborative LinkedIn Live session, we discussed:

• Who the leading insurance technology providers are for both P&C and L&A insurers 
• What makes the top providers stand out in today’s competitive landscape 
• How cloud , data , and AI  are transforming the insurance industry, and which providers are driving these innovations 

The digital transformation landscape is rapidly evolving, and mid-market enterprises (MMEs) are emerging as significant drivers of demand.  

While they may be smaller than Fortune 500 companies, MMEs are often more agile and willing to adopt innovative technologies  to gain a competitive edge.  

This has led service providers to recognize the untapped potential of this market and tailor their solutions to meet the specific needs of mid-sized businesses. 

Read on to discover how this has led to service providers recognizing the real untapped potential of this market, as they tailor their solutions to meet the specific needs of mid-sized businesses, and get in touch if you’d like to speak to an analyst on this subject. 

The Demand-Side Perspective: What Mid-Market Enterprises Want

Mid-market enterprises, traditionally overshadowed by larger corporations, are increasingly becoming the focus of digital transformation services. Unlike giants, MMEs demand personalized, cost-effective, and agile solutions. Their digital transformation initiatives often center on several key priorities, such as: 

• Operational Efficiency: Leveraging technology to streamline operations and reduce costs. 

• Customer Experience: Using digital tools to enhance customer interactions and satisfaction. 

• Scalability: Implementing scalable technologies like cloud computing, artificial intelligence (AI), and data analytics to allow rapid growth. 

A notable trend is the growing adoption of cloud computing and AI-driven automation, which help MMEs extract valuable insights from their data, improve decision-making, and optimize operations.  

Additionally, many MMEs prefer bite-sized, phased digital transformation projects that minimize risks and provide quicker returns on investment. This preference for shorter, milestone-based engagements creates an opportunity for service providers to establish long-term partnerships based on incremental, success-driven outcomes. 

The Supply-Side Perspective: How Service Providers Are Responding

Service providers are increasingly adapting their strategies to align with the unique needs of mid-market enterprises. Key approaches include: 

• Flexible Pricing Models: Given the limited budget MMEs typically have for large-scale, long-term transformation projects, service providers are adopting innovative pricing models that reflect the need for flexibility and scalability. These models include subscription-based or usage-based pricing, which can grow with the client’s evolving needs. 

• Scalability and Hybrid Solutions: Cloud solutions, automation tools, and data analytics platforms are some of the most in-demand services. Providers are responding by offering scalable andhybridand hybrid solutions that allow MMEs to gradually expand their capabilities. 

• Client Intimacy and Agility: Smaller service providers often have an edge when it comes to client intimacy, as they can deliver more personalized engagement and quicker responses compared to larger competitors. MMEs value hands-on support and strong partnerships, and they prefer service providers who demonstrate agility and a deep understanding of their business challenges. 

Strategic Partnerships and Co-Innovation

To effectively serve the mid-market segment, service providers must emphasize strategic partnerships and co-innovation with technology vendors. This is particularly evident in collaborations with hyperscalers and cloud service providers, where smaller vendors team up with larger players to develop solutions tailored to mid-market needs.  

These partnerships enable service providers to offer proven tools and accelerators, which can significantly reduce the time and cost required to implement digital transformation initiatives. 

Moreover, consulting-led engagements are another way service providers differentiate themselves. By providing strategic guidance alongside implementation services, providers position themselves as long-term partners, helping MMEs navigate complex digital landscapes while consistently delivering value. 

Growth Opportunities in the Mid-Market Segment

The mid-market segment is experiencing rapid growth, with service providers specializing in this area seeing compound annual growth rates (CAGR) of 9-10%. This surge is fueled by MMEs’ desire to modernize legacy systems, enhance customer experiences, and secure a competitive advantage through digital innovation.  

Unlike their larger counterparts, MMEs are often more willing to embrace  generative AI  (gen AI) and other cutting-edge technologies due to their agility and lower complexity. 

For service providers, this presents an excellent opportunity to engage with a forward-thinking, fast-moving segment of the market that is eager to invest in the future. 

Conclusion

The mid-market enterprise segment is ripe for digital transformation, offering a wealth of opportunities for service providers that can meet their unique needs.  

As MMEs continue to prioritize agility, cost-effectiveness, and customer experience, service providers must adjust their strategies to offer scalable, innovative solutions that can deliver tangible business outcomes.  

By focusing on flexibility, personalized engagement, and strategic partnerships, service providers can position themselves as indispensable partners in the digital journeys of mid-market enterprises. 

Looking to capture the untapped potential of mid-market enterprises? Connect with our team to explore strategic insights from our recent study— Digital Transformation Services for Mid-Market Enterprises PEAK Matrix® Assessment 2024. 

If you found this blog interesting, check out our blog focusing on How Has Generative AI Evolved And Is Its Evolution Now Supporting CX Leaders More On The CXM Journey? | Blog – Everest Group (everestgrp.com), which delves deeper into another topic in the world of artificial intelligence. 

If you have any questions, would like to delve deeper into the Engineering & Information Tech market, or would like to reach out to discuss these topics in more depth, please contact Alisha Mittal and Parul Trivedi.

Retail media networks (RMNs) are transforming the way retailers and consumer packaged goods (CPG) brands collaborate in the digital advertising space. Retailers are now seizing the opportunity to monetize their digital assets, while brands are eager to invest in retail media networks (RMNs) to engage consumers directly at the point of sale. 

Just this year, Lowe’s rebranded its network with a simplified name—Lowe’s Media Network—and expanded its channels to include email, in-store audio, paid search, and app-based ads. 

Meanwhile, Macy’s integrated artificial intelligence (AI)-powered technologies to its RMN, to improve post-purchase engagement through its partnership with Rokt. Similarly, Albertsons Media Collective is working with commerce media platform Criteo to extend its in-store media offerings for advertisers. 

With so much evolution in the sector, our analysts have looked into what the future holds for a space that is currently incredibly lucrative… 

Reach out to discuss this topic in depth. 

What exactly does all this mean, and how are brands and retailers making money from it? 

A retail media network (RMN) is an advertising platform run by a retailer , allowing brands to purchase ad space on its website, app, and other digital properties. The key appeal is the retailer’s use of first-party customer data, enabling precise targeting, which is often more effective than traditional digital marketing channels. 

For brands, RMNs are a goldmine. They offer a way to target consumers with highly relevant ads while they’re actively shopping, increasing the likelihood of a purchase and boosting return on ad spend (ROAS). This combination of contextual relevance, first-party data, and seamless integration with the shopping experience, gives RMNs a strong edge over traditional marketing platforms. 

Why are RMNs growing while traditional digital marketing still exists? 

There are several reasons behind the rise of retail media networks: 

• Privacy-First Advertising: With regulations like general data protection regulation (GDPR) and the phase-out of third-party cookies, RMNs provide a privacy-compliant way for brands to connect with their audiences using first-party data 

• Seamless Shopping Integration: RMN ads appear while consumers are already in buying mode—unlike traditional ads that interrupt browsing or social media activities 

• Enhanced Measurement and Attribution: RMNs offer closed-loop attribution, enabling brands to see exactly how their ads drive purchases, providing transparent and accurate ad performance data 

• Retailer Competitive Advantage: Retailers with strong loyalty programs and large online presences control valuable first-party data, giving them an edge in the advertising space 

If it’s so amazing, why isn’t every retailer running their own RMN? 

Despite the many benefits, only top-tier retailers like Amazon, Walmart, Target, and Kroger have been able to successfully manage profitable RMNs. Even for retailers with large customer bases, several challenges arise: 

• Data Privacy and Security: Handling large volumes of first-party data comes with immense responsibility. Retailers must adhere to regulations like GDPR and the Californian Consumer Privacy Act (CCPA), while avoiding breaches that could harm consumer trust 

• Ad Fraud: Like any digital advertising channel, RMNs are vulnerable to fraud. Robust fraud detection tools are essential to maintain advertiser trust and campaign performance 

• Balancing Ads and User Experience: Too many ads can disrupt user experience, so retailers need to strike a careful balance between monetizing traffic and maintaining a smooth shopping journey 

• Technological Infrastructure: Building a scalable RMN requires significant investment. Not all retailers have the technology stack or resources to develop such platforms without external support 

 Can outsourcing help? Where and how? 

For retailers lacking in-house expertise, outsourcing can be a powerful solution. Case in point, Macy’s partnership with Rokt, which brought in AI capabilities without the need for internal development. 

Key areas where outsourcing can help include: 

• Technology Development: Building the right tech stack can be time-consuming and expensive. By outsourcing to technology vendors or global system integrators (GSIs), retailers can launch their RMNs more efficiently 

• Ad Operations: Managing ad inventory, targeting, and performance measurement can be handled by specialists, allowing retailers to focus on their core operations 

• Data Management: Safeguarding and analyzing first-party data requires expertise in privacy and compliance, which can be outsourced to trusted partners 

Global System Integrators (GSIs) are instrumental in helping retailers scale their RMNs by providing the technical backbone and operational expertise required to do so. 

Retailers can also outsource day-to-day operational tasks, such as managing advertiser partnerships or designing creative ad formats. This allows them to scale faster without having to build large internal teams. 

The future of retail media networks: 

As RMNs evolve, they represent one of the most exciting opportunities for retailers and CPG brands to enhance customer engagement and drive sales at the point of purchase. Below are key considerations for retailers and brands: 

• People: 

• • Tech-driven upskilling: Technology vendors and service providers will play a key role in upskilling the teams at retailers and brands, helping them deepen their technical and functional understanding of RMNs and its evolving trends 

• • Need for deeper partnerships: As the number of RMNs grows, brands with stronger connections with retailers will gain a competitive edge by securing better visibility and premium placements within retailers’ advertising properties  

• Process: 

• • Customer Experience : Retailers will prioritize non-intrusive ads that enhance the customer journey. RMNs will shift from basic product placements to immersive, personalized, data-driven ads 

• • Data management and privacy: As concerns over data privacy grows, transparency in data collection and usage will become crucial. Retailers and brands will need to communicate clearly about how consumer data is handled, building trust, and fostering acceptance of ads in retail environments 

• Technology: 

• • Closed-loop reporting: Brands will demand closed-loop reporting that is detailed, unambiguous, near real-time, and continuously accessible, providing insights that drive better marketing outcomes 

• • Integration and cybersecurity: Tech solutions must integrate ads seamlessly into retail environments, ensuring consistent delivery across online, in-store, and mobile platforms, while prioritizing cybersecurity and data protection 

In a nutshell, the future of RMNs will see brands and retailers working together more strategically, making every touchpoint a moment to connect and convert. 

Technology and service providers will act as key partners, connecting advanced RMN technologies with retailers and brands. They will help teams understand and utilize these tools effectively, enabling optimized targeting and seamless integration. 

We are actively tracking the evolution of retail media networks and their impact on the future of the Retail And CPG sector. To discuss the latest trends and their implications for CPG brands, retailers, technology vendors, and service providers, feel free to reach out to Manu Aggarwal, Abhilasha Sharma, or Aakash Verma. 

If you found this blog interesting, check out our blog focusing on Composable Commerce: For Composing The Best-of-Breed Customer Experience, which delves deeper into another topic worked on by our HLS service line. 

Join us at NRF ’25 to connect with our retail and CPG leaders. We look forward to exploring the insights and strategies shaping the industry. 

For more information regarding NRF ‘25, visit website and their LinkedIn page. 

Gen AI’s rapid adoption is evident from its early success; for example, ChatGPT 3.5 amassed one million users within five days of its 2022 launch, and now has over 180 million users – these numbers simply can’t be ignored!  

Organizations across industries are now leveraging gen AI to transform operations, enhance decision-making, personalize customer experiences, and foster innovation.  

However, this rapid adoption comes with significant environmental and social challenges. Our analysts have delved deeper into the topic, to decipher how and why gen AI needs to be nurtured and understood throughout every ‘step of the ladder’ in the marketplace. 

Reach out to discuss this topic in depth. 

The current landscape: 

The environmental footprint of gen AI is notable; generating a response from gen AI uses six to ten times more energy than traditional internet searches, exacerbating the information technology (IT) carbon footprint in every sector.  

Socially, gen AI also faces issues such as bias and ethical concerns, with biases in gen AI outputs perpetuating discrimination and misinformation. The particular concern around fair use doctrine is also emerging, with the New York Times suing OpenAI to use its news articles without permission, to train its model.  

To address these multifaceted challenges, it is crucial to understand the roles of various stakeholders in the gen AI ecosystem. Each plays a distinct part in promoting sustainability and mitigating negative impacts.  

The gen AI’s ecosystem involves various stakeholders—technology providers, service providers, enterprises, regulatory bodies, and research/coalition building organizations. Technology providers can enhance model efficiency and inclusivity, while service providers develop energy-efficient and responsible artificial intelligence(AI) solutions.  

Enterprises, as end users, can demand sustainable practices and influence market demand. Regulatory bodies also play a crucial role by establishing and enforcing standards and regulations. Meanwhile, research and coalition building organizations drive innovation and offer insights into emerging best practices and technologies for sustainable gen AI. Together, these stakeholders form a cohesive ecosystem essential for advancing sustainability in the gen AI landscape. 

Everest Group explores how key stakeholders influence gen AI’s path to sustainability

At Everest Group, we view gen AI’s sustainability through the lens of the planet and people. To ensure a sustainable future for gen AI, we have identified three themes:  

• Decarbonization and energy management: Reducing energy consumption and lowering the carbon footprint of gen AI technologies. 

• DEIB (Diversity, Equity, Inclusion, and Belonging): Promoting inclusive and equitable practices within gen AI development and deployment. 

• Accessibility: Ensuring gen AI technologies are accessible and usable for everyone, regardless of their disability status. 

Three stakeholders—technology providers, service providers, and enterprises—are pivotal in translating these mandates into practical actions. Understanding their contributions is essential for advancing gen AI sustainability.  

Technology providers, service providers, and enterprises are directly involved in implementing and influencing sustainable practices, making their involvement critical for tangible progress. While regulatory bodies, governments, and research organizations and industry coalitions play a complementary part by establishing standards, regulations, and guiding research, the immediate impact on sustainability stems from the actions and commitments of these primary stakeholders. 

Everest Group has developed an assessment framework to define the roles the primary stakeholders play in making gen AI more sustainable.  

Our ROLE framework evaluates how much pressure existing AI regulations place on stakeholders, their operational control across the gen AI value chain (from conceptualization to end-of-life), their leadership in partnerships and engineering research & development (ER&D), and their expertise in shaping sustainable gen AI.  

The ROLE framework is depicted in Exhibit 1.

After scoring the three stakeholders across the parameters defined in our ROLE framework, our assessment has categorized stakeholders into three roles:

• Architect: Reflects stakeholders with high engagement and significant influence on advancing gen AI sustainability.

Technology providers are currently in the role of Architect. They drive innovation and set the standards for sustainable gen AI technologies. Their involvement spans the entire lifecycle of gen AI, from development to deployment, and they are at the forefront of integrating sustainability into their solutions.  

• Contributor: Indicates stakeholders who actively support and engage with sustainability efforts but do not lead them.

Service providers fall into the Contributor category. They play a vital role in implementing and supporting sustainable practices within gen AI solutions, yet their influence is more supportive rather than leading the charge in sustainability initiatives.  

• Influencer: Denotes stakeholders who monitor or follow sustainability developments with minimal direct involvement but may shape discussions and perceptions through their observations.

Enterprises are classified as Influencers. While they adopt gen AI solutions, their involvement in driving sustainability is limited. They largely follow industry trends without actively shaping or leading sustainability efforts. However, they can shape the demand for more sustainable gen AI through discourse, forming industry-coalitions to adopt best practices, or co-innovating sustainable gen AI solutions with tech partners. 

The ROLE framework provides a comprehensive assessment of the market and the contributions of various stakeholders.  

It categorizes stakeholders based on their overall impact within the ecosystem. However, we recognize that some players are making exceptional efforts that could elevate their roles—from Influencers to Contributors or from Contributors to Architects. This nuanced view acknowledges that individual players can surpass their general category and assume a more influential position in driving sustainability. 

The evolving roles of technology providers, service providers and enterprises present valuable opportunities for further advancements. By exploring these dynamics, we can better understand how each stakeholder can contribute to a more sustainable gen AI ecosystem. 

Everest Group will keep digging deeper to understand the gen AI sustainability ecosystem better. Stay tuned for our upcoming blogs, where we’ll explore strategies for tackling gen AI’s complex sustainability challenges. We’ll delve deeper into each stakeholder’s evolving role and offer insights on bridging the gaps in their current efforts. 

If you found this blog interesting, check out our recent blog focusing on Unleashing The Power Of Advanced AI Engines: Transforming Business Operations For The Future | Blog – Everest Group (everestgrp.com), which delves deeper into the topic of advance AI and gen AI. 

If you have questions or want to discuss these topics in more depth, please contact Meenakshi Narayanan, Rita N. Soni and Cecilia Van Cauwenberghe.