Tag: Generative AI

Why TCS and Infosys’ Gen AI Commentaries in Q3 Felt Lackluster | In the News

India’s two biggest IT services companies, Tata Consultancy Services (TCS) and Infosys, reported their third-quarter earnings for FY24 on January 11. Amid a seasonally weak quarter and an uncertain demand environment, all eyes were on their generative AI updates. However, they shied away from sharing definitive numbers or updates on deal pipeline opportunities or prospects of revenue in coming quarters.

Peter Bendor-Samuel, founder of Everest Group, believes most of the PoCs (almost 90%) undertaken in 2023 will not move into production in 2024. “However, 10% which does move (to production) will indicate to the market where gen AI does work, and we expect significant opportunities to arise in these areas for the tech services firms by the third quarter of 2024,” he said.

Read more in Money Control.

Content Supply Chain – The Time is Ripe to Reimagine the Content Ecosystem Lifecycle | Blog

Content is key to creating connected and engaging experiences. By effectively managing the content supply chain, enterprises can achieve greater productivity and scalability and produce high-impact content. Discover how the content ecosystem has evolved, explore the potential of generative Artificial Intelligence in reshaping the content supply chain, and gain insights into what’s next in this blog.

In today’s hyper-connected world, where customers interact with brands across multiple touchpoints, the demand for seamless experiences and one-to-one personalization has reached unprecedented levels. This has exponentially increased the desire for all forms of quality content that aligns with customer expectations.

The increasing appetite for content, coupled with rising customer expectations, poses a challenge for marketers to create, share, and track quality content at scale. Marketers need strategies and implementation mechanisms that can streamline the workflow, deliver content at scale, and track results to gain a competitive differentiation.

Evolution of the content ecosystem – then, now, and forever

The content ecosystem evolution has been nothing short of transformative. The 1990s saw the emergence of the internet, leading to the inception of digital content (mostly text-based with limited interactivity) and its eventual breakout from traditional media.

The dawn of Web 2.0 brought dynamic and user-generated content. This coincided with the rising popularity of blogging platforms in the 2000s, the proliferation of smartphones, and the dominance of social media platforms in the 2010s. These factors significantly boosted the content ecosystem.

The advent of the COVID-19 pandemic provided further fuel to accelerate into the next generation of content preference – short-form, engaging, and snackable content.

In this multi-form content phase, ranging from text and videos to virtual/augmented reality (AR/VR) content, the ever-changing ecosystem dynamics continue to redefine content consumption behavioral shifts.

As we step into the connected future, consistent omnichannel content might not only define which form survives but also lead to the emergence of newer and more engaging content formats.

Content supply chain – another jargon in the marketing world?

One might question the need to adopt a content supply chain when the current content ecosystem seems to function smoothly. However, the demand is driven by the ever-evolving content lifecycle with fast-changing consumer preferences and demands. The need to create personalized omnichannel experiences that can grab customers’ eyeballs in today’s crowded internet adds to the challenge.

A content supply chain is essentially a process to streamline content ideation, creation, management, and distribution in a structured and efficient manner. It ensures a seamless workflow, from ideation to delivery, optimizing collaboration, maintaining quality, and meeting diverse content delivery platform demands.

Effectively managing the content supply chain enhances productivity, enables scalability, and ultimately allows organizations to consistently produce and deliver impactful content in today’s dynamic digital landscape.

Exhibit 1: Defining a content supply chain lifecycle


While the term “content supply chain” might be new and gaining traction, consolidating multiple components of the content ecosystem lifecycle has become increasingly important over the past few years.

Enterprises and marketers also face technology and internal enterprise challenges beyond content. These include the lack of quality content, plagiarism, siloed communication, high manual involvement, omnichannel inconsistencies, and a fast-evolving landscape. To adopt a content supply chain at scale, the inefficiencies surrounding the fragmented content landscape need quick resolution.

The absence of a content supply chain greatly impacts content developers, marketers, enterprises, consumers, and other stakeholders involved in the content lifecycle. Without key performance indicators (KPIs), developers lack sufficient information and feedback on content to gauge effectiveness. Similarly, marketers are unable to precisely target their desired audiences due to a lack of relevant content. Enterprises also cannot tap into potential leads and manage content quickly at scale. Ultimately, without a content supply chain, end consumers would be barraged with an excess of irrelevant and annoying information, leading to a reduced experience.

Exhibit 2: Benefits of adopting a content supply chain


Generative AI and the content supply chain – reshaping the content ecosystem lifecycle

Generative AI (gen AI) has brought about a technology revolution. Touted as the next chapter in human-machine interaction, its impact on the content supply chain is extraordinary.

Gen AI can potentially revolutionize the content supply chain by assisting humans across the proposal, development, activation, and insights stages. It also can automate many manual tasks involved in creating and distributing content.

This technology could significantly reduce costs, improve efficiency, and produce better content quality and consistency. As a result, many enterprises have already invested in gen AI tools and solutions to supplement their workforce across the lifecycle stages.

Exhibit 3: Optimizing the content at scale for increased efficiency of marketing teams


Almost 50% of marketers either use or experiment with gen AI during their work according to our report, Content Supply Chain – Revolutionizing the Content Development Lifecycle. With the increasing adoption of gen AI in the content ecosystem, analyzing its degree of adoption and complexity provides deep insights into its usefulness as illustrated below.

Exhibit 4: Comparison of the complexity with the adoption of creative use cases


It is not just about the content supply chain platform, but how it must be implemented

The content supply chain product market is heating up, with newer entrants joining well-established tech vendors, offering organizations many new options. However, it becomes imperative to ensure any new products enterprises adopt can be seamlessly integrated into their existing infrastructure and content pipeline.

With very few major tech vendors providing professional services for content supply chain product offerings, Global System Integrators (GSIs) have become essential. While GSIs offer consulting, implementation, or managed services for individual content supply chain components, the fragmented nature often can lead to integration issues. Thus, GSIs must develop end-to-end capabilities across the content supply chain ecosystem to meet growing enterprise needs and preferences.

GSIs must adhere to a strict framework that will enable them to offer strategy planning, design and implementation, run and operate, and manage services across the four content supply chain layers. This will enable GSIs to partner with enterprises to transform their content workflow process. A detailed framework can be found in the report, Content Supply Chain – Revolutionizing the Content Development Lifecycle.

What does the future hold?

Connected experiences will power the future. Overall, the personalization and interactive experience landscape has become increasingly complex and diverse. This requires brands to constantly adapt and stay up to date on the latest trends and technologies to reach and engage customers. Content is key to achieving connected experiences.

Having a predefined clear vision and strategy before adopting a content supply chain is essential to avoid wasting organizational resources. A thoroughly defined content strategy, optimized activation and delivery pipelines, and investment in proper content creation solutions and business KPIs are crucial for success.

Overall, a well-designed content supply chain can help enterprises stay ahead of the curve and break down internal siloes between different teams. This promotes consistency and responsiveness to changing market conditions. By implementing a content supply chain, enterprises can reduce duplication of efforts and meet KPIs in a standardized manner.

For questions about selecting the right content supply chain platform or to learn more about personalization, interactive experiences, or developments in this space, contact the Everest Group team at [email protected], or [email protected].

Register for our webinar, The Generative AI Odyssey: A Year in Review and What’s Ahead in 2024, to learn more about future themes across gen AI.

AWS re:Invent: The Story Beyond Generative AI | Blog

While Generative Artificial Intelligence was a major focus at the recent Amazon Web Services (AWS) annual user conference in Las Vegas, other important themes stood out to our analyst team. These include an increased focus on partnerships, cost optimization, and new growth channels. Read on for our analysis of the trends to pay attention to from AWS re:Invent 2023.

Since attending AWS re:Invent from Nov. 27 to Dec. 1, we have been digesting the newer offerings, alliances, and strategic focus areas for AWS. This blog explores the top three themes we believe make their mark in the sea of announcements, initiatives, and even unspoken priorities.

Increased focus on the partner ecosystem

Much like prior years, a significant focus was put on the partner network, including service providers (global system integrators, niche system integrators, etc.), technology partners (large and small vendors), and others (e.g., resellers).

The AWS Marketplace witnessed significant changes to help partners that are already well supported. Many service providers demonstrated high-value client transformation case studies during keynote sessions, round tables, and in expo booths.

With the massive spectrum of service partners and their diverse wish lists, we firmly believe a key focus area for AWS should be improving the profitability of its AWS business. AWS needs to make it simple to use its many offerings, scale personnel training, and help build tools and intellectual property (IP).

AWS continuously assesses its partner program and the results shared were encouraging. However, with the market transitioning to Generative AI, the earlier approach of building partnerships based on core infrastructure will need to evolve.

We observed that clients want service partners to proactively have strong views on specific cloud vendors they should work with rather than be indecisive. To remain the preferred choice, AWS needs to continue engaging its service partners, especially with the newer demand for cloud services.

Nonetheless, AWS will need to work with service partners to strike a balance between being the primary cloud partner, which AWS wants, while maintaining the partner’s professed cloud agnosticism to ensure they both deliver client value.

Service providers’ industry expertise is another critical engagement area. This can explain why the event did not heavily emphasize the “industry cloud” because a large part of industry-centric development will be done in collaboration with service partners.

We believe the earlier witnessed “client ownership” friction between service partners and AWS is now resolved. However, as some service partners still raise this concern with us, AWS should address this issue through partner communication and stronger action.

Cost optimization at the center of all conversations

One notable feature of AWS re:Invent 2023 was the presence of a large number of financial operations (FinOps)-focused providers in the booths. While FinOps is not new and cost optimization has always been a CIO agenda, the sudden surge in their relevance can be attributed to the current macroeconomic situation and the frantic, unplanned post-COVID cloud adoption. As a result, most enterprises ended up having complex, hybrid cloud estates and a lack of visibility, leading to spiraling costs.

According to an Everest Group survey of 450 enterprises, 63% dedicate more than 7% of their cloud spend to FinOps as they are becoming more aware of the potential cost-saving available through investments in FinOps.

The FinOps space has become quite crowded with several specialists, global and regional system integrators, and technology providers, including AWS, offering these solutions. Enterprises currently have too many choices and identifying the right partner is difficult.

The provider type also varies by their offering within FinOps and typically can be categorized by: reseller, Reserved Instances (RI)/Savings plan (SP) management provider, consulting and managed services provider, visibility and recommendations provider, and end-to-end FinOps capability and offering provider.

Some of the specialist providers that caught our attention at the event include Alteryx, Archera, Aviatrix, CAST, Chronosphere, Cloudability, CloudFix, Cloudflare, CloudKeeper, CloudZero, Coralogix, DoiT, Finout, Flexera, Harness, Kubecost, LogicMonitor, Ollion, ProsperOps, Splunk, Stacklet, Ternary, Vantage, Vega, Virtasant, Xosphere, and Zesty.

Each enterprise should identify the right solution to meet its requirements. A few considerations to keep in mind when choosing a FinOps solution or service include the ability to manage environment complexity, the metrics and key performance indicators (KPIs) used to track progress, cross-team collaboration features, availability of skilled FinOps personnel, and the visibility and dashboarding quality.

Newer channels for growth acceleration

In the third quarter of 2023, AWS reported US$23.1 billion in revenue, up 12% year-on-year, but the growth rate was below the company’s typical historical increases in the mid-20 to low-30% range. The same trend is visible across its partner ecosystem, except for a few specialist players.

The growth rate of most global cloud system integrators has diminished by more than half compared to 2021 and 2022. Amid this slowdown, we sense an emphasis on identifying channels for growth acceleration within AWS and across its entire ecosystem partners.

Generative AI was the biggest growth bet and talking point for all attendees at the event. Almost every major announcement by AWS was around Generative AI. However, it’s worth noting that Generative AI has had little influence on the top line of hyperscalers, technology vendors, or system integrators.

Most Generative AI implementations are still in the proof of concept stage, with more than 90% of deal sizes being under US$1 million. AWS expects that many Large Language Models (LLMs) will require public cloud computing capacity and is pushing all its partners to drive enterprise adoption.

However, the founder of a leading Generative AI company at the event mentioned that while the potential is enormous, the shape and form of future adoption are completely uncertain. Interestingly, he suggested with the rapid rate AI models are evolving, LLMs might get replaced by something completely new in two years. While AWS and the entire ecosystem need to continue investing and exploring Generative AI use cases, placing big bets on it could be a risky short-term proposition.

Other Focus Areas at AWS re:Invent

If not for the emergence of Generative AI, industry cloud and complex workload migration to AWS would have dominated the event. These AWS industry cloud solutions had dedicated booths right at the center of the expo hall.

However, its impact was muted by limited announcements by AWS and the lack of a serious investment intent displayed. As suggested earlier, this could be due to AWS focusing the industry cloud narrative with service provider partners who have a better understanding of industries. The impression created by AWS at re:Invent in this area was low, making it appear the hyperscaler is taking a wait-and-see approach.

Another growth area AWS is expected to pursue is the migration of complex workloads, like mainframes, to its platform. It announced partnerships with a few system integrator partners and showcased its intent to help enterprises migrate.

With a significant portion of simple workloads already migrated to the cloud, complex workload migration could be the most stable growth potential for AWS in the next few years. AWS and its partners should double down on investments in this area.

Undeniably, AWS re:Invent 2023 turned out to be a delicate balancing act of strengthening the partnership network, investing in emerging innovation areas, maximizing client value, and ensuring cost optimization in the current macroeconomic environment. We would love to hear your observations from AWS re:Invent. To share your views or to discuss other details, please reach out to [email protected] or [email protected].

Learn more about the AWS services market, including trends, demand drivers, and key considerations for enterprises.

Key Issues Affecting the Effectiveness of Generative AI | Blog

Generative AI seems so compelling. However, it carries significant issues that will likely cause initiatives to fail or substantially underperform against their potential. This blog presents information about several issues. We will look first at a key issue causing a lot of resistance to generative AI adoption: the technology presents a probabilistic answer as though it is a deterministic answer. This blog will help your company better understand where and how to apply generative AI.

Read more in my blog on Forbes

Everest Group’s AI Top 50™ List: Who’s Leading a Decade of Disruption? | Blog

With its first-ever AI Top 50™ list, Everest Group recognizes the exceptional business performance of AI technology providers globally. This valuable ranking serves as a useful tool for enterprise decision-making and provider comparisons. Continue reading to learn more about the leading AI-first providers featured, as determined by Everest Group’s comprehensive research.

Everest Group has launched its inaugural AI Top 50 list of the most prominent technology providers worldwide that prioritize artificial intelligence (AI) at their core. The list was determined by evaluating rigorous, objective criteria, which include AI revenues, total funding secured, share of funding received in the past two years, and company valuation. This vital report will be released annually.

See Everest Group’s Top AI 50 list.

Why is the Everest Group AI Top 50 important today?

In the past decade alone, over 5,000 AI technology providers have emerged with different specializations across various domains and geographic regions. The remarkable growth underscores AI’s critical role in shaping the future of technology and its enduring impact on daily lives. Recently, the introduction of large language models (LLMs) has further sparked an innovation wave across various industries, shaping interactions and decision-making and revolutionizing everything from chatbots to content creation.

With this rapid progression in the past few years, and even months, businesses across all industries are rushing to gain insight into the technology to stay competitive as it reshapes industries, streamlines processes, and redefines interactions between humans and machines.

The list will assist enterprises in identifying AI technology providers that have achieved notable scale. Businesses, investors, and industry professionals can gauge the providers’ competitive positioning and potential in the ever-evolving AI industry. Additionally, the ranking is a resource for AI-first technology providers to benchmark themselves against industry peers.

How does Everest Group define AI?

To define the terms in the report, we used a multi-faceted approach, drawing on a combination of exclusive proprietary data, including insights gathered directly from AI technology providers. Along with that, the methodology incorporates publicly accessible information such as reported revenue, total funding, and valuation data obtained from publicly accessible sources. This thorough methodology ensures the accuracy and breadth of the data.

How is the Everest Group AI Top 50 list determined?

From a database of 2500-plus AI providers, we narrowed the pool to 250 firms after initial preliminary screening. The selected AI technology providers were then evaluated and positioned based on a set of qualification criteria. The research findings provide a comprehensive snapshot of their standing in the dynamic AI landscape.

The AI Top 50 list represents AI technology providers that meet the following criteria:


All the companies listed develop and integrate AI as a central and indispensable component of their products and solutions. In other words, their offerings would be fundamentally incomplete without AI.


The featured providers develop and offer software-based AI solutions as their primary offering, meaning their core focus revolves around delivering AI through software applications.

Business-to-business (B2B) focus/offerings

The providers on the list tailor their software products and solutions to meet the technology needs of other businesses.

With AI becoming a more essential component of business and individuals’ daily lives, the Everest Group AI Top 50 list empowers organizations to navigate this dynamic field. See the Everest Group AI Top 50 list.

To learn more about the 2023 Everest Group AI Top 50, reach out to Priya Bhalla, [email protected], Vishal Gupta, [email protected], and Niraj Agarwal, [email protected].

Exploring Generative AI in the Future of Work | Webinar


Exploring Generative AI in the Future of Work

November 15, 2023
9:00 AM PT | 12 PM ET

Everest Group Practice Director, Sailesh Hota, will join industry experts to explore how companies are looking to advances in generative AI technology to drive human-centered opportunities and outcomes. They will discuss:

  • Potential use cases in talent acquisition and HR
  • Considerations and risks in the world of work
  • How an enterprise could be successful with AI
  • Human-centered opportunities and outcomes with AI


Sailesh Hota
Practice Director, Everest Group
Matt Malden
Chief Product Officer, Globality

How Generative AI is Changing Creative Work | In the News

Text, image, and audio generators offer new content creation capabilities, but they raise concerns about originality, ethics, and the impact of automation on creative jobs.

Nishant Jeyanth, Practice Director at research firm Everest Group, sees generative AI tools playing a broader role in creative content lifecycles in enterprises. Tools such as ChatGPT and Dall-E are especially useful for crafting first drafts.

Read more in TechTarget

Humans at the Heart of Generative AI | In the News

Generative AI is becoming a key component of business operations and customer service interactions today. According to Salesforce research, three out of five workers (61%) either currently use or plan to use generative AI in their roles.

The public release of generative AI technology over the past year has improved the use of Chatbots dramatically in a short time. “Chatbots were around before, but generative AI has further increased their efficacy, as well as the quality of output,” notes Vishal Gupta, Vice President at Everest Group.

Read more in this MIT review.

Enterprise Generative AI Adoption: Risk Evaluation for Competitive Advantage | Blog

The adoption of generative AI technology poses four major types of threats to enterprises: data privacy and security, reliability and explainability, responsibility and ownership, and bias and ethics. By assessing current risk levels and implementing practices, tools, and systems to manage these challenges, enterprises can realize the most value from this transformative technology. Learn more about evaluating generative AI risk to gain an edge in this blog.  Learn more about our Generative AI Risk Assessment.

Generative Artificial Intelligence (AI) has captivated popular imagination like nothing else, promising a future filled with endless possibilities. For the first time, this technology can create art, synthesize human voices, and generate human-like responses to questions.

Open AI’s ChatGPT triggered the mainstream adoption of generative AI, racking up more than 100 million monthly active users within just two months of its launch. Today, more than 300 startups are developing various generative AI-related applications.

Enterprises globally have recognized generative AI’s emergence as a watershed moment and are scrambling to identify the best way to leverage its capabilities. Numerous use cases across industries and functions have already emerged and are being piloted.

Many technology providers have incorporated generative AI as an integral part of their solutions, and others are forging relevant partnerships to jump on the bandwagon.

However, while many organizations are excited about long-term generative AI adoption, few fully consider the potential risks. In this blog, we will delve deeper into the importance of generative AI risk assessment.

To realize maximum value from generative AI adoption, enterprises must undertake a structured incremental approach (as illustrated in Figure 1). This framework involves prioritizing use cases, assessing adoption risks, identifying suitable providers, adapting existing operating models, providing effective governance and change management, and reviewing performance against expectations.

Figure 1: Generative AI adoption framework
Figure 1: Generative AI adoption framework

Generative AI risks

Generative AI’s ease of usage has accelerated its adoption, highlighting both its value and its risks. Broadly, generative AI risks can be grouped into four categories: data privacy and security, reliability and explainability, responsibility and ownership, and bias and ethics (as shown below in Figure 2).

Figure 2: Generative AI risk categories
Figure 2: Generative AI risk categories

Let’s look at how these risks typically manifest and some examples:

Data privacy and security: Regulatory fallout from undisclosed data collection and retention is a key issue with generative AI models. This stems from the practice of developing AI models that can address a broad range of topics, rather than training data for a specific purpose. Further concerns include employees inadvertently sharing confidential enterprise data through user prompts or training data. In some cases, unfiltered prompts may allow employees access to data beyond their purview. From a cyber threat perspective, generative AI raises the risk of data breaches through malware, phishing, and identity theft

Samsung employees pasted confidential source code into ChatGPT to look for errors and optimize the data, inadvertently adding it to ChatGPT’s training data pool that can possibly be accessed by others.

Reliability and explainability: The quality and representativeness of training data greatly influence the accuracy of output produced by generative AI models. Deficiencies in the training data manifest as errors in generated content that may have serious legal ramifications beyond eroding customer trust. Furthermore, in the absence of required information, generative AI models may even fabricate information to answer a question. This leads to a false sense of expertise and can mislead the average user. Without a confidence score that estimates the likely accuracy of the generated content or some other equivalent mechanism, enterprises will need to develop and operationalize fact-checking of AI-generated content

During Microsoft’s Bing chat demo, the search engine was asked to analyze earnings reports from Gap and Lululemon and in comparing its answers to the actual reports, the chatbot missed some numbers and made some up. 

Responsibility and ownership: The legal ownership of a piece of content produced by generative AI raises complex questions. Does it belong to the enterprise that licensed the generative AI product or the company that owns the generative AI product? Moreover, do individuals or organizations whose content was used to train the AI model partially own any subsequent content produced by the AI? These legal quandaries currently lack clear answers. An evident problem is generative AI producing output that contains distinct and identifiable pieces of Intellectual Property (IP) owned by others. This can lead to potential legal fallout for the entity that deployed the generative AI model. Enterprises need to work with their legal teams to evolve their IP management amid widespread generative AI adoption

“Zarya of the Dawn” is a graphic novel written by Kris Kashtanova who used an AI based image generation software called Midjourney to create illustrations for the novel. After having initially given full copyright protection for the novel, the US Copyright Office later restricted the copyright to only the text and the arrangement of the illustrations and not the illustrations themselves. The justification provided was that copyright protection could only extend to human creators. 

Bias and ethics: An AI trained on biased data will propagate those biases, potentially leading to the generative AI producing discriminatory and stereotypical content. Failing to identify and preemptively remove such content through effective moderation can lead to severe reputational and legal ramifications for the enterprise and the generative AI provider.

Widespread generative AI adoption has the potential to ramp up carbon emissions from training and operating AI models. This can have significant implications for an enterprise’s Environmental, Social, and Governance (ESG) goals

In a study conducted by Bloomberg on Stable Diffusion (an AI-based text-to-image software), the rendering of more than 5,000 images for people with high- and low-paying jobs was full of racial and gender stereotypes. The results indicated men and individuals with lighter skin tones accounted for most high-paying roles.

How can enterprises assess their risk exposure to generative AI?

While the risks emanating from generative AI usage are notable, its benefits are too significant for enterprises to ignore. Consequently, enterprises that can leverage generative AI’s strengths while effectively mitigating its risks will outperform their peers. To effectively draw up a risk management plan for generative AI, enterprises need to first assess their current risk exposure to generative AI.

Everest Group has developed a multi-dimensional risk assessment framework (see Figure 3) to help enterprises take stock of their current risk profile for generative AI adoption. This framework is deployed through a tool that comprises 21 questions spanning the four risk categories mentioned above.

Figure 3: Everest Group’s generative AI risk assessment framework
Figure 3: Everest Group’s generative AI risk assessment framework

Responses provided by the enterprise across the four categories are weighted and aggregated to arrive at a risk score (see Figure 4).

Figure 4: Generative AI risk assessment outcomes
Figure 4: Generative AI risk assessment outcomes

Evaluating the risk exposure from generative AI is a necessary step to successfully implement and leverage generative AI to create value for customers. Incorporating appropriate risk management practices, tools, and mechanisms in the generative AI ecosystem can instill the confidence needed to take bigger bets, create differentiation, and fully harness this transformative technology.

Deploy our Generative AI Risk Assessment Tool. To discuss this tool and generative AI adoption strategies, please reach out to: [email protected], [email protected]; [email protected]; [email protected]; [email protected].

Check out our 2024 Key Issues webinar, Key Issues 2024: Creating Accelerated Value in a Dynamic World, to learn the major concerns, expectations, and trends for 2024 and hear recommendations on how to drive accelerated value from global services.

Exploring Emerging Generative AI Trends in Technology | Blog

Generative Artificial Intelligence’s rapid evolution holds the promise to transform enterprise operations and decision-making across many industries. Several emerging key generative AI (GAI) trends can profoundly impact automation, productivity, and human expertise, but harnessing GAI’s many opportunities will come with risks that will require enterprises to make complex choices and strategically adapt. Read this blog for valuable insights to prepare for this new frontier. 

Developing Generative AI Trends and Innovations

The trends to watch in the near and mid-term:

  • The move from general to specialized models – As generative AI moves into specific industries and domains, more examples of models fine-tuned for specific purposes are expected to emerge. For instance, models could be specifically trained for banking, insurance, or Human Resources domains, with the capability to speak the language of these narrower fields
  • Applications built on top of foundational GAI models – Apps built on top of large language models (LLMs) or conditioned LLMs to solve for specific needs will likely proliferate. Beyond ChatGPT, we already see early-stage web navigation concierges, code development assistants, and more. Initially, business-to-consumer (B2C) contexts will rise, but once the risks around GAI are solved, business-to-business (B2B) or business-to-employee (B2E) applications also will surge in activity
  • Lower costs – GAI is still relatively expensive but prices already have dropped significantly. As infrastructure, hosting, training, and inference become more efficient and economies of scale improve, we expect further cost reductions

What the generative AI trends mean for enterprises

  • Automation, productivity, and skills – Automation of tasks by GAI will boost employee productivity and also change the nature of expertise. This shift will require enterprises to rethink their talent agenda, workforce planning, learning and development (L&D) programs, and so on. Consider the example of an entry-level developer. With the benefits of GAI, the traditional “skill” of knowing a particular syntax for a specific language will become much less important. As a result, the bar of “valuable” human expertise will be raised. Enterprises need to account for these changes by rebuilding skill taxonomies and subsequently reassessing talent planning
  • Focus on enterprise data strategy – The true power of GAI comes into play once enterprises go beyond the low-hanging fruit of using it to generate generic outputs, like text, images, or other media. For instance, we could envision a world where GAI creates appropriate business or IT workflows, creates complex documents from scratch, or generates marketing collateral tailored to a company. Getting to these use cases will require seamless access to enterprise data, regardless of the approach (whether specialized models built from scratch, fine-tuning, or in-context learning). While GAI will unlock the power of this data, enterprises will need to surface it for use. The enterprise data journey is not new, but GAI will require a renewed focus and potentially more investments to advance it
  • Competition, disruption, and lowered barriers to entry – As GAI enables significant automation, organizations can do more with less. With lower costs, fundamentally new business models will become more feasible in multiple domains. Similar to how digital banks, built from the ground up, started nipping at the heels of established brick-and-mortar ones, this technology can potentially give birth to new contenders. One possible scenario to imagine is a new video game company creating complex video games relying heavily on GAI with a dash of human ingenuity. Similarly, GAI has the potential to disrupt stock media, customer service, entertainment, and other industries.

Enterprises may face difficult future choices, including making massive pivots, cannibalizing existing revenue streams, etc. While these decisions will naturally be difficult, enterprises must be willing to make hard calls to rapidly evolve and stave off existential threats further down the line.

However, there is no need to press the panic button yet. By investing in leadership education, keeping on top of developments, being open to innovations, and investing in home-grown and external GAI solutions, enterprises can position themselves well for when the time comes to make those hard choices

But before putting the horse before the cart, the many primary risks around GAI need to be addressed for broad-based enterprise adoption. These include regulatory concerns (including intellectual property), data and privacy, explainability (to some extent, at least), and others. Based on early trends, at least partial workarounds or mitigation mechanisms will be developed, in the short-term.

Everest Group provides insights and guidance on the risks, use cases, pricing, and implementation strategies to best position enterprises across industries for GAI adoption success. To learn more about Everest Group’s generative AI research or to discuss generative AI trends, reach out to Anil Vijayan.

Don’t miss our webinar, Key Issues 2024: Creating Accelerated Value in a Dynamic World, to hear our analysts discuss major concerns, expectations, and trends for 2024.

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