Tag: artificial intelligence

Agentic Artificial Intelligence (AI): The Next Growth Frontier – Can It Drive Business Success for Banking & Financial Services (BFS) Enterprises? | Blog

October 4, 2024

Artificial intelligence is evolving faster than a quarterly earnings report, and just when we’ve started to master generative AI , a new breakthrough is emerging: agentic AI!  

This isn’t just another buzzword to add to your corporate lexicon either—it’s a game-changer that’s set to redefine AI’s capabilities.  

Reach out to discuss this topic in depth. 

What is agentic AI? 

Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets. Key characteristics of agentic AI include: 

  • Autonomy: perform tasks independently 
  • Reasoning: make advanced decisions 
  • Flexible planning: adjust plans based on prevailing circumstances 
  • Workflow optimization: efficiently execute multistep, complex processes 
  • Natural language understanding: comprehend and follow complex instructions 
  • Continuous improvement: learn from historical data and feedback 
  • System integration: integrate with diverse enterprise systems 

The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions. 

What does it mean for the banking and financial services industry? 

In Banking and Financial Services, agentic AI could be the key to optimizing operations, automating complex processes, and delivering hyper-personalized customer experiences.  

Agentic AI assesses the need for actions before executing them and continuously learns from its experiences to improve decision-making.  

Now let’s dive into why this innovation is catching the attention of technology  and financial leaders and how it could now transform the financial services industry. 

In the fast-moving world of trading and investment , agentic AI has the potential to transform portfolio management. These AI agents can analyze market trends, make rapid trading decisions, and adapt investment strategies in real time based on economic data and news events.  

Beyond trading, agentic AI could enhance risk management by autonomously identifying potential market disruptions or regulatory changes and adjusting exposure accordingly. In personalized banking, it could optimize customer service, offering tailored financial advice, automated portfolio management, and fraud detection systems that continuously learns and adapts by the second.  

This combination of real-time decision-making and autonomy could lead to more efficient markets, improved risk mitigation, and potentially higher returns for investors and clients alike. 

What are the high priority use cases for agentic AI in banking and financial services? 

Agentic AI is a transformative force driving exponential growth for banks by revolutionizing customer engagement, decision-making, and operational efficiency. 

With its ability to incorporate a “chaining” capability in decision making, banks can deliver hyper-personalized products and services, significantly boosting customer loyalty and unlocking new revenue streams through targeted cross-selling and upselling.  

Agentic AI will empower banks to make smarter, faster decisions on investments and lending, while superior risk management enables more aggressive growth with minimized losses.  

The following exhibit highlights the most relevant use cases from a banking and financial services perspective. 

Agentic AI blog infographic scaled

Which technology providers are riding the agentic AI wave already? 

The vast ecosystem of core banking technology providers, are still familiarizing themselves with the nuances of embedding AI into core baling modules offered via their plaforms. Our conviction is that core augmentation providers, hyperscalers, and niche agentic AI start-ups are going to lead the agentic AI revolution for this industry. 

From a  core augmentation provider perspective, we see technology platforms in the areas of experience, data & analytics androbotic process automation (RPA) leading the way , in order to guide and augment the core banking platforms, and enabling access to latest technologies.  

In recent days, we have already seen the launch of Agentforce by Salesforce that is positioned as suite of autonomous, and personalized assistive agents to support employee’s workflow with specific tasks.  

On the other hand, RPA providers are sitting on a base architecture that enables them to manage and automate tasks. Automation Anywhere is offering AI Agent Platform to build its own AI agents, while UiPath is also incoporating these capabilities into its existing RPA offerings.  

Additonally, financial crime remains a particularly ripe area for disruption by agentic AI, as technology providers are deploying AI agents to fight financial crime such as WorkFusion.  

We also see Google with its Vertex AI Agent Builder and Microsoft with its AutoGen, offering to build AI agents, that provide the necessary frameworks to accelerate agentic AI development. 

There are also a few niche providers such as EMA that are catering to use cases for the financial services industry and it will be interesting to see how other firms evolve and adapt in the weeks and months to come. 

Potential challenges on the road to adoption 

Adopting agentic AI faces several challenges, including high costs and an uncertain return on investment (ROI). Change management and acquiring the right talent  are critical hurdles. 

Existing technology investments, such as process automation, orchestration, and core modernization efforts, can complicate integration. Additionally, data readiness for training AI models and the substantial effort required to train and integrate these solutions into the value chain are among the other obstacles currently facing firms. 

What support do banks and financial service (FS) firm need? 

Looking at the technology estate of banking and financial services firms, we see a spider like mesh of various systems and applications that have evolved over the years.  

Streamling them to accomplish a workflow, retrieving the right set of data, and arriving at the meaningful insight is no singular feat and one that continues to be amongst the biggest challenges for enterprises today.  

Agentic AI can help jump through various of these applications to automate tasks while needing support from other agents to complete the tasks.  

Banking and financial services enterprises thus need to ensure their data assets are ready to be uttilized by agents while the workflow and processes are clearly defined. It is on this bedrock that these enterprises will be able to deploy agents. 

 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 (everestgrp.com), which delves deeper into the topic of artificial intelligence.  

If you have any questions, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Pranati Dave, Ronak Doshi and Kriti Gupta.

 

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

October 1, 2024

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.

Exhibits Generative AI gen AI – why we all have a role to play in the future success of the gen AI ecosystem 2

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

Screenshot 4

 

  • 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. 

 

Race for Artificial Intelligence (AI) Infrastructure: Navigating the Best Path to Supercharge Your AI Strategy | Blog

September 27, 2024

As we stand on the brink of a new technological era, the rise of AI is reshaping our interactions with the digital world.  

The rapid proliferation of AI has intensified the demand for scalable, high-performance computing resources, in the process exposing the limitations of traditional infrastructure.  

Enterprises are now seeking significant upgrades and expansions to their traditional information technology (IT) infrastructure, in order to keep up with the rising demands of AI workloads.  

This has since driven considerable investment into specialized AI infrastructure and tools and services, that can now create the necessary environment for core hardware and infrastructure components to operate at their best.

  • According to Everest Group research, 81% of enterprises plan to allocate 50% or more of their infrastructure budget this year to upgrading capabilities specifically for AI

Managing Investments in the Face of Rising AI Demands and Evolving landscape 

To accelerate AI development and maintain an edge in the evolving digital landscape, enterprises are increasingly investing in core hardware and infrastructure components. This had led to the surge in demand for high-performance critical computer hardware, networking, and storage infrastructure necessary for AI computations and data management including (Graphics Processing Units) GPUs, (Tensor Processing Units) TPUs, and Virtual Storage Platforms (VSP).  

  • As per Everest Group research, 46% of enterprises prioritize upgrading computing power such as, graphics processing units (GPUs), central processing units (CPUs), and tensor processing units (TPUs), as one of their top three priorities in AI infrastructure investments

Reach out to discuss this topic in depth. 

Providers are now significantly increasing investments to upgrade their supply and secure their positions in a rapidly evolving marketplace.  

They are adopting multifaceted strategies to differentiate themselves, secure market share, and address the evolving needs of enterprises for their AI needs. As the market transforms, leading players are making bold strides in the AI arena: 

As Nvidia rides the AI wave, AMD battles to disrupt its market dominance in the GPU market 

Nvidia, known for its high-end graphics cards for gaming personal computers (PCs), has now crossed US$3 trillion in market cap, owing to the rising demand for its AI chips, critical for advanced AI infrastructure. As Nvidia stands at the forefront of the AI infrastructure market, its GPUs are indispensable for training and deploying sophisticated AI models, including OpenAI’s ChatGPT, leading to its market dominance in the GPU sector.  

While Nvidia remains a dominant force in the AI field, other competitors are gradually emerging, aiming to gain market share and driving innovation to break Nvidia’s dominance.  

AMD presents a significant challenge to Nvidia in the GPU sector and is working on providing compelling alternatives, particularly for budget-conscious buyers. AMD’s MI300 chip has gained substantial traction amongst startups, as well as with technology giants like Microsoft. It is also constantly investing in this space to bolster its position, as evidenced by its recent multi-billion-dollar acquisition of ZT Systems.

Intel – the computing giant facing challenges, but could that change soon with Gaudi 3? 

Intel, traditionally focused on CPUs, has faced challenges in gaining a strong foothold in the GPU market and has been facing stiff competition from competitors, with Nvidia surpassing Intel in annual revenue.  

Intel is now intensifying its efforts to close the gap in the AI market. At the recent Intel Vision event, Intel highlighted the forthcoming release of Gaudi 3, an AI accelerator, claiming to be able to outperform Nvidia’s powerful H100 GPU in training large language models (LLMs).  

Intel also stated that the Gaudi 3 could deliver similar or even superior performance compared to Nvidia’s H200 for large language model inferencing. Additionally, it claims that Gaudi 3 is focused on reducing energy consumption and has greater power efficiency than the H100, for specific use cases.  

Intel’s strategic push to challenge Nvidia’s dominance occurs against a backdrop of persistent shortages in AI accelerator chips, which has created substantial obstacles for tech companies. 

Hyperscalers – Nvidia’s largest customers today, potential rivals tomorrow? 

Major cloud providers such as Google, Microsoft, Amazon, and Oracle, who together contribute significantly to Nvidia’s revenue, are making a strategic shift toward developing their own processors and in-house chips, to reduce dependency on Nvidia’s GPUs, as well as to drive their own innovation.  

Amazon has been rolling out its AI-focused Inferentia and Tranium chips for AI inference and training, offering these through Amazon web services (AWS), as cost-effective alternatives to Nvidia’s products.  

Google, a long-time advocate of its Tensor Processing Units (TPUs), recently introduced Trillium, its sixth generation TPU to power its AI models, which it claims is 5 times faster than its predecessor.  

Microsoft is also making strides by developing its own AI processors and chips, including the Cobalt 100 CPU, an arm-based processor used for running general purpose computer workloads on the Microsoft Cloud and Maia 100 AI Accelerator. 

Emergence of new players and trailblazing startups – Disrupting the AI landscape with innovative approaches? 

Several startups are making significant strides within the AI infrastructure landscape, with their innovative approaches.  

Cerebras Systems, known for its Wafer-Scale Engine (WSE) designed for high-performance AI workloads, has recently introduced an AI inference service that it claims to be the fastest in the world.

Groq’s Language Processing Unit (LPU) stands out for its high speed in AI inference tasks, offering substantial performance gains for large language models. Groq has also recently raised $640 million for its AI chips.

Groq’s rival SambaNova, has also launched its AI inference platform SambaNova cloud. Similarly other startups like Blaize, an AI chip maker, is developing competitive AI chip technology, with its own unique focus and specialization.  

Although Nvidia holds a dominant position, Groq, Cerebras Systems, SambaNova, and other startups are emerging as serious contenders in the marketplace, offering innovative and competitive solutions. It will now be interesting to see how the new players in this space can challenge the technological giants. 

AI chips and accelerators 1

Exhibit 1: AI chips and accelerators landscape 

How to take the next steps? 

As the AI landscape continues to evolve, challenges remain, as enterprise demand for GPUs exceeds supply, leading to a shortage.  

This imbalance, combined with high demand, has also driven up GPU prices, making it challenging to find affordable alternatives. As a result, organizations are increasingly exploring alternatives to the dominant players in the AI chip and accelerators market.  

But, with so many options, it’s crucial for organizations to carefully evaluate their requirements, budget, and strategic goals, to choose the most suitable options for leveraging AI power effectively. We suggest a two-pronged approach to align organizational AI strategy: 

Assess and analyze

Assess your requirements on parameters such as:  

  • Organizational capabilities and budgetary flexibility: Assess which strategy would suit your budget – purchasing or renting GPUs. Weigh in the initial investment needed, maintenance costs, and long-term operational savings
  • AI current workload requirements: Analyze your requirements based on the types of AI workloads and business use-cases (e.g., is your need centered around high-performance training or low latency inference or both)
  • Future adaptability: Consider whether your AI workloads may evolve, necessitating reconfigurable hardware or if the efficiency of specialized chips is more important
  • Power and space: Assess your organization’s energy efficiency, hardware footprint, and power consumption needs

Align and augment

After the initial assessment and once you have a clear understanding of your AI requirements, develop a roadmap that supports your AI strategy, taking the 5 S into consideration – Scalability, Sustainability, Security, Simplicity, and Stability.  

  • Ensure your AI strategy is directly aligned with business objectives, such as innovation, operational efficiency, or scaling products, while also being adaptable to future AI workloads
  • Augment existing AI infrastructure by partnering with the right vendors that can help you meet your AI workload demands

Slide2 1 

Exhibit 2: By adopting this two-pronged approach, you can effectively chart the best path to supercharge your AI strategy.  

If you found this blog interesting, check out our report, Navigating AI Infrastructure: The Backbone of the AI-Driven Era.

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 Praharsh Srivastava, Zachariah Chirayil, and Tanvi Rai.

From Banking Giants to Tech Innovators: FNZ’s New Leadership, 1-billion-dollar Investment, and its Impact on the Wealth Management Industry | Blog

September 25, 2024

The wealth technology industry has recently witnessed significant changes, particularly within one of its leading firms, undergoing a substantial strategic transformation.  

This comes in the wake of Adrian Durham, the founder and long-serving CEO of FNZ Group, announcing his decision to step down after 21 years at the helm.  

Durham’s departure marks a strategic shift for FNZ and the wealth management industry. As a key figure, this move signals a new direction for FNZ. He will stay on as a non-executive founding director and senior advisor, continuing to contribute his expertise – but what does the future now hold? 

Reach out to us to discuss this topic further with our expert analysts.  

New leaders take the helm

FNZ’s leadership transition introduces both opportunities and challenges with the arrival of Blythe Masters, a former JP Morgan executive, joining as CEO.  

Joining her are Roman Regelman, a former BNY executive, as group president, and Stephen Daffron as strategic advisor. This new, diverse leadership team is expected to drive growth and innovation at FNZ, with a focus on integrating technology to enhance client experiences and operational efficiency. 

Masters’ background in investment banking and technology also suggests a strategic shift towards digital transformation, aligning FNZ with broader industry trends within the wealth management industry.  

The leadership changes have been announced as FNZ’s existing institutional shareholders, have committed $1bn of capital to support the enduring success of the business over the long term. 

Technology trends in the wealth management industry and the opportunity for FNZ

  • Infusing data and intelligence into wealth operations across front, mid and back-office: Wealth management firms are looking to accelerate the infusion of data and intelligence into their operations, to drive productivity, better stakeholder experiences, and business agility. This places a focus on enabling analytics and artificial intelligence (AI) adoption, by streamlining the business processes, data, applications, and information technology (IT) infrastructure stack
  • Building customer trust in a hybrid channel model of intelligent self-serve and high-touch advisor model: As the industry undergoes intergenerational wealth transfer, catering to different personas requires a tailored approach to increase adoption of self-serve modules in conjunction with advisors. Wealth management firms thus need to leverage technology with the right balance of human touch and digital touch
  • Enabling the next-generation advisor experience powered by cloud and AI:  AI and generative AI (gen AI) remain key buzzwords with cloud serving as the foundational backbone, to enable production grade availability of these technologies. Advisors need access to the AI-enabled tools that can help streamline their day-to-day workings, so they can focus on serving clients better. As the industry adopts next-generation advisor experiences powered by AI and cloud, FNZ can build on top of its existing machine learning (ML) models to determine the most suitable exchange-traded funds (ETF) / Mutual Funds, and enhance the data search feature that currently looks at multiple documents for relevant data that can be shared with advisors
  • Access to alternate investment classes and increasing sustainability preferences: Investors and clients are now asking for alternative investment classes and different products to cater to their investment philosophies and visions.

Technology providers are now playing catchup to this unique demand trend that has shades of hyper-personalization. With this going beyond contextualizing experiences, instead bringing a material impact to portfolios.

FNZ has a sustainable finance platform, and it will be interesting to see what innovation happens in this platform area 

  • Cost takeout demand across technology and wealth operations: The wealth management industry is looking at minimizing the total cost of ownership of each value streams, over indexing on the four factors of software, IT services, business process services, and IT infrastructure (including cloud and compute as AI adoption scales).

We have already seen FNZ taking forth the joint value proposition of technology, infrastructure, and operations in a single platform. The cost takeout theme will now continue to take centerstage in this volatile macro-environment.

The wealth management industry is undergoing rapid transformation, and FNZ’s $1 billion investment is an opportunity to capitalize on key technology trends and revolutionize decision-making across front, mid, and back-office functions through AI and analytics.

In the shift towards a hybrid channel model, FNZ can build upon self-serve tools combined with high-touch advisor support 

Implications for FNZ and the broader wealth management industry: 

We see the following implications and impact coming together for this industry. 

  • Increased investments and innovation will expand and enhance the current portfolio of FNZ’s offerings as outlined above, leading to more sophisticated offerings tailored to wealth management firms’ needs 
  • Penetration into different geographic regions may be on the cards as we see FNZ’s consistent investments via acquisitions, platform launches, partnerships in last 12-18 months. We have already seen the APAC region to be a key focus area for FNZ’s next rung of growth charter as outlined by Asian leadership 
  • Environmental, Social and Governance (ESG) is expected to be a key investment area for the firm going forward, given the customer demand themes and renewed focus on the space 

Net-net, the investment of US$1 billion and new leadership puts the organization in a good spot to accelerate product innovation and expand its offerings.  

If you found this blog interesting, check out our Core Banking In The Age Of Transformation: A Ride From Legacy To Modernity | Blog – Everest Group (everestgrp.com), which delves deeper into the Banking, Financial Services and Insurance (BFSI) sector. 

 To discuss this topic in more detail, to hear more about wealth management technology and the latest trends or for an even more detailed analysis, please contact Ronak Doshi ([email protected]), Kriti Gupta ([email protected]) and Priyanshi Gupta ([email protected]).

Unleashing the Power of Advanced AI Engines: Transforming Business Operations for the Future | Blog

September 19, 2024

The digital age has transformed the way businesses operate, raising the bar for efficiency and innovation. They are now expected to be more agile and responsive than ever. As a result, integrating Artificial Intelligence (AI) in business operations has become a necessity for businesses aiming to stay competitive in the market.

Advanced AI engines are reshaping both front and back-office processes for enterprises by driving efficiency, accuracy, and scalability at new levels. In this blog, we explore how these cutting-edge AI engines are transforming business operations.

Reach out to discuss this topic in depth.

Unleashing the potential: how advanced AI is reshaping business operations

From rigid rules to cognitive flexibility

Use of AI for business operations has evolved beyond early AI systems that were limited by predefined algorithms and effective only for basic tasks, often failing when faced with complex queries. Advanced AI engines are at the core of the next generation of business operations. AI systems today are designed to learn, adapt, and improve over time, ushering in a new era of cognitive flexibility that elevates customer interactions across every touchpoint.

These AI engines are becoming integral to business operations, automating mundane tasks, providing deep insightsand unifying disparate operational silos. They help identify inefficiencies and drive end-to-end automation, enabling organizations to operate with greater efficiency and agility in an ever-evolving market

Foundations of advanced AI engines

Three pillars form the foundation of advanced AI engines:

Picture1 1

Generative AI (gen AI), built on the robust foundations of advanced AI engines, is revolutionizing business operations by automating complex tasks, enhancing decision-making, and enabling hyper-personalization. AI-driven tools are helping streamline workflows, optimize supply chains, and improve data analysis, leading to greater efficiency and cost savings.

These solutions also elevate customer experiences through personalized interactions, advanced self-service options, and consistent, context-aware engagement across all touchpoints. By bridging the gap between front office and back-office, AI solutions also enable quicker, more seamless, and richer experience for the customers. By adopting gen AI, enterprises can gain a competitive edge by streamlining customer service processes, anticipating customer needs, and delivering tailored experiences that drive customer acquisition, loyalty, and retention.

Human-AI collaboration: enhancing, not replacing

Contrary to dystopian predictions, AI isn’t here to replace human agents—it’s here to supercharge them. This collaboration between AI and humans enhances overall efficiency and customer satisfaction with AI efficiently managing lower complexity and repetitive queries, freeing up human agents to focus on intricate problem-solving that requires empathy and nuanced understanding, while leveraging insights from AI engines for a better experience.

This synergy between AI and human workers not only enhances operational efficiency but also drives innovation, allowing businesses to stay competitive in an increasingly digital world.

Front office evolution: delivering smarter, faster customer engagement

Front office operations, the heart of customer interaction, have traditionally been resource-intensive, often requiring significant human input. Advanced AI engines are now revolutionizing these operations, creating smarter, faster, and more personalized customer experiences:

  • Smarter customer interactions: AI engines empower front office teams with real-time data and actionable insights, enabling them to engage with customers more effectively. This allows for enhanced customer satisfaction and loyalty as interactions become more personalized, context-aware, and relevant
  • Automation at scale: Integration of AI systems enable seamless automation of tasks such as scheduling, responding to queries, and follow-ups, now freeing up human agents to tackle more complex issues. AI-driven chatbots efficiently manage a high volume of inquiries, ensuring customers receive quick, accurate responses while enhancing the overall experience
  • Proactive customer engagement: With predictive analytics, advanced AI engines can anticipate customer needs and behaviors, allowing businesses to engage proactively. Whether it’s offering personalized recommendations, addressing potential issues before they escalate, or optimizing marketing efforts, AI helps businesses boost customer lifetime value and drive revenue growth

Back-office revolution: streamlining operations for maximum efficiency

Advanced AI engines are also revolutionizing back-office operations streamlining critical processes, driving operational excellence, and enabling businesses to focus on strategic growth:

  • Automation and efficiency: AI is automating repetitive and routine tasks including data entry, invoice processing, and inventory management. Gen AI technologies are expected to deliver significant benefits across business operations. When successfully implemented, they could lead to 15-25% cost savings across operations within 18-36 months. Additionally, agent training time is projected to decrease by 20-30%, as these technologies continuously learn from past interactions, providing agents with targeted training tailored to specific areas for development. The result is a leaner, more efficient operation that can adapt quickly to changing demands
  • Intelligent decision-making: Advanced AI engines analyze vast amounts of data in real-time, providing insights that inform better decision-making. In finance, AI forecasts cash flow, detects fraud, and optimizes investments. In HR, AI streamlines talent acquisition by matching candidates with roles that best fit their skills. This data-driven approach enables businesses to make quick, informed decisions, gaining a competitive edge
  • Optimized supply chain: AI is revolutionizing supply chain management by enhancing demand forecasting, inventory control, and logistics planning. By analyzing historical data and market trends, AI ensures optimal inventory levels and efficient logistics, reducing costs and improving delivery times

The AI-driven future: innovation, efficiency, and growth

As businesses increasingly adopt advanced AI engines, their transformative impact on both front and back-office operations is set to accelerate. These tools are doing more than just automating routine tasks—they’re unlocking new business models and driving innovation. Integrating AI into operations is allowing enterprises to unlock unprecedented efficiency, creativity, and growth.

By enhancing customer engagement and streamlining processes, AI empowers businesses to operate smarter, faster, and leaner. The real question isn’t whether to adopt AI—it’s how quickly businesses can harness its potential to lead the future.

If you found this blog interesting, check out our recent blog focusing on Revolutionizing Customer Journeys: Creating a Unified Customer Experience through AI).

If you have questions or want to discuss these topics in more depth, please contact Jagrit Kasera or Sharang Sharma.

Will Every Enterprise Platform Become a Data Company? Salesforce Acquires Own Company in a Deal That Will Now Send Ripples Through the Sector | Blog

September 16, 2024

Salesforce has announced the acquisition of New-Jersey based Own Company, a prominent provider of data protection and data management solutions, for approximately $1.9 billion, with this move underscoring Salesforce’s strategic commitment to expand its capabilities as a comprehensive enterprise platform.  

Originally focused on backup and restore, Own offers a comprehensive suite of data-centric solutions, including security, compliance, sandbox seeding, archiving, and a new artificial intelligence (AI)-powered product for extracting insights from historical data.  

Its product portfolio extends to other specialized Salesforce solutions like nCino and Veeva Systems, with Own’s capabilities complementing Salesforce’s existing data security offerings. Notably, Own Company was also recently recognized as one of the preferred data ecosystem partners for Salesforce newly established Zero Copy Partner network. 

With this acquisition causing ripples through the tech, AI and data sectors, we have used our insight and knowledge to decipher what this acquisition may mean for the future of Salesforce, as well as the rest of the marketplace. 

Reach out to us to discuss this topic further with our expert analysts. 

Strategic intent behind the acquisition: 

Salesforce has doubled down on building data-centric product capabilities. This strategic shift, primarily focused on developing new products and forging strategic partnerships. In the process reflecting the company’s recognition of data as a cornerstone for its ‘Data + AI + customer relationship management (CRM)’ vision. 

On the product side, Salesforce has consistently innovated its data-specific offerings. Its ‘Genie’ offering, introduced in 2022, has evolved into the Data Cloud, which has since become its fastest-growing product and is the very core of its AI revolution, in addition to sustained growth from Einstein and Tableau. 

Following this, Salesforce has plans to launch one of its key offerings at the upcoming Dreamforce event, notably its new autonomous agent platform, Agentforce. Salesforce has made significant strides in product evolution, from the initial launch of Einstein in 2016, to the Einstein 1 Platform and the recent introduction of autonomous AI agents – Service Agent and Sales Agent. 

On the partnerships, Salesforce has been actively forming data-centric partnerships with service providers, data companies, platforms, and enterprise platforms. Its recent Zero Copy Network brings together a wide range of ecosystem partners to deliver superior data integrations to enterprises while eliminating data duplication and dynamic data changes. 

Salesforce’s recent partnership with Workday aims to establish a unified data foundation by leveraging back-office data from Workday and front-office data from Salesforce. This will enable new AI-powered use cases to enhance employee experience and introduce a new employee service agent offering. The partnership also includes seamless integration of Workday into Slack to facilitate easier access to core employee human resources (HR) and financial records within Slack. 

Data will become increasingly pivotal for the successful scaling of enterprise AI initiatives. As businesses assess their AI investments, concerns around data-related topics such as management, readiness, privacy, and security are growing.  

Overall, for Salesforce, the acquisition of Own Company at this juncture is strategic, addressing the growing data-specific concerns in the market. Notably, this move comes amidst a period of challenge for Salesforce. 

The company has been actively seeking new avenues for growth, including expanding its international presence and targeting the small and medium-sized business (SME) segment to diversify its revenue streams. Given Own’s synergistic product portfolio and substantial customer base of over 7,000 clients, this will make sure to add to Salesforce’s growth ambitions.

Acquisition implications –  

  • For enterprises, this partnership will instill more trust and confidence in their generative AI (gen AI) investments with the Salesforce platform, in terms of data security and compliance, which may encourage them to make further strategic investments towards their AI-specific objectives 
  • For service providers, it will become important for them to proactively build capabilities and focused partnerships across such independent software vendor (ISVs) and take these to the market through their Salesforce-specific go-to-market (GTM) activities. This also presents an opportunity for them to collaborate with similar partners and jointly identify and address whitespaces in areas such as talent, internet protocol (IP), and market education to further augment their Salesforce services capabilities 
  • For ISVs and solution partners, the potential for additional and similar partnerships and acquisition opportunities is going to increase from here on. ISVs seeking to be acquired, should present a compelling case to Salesforce to position themselves for inclusion in the company’s ongoing acquisition spree  

Going forward, we can expect Salesforce to make additional acquisitions of other successful ecosystem partners such as the previously mentioned nCino and Veeva Systems, as well as the likes of Conga, Copado, Pimly, Provar, and Flosum, to fill the existing gaps in its product portfolio and augment it further. 

This will not just be limited to Salesforce either, as we expect other large enterprise platforms such as SAP, Oracle, Microsoft Dynamics, ServiceNow, and Pega to follow suit. Moreover, the marketplace can now anticipate Salesforce to acquire some of its specialized service providers such as Coastal, OSF digital, and Slalom to bring life to its dying professional services arm, which the firm seemed to be struggling with for quite some time.  

Our analysis and insight see us conclude that Salesforce’s acquisition of the Own Company is as a prominent one for the industry as a whole and will be sure to now start a domino effect globally, especially across major enterprise platforms with lofty AI ambitions. 

If you found this blog interesting, we have recently published an in-depth analysis of key Salesforce service providers which analyses their key capabilities and the impact they deliver to the market through their Salesforce services https://www2.everestgrp.com/reportaction/EGR-2024-50-R-6551/Marketing. 

If you have any questions or want to discuss this acquisition and the effects it’ll have on the sector in the future, please contact Arun Prateek [email protected] and or AS Yamohiadeen [email protected].  

Retail and CPG Data, Analytics, and AI Services PEAK Matrix® Assessment 2024

September 16, 2024

Retail and CPG Data, Analytics, and AI Services PEAK Matrix® Assessment

Data, Analytics, and AI (DAAI) services are transforming Retail and Consumer Packaged Goods (RCPG) enterprises by enhancing operations and improving customer experiences. Data services integrate and manage data from various sources, ensuring accuracy and security, while centralized data warehousing facilitates efficient retrieval and analysis. Analytics services provide insights through descriptive, predictive, and prescriptive analyses, helping businesses understand past performance, forecast future trends, and optimize decision-making. Customer and supply chain analytics further enable enterprises to tailor marketing strategies and streamline operations. AI services, including machine learning, natural language processing, and computer vision, further automate and enhance decision-making processes. These technologies enable personalized marketing, demand forecasting, pricing optimization, and customer sentiment analysis, driving business growth.

Retail and CPG Data, Analytics, and AI Services PEAK Matrix® Assessment

What is in this PEAK Matrix® Report

Implementing these solutions requires a strategic approach and a reliable service partner with strong DAAI capabilities, along with RCPG domain expertise and a robust partner ecosystem. In this report, we assess 27 providers featured on the RCPG DAAI Services PEAK Matrix®. Each provider profile comprehensively describes its service focus, key intellectual property solutions, domain investments, and case studies.

Scope:  

  • Industry: RCPG
  • Geography: global
  • Services: DAAI
  • The assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with leading providers, client reference checks, and an ongoing analysis of the RCPG DAAI services market

Contents:  

This report features detailed assessments, including strengths and limitations, of 27 providers that focus on DAAI services in the RCPG industry.

Related PEAK Matrix® Assessments

APSPM2024
PEAK Matrix®

Application Management Services for Mid-market Enterprises PEAK Matrix® Assessment

Learn More
Healthcare Payer Business Process as a Service (BPaaS) – Solutions PEAK Matrix® Assessment
PEAK Matrix®

Healthcare Payer Business Process as a Service (BPaaS) – Solutions PEAK Matrix® Assessment 2024

Learn More
Digital Transformation Services for Mid-market Enterprises PEAK Matrix
PEAK Matrix®

Digital Transformation Services for Mid-market Enterprises PEAK Matrix® Assessment 2024

Learn More
Product Engineering Services
PEAK Matrix®

Key Verticals, Value Chain Elements, and Nearshore Delivery Software Product Engineering Services PEAK Matrix® Assessment 2024

Learn More

Our Latest Thinking

GettyImages 1961127788
Blog

Mars’ Acquisition of Kellanova Sparks Significant Opportunities for IT Service Providers

Learn More
ESG
Blog

Sustainability in Retail and CPG: A Reactive Approach Will No Longer Work

Learn More
GettyImages 1196432085
Blog

Striking the Right Chords: Composable Platforms to Orchestrate Supply Chain Platformization in the Retail and CPG Industry

Learn More
Digital Effectiveness in the Retail Industry
Market Insights™

Digital Effectiveness in the Retail Industry

Learn More

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

Change Management Platforms: Are We Still Just Rearranging Deck Chairs on the Titanic? | Blog

September 9, 2024

In an era of rapid digital transformation, organizations are increasingly turning to Change Management (CM) platforms to navigate complex transitions, however are many of these efforts meeting expectations?

According to Everest Group’s research with over 180 CXOs and business heads, 68% of enterprises have not realized the envisioned value from their digital transformation initiatives.

Additionally, 53% of enterprises cite change resistance as a key obstacle to achieving intended outcomes. These statistics highlight a critical question…are our current change management approaches truly effective, or are we simply rearranging deck chairs on a sinking ship?

Reach out to us to discuss this topic further with our expert analysts.

As organizations grapple with these challenges, the demand for robust change management platforms has surged. These platforms, designed to provide visibility, tracking, and insights into change programs, are becoming critical tools in the quest for successful transitions. Yet, the change management platform market is marked by diverse capabilities, emerging trends, and persistent challenges that are reshaping the future of change management.

In this landscape of unrealized potential and resistance to change, it’s crucial to examine the key trends, challenges, and strategic imperatives for both enterprises and service providers in the CM platform market.

Capture

Artificial Intelligence (AI) in change management: promise vs. reality

One of the most significant trends in the CM platform market is the increasing focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) capabilities. While most providers have developed platforms that provide basic visibility and insights into change programs, there is a clear shift towards enhancing AI-related features.

Some mature players are moving quickly to improve AI within their platforms through new partnerships and by building internal capabilities that align with market demands. However, despite the potential of AI, many platforms still fall short of client expectations, particularly when marketed as prescriptive recommendation generators. With 60% of CM professionals reporting little to no implementation of AI in their engagements, the infusion of AI remains a critical area of focus for future platform development.

Building on giants: the power of enterprise platforms

Many CM platforms are developed on large enterprise platforms such as Salesforce, which offer familiar user interface (UI) elements, robust security controls, and seamless integrations. Building on such platforms allows providers to leverage existing infrastructure while enhancing their tools with advanced features. Additionally, major out-of-box integrations with large enterprise resource planning (ERPs) and other information technology (IT) assets are becoming increasingly common, enabling providers to cross-sell and upsell their services more effectively.

These integrations not only facilitate smoother implementation, but also enhance the overall value proposition of CM platforms, by incorporating industry research data, employee sentiment analysis, and other critical insights.

Show me the value: monetization strategies in CM platforms

The monetization strategies for CM platforms are evolving, with providers typically charging for their tools separately from the services they offer. In many cases, CM platforms are sold alongside advisory services, allowing providers to position their tools as integral components of broader transformation initiatives.

However, it’s not uncommon for these platforms to be purchased as standalone offerings, implemented either by the client themselves or a third party. In larger transformation deals, some providers even position their tools at no extra cost, framing them as an “investment” to drive adoption and differentiate their offerings. This approach reflects the strategic importance of CM platforms in securing and enhancing multi-million-dollar deals.

Beyond pretty dashboards: the quest for actionable insights

Despite the advancements in CM platforms, several challenges persist. The market is characterized by varying degrees of maturity, particularly in the use of machine learning. While platforms are increasingly focused on creating visibility and deriving high-level insights, they still offer limited intelligence and prescriptive capabilities. Basic visualizations and dashboards are prevalent, but the need for more sophisticated tools that deliver actionable insights remains un-met.

Moreover, the demand for stakeholder management, activity tracking, and impact analysis is high, yet many platforms struggle to fully meet these needs. To stay competitive, providers must continue to innovate and expand their AI capabilities while also enhancing the core features that clients value most.

The future of CM Platforms: what enterprises really want

  • The change management platforms market is evolving rapidly, with enterprises demanding more than just basic change monitoring and adoption support. In addition to these core capabilities, organizations now seek industry-specific insights and strategic recommendations from their platforms. They expect CM solutions to be tailored to the unique needs of their business functions and industries, offering customization that drives more relevant and actionable outcomes
  • Beyond visibility, efficiency gains are becoming a critical expectation. CM platforms are anticipated to automate diverse processes, reducing manual interventions, minimizing errors, and ultimately improving operational efficiency. This automation enables enterprises to achieve better business outcomes with less effort
  • Furthermore, managing employee resistance is a key focus for CM platforms. Enterprises look at these solutions to deeply analyze employee behaviors and develop creative methods to motivate and align the workforce with change initiatives. CM platforms that provide clear roadmaps for driving change adoption and overcoming resistance will become essential in managing the human aspects of transformation
  • Finally, strategic recommendations are becoming increasingly crucial for enterprise clients. CM platforms should offer self-serve capabilities that deliver actionable insights and guidance with minimal reliance on consulting services. The future of change management lies in platforms that can offer automation, customization, and strategic intelligence—all while enhancing workforce alignment and driving better business outcomes

If you found this blog interesting, you can read our 12 Steps To Effective Change Management In Global Business Services (everestgrp.com) blog, which delves deeper into the topic.

If you have questions or want to discuss CM platforms and solutions, please contact Krishna Zawar at Krishna Zawar or Parul Trivedi.

Optimizing Pricing Strategies for Healthcare AI Startups: Expert Insights for Payer and Provider Innovation | Blog

August 28, 2024

Recently, I had the opportunity to judge Accenture’s Healthtech Innovation Challenge. The focus was on AI and its applicability in healthcare. For me, what stood out among the many brilliant ideas was the question of how to price technology products for healthcare. The following is not only a summary of what I could glean from the many conversations I had on this topic but also advice for startup CEOs who aim to succeed in the healthcare technology space through a successful pricing strategy. Reach out to discuss this with us further.

As AI reshapes healthcare, startup CEOs need to think of strategic pricing to drive adoption among payers and providers. Two key observations:

  1. “Focus on cost savings” may sound like a cliché, but in healthcare, cost is an emotive subject. If cost savings on X is not one of the outcomes of your product X, you are barking up the wrong tree
  2. Prepare to flex models as you evolve in the environment. Moving from a flat fee to pay per use to tiered pricing may be considered hara-kiri in big tech pricing strategies, but in the startup world it is considered “having your ears to the ground”

Now, on to how healthcare buys. Traditionally, healthcare was not a big buyer of large monolithic platforms such as CRM and ERP. The industry was (and continues to be) fragmented, and hence, technology purchases were also erratic – a mix of bespoke and non-standard COTS. This world underwent a change post meaningful use. Core administration (payers) and EMRs (providers) became that monolith. However, instead of solving for that erratic product buying behavior, what we got was just a better name for it – point-solution centricity. Two ways to explain this phenomenon:

  • Healthcare loves bolt-on solutions that are not only cheaper to adopt but also plug into their legacy technology
  • Healthcare also desires an evolved “platform” that herds together the benefits of all the bolt-ons and legacy technologies

This is the reason why technology adoption strategies vary by organization. Across functions and stakeholders, buyers’ willingness to pay depends not only on direct business outcomes but also on tailored solutions that correlate spend with outcomes.

Hence, a nuanced approach to pricing is crucial in driving adoption and ensuring that AI solutions are accessible and valuable across the diverse landscape of healthcare organizations.

These strategies not only enhance market penetration but also build long-term relationships with healthcare providers and payers by aligning pricing with organizational needs and capabilities. What we have noted below is drawn from a slew of interactions with buyers of technology at different healthcare organizations and CEOs of startups who are working with them.

  1. Large payers and providers

Large healthcare organizations typically have significant budgets and complex needs, necessitating flexible and comprehensive pricing models. The key strategies observed in this segment include:

  • Enterprise pricing model (most preferred model currently):
    • This model offers a flat fee for unlimited access across the organization. It simplifies budgeting and supports widespread adoption across multiple departments
    • Common among large hospital networks and national insurers like UnitedHealthcare and Kaiser Permanente
  • Value-based pricing:
    • Pricing is aligned with the outcomes achieved, such as improvements in patient outcomes or operational efficiencies. This model resonates well with large entities focused on ROI
    • Utilized by companies like Epic Systems in their AI-driven EHR enhancements
  • Bundled services:
    • Comprehensive packages that include not only the AI tools but also integration, training, and ongoing support. This model adds significant value and ensures that the AI solution is fully embedded in the organization’s operations
    • Seen in offerings by Teladoc Health for their AI-driven telehealth services
  1. Mid-sized payers and providers

Mid-sized organizations, such as regional hospital systems or mid-tier insurance companies, often require scalable solutions that can grow with their needs. The pricing strategies here are more varied to accommodate differing capabilities and budgets:

  • Tiered pricing (most preferred model currently):
    • Offers different levels of service and functionality, allowing organizations to choose a package that best fits their current needs and budget
    • Common in platforms like Olive AI, where mid-sized hospitals can start with basic automation tools and scale up to more advanced AI-driven operations
  • Pilot programs with clear ROI metrics:
    • Healthcare AI startups often implement pilot projects that allow these organizations to test solutions with clear, short-term ROI metrics before committing to larger investments
    • Examples include pilot programs from startups like Aidoc, which provides AI-driven radiology solutions
  • Volume discounts:
    • Discounts are offered for bulk commitments or long-term contracts, helping mid-sized entities manage costs while planning for future growth
    • Seen in pricing strategies from Change Healthcare for their AI-driven revenue cycle management tools
  1. Small payers and providers

Small healthcare providers, such as independent clinics or small regional insurers, have limited budgets and require affordable, flexible solutions. The pricing models in this segment are designed to lower barriers to entry:

  • Subscription-based pricing (most preferred model currently):
    • Monthly or annual subscriptions make AI solutions more accessible by spreading costs over time, which aligns with the cash flow constraints of smaller organizations
    • Healthcare AI startups like Zebra Medical Vision utilize this model to provide AI-driven imaging solutions to small clinics
  • Freemium models:
    • Basic features are offered for free, with the option to upgrade to premium versions. This model allows small providers to try the technology before committing financially
    • Used by some digital health startups like Buoy Health for their AI-driven symptom checkers
  • Pay-as-you-go:
    • This usage-based pricing model allows small providers to pay only for what they use, making it ideal for those with fluctuating patient volumes
    • Common in AI-driven telemedicine platforms like Amwell, which serves small practices

As healthcare AI startups navigate the complexities of pricing strategies, it is crucial to continuously monitor the evolving demands of payers and providers. Market dynamics, technological advancements, and regulatory changes can all influence what organizations value in AI solutions.

Startups must remain agile, regularly reassessing their pricing models to ensure they align with the shifting priorities and financial capacities of their target segments. By staying attuned to these changes, startups can not only maintain relevance but also capture new opportunities for growth, ensuring that their solutions remain accessible and attractive across stakeholders in the healthcare landscape.

To explore AI in the healthcare industry and how startup CEOs can succeed in the healthcare technology space, reach out to Abhishek Singh, Rahul Gehani, or Abhishek Sharma.

Watch our LinkedIn live event, The Role of Technology in Advancing Member and Patient Engagement, to learn about potential investments in this space and strategies for their implementation.

How has Generative AI Evolved and is its Evolution Now Supporting CX Leaders More on the CXM Journey? | Blog

August 27, 2024

The landscape of Customer Experience Management (CXM) has witnessed a remarkable transformation within the advent of Generative AI (generative artificial intelligence). Based on periodic comprehensive studies conducted by Everest Group with customer experience (CX) leaders from over 300 enterprises globally, we present comparative insights that highlight the progress made in the past year (2023 to 2024).

Using two different primary studies, research has been conducted regarding gen AI in CXM operations, in the process providing our perspective on future developments.

Reach out to us to discuss this topic further with our expert analysts.

Adoption of digital CX solutions – 2023 vs 2024

Propelled by gen AI, a significant shift has been observed in the adoption of digital CX solutions such as automation, self-service, conversational artificial intelligence (AI), data and analytics, and migration to cloud contact centers.

There was a 15-30% increase in the number of enterprises having deployed these solutions from 2023 to 2024.

Blog The Evolution of Generative AI Exhibit 1

 

Generative AI awareness and its potential

Noteworthy changes in the awareness and potential of various gen AI use cases were also observed during this analysis. In 2023, while most enterprises had a good understanding of applications such as text, image, and code generation, few had robust knowledge of other application areas.

However, this scenario changed significantly in 2024. The majority of enterprises across industries now report having a solid working knowledge of various gen AI applications. Many are even considering synthetic data generation and audio and video generation as high-potential applications for gen AI in CXM.

Blog The Evolution of Generative AI Exhibit 2

The role of third-party providers

The role of third-party providers has become pivotal for enterprises, as they look to navigate complexities. Their importance is increasingly becoming more significant as enterprises realize the various nuances required in developing these solutions.

Blog The Evolution of Generative AI Exhibit 3In 2024, there is a significant uplift in enterprises opting for tech-heritage or specialized AI companies, to use for implementation of gen AI, to be able to leverage their expertise in this technology and achieve faster time to market.

Additionally, more enterprises are outsourcing to contact center providers for gen AI integrations, capitalizing on their CXM domain expertise to better customize customer journeys and improve productivity and CX metrics.

Conversely, there has been a notable decline in the hybrid approach to gen AI development which combines both in-house and outsourced development. From a whopping 70% in 2023, the percentage of enterprises preferring this mode has reduced to only around 30%. This decline, accompanied by a decline in internal development, can be attributed to the change in business priorities for organizations and their need to have eagle-eyed focus on improving their core competencies and achieving their business objectives of revenue improvement, cost reduction, and adapting to new business challenges.

Enterprises choosing to invest wisely in their long- and short-term approach to Gen AI

From a financial perspective, enterprises exhibited a more optimistic stance toward generative AI adoption in 2023, with nearly two-thirds planning to invest over US$1 million in the next 12-18 months on gen AI solutions in CXM.

However, as the technology has matured, enterprises now have a clearer understanding of the returns these investments can generate. Over the past year, many enterprises observed that a significant number of gen AI pilots failed to progress to the deployment phase.

Consequently, in 2024, enterprises have taken a more cautious approach toward gen AI adoption. They now prefer to evaluate each application on a use-case basis before committing to full-scale investments. This shift is reflected in the investment budgets for gen AI, with only half of the enterprises (down from two-thirds) now planning to spend more than US$1 million on these initiatives. This decrease in investments on gen AI is propelled further by the current difficulties in the macroeconomic and business environments, where organizations are placing cost reduction and revenue enhancement as their top priority.

Blog The Evolution of Generative AI Exhibit 4

This cautious stance, however, does not mean that there is a decrease in the perceived potential of gen AI. 2025 continues to hold promise of a booming gen AI adoption. In fact, more than 80% of the enterprises plan to invest more than US$1 million in 2025. As gen AI continues to demonstrate its potential and deliver its promised outcomes, enterprises are likely to embrace it with increased enthusiasm.

If you found this blog interesting, registrations are now open for our Gen AI Unhyped: How It Is Evolving And How To Plan For Success | LinkedIn Live – Everest Group event LinkedIn Live event on September 11, 2024!

If you have questions or want to discuss CX strategies and solutions, please contact Mohit Kumar at [email protected] or Aishwarya Barjatya at [email protected].

Linkedin-in Facebook-f Play Instagram
Better Decisions Powered by Tenacious Research    

Contact Us

Who We Are

About Us

Newsroom

Office Locations

Partners & Affiliations

What We Offer

Memberships

Custom Decision Support

Published Research

Careers

Why Everest Group

Explore Openings

Equal Opportunity Employer

Resources

Access Reports

Citation Policy

Everest Group Market Intelligence (EGMI)

Legal

Using the Reports Portal

©2023 Everest Global, Inc.         Privacy Notice          Terms of Use        Do Not Sell My Information

How can we engage?

Please let us know how we can help you on your journey.

Contact Us

"*" indicates required fields

Please review our Privacy Notice and check the box below to consent to the use of Personal Data that you provide.
Consent*