Category: IT Services

From Sci-Fi to Reality: Unraveling the Risks of Superintelligence | Blog

Superintelligence promises incredible advancements and solutions to the world’s biggest challenges, yet it also presents an ominous threat to society. As the lines between innovation and catastrophe blur, understanding the risks of AI is crucial. Read on for recommendations for moving forward in this uncharted territory.

Generative Artificial Intelligence’s emergence has led enterprises, tech vendors, and entrepreneurs to explore many different use cases for this disruptive technology while regulators seek to comprehend its wide-ranging implications and ensure its responsible use. Learn how enterprises can leverage GAI in our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

Concerns persist as tech visionaries warn that AI might surpass human intelligence by the end of the decade. Humans are still far from fully grasping its potential ramifications and understanding how to collaborate with the technology and effectively mitigate its risks.

In a groundbreaking announcement in July, OpenAI unveiled that it has tasked a dedicated team with creating technologies and frameworks to control AI that surpasses human intelligence. It also committed to dedicating 20% of its computing resources to address this critical issue.

While initially exciting, the prospect of superintelligence also brings numerous challenges and risks. As we venture into this uncharted territory, understanding AI’s evolution and its potential implications on society becomes essential. Let’s explore this further.

Types of AI

AI can be broadly categorized into the following three types:

  • Narrow AI

Narrow AI systems are designed to excel at specific tasks, such as language translation, playing chess, or driving autonomous vehicles. Operating within well-defined boundaries, they cannot transfer knowledge or skills to other domains. Common examples include virtual assistants like Siri and Alexa, recommendation algorithms on streaming platforms, and image recognition software.

  • General AI

General AI possesses human-like cognitive abilities and can perform various intellectual tasks across various domains. Unlike narrow AI, general AI has the potential to learn from experiences and apply knowledge to different scenarios.

  • Super AI (Superintelligence):

Super AI represents a hypothetical AI that surpasses human cognitive abilities in all domains. It holds the promise to solve complex global challenges, such as climate change and disease eradication.

Tech thinkers across the globe have raised an alarm

Amidst growing concerns about the risks of superintelligence, the departure of Geoffrey Hinton, known as “the Godfather of AI,” from Google was one of the most significant developments in the AI realm. Hinton is not alone in his concern about AI risks. More than 1,000 tech leaders and researchers have signed an open letter urging a pause in AI development to give the world a chance to adapt and understand the current developments.

These leaders emphasized that development should not be done until we are certain that the outcomes will be beneficial and when the AI risks are fully known and can be managed.

In the letter, they highlighted the following five key AI risks:

  1. Machines surpassing human intelligence: The prospect of machines becoming more intelligent than humans raises ethical questions and fears of losing control over these systems. Ensuring that superintelligence remains beneficial and aligned with human values becomes crucial
  2. Risks of “bad actors” exploiting AI chatbots: As AI technologies evolve, malicious actors can potentially exploit AI chatbots to disseminate misinformation, conduct social engineering attacks, or perpetrate scams
  3. Few-shot learning capabilities: Superintelligent AI might possess the ability to learn and adapt rapidly, presenting challenges for security and containment. Ensuring safe and controlled learning environments becomes essential
  4. Existential risk posed by AI systems: A significant concern is that superintelligent AI could have unintended consequences or make decisions that could jeopardize humanity’s existence
  5. Impact on job markets: AI’s rapid advancement, especially superintelligence, might disrupt job markets and lead to widespread unemployment in certain sectors, necessitating measures to address this societal shift

As we already have seen some risks associated with this technology materialize, cautiously approaching the advancement of its progress is necessary.

Recommendation for moving forward

To mitigate AI risks and the risk of superintelligence while promoting its development for positive societal outcomes, we recommend enterprises take the following actions:

  • Create dedicated teams to monitor the development – The government needs to appoint relevant stakeholders in regulatory positions to monitor and control these developments, particularly to protect the large population that does not understand the technology from its potential consequences
  • Limit the current development – As the letter suggested, the government should implement an immediate moratorium on developing and using certain types of AI. This pause would give everyone enough time to understand the technology and associated risks better. While Italy has used its legal architecture to temporarily ban ChatGPT, efforts like this will not have a significant impact if carried out individually
  • Define policies – Regulatory agencies should start working on developing policies that direct researchers on how to develop the technology and define key levels for alerting regulatory agencies and others
  • Promote public awareness and engagement – Promoting awareness about AI and superintelligence is crucial to facilitate informed debates and ensure the technology aligns with societal values
  • Form international collaborations – Isolated initiatives won’t help the world. Larger collaboration among governments to define regulations and share knowledge is needed

While new technologies have always brought changes to the existing norms, disrupted established industries, and transformed societal dynamics, ensuring these advancements are beneficial to a larger audience is essential.

To discuss the risks of Generative AI, its use cases, and its implications across different industries, contact Niraj Agarwal, Priya Bhalla, and Vishal Gupta.

BigTechs’ Play in Generative AI in Healthcare | Blog

The healthcare sector is increasingly interested in harnessing generative AI capabilities to drive operational and cost efficiencies. Leading technology players are heavily investing in this space and partnering with healthcare enterprises to take advantage of its potential. Discover the latest developments, challenges, and outlook for generative AI in healthcare in this blog.  

The adoption of generative artificial Intelligence (GAI) by the healthcare industry has gained momentum in recent months with technological advances such as ChatGPT and Dall-E 2.

To tap into this growing demand for generative AI in healthcare, BigTechs (Amazon, Google, Microsoft, and Oracle) are leaving no stone unturned as they pivot toward developing capabilities at the intersection of healthcare and GAI. Let’s explore this further.

Recent healthcare-specific GAI announcements by BigTechs

Picture1

The frequency of recent investments by these tech giants signals that more funding will follow in the GAI-healthcare space.

BigTechs’ partner ecosystem – a key differentiator in developing industry-specific generative AI capabilities

As BigTechs make huge strides in ramping up their healthcare-specific generative AI capabilities, accessing large volumes of healthcare data is a major obstacle. To address this challenge, BigTechs are forging strategic partnerships with niche health tech firms or third-party data providers to obtain healthcare data for training Large Language Models (LLMs).

BigTechs also are leveraging partnerships to foster co-innovation and adopt a joint go-to-market strategy for healthcare-specific GAI solutions. For example, Oracle partnered with AI specialist firm Cohere to develop GAI solutions. Through the partnership, Cohere will train, build, and deploy its generative AI models on Oracle’s cloud infrastructure. Oracle plans to deploy new models for healthcare and embed GAI throughout its industry-specific applications.

BigTechs’ GAI capabilities are predominantly provider-focused  

Most investments by BigTechs in generative AI in healthcare are focused specifically on enhancing administrative processes for providers and physicians. Some of the prominent use cases are centered around streamlining clinical documentation and drafting automatic message responses to patients, as illustrated below:

Picture2

Seeing how the technology giants develop capabilities in care coordination and delivery will be interesting. Providing health and lifestyle recommendations, reminders for medications and appointments, and other proactive communications could significantly transform the way patient care is delivered.

When developing care coordination and delivery capabilities, BigTechs and other technology players must ensure the GAI models are built on huge volumes of patient data. This will significantly reduce the margin for error and help improve patient care outcomes.

Adoption of generative AI in healthcare is currently limited but will surge before long

While investments by suppliers are rising, GAI adoption in healthcare is still in its nascent stages. However, we have observed a few mid-to-large-sized hospitals and providers taking the leap of faith and becoming early adopters. For example, UC San Diego Health and UW Health in Madison, Wisconsin, are adopting GAI solutions developed by Microsoft and Epic to automatically draft message responses to patient queries.

To increase GAI adoption in healthcare, technology players and enterprises will have to jointly address these key considerations:

  • People – determining and acquiring the right skills
  • Process – change management and identifying the business functions
  • Technology – ensuring security and maintaining infrastructure

Most importantly, successfully navigating security challenges will be key to increasing adoption, as enterprises are skeptical about sharing sensitive patient data to train GAI models. Technology players will have to proactively alleviate this concern by ensuring patient data is protected and that the development of GAI models adheres to data privacy and state and federal government security mandates.

Moving forward, what will BigTechs focus on?

While most current use cases revolve around administrative and operational provider functions such as clinical documentation and medical coding, BigTechs are expected to invest in other provider-related functions shortly. Some provider-specific functions that are ripe for GAI investments are:

  • Billing and payments – Analyzing patient billing data, payment history, and due dates to generate personalized payment reminders for patients
  • Post-discharge follow-ups – Generating personalized care instructions and sending tailored health reminders to individuals, including medication adherence and lifestyle recommendations

Pre-care and post-care services, such as providing appointment reminders and billing options, could be near-term investment areas for BigTechs. By working together, BigTechs and enterprises can potentially leverage GAI to analyze patient data and proactively recommend care interventions – an exciting concept to keep an eye on.

Surprisingly, we have not seen much traction or activity in the payer segment on the supply or demand side. Considering payers are generally more inclined to embrace technology than providers, we anticipate demand and supply to grow in the short term.

It will be intriguing to watch the types of applications BigTechs create for payers when they dive deeply into this segment. Streamlining contact centers and automating benefit verification are low-hanging fruits BigTechs may target.

We will continue to closely follow how BigTechs’ GAI play evolves in the payer market. To discuss generative AI in healthcare, contact [email protected] and [email protected].

Learn more about technology in healthcare in our LinkedIn Live session, How Technology is Reshaping the Delivery of Care in the Healthcare Industry.

Unlocking Success: The Rising Importance of Service Providers in the SAP Mid-Market Growth Strategy | Blog

Service providers are expected to play a vital role in meeting the unique needs of customers in the strategic SAP mid-market. To help the software company achieve its growth targets, partnerships will be crucial. Discover the five customer priority areas where service providers can make a difference in this blog.       

SAP expects the mid-market to be a large contributor to meet its revised growth forecast of 8.3% over the next three years. To effectively attract clients in this segment, the company recognizes it will need to invest in extensive industry application development and form substantial agile partnerships with service providers.

To better evaluate this market, Everest Group’s Enterprise Platform Services (EPS) practice is launching a new PEAK Matrix® assessment. Learn more to participate

SAP mid-market customers have distinct priorities compared to large clients. Service providers who can tailor solutions to meet the following five key priorities will be valuable partners:

  1. Flexibility: SAP mid-market customers need providers that offer flexible services to adapt to their changing business requirements
  2. Responsiveness: Since SAP is typically a new platform for mid-market customers, providers need to proactively educate them on its capabilities and have responsive services teams available to address queries and concerns
  3. Regional support: Mid-market enterprises demand a higher regional presence for services because they want to establish robust communication and agile collaboration
  4. Cost attractiveness: Mid-market customers seek service providers that offer the most value for their SAP and services investments, aligning with their budget needs
  5. Customized services: These enterprises want service providers that invest in understanding their business processes and deliver SAP services tailored to specific requirements

While providers of SAP services focused on this segment may lack the scale or global footprint, they are laser-focused on addressing these key priorities of mid-market SAP customers. Beyond implementation, data migration, and customization, they also offer the benefits of training and support services.

SAP’s mid-market services have already made a significant impact, contributing an impressive US$ 13 billion with a staggering year-over-year growth rate of approximately 20%. With the introduction of the RISE with SAP and GROW with SAP programs, momentum is growing for small and medium-sized businesses to adopt the SAP suite for modernization and consolidation initiatives.

SAP services market divided by buyer segmentsSource: Everest Group (2023)
SAP services market divided by buyer segments | Source: Everest Group (2023)

Based on our analysis of the service provider landscape, the below exhibit shows the prominent providers in the mid-market segment across key regions:

Prominent mid-market-focused service providers strategically located across key regions | Source: Everest Group (2023)
Prominent mid-market-focused service providers strategically located across key regions | Source: Everest Group (2023)

To better evaluate this market and assist enterprises in key sourcing decisions, Everest Group’s Enterprise Platform Services (EPS) practice is launching a new PEAK Matrix® assessment, SAP Business Applications Services PEAK Matrix® Assessment 2023 for Mid-market Enterprises.   

Providers that derive at least 40% of SAP services revenue from mid-market clients with annual revenue below US$5 billion can participate in this assessment. To participate, reach out to EPS ([email protected]) or complete the form: SAP Business Applications Services PEAK Matrix® Assessment 2023 for Mid-market Enterprises.

For information on the SAP mid-market and service providers in SAP, contact [email protected], [email protected], and [email protected].

Change Involved in Moving to the Platform Operations Model | Blog

Many companies are now 5-10 years into their digital transformation journey. I have blogged for two years (here for instance) about how the journey drives companies to forge a more intimate relationship between their technology and their business operations, where they operate as one integrated team. At Everest Group, we refer to this convergence as “platform operations.” Companies over time find that the platform operations model accelerates their progress toward achieving their business objectives and key results (OKRs) while often decreasing their cost to serve.

Read more in my blog on Forbes

Navigating the Generative AI Conundrum in Life Sciences: Insights into Challenges, Implications, and an Adoption Roadmap for Commercial Technology Functions  

The life sciences industry can reap the many benefits of Generative Artificial Intelligence (GAI) by effectively overcoming challenges in this highly regulated industry to responsibly implement the technology. Discover key implications for technology players and a roadmap for enterprises to successfully adopt GAI for commercial functions.  

Help us learn more about the potential of gen AI in the life sciences commercial function by participating in this short survey and receive a complimentary summary of the survey findings.

In the first blog in this series, we explored Gen AI life sciences commercial use cases, shared industry leaders’ skeptical to optimistic perspectives on its potential, and uncovered new technology offerings. Read on for more insights into key risks, repercussions, and recommendations to adopt generative AI in life sciences.

“With great power comes great responsibility.” – Uncle Ben, Spiderman

Undoubtedly, Gen AI has massive potential to disrupt most processes and create new opportunities across industries, including the life sciences commercial function. But the highly regulated nature of this industry brings significant risks and challenges that will need to be overcome to adopt GAI at scale. Let’s explore this further in the illustration below:

Picture1
Risks and challenges associated with generative AI in life sciences

Key implications for the life sciences commercial technology ecosystem

“A journey of a thousand miles begins with a single step.” – Lao Tzu

While the Gen AI journey can appear long and daunting, commercial technology players may have a head start over their peers across the life sciences value chain. While certain use cases, such as personalized campaign generation and brand reputation monitoring, will require complex integrations and domain-specific development, other applications like content generation/analytics, market research, and autonomous customer support can be quickly implemented and brought to market.

Next, let’s take a look at six recommendations for life sciences technology providers to seize opportunities that GAI presents.

Picture2
Key Implications for life sciences commercial technology players
  1. Take a safety-first approach: First and foremost, commercial technology players need to address the safety aspects of Gen AI adoption and applications – from healthcare personnel/patient data safety and security to compliance with regulations and tackling ethical and legal risks. Providers that successfully address safety questions will instill trust and reliability with customers and gain a foot in the door to discuss and foster responsible Gen AI application across the commercial function
  1. Seize the opportunity to achieve domain specificity at scale: By combining the domain-data trained language models and large language models (LLMs), Gen AI provides a great opportunity for commercial technology players to offer domain specificity at scale across a wider range of solutions and areas. This integration enables the generation of more accurate, relevant, and specific outputs in the life sciences commercial function context, ensuring quicker model training, fine-tuning of responses, and domain-specific prompting
  1. Recognize that speed-to-market is essential: Technology providers must quickly identify, prioritize, and bring viable go-to-market opportunities and use cases to capture market attention, and, ultimately, the enterprise mindshare. While enterprises are still determining next steps with Gen AI, they are eager to learn more, explore potential use cases, and become better educated. Therefore, the velocity of go-to-market initiatives is immensely valuable
  1. Balance incremental and disruptive innovations: To succeed in the market, players will need to balance their bets between simpler quick-to-market propositions that augment existing capabilities and more strategic long-term opportunities that explore new segments, functionality, etc. With the abundance of possibilities, providers should carefully weigh options
  1. Partner with service providers: Service providers can be important allies in ensuring enterprise-wide acceptance and adoption of AI-enabled services. Technology players should look to forge strategic ties with service providers who need to be the flag bearers for technology modernization, data architecture, and process and change management initiatives
  1. Prepare to win the talent war: As demand for new skills (such as generative modeling, data engineering, and ethical AI) rises, the talent war is expected to get more vigorous. Players must proactively plan for strategic hiring and upskilling/cross-skilling initiatives

­

Enterprises are still evaluating the Gen AI conundrum across the entire life sciences commercial function, including the risks, challenges, costs, return on investment (RoI), talent, and processes. Our five-step GAI tools adoption guide can help enterprises accelerate this process, as illustrated below:

Picture3

While Gen AI holds immense promise for transforming the life sciences commercial landscape, it comes with its fair share of challenges, including ethical considerations, data quality, interpretability, and integration hurdles that need to be addressed to ensure responsible and successful adoption.

Technology providers can proactively develop strategies and solutions to overcome these obstacles. By crafting a thoughtful roadmap, committing to ethical practices, and focusing on continuous learning and improvement, the life sciences commercial solutions supply ecosystem can harness the power of Gen AI to unlock new opportunities, enhance customer experiences, and drive sustainable industry growth. While the journey to adopt Gen AI may be complex, the rewards for successful navigation are boundless.

Help us as we research the possibilities of Gen AI in the life sciences commercial sector by taking part in this brief survey. As a token of appreciation, you will receive a complimentary summary of the survey results.

To discuss Gen AI in life sciences and its impact on the commercial technology landscape, contact Rohit K, Durga Ambati, Panini K.

Six Key Takeaways from the Unisys Analyst and Advisor Event | Blog

The 2023 Unisys Analyst and Advisor Event provided a platform to showcase the company’s commitment to innovation and forward-thinking approach, exemplified by its new branding image in the IT services sector released last year. The company’s digital workplace strategy emerged as one of the key themes attracting our attention. To learn valuable insights from our analysts who attended the leadership presentations, read on.

Contact us directly to expand further on this topic.

Digital workplace artificial intelligence (including Generative Artificial Intelligence), cybersecurity, multi-cloud, and quantum computing were among the most prominent industry trends grabbing attention at the Unisys Analyst and Advisor Event, June 13-14 in New York.

Unisys presented a comprehensive update on the performance and prospects of its four business units: Cloud, Applications & Infrastructure Solutions (CA&I), Digital Workplace Solutions (DWS), Enterprise Computing Solutions (ECS), and Business Process Solutions (BPS).

Peter Altabef, Unisys CEO, set the stage with a fireside conversation preceding the event that focused on the ever-present concerns of cyber-attacks and vulnerabilities. Discussions over the two days covered important topics such as Unisys’ merger and acquisition (M&A) cloud solutions investment approach, its ClearPath Forward strategy for ECS, and increasing its presence in Banking, Financial Services, and Insurance.

Here are six takeaways of the main points we heard from Unisys leadership at the event:

  1. Reinforcing a commitment to innovation

Themed “Imagination to Realization,” the event provided an opportunity for the Unisys leadership team to emphasize its new brand proposition focused on creating an internal culture dedicated to providing “experience breakthroughs” for clients.

Given Unisys’ legacy-heavy business history, the rebranding aims to update the company’s image and make it more relevant to modern consumers’ requirements. The initiative emphasizes innovation and new technology solutions, increasing brand awareness and customer engagement.

  1. Focus on next-generation technologiesAligning with high-growth markets of next-generation technologies, specifically GAI, was a hot discussion topic dominating both formal and informal conversations. Unisys emphasized its goal of increasing next-generation revenue to 45 percent by 2026.

With its potential to disrupt and revolutionize the industry, the chatter surrounding GAI has even surpassed the buzz surrounding AI/Machine Learning (ML) solutions over the past few years. GAI’s immense promise vastly transcends any data privacy and governance concerns.

We envision GAI playing a larger role in such areas as summarizing incidents and findings, generating clear and concise reports and presentations, and augmenting human analyst capabilities by adapting responsibilities to the organization’s landscape and enhancing the analyst experience.

  1. Expansion in the US mid-market segment

Unisys is proactively diversifying its growth strategy by targeting mid-market US corporations, especially those between $2 billion and $5 billion, while also pursuing major deals. This segment holds promise since most mid-market clients feel overlooked or are forced to pay higher prices for less personalized services. By expanding their focus, companies can gain a first-mover advantage and capture a sizeable market share in this segment.

Large US IT service providers’ approach to the mid-market should provide simpler access to IT function heads and leadership teams, Experience Level Agreements (XLAs), and Requests for Proposals (RFPs). This presents Unisys with an opportunity to forge stronger client relationships that can be leveraged to support the enterprise’s digital transformation initiatives.

  1. Dedication to the digital workplace

We were most intrigued by the DWS update. Unisys continues to be a player with a dedicated focus and investment mindset on DWS, focusing on applying ML/AI to support capabilities across the workplace.

To address market opportunities across DWS, the company also is prototyping GAI use cases. Traditionally a strong desk-side support organization, Unisys is now pivoting its workplace portfolio around the acquisition of Unify Square and Mobienergy, providing balance and a future-ready portfolio.

With all the talk about XLA 2.0, Unisys shared that it designed an Experience Governance Board (XGB) in collaboration with a client to manage the evolution of XLAs. This demonstrates Unisys’s commitment to advancing beyond traditional XLAs. As XLAs reach their peak, the Board focuses on new XLAs and business needs to tackle more pressing concerns and find resolutions.

  1. Emphasis on organizational change management

The digital workplace landscape evolution has profoundly impacted Human Resources (HR) functions. HR now plays an expanded role in change management, with AI and automation boosting productivity by facilitating administrative and training processes. Service providers increasingly focus on organizational change management to cultivate a cohesive digital workplace environment.

Realizing that end-user acceptance of digital adoption is the most crucial step, Unisys leadership is actively working to institute change management in every transaction and not just the transition phase.

Unisys has implemented a targeted organizational change management program for customers that assesses users’ digital dexterity. This approach allows the company to determine the adoption level and utilization of digital workplace services among different user personas. By understanding workplace adoption patterns, Unisys can optimize digital workplace investments.

This exhaustive strategy guarantees successful technology implementation, positive user experiences, and measurable business outcomes.

  1. Partnering with clients

Unisys demonstrated its ability to foster client relationships by sharing compelling client success stories. The company’s prowess in fostering innovation and delivering tangible value is evident by its long-standing relationships with its top 50 clients, with an average collaboration of 20 years.

In one notable partnership with Elekta, Unisys effectively employed its digital transformation proficiency to enable the precision radiation therapy solution provider to enhance the end-user experience.

Unisys delivered its Intelligent Workplace Services, ensuring comprehensive 24/7 support of employees and partners across various communication channels, contributing to high-quality radiation treatment availability for cancer patients worldwide.

Unisys also highlighted its partnership with Dell Technologies. Working together, the companies efficiently and reliably resolve customers’ technological issues by delivering end-to-end solutions created for even the most demanding IT settings.

Both of these achievements exemplified Unisys’ ability to deliver measurable business outcomes and its commitment to innovation driven by technology solutions.

Udit Singh and Ronak Doshi represented Everest Group at the event. Reach out to this team with questions about digital workplace artificial intelligence and IT services markets.

Learn more about digital workplace technologies in the first video in our digital workplace opportunities series, Unlocking Digital Workplace Opportunities in 2023 | Episode 1: Exploring the Benefits of ChatGPT.

And discover cyber resiliency and cybersecurity trends, key enterprise investment themes, and the pricing and solution themes underlying the cybersecurity and cyber resiliency market in our webinar, Cyber Resiliency Strategy: Key Themes and Pricing Trends for 2023.

Navigating Disruptions in BFSI: The Role of Desktop Infrastructure Transformation (DIT) | Blog

The Banking, Financial Services, and Insurance (BFSI) industry faces various challenges in today’s evolving environment, from inflation and cybersecurity to increased competition from fintechs, and changing customer expectations. Desktop Infrastructure Transformation (DIT) has emerged as an attractive solution to combat these market disruptions because of its ability to optimize costs, empower users, and enhance IT efficiency. In this blog, we’ll explore how DIT can help the BFSI industry tackle pressing issues.

Contact us directly to discuss this topic further.

BFSI in the “Age of Disruption”

Benjamin Franklin’s wise words, “When you’re finished changing, you’re finished,” still hold true today, particularly in the rapidly evolving world of BFSI. Recent events such as the collapse of Silicon Valley Bank and UBS Bank’s acquisition of Credit Suisse show that those who fail to adapt will be left behind even quicker than they can Google “subprime mortgage crisis.”

Facing various internal and external disruptions, BFSI enterprises struggle with difficult questions. However, a recent Everest Group survey of 500 senior stakeholders supports that “fortune favors the bold.” The survey found 59% of respondents identify digital transformation maturity as a critical priority to withstand disruptions.

Considering these findings, the following framework provides an overview of disruptions BFSI enterprises face and outlines the actions to offset them:

Picture1 6
Source: Everest Group 2023

BFSI enterprises need to act swiftly and effectively to mitigate the impact of these disruptions. As highlighted in the framework above, DIT and its two sub-components – as part of an overall mature digital transformation approach – can provide a strong buttress against disruptions.

These sub-components can be broadly defined as follows:

  • Virtual Desktop Infrastructure (VDI): Technology that allows a user to access a desktop operating system and its applications from a remote server and thin clients
  • Full-stack Desktop-as-a-Service (DaaS): Cloud computing environment with bundled pricing for hardware, software, and ancillary management services in a pay-per-use model

In the following section, we examine the most pressing disruptions BFSI enterprises face and explore how DIT can provide a solution to address them:

Disruption Evidence Complication Question DIT to Rescue
Compounding impact of concurrent inflation and recession Inflation is at a 40-year high in most developed countries such as the US and UK –      Reduction in banking payments and transactions

–      Dip in insurance investments and higher payout expenses

–      Deterrence of new bond issuances and Initial Public Offerings

How can enterprises offset the impact of inflation and be prepared for a recession? Cost effective and pay-as-you consume model through DaaS or VDI
Embracing Banking 4.0 and seizing new business opportunities The customer acquisition cost for a physical branch is approximately 50 times higher than for digital banking –      BFSI companies are under pressure to digitize their platforms/services immediately

–      Automation and data-driven decision-making has become pertinent

How can BFSI enterprises effectively leverage the Banking 4.0 approach and seamlessly launch related businesses and products? Cloud-based desktop infrastructure for agility and to ensure faster time-to-market for digitized products/services
Increasing prevalence of cybersecurity attacks More than 60% of global financial institutions with at least $5 billion in assets were hit by cyberattacks in 2022 –      Higher risks of financial losses and reputational damage

–      Increased regulations and compliances, creating operational complexities

How can BFSI companies manage cybersecurity threats while maintaining productivity and profitability? Embedded security over bolt-on security through centralized security controls and Artificial Intelligence (AI)-based threat analytics within VDI
Encroaching fintech startups, reshaping traditional BFSI Venmo’s users increased by 11% year over year in 2022, while the traditional bank growth on average is about 2-5% –      Increased pressure for collaborations between fintech startups and traditional banks

–      M&As leading to business process changes

How can enterprises seamlessly transition to new business models and strengthen collaborations? On-demand desktop infrastructure scalability and seamless integration across enterprises through VDI and DaaS

Source: Everest Group 2023

Empowering BFSI Organizations through DIT

To better understand the composition of VDI and full-stack DaaS in a typical enterprise environment, the below framework provides more detail of the two previously defined key DIT components and their enablers:

Picture2 5

Note: The above framework is not an exhaustive representation of all the components within DaaS and VDI.

Source: Everest Group 2023

Now, let’s take a look at the benefits of this transformation initiative by exploring some applications of DIT that ideally align with the needs of the BFSI sector:

  • Cost optimization: With agile capacity management, increased device lifespan, and a pay-as-you-consume model, BFSI organizations can achieve cost efficiency while maintaining desktop infrastructure quality
  • Single pane of observability: AI-led analytics, synthetic bots for application performance testing, and proactive alerts help IT resources within a BFSI enterprise effectively monitor and manage their desktop infrastructure, achieving operational excellence
  • User empowerment: Personified Virtual Machines (VMs), a self-help marketplace, and DevOps-based feature development enable organizations to empower their end users and improve their experience
  • IT efficiency: Scalable architecture and limited upfront investment support expansion to alternative business models, geographies, and product lines. Cloud-hosted models also allow firms to seamlessly integrate with other IT stacks during mergers and acquisitions (M&As), and divestitures
  • Security and reliability: Automated patch management, trust zones, centralized security controls, and role-based access are some DIT features that enable continuous compliance with industry regulations and help BFSI enterprises avoid security breaches

Making DIT Real for BFSI Enterprises: Balancing Stability and Change

Let’s walk through the following use cases of DIT in various BFSI segments to demonstrate its value for employees ranging from investment traders to data scientists and knowledge workers:

Use case 1: Ensure zero downtime in a trading environment Scope: DaaS
Industry: BFSI Sub-segment: Investment banking Category: Emerging Persona: Power worker (traders)
The business need:

  • Supporting resource-intensive tasks, such as pre-trade processing, trade confirmation, and trade clearance
  • Enabling work on network-heavy applications, such as Bloomberg and Reuters
  • Embedding security and compliance
  • Supporting high-resolution audio-visual tasks
DaaS Benefits:

  • Lag-free, high-performance machines
  • Seamless access to critical trading systems through inexpensive thin clients
  • Enhanced user productivity and experience through improvement in metrics such as win rate, loss rate, and winning trades
  • Interactive, multi-screen support to bolster decision-making

 

Use case 2: Facilitate data-driven, rapid decision-making Scope: VDI, DaaS
Industry: BFSI Sub-segment: All Category: Emerging Personas: Data scientists, business analysts
The business need:

  • Identifying meaningful data patterns from large data sets for smarter decision-making
  • Leveraging data analytics for cyber risk insurance analysis, fraud management, actuarial analysis, and credit record management
  • Identifying potential opportunities and threats
Benefits:

  • Equips data scientists/analysts with a high-performance computing environment, accelerating decision-making
  • Enables secure remote access to custom platforms and tools to run compute-heavy Artificial Intelligence (AI) and deep learning workloads
  • Proactively identifies malicious transactions and requests

 

Use case 3: Realize synergies from M&A activities sooner Scope: VDI, DaaS
Industry: BFSI Sub-segment: All Category: Prevalent Personas: Knowledge workers and power workers
The business need:

  • Accelerating consolidations divestitures, and M&As
  • Providing omni-channel access during transition to critical functions such as wealth management
  • Avoiding business disruptions, cost leakages, and productivity loss
Benefits:

  • Seamless accrual of targeted synergies
  • Efficient onboarding of new workforce, improved productivity, and reduced employee downtime
  • Cost optimization through models such as pay-per-use in device infrastructure

These use cases demonstrate the substantial value DIT offers in addressing the vital requirements of the BFSI sector and mitigating market disruptions. Several key benefits of DIT include cost optimization, operational excellence, user empowerment, and enhanced IT efficiency.

Yet, it is essential to recognize and thoroughly assess the associated risks of this technology, such as user acceptance and training challenges, as well as potential dependencies on network infrastructure. By carefully evaluating these factors, enterprises can make informed decisions about investments like DIT aimed at enhancing the IT infrastructure and diminishing market disruptions.

Ultimately, however, understanding the risk of inaction is critical. As Tony Robbins, life coach and author, aptly notes, “Risk comes in many forms, but the most common one is simply not investing.”

To discuss Desktop Infrastructure Transformation, contact Prabhneet Kaur and Udit Singh.

Generative AI in Retail and CPG: Revolutionizing Operations and Customer Experience | Blog

Generative Artificial Intelligence (GAI) can transform multiple facets of retail and consumer packaged goods (CPG) industries, from product development and digital commerce to sales and marketing, supply chain, and in-store operations. Explore GAI’s exciting future in this blog.

Contact us to speak to an analyst on this topic.

Following the growing trend in other industries, retail and CPG enterprises are now investing in GAI to harness its potential to enhance operations and customer experience.

While not a completely new technology, the current enthusiasm for GAI stems from the introduction of user-friendly interfaces (ChatGPT) that enable users to effortlessly generate high-quality text, graphics, and videos in seconds, marking a significant advancement in content creation.

But the question remains: Will GAI leave a lasting impact on the retail and consumer goods value chain, or will it follow the trajectory of numerous other technologies and fade into obscurity? Let’s explore this further.

To comprehensively grasp the impact of generative AI in retail and the CPG industry, we have identified and highlighted specific use cases across the value chain. By examining its potential and ease of adoption, we can better understand how GAI can revolutionize and drive transformative change in this sector.

The matrix below explores the more promising application areas and roadblocks to adoption across five areas of the retail and CPG industry value chain: product development, digital commerce, sales and marketing, supply chain, and in-store operations.

Picture2 1

From our analysis, the following key takeaways emerge:

  1. Product Development: GAI’s integration with data, analytics, and customer preferences empowers product development in retail and CPG, propelling innovative and efficient design processes. GAI enables businesses to create products that resonate with customers by enhancing decision-making capabilities and driving success in the competitive retail and CPG market.

Use Case: Anheuser-Busch’s AB InBev developed the world’s first AI-created beer called Beck’s Autonomous by leveraging GAI to analyze customer preferences. GAI tools such as ChatGPT and Midjourney were used to create the recipe and logo, name the beer, and design the packaging

  1. Digital Commerce: By leveraging GAI, businesses can create personalized and interactive customer experiences to optimize engagement and satisfaction. From dynamically generating product recommendations to creating virtual fitting and styling experiences, GAI opens new avenues for transforming the way customers interact and engage with retail and CPG brands.

Use Case: Levi’s has introduced a tool that allows customers to virtually try on apparel by capturing their body shapes with a smartphone. Integrated with GAI technology, this feature then generates images of them wearing different attire options

  1. Sales and marketing: GAI holds the transformative potential to revolutionize sales and marketing strategies in the retail and CPG sector, particularly by customizing messaging. Businesses can use GAI to create highly tailored and personalized marketing campaigns that resonate with individual customers, increasing engagement and conversions. Additionally, GAI enables precise targeting of localized market segments, allowing brands to deliver relevant and targeted messages that effectively capture the attention of specific regions or demographics.

Use Case: Utilizing GAI, IKEA collects consumer preference data from various regions and cultures, allowing the retailer to create localized marketing campaigns that successfully resonate with local audiences. This approach includes tailored campaigns for furniture products targeting young urban professionals to suburban families

  1. Supply chain: GAI can be a game-changer in optimizing supply chain operations for the retail and CPG industry, unlocking its vast potential to drive efficiency and productivity. By combining GAI with advanced analytics, businesses can gain valuable insights into demand forecasting, inventory management, and logistics optimization, resulting in streamlined operations and reduced costs. Moreover, automating documentation processes through GAI simplifies paperwork, saves time, and improves accuracy, contributing to smoother and more efficient supply chain workflows.

Use case: Walmart is harnessing GAI to develop efficient and sustainable supply chains. The retail giant employs algorithms for accurate demand forecasting, waste reduction, improved inventory management, and optimized transportation networks, resulting in lower costs and emissions

  1. In-store operations: GAI has immense promise to revolutionize in-store retail operations, offering a powerful tool to optimize various customer experience aspects. Applying GAI’s capabilities, retailers can analyze vast amounts of data and extract actionable insights to enhance sales performance. Additionally, GAI can automate and optimize tasks like managing inventory, creating visual merchandising, and personalizing product recommendations, ultimately improving operational efficiency and increasing customer satisfaction.

Use case: Amazon Go is enhancing its contactless checkout process by leveraging GAI to track customer movement, identify selected products, and automatically charge customers’ Amazon accounts when they exit the store

Outlook for Generative AI in retail and CPG

The future potential of generative AI in retail and CPG demonstrates encouraging prospects for enhancing customer engagement and operations. However, conducting thorough research before implementing this technology is crucial.

As the retail and CPG industry is still experimenting with GAI, it is advisable for businesses to first identify specific problems and understand how GAI can effectively solve them, rather than investing in flashy use cases.

Enterprises interested in experimenting with GAI technology should begin with customer-centric use cases as pilot projects before expanding to more complex applications. By taking this approach, retailers and consumer goods companies can ensure a smoother transition and realize the many benefits this technology offers.

Everest Group will continue to follow the evolution in this space. To discuss generative AI in retail and the CPG industry, please reach out to [email protected], [email protected], and [email protected].

Learn about the vast potential of GAI in customer experience in our LinkedIn Live, Generative AI in Customer Experience: Use Cases and Responsible Adoption.

Driving Sustainable Change: A Look into the Insurance Industry’s Commitment to Sustainability | Blog

Embracing sustainability in the insurance industry is not just a choice, but a necessity for a resilient future. By integrating Environmental, Social, and Governance considerations into their practices, insurers can mitigate risks and foster long-term value for customers, shareholders, and the planet.

Sustainability has been a pivotal issue for years, but the recent conditions induced by the storm of the COVID-19 pandemic’s economic effects and the escalating climate change impacts across the world have increased pressure on industries across the globe to be aware of their Environmental, Social, and Governance (ESG) footprint. The financial services sector has not been behind in the race to drive the global sustainability agenda, largely driven by the BFS industry in the past. However, over the past few years, the insurance industry, being a key player in this sector, has also recognized the importance and urgency of embracing various practices in its operations to contribute to a sustainable planet. By integrating sustainability into various aspects of their operations, insurers are not only mitigating risks associated with climate change and environmental degradation but also fostering long-term resilience and contributing to a more sustainable future. This blog will explore how the insurance industry is driving the sustainable change through technological investments, product innovation, business processes, and disclosures.

With the increasing pressure from regulatory authorities, customers, employees, shareholders, and other market participants, insurance enterprises are striving to incorporate various aspects of sustainability into their business. Insurance firms are embracing sustainable change in a variety of ways, including through their investments, underwriting choices, and the structure of their insurance products, as well as using their own office buildings and making the vehicle fleet available to executives and staff. By integrating ESG considerations into their risk management, product design, internal operations, long-term strategies, and workforce management, many insurance firms have already started their journey toward becoming purpose-driven organizations and have begun to integrate sustainability with their core businesses.

Picture1 1
Exhibit 1: A look at various internal and external ways to incorporate sustainability

Incorporating sustainability in workforce management and internal processes has been the first step in creating sustainable change for most insurance enterprises. However, with the high awareness and responsibility in the play, insurers are now also increasingly moving toward adding sustainable insurance products in their catalog that address environmental and social challenges to become champions in the maturity continuum [Exhibit 2]. For instance, insurers offer green insurance policies at lower premium rates to incentivize environmentally friendly practices and offer coverage for renewable energy installations, energy-efficient buildings, and sustainable agriculture. Similarly, parametric insurance products provide rapid and efficient payouts in the event of natural disasters, helping communities recover faster and build resilience against climate change impacts. These innovative products not only protect clients against risks but also encourage sustainable change behaviors and contribute to a greener future.

Another impactful way in which insurers can increase their top line while promoting sustainability is by incorporating sustainability criteria into their investment policies, divesting from environmentally harmful industries, and investing in renewable energy projects. These actions not only align with the insurers’ values but also offer potential financial returns while mitigating climate-related risks.

Picture2
Exhibit 2: Sustainability maturity continuum for insurance enterprises

Insurers need to prepare for sustainable change with the right technology and data architecture to achieve their sustainability goals, maintain transparency, and stay ahead of the regulatory disclosures requirements.

Insurers have been leveraging consulting partners to help them define their roadmap and strategies to achieve their sustainable agenda. But one of the biggest challenge  insurers face in this pursuit is the lack of robust data architecture to provide an understanding of the current ESG footprint, such as carbon emissions, energy consumption, energy mix, and employee well-being. As more insurer enterprises move toward becoming sustainability champions and provide transparency and disclosures to the regulatory bodies and other stakeholders, there will be increased opportunity for data and analytics providers to partner with the insurers to help them align their insurance portfolios with sustainability goals and manage ESG-related risks.

Additionally, collaboration with technology and IT service providers can help insurers build new products and solutions by leveraging cutting-edge technologies such as data analytics, AI, cloud computing, AR/VR, and blockchain that can boost the sustainability agenda along with unlocking fresh opportunities for generating revenue. Moreover, using technologies such as green/sustainable cloud to minimize operating expenses and carbon footprint while optimizing energy demand, predictive/prescriptive maintenance of equipment using IoT to limit energy and materials waste, and processing claims efficiently and sustainably by uploading photos and videos of damage through an AR/VR interface are some of the ways insurers can leverage technology to achieving their internal sustainability initiatives as well.

Picture3
Exhibit 3: Utilizing cutting-edge technology to drive sustainable change

The insurance industry has recognized the urgent need to embrace sustainability and is taking significant steps to drive positive change. By integrating sustainability into investments, leveraging technological innovation, offering sustainable products, adopting environmentally responsible business processes, and promoting transparency through disclosures, insurers are playing a crucial role in addressing global sustainability challenges. As the industry continues to evolve, the integration of sustainability practices will become even more critical, enabling insurers to manage risks effectively, foster resilience, and contribute to a more sustainable future for all.

For more details on how the insurance industry is moving toward driving sustainable change and insuring a sustainable tomorrow, please refer to our report Insuring a Sustainable Tomorrow: How the Insurance Industry is Driving Positive Change.

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

Confronted with significant challenges in managing their supply chain due to fragmented software solutions and data silos, retail and consumer packaged goods (CPG) enterprises need unified platforms that support the demand for customization while maintaining agility. Learn about the benefits and components of composable platforms as well as the collaborative role ecosystem stakeholders can play to bring together the supply chain landscape in this blog.

Reach out for more information on this topic.

The retail and CPG industry supply chain is a complex web of suppliers, manufacturers, distributors, and retailers. Daily fluctuations in consumer demand patterns and the rapid growth of e-commerce and newer business models have further increased the intricacy.

Yet, half of the industry has not moved past using spreadsheets and custom-built discrete solutions to manage their operations. Based on an Everest Group study, almost 48% of retailers and consumer goods companies still track their supply chains using spreadsheets. While these solutions are powerful tools, they often lead to siloed data and disjointed processes, resulting in delays and poor supply chain visibility. Let’s explore these limitations and a better solution.

Fragmented supply chain software solutions

The supply chain is a core function not only in retail and CPG but a building block of the economic infrastructure for many other industries. However, no multi-billion-dollar end-to-end supply chain platform company exists like Salesforce in customer relationship management (CRM), Workday in Human Resources (HR), ServiceNow in IT service management (ITSM), and Oracle and SAP in Enterprise Resource Planning (ERP).

The application landscape is fragmented across different departments, such as transportation, warehousing, procurement, planning, and inventory management, with each having its own goals and limited alignment, leading to distinct silos.

Software providers also target these separate buying centers, resulting in various supply chain software categories having great diversity. Due to this heterogeneity and the lack of unified ownership, no comprehensive solution that covers the entire end-to-end supply chain is available.

Data silos across the value chain

The fragmented nature of the application landscape also creates data silos that pose significant challenges within the retail supply chain, hindering efficiency and inhibiting strategic decision-making.

According to our recent study, almost 83% of retailers struggle with data silos across various functions such as inventory management, procurement, logistics, and point of sale (POS) systems. This disconnected data landscape not only impedes supply chain visibility but also results in missed opportunities for cost savings and improved customer experience.

Need for customization

Customizing supply chain is a top demand for retail and CPG enterprises. Many companies have spent decades building software that uniquely fits their purposes.

Enterprises transforming their supply chain are either migrating or replicating these solutions to the cloud. However, they are finding out-of-the-box solutions such as Blue Yonder, SAP, Manhattan, and others do not fit the purpose in most cases. Roughly 30-50% of enterprises, even digitally mature ones, still need customization.

Moreover, the RCPG industry also requires workflow applications and other low-code applications to augment the day-to-day decision-making of different system stakeholders. For these reasons, a unified platform that supports customization while maintaining agility is crucial.

Target state of supply chain platformization

By integrating suppliers, manufacturers, distributors, and retailers on a unified platform, organizations can achieve end-to-end visibility, optimize inventory levels, reduce stockouts, and improve customer satisfaction. Real-time data analytics empower stakeholders to anticipate demand, optimize production schedules, and minimize waste.

This unified supply chain management platform should have the following five components:

  1. Orchestration – The platform should have end-to-end capabilities that not only orchestrate core business applications such as inventory management and supply chain planning but also value-add applications such as sustainability monitoring and supplier risk management, among others
  2. Composability – The platform architecture should be a composite structure of granular components interconnected by business logic and extensible as required. Components in composable platforms promote interoperability, allowing different components developed using various technologies or programming languages to work together seamlessly. This interoperability is typically achieved through standardized protocols, data formats, or communication mechanisms
  3. Scalability – The platform should be built on the cloud to provide scalability as the supply chain process scales up in volume and complexity. The platform should also have integration capabilities that support seamless data exchange and communication between on-premise systems and cloud services. This includes connectors, application programming interfaces (APIs), or middleware solutions that enable smooth data flow and interoperability between the different environments
  4. Unified data fabric – The traditional linear data value chain should be replaced by a collapsed one with structured and unstructured internal and external data all in one location. The platform should act as a single repository of all the supply chain data that is standardized and can be accessed in real-time
  5. Extendibility – The platform should provide the ability to extend existing applications as the business scales. It should have developer portals to build supply chain services/products and a marketplace for technology partners to integrate their solutions on the platform

Picture1

Consolidating the current fragmented supply chain platform landscape is no easy feat and requires collaboration by hyperscalers, data cloud vendors, and enterprise application providers. Some of the players to roll out collaborative initiatives include:

  • Blue Yonder, in partnership with Snowflake and Azure, is consolidating the majority of its solutions offerings on the Luminate platform
  • Microsoft launched its supply chain platform late last year, which aims to provide platformization building blocks across Azure, Dynamics 365, Microsoft Teams, and Power Platform

Technical debt prevents many large enterprises from undergoing supply chain platformization. Our analysis of supply chain investments by retail leaders indicates the end-to-end platformization journey needs to be iterative and not a big-bang transition. It also requires a balanced approach of adopting out-of-the-box applications and building composable applications from the ground up to fit the organizational context.

Everest Group will continue to follow the evolution in this space. To discuss composable platforms and other supply chain management trends in the retail and CPG industry, please reach out to [email protected] and [email protected].

Learn the key technology investment priorities for retail and CPG in our LinkedIn Live session, The Future of Retail and CPG: Balancing Economics, Efficiency & Experience.

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.