Category: IT Services

Decisions for the Future of the Office | Blog

At Everest Group, we work with a lot of companies currently wrestling with the challenge of moving people back to the office. They went from everyone working at the office or company facility to everyone working from home during the COVID-19 pandemic.

There has been a lot written about the rising sentiment in management circles for the need to return to the office, with numerous examples of CEOs proclaiming that said move is essential. However, we can also observe from vacancy rates that this movement has stalled and is being actively resisted by a wide swath of employees. Short of a major realignment in the relative power of employees vs their management, it appears that firms will have to come to an accommodation forging a partnership with their employees which accommodates the needs of both sides into a better working situation. What is clear is that firms that attempt a one-size-fits-all receive the greatest pushback, and those that craft solutions that fit departments and functions achieve the best results.

Read more in my blog on Forbes

Unlocking Success: The Vital Role of Digital Transformation Consulting in Today’s Challenging Climate | Blog

As enterprises face mounting challenges in adopting complex digital solutions, digital transformation consulting continues to grow. But economic pressures, heightened digital intricacies, and new opportunities in sustainability will impact the industry’s future. To thrive in this rapidly evolving environment, consulting firms must offer tailored solutions that deliver measurable outcomes. For more insights, read this blog.     

Reach out to us directly to learn more.

Digital transformation consulting has gained market prominence in recent years due to the demand for experts who can help organizations effectively embrace technology-driven processes and strategies. With enterprises’ constant demand for digital relevance continuing, the sector is expected to grow 8.5 to 9.5% through 2025.

Consultants have a critical role in helping guide enterprises through the why, what, and how of digital business transformation. The success of digital transformation initiatives hinges largely on identifying the right objective, determining the best strategies, and properly planning digital initiatives, which is where consultants can provide invaluable guidance and expertise.

Service providers also have opportunities to display market-leading thought leadership, influence technology decisions by becoming strategic partners, and build long-term senior stakeholder relationships through consulting. These factors make digital transformation consulting one of the most important segments of the IT industry. Analyzing this industry’s movements helps decode the overall direction of digital change.

Macro-economic conditions are pushing enterprise priorities toward operational benefits

With the increasing economic pressures, cost optimization and productivity improvement have become top-of-mind priorities for enterprises in 2023. Companies are looking to optimize operations, streamline processes, and reduce expenses. In response, consulting firms should rebalance their priorities on operational segments such as supply chain management, production, finance, Human Resources, or sales and marketing.

This shift towards operational benefits will likely impact consulting service delivery. Clients increasingly are seeking outcome-based pricing models that tie consulting fees to specific cost savings or productivity gains. To meet this demand, consultants must demonstrate a deep understanding of their client’s business processes and operations and develop customized solutions that deliver measurable results. Many large consulting houses have also leveraged lower-cost locations to address their delivery cost uptick.

Digital pragmatism is leading enterprises to eye scope and vendor consolidation

Many enterprises are struggling to see the expected returns on their digital investments and are looking to optimize their value. This has led to a surge in demand for consulting services that can help businesses rationalize their digital scope and streamline their vendor portfolios. In 2023, the number of enterprises seeking to critically rebalance or rationalize their service provider portfolio increased by 35%.

To meet this wave of digital pragmatism, professional services firms need to provide end-to-end services that can guide clients throughout the process of IT portfolio rationalization. This includes identifying areas for consolidation, developing an implementation roadmap, and providing ongoing support to ensure successful execution. By taking a more strategic approach to IT investments and vendor selection, enterprises can optimize their value and drive better business outcomes.

This also highlights why digital consulting providers have been attempting to expand their footprint across overlapping opportunities among peer groups. For quite some time, the Big Three consulting firms have targeted downstream revenue with products and solutions for enterprise decision-making. Meanwhile, traditional IT services vendors are leaning on the importance of digital to engage top-brass executives and expand into upstream revenue more strategically. This all comes alongside the Big Four accounting firm’s efforts to exert dominance across end-to-end services capabilities.

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This razor focus on value also forces consulting service providers to repair delivery inefficiencies. Everest Group’s Digital Transformation Consulting Services PEAK Matrix® Assessment found that costly engagements with large management consulting houses are not perceived as delivering sufficient value by most stakeholders, and organizations are receptive to working with IT service providers that have a stronger technical focus.

However, IT service providers who excel in technology expertise may fall short in delivering domain or industry expertise. The study showed clients were 10 percent less satisfied with providers’ domain/industry expertise than their technical expertise.

Winning in the “Value Market” will require consulting service providers to deliver well-rounded engagements supported by forward-thinking talent at effective price points that bring technical and domain prowess.

Sustainability will be the next game-changer in consulting

As businesses become more conscious of their environmental impact, many seek consulting services to help them develop and implement sustainable practices. Everest Group research found four out of every 10 Environmental, Social, and Governance (ESG) engagements are focused purely on consulting elements. As this trend is expected to accelerate in the coming years, sustainability will likely become a key driver for growth in the consulting industry.

Consultants are uniquely positioned to help clients navigate the complex sustainability ecosystem, working with diverse partner segments such as rating agencies, global standards organizations, data and reporting vendors, as well as independent software vendors (ISVs) and original equipment manufacturers (OEMs.) To capitalize on this trend, consulting firms need to invest in creating industry and function-focused expertise on sustainability. This includes building teams with deep domain knowledge in areas such as carbon accounting, circular economy, and ESG reporting.

Looking ahead, the consulting industry is expected to continue to undergo significant change, driven by macroeconomic conditions, digital predicaments, and sustainability. Consulting firms today must have a deep understanding of their client’s business processes, operations, and priorities. Providing customized solutions that produce measurable results will be crucial to thrive in this rapidly evolving environment.

To discuss digital transformation consulting and digital strategies, contact [email protected], [email protected], and [email protected].  Stay tuned for our perspectives on generative Artificial Intelligence’s impact on the digital transformation consulting market.

Don’t miss our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences, to learn how enterprises can leverage Generative AI to unlock business value and about current use cases.

Generative AI in Consulting: A New Era of Strategic Decision-Making and Digital Transformation | Blog

Generative Artificial Intelligence (GAI) has the potential to revolutionize strategic decision-making and consulting. With its power to simulate business scenarios and generate comprehensive data, GAI can be a game-changer for functions ranging from client onboarding to performance tracking. This blog explores the profound implications of GAI in digital transformation consulting, highlighting opportunities and addressing ethical and regulatory considerations.

To learn about use cases and the potential of GAI technology watch our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

Consulting firms are embracing innovative approaches like GAI to enhance their value propositions. By combining human intelligence with AI capabilities, GAI is a ground-breaking technology offering an exciting future.

While challenges and investments are inevitable, early adopters stand to gain immense rewards, accelerating the industry’s growth and gaining a competitive advantage in the digital era. Continue reading to learn more.

Unleashing the power of GAI for strategic decision-making

By leveraging GAI’s capabilities, consultants can navigate the intricate labyrinth of available data, making sense of multifaceted patterns and trends that would otherwise remain elusive. With its proficiency in generating data and simulating varied business scenarios, GAI can offer strategic insights that can bolster enterprises’ decision-making processes.

These potential benefits are presented in a 3×3 framework that maps the impact and adoption rate of GAI in various consulting processes, as illustrated below:

Mapping the impact and adoption timeline of generative AI in consulting

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Navigating ethical and regulatory considerations

As we venture into the GAI era, maintaining ethical and regulatory balance is critical.

The following four considerations must be taken into account:

  • Data privacy – As GAI models rely heavily on data, sensitive data could be involved in a consulting scenario. Consulting firms must ensure robust data governance policies and procedures are in place, including measures for data anonymization and secure data handling
  • Algorithmic bias – GAI models, like all machine learning models, are only as good as the data they are trained on. If the training data contains biases, the AI models can inadvertently perpetuate these biases, leading to unfair or even harmful outcomes. To mitigate this, consulting firms must regularly audit their AI models, ensuring any biases are detected and corrected promptly
  • Transparency or explainability – Clients may mistrust AI’s opaque decision-making processes, especially when AI’s recommendations significantly influence strategic decisions. To overcome this challenge, consulting firms should consider adopting explainable AI technologies that can shed light on the “black box” of AI, enhancing transparency, and building trust
  • Regulatory compliance – Adhering to varying regulatory standards across regions and sectors is imperative. Consulting firms need a comprehensive understanding of relevant regulations in their jurisdictions and ensure that they adhere to these rules

The future of consulting

Integrating GAI into consulting can reshape the industry’s skill requirements, service offerings, and delivery models. The table below provides a glimpse of these potential changes:

Aspect Current State Potential Future State with Generative AI Key Changes Business Impact
Skills Heavily relies on human expertise and analysis Greater need for data science, AI expertise, and understanding of AI in a business context Development of AI-related skills, especially understanding and managing AI tools and algorithms Enhanced data-driven decision-making, improved efficiency, more sophisticated analysis
Service offerings Traditional consulting services Expanded offerings with AI-powered services such as predictive analysis, scenario generation, and more Addition of AI-enabled services to the portfolio, transforming the way consulting services are designed and delivered Diversified service offerings, potential for new revenue streams, increased competitive edge
Delivery models Primarily human-led engagements Hybrid model with AI augmenting human-led engagements, enabling more efficient and impactful delivery Transition to a blended delivery model with AI augmenting human expertise, redefining the consultant’s role Increased client engagement, enhanced value delivery, improved scalability
Client engagement Personalized, but time-intensive interactions AI-enabled personalization in real-time, optimizing engagement through AI tools like chatbots and virtual assistants Implementation of AI in client interactions, necessitating changes in engagement strategies Enhanced client experience, real-time interactions, increased client satisfaction
Data analysis Manual data analysis, potentially time-consuming and error-prone AI-powered data analysis, providing accurate insights quickly and efficiently Incorporation of AI tools in data analysis, enhancing speed and accuracy Improved insights, faster decision-making, increased reliability
Business development Traditional methods of identifying and pursuing opportunities AI-enabled opportunity identification and business development strategies Adoption of AI tools for market research, lead generation, and opportunity analysis More effective business development, increased revenue, higher growth potential
Continuous learning and improvement Based on feedback and personal experience AI-facilitated learning from every engagement, leading to continuous improvement of consulting methodologies Integration of AI tools for feedback analysis and learning, enhancing the consulting approach Continual improvement, staying relevant, higher client satisfaction

Positive outlook for generative AI in consulting

Adopting GAI in consulting can be a game-changer, offering a competitive edge and driving industry growth by delivering superior value. However, remember these potential rewards are not without risks. Firms must consider the significant investments in technology and skills development, as well as navigate the intricate ethical and regulatory landscape.

As digital transformation consulting firms set sail on their journey to integrate GAI into their operations, proactively understanding and adopting these advanced technologies can set them apart. By navigating the challenges and capitalizing on the immense potential of Generative AI in consulting, firms can seize the opportunity to lead in this new era of digital transformation consulting.

To discuss Generative AI in consulting, contact [email protected], and mailto:[email protected].

Don’t miss our virtual event, The Possibilities for Generative AI in Sourcing, to learn about opportunities to integrate generative AI into sourcing processes.

Generative AI in Clinical Development: Unlocking Possibilities and Addressing Challenges | Blog

The prospects for Generative AI in clinical development look encouraging. GAI can help speed drug development, improve protocol design, personalize treatment, and bring other benefits. However, the industry must be aware of the risks and responsibly operate GAI. Read on to learn more.

Contact us directly to discuss this topic further.

Given the lengthy, labor-intensive, and expensive process of bringing a new drug to market and conducting a clinical trial, clinical development stakeholders are constantly searching for technological solutions to automate workflows, streamline operations, reduce site and patient burden, and accelerate trial timelines.

Leveraging cutting-edge technology, Generative Artificial Intelligence (GAI) may unlock new horizons and revolutionize the entire clinical development value chain. Let’s explore this further.

GAI works by training underlying large language models (LLMs) on huge datasets containing billions of parameters. For example, ChatGPT is built on GPT-3, a model trained with over 175 billion parameters, far exceeding prior LLMs.

GAI outperforms conventional AI by not only analyzing and interpreting existing data but also generating text, images, audio, video, and other content. The latest innovation, GPT-4, takes this a step further by introducing multi-modality, allowing it to process non-text inputs like images and generate high-quality outputs.

The potential for generative AI in clinical development 

In the clinical development field, the opportunities with GAI are simply too big to ignore. It has the power to accelerate drug development, enhance patient engagement, improve protocol design, personalize treatment approaches, generate synthetic data, and much more.

Let’s take a closer look at the potential applications in clinical development to better understand the role that GAI can play in this domain:

  • Patient recruitment and screening: By scanning a plethora of health and medical records, GAI can reduce the recruitment funnel and better identify suitable patients for clinical trials. The outcome of these interactions can be fed to a validated digital biomarker for the desired indication. This, in turn, can accelerate patient recruitment and improve diversity ratios
  • Synthetic data generation: Synthetic datasets that closely resemble real-world patient data help researchers with limited data or no control group in randomized controlled trials (RCTs). GAI can help conduct simulations, test hypotheses, and accelerate the time to market. With the regulatory push in favor of synthetic control arms, GAI can be instrumental in generating synthetic data
  • Protocol authoring: GAI can optimize the process of authoring protocols for clinical trials. It has the ability to scan through vast amounts of scientific literature, past trial histories, and databases and generate insights on the appropriate endpoints, dosage, patient population, treatment arms, and analysis procedures
  • Patient engagement: AI-driven by LLMs can analyze patients’ medical history data and preferences and create personalized content. GAI-powered digital avatars can significantly improve patient engagement by providing personalized communication and educational information that ensures patients are included in the process and remain engaged and informed
  • Real-time decision-making: During a trial, GAI can continuously monitor patient conditions (through data coming from wearables and sensors) and provide real-time support to investigators and researchers. This may entail preventive interventions, dosage modifications, improving medication adherence, and early detection of adverse events
  • Study Data Tabulation Model (SDTM) transformations: SDTM involves mapping clinical data to a standard structure for regulatory submissions. GAI can analyze data from multiple sources and generate mappings that meet SDTM standards. This use of GAI would provide validation and quality controls, and automate repetitive tasks, expediting the whole process

Figure 1: Prominent use cases and the potential impact of Generative AI in clinical development in the near future

While GAI is generating buzz across industries, like any new technology, its benefits come with challenges. Pharmaceutical enterprises must be aware of the following risks and biases so they can be prepared to address them:

  • Data quality and bias: GAI relies on the quality of training data for generating meaningful outcomes. The data used to train the models can have biases that can lead to disparities in patient recruitment and treatment recommendations. New AI models may not be well-suited to handle diverse languages, dialects, and cultures effectively
  • Data security and privacy: A crucial consideration for using Generative AI in clinical development is patient data security. Ensuring compliance with the ambiguous regulations for digital technologies and AI in clinical development is complicated. AI models should comply with existing regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) and be prepared for potential upcoming ones
  • Ethical considerations: The use of GAI raises ethical questions, especially in using patient information to train the models. Patient data must be handled with strict confidentiality, and consent must be given at all times before using it to train any model
  • Accountability: GAI cannot be held accountable for inaccurate treatment recommendations or a mistake in the protocol design. Completely relying on automated systems raises liability concerns and makes determining accountability challenging
  • Sustainability: GAI models are trained on billions of parameters that require extensive computational resources often managed at large-scale data centers. As the use of GAI models and queries continues to grow, the carbon footprint also rises

Over-reliance on AI models without human intervention for decision-making can lead to unwanted consequences. Domain experts, clinicians, and researchers must be present to validate the outputs and use GAI responsibly while having minimal environmental impact.

Technology advances have always brought disruptions, and GAI is no exception. GAI can revolutionize data management processes and become an invaluable tool in clinical development. By better understanding its risks and biases, pharmaceutical enterprises can use GAI responsibility and reap its full benefits – making GAI’s future in clinical development look promising.

To discuss the future of Generative AI in clinical development, contact Anik Dutta, Nisarg Shah, and Madhur Kakade.

Learn more about the use cases and potential of GAI technology in our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

Four Preparation Activities Companies Are Taking Now for the Looming Recession | Blog

The first quarter of this year was a story of an economy that was slowing but still progressing. The story suddenly turned negative in mid-April. At that time, we at Everest Group detected a significant step back in customer sentiment with regard to third-party services and business technology services spend.

A broad number of industries collectively seem to have come to the conclusion that we are, in fact, heading into recession and that it’s time to start changing plans and budgets to reflect a recession mentality. Consequently, CIOs and CTOs are taking a look at their responsibilities and dividing them into four specific agendas.

Read more in my blog on Forbes

Capabilities Necessary For Evolving Operational Platforms | Blog

Today, most companies are in the process of assembling digital operations platforms or are in the process of evolving them. Software-defined operations platforms enable companies to integrate technology and services so they can operate differently and better compete in the marketplace. These platforms become differentiators and create new value.

They also create a more intimate, dynamic relationship between the tech stack and business operations. I blogged often in the past few months (here, for instance) about operations platforms. The platforms’ constantly evolving nature requires continual investment in maintaining the platform components as they evolve. Operations platforms also have huge requirements for engineering and IT talent.

Read more in my blog on Forbes

Decoding the Generative AI Buzz in Life Sciences | Blog

Advances in Generative Artificial Intelligence (GAI) have sparked interest in its potential to drive growth and innovation in the biopharma and medical devices industries. Despite challenges and regulations, the life sciences industry is actively exploring GAI’s possibilities. Learn about the current state of Generative AI adoption, the supplier landscape, and proactive actions stakeholders should take to stay at the forefront of this technology. 

Contact us for questions or to have a discussion.

Life sciences, just like all other industries, is actively seeking to understand the intricacies of Generative AI (GAI) to gain a competitive edge. Enterprises in this industry are gearing up to embrace this generational shift in AI-enabled technology. Continue reading for the first part of our series on Generative AI adoption.

GAI is a type of machine learning that uses neural networks to learn patterns in the input data. Based on the input data it was trained on, GAI then generates the most appropriate response. GAI’s promise of delivering significant operational and tactical benefits in the short term and hyper-personalization and intelligent decision support over time is pushing life sciences enterprises to evaluate their Generative AI adoption readiness.

Although GAI can potentially disrupt the life sciences technology ecosystem in many significant ways, navigating the various risks and challenges that come with its implementation in this highly regulated industry will be critical.

Generative AI adoption outlook

Let’s take a look at the potential impact of GAI on the life sciences value chain:

  • Building on the solid AI foundations in place, GAI is expected to have the greatest impact on the areas of drug discovery and research, and sales and marketing
  • As organizations prioritize customer experience and new AI-based products emerge, the sales and marketing function is adopting GAI at a solid rate compared to the previous generation of AI applications
  • Due to its huge potential across the development lifecycle – from novel design ideation to final prototyping – GAI is expected to impact medical device design and development, in addition to impacting R&D and commercial functions
  • The biopharma and medical devices value chains have not yet fully embraced GAI because tools/GAI-based solutions trained on good quality LS-specific datasets are limited

The graphic below explores the short and long-term impact of GAI on various life sciences functions:

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GAI can be used to model certain aspects of drug discovery. Some prominent cases include Insilico Medicine’s GAI platform Chemistry42 which generates ideas for novel chemical compounds, and AstraZeneca’s transformer-based model MegaMolBART for reaction prediction, molecular optimization, and de novo molecule creation.

As life sciences enterprises look to unlock GAI’s true value, its various stakeholders have exciting opportunities to collaborate and form next-generation partnerships to successfully drive GAI implementation. The supply ecosystem across the GAI technology stack is illustrated below:

Picture2 4

The following stakeholders have key roles in GAI’s future:

  • Pharmaceutical and medical device enterprises: Enterprises such as Pfizer and Eli Lilly are partnering with independent software vendors to co-develop GAI solutions focused on enhanced efficiency, cost optimization, faster drug discovery, and remote patient monitoring while maintaining compliance with regulations
  • Independent Software Vendors (ISVs): These firms offer highly specialized (pharma value chain element and/or technology-specific) productized GAI-enabled tools and have a wealth of domain expertise (cheminformatics, bioinformatics, genomics, etc.). Some players include Iktos, Yseop, and Huma.AI
  • Hyperscalers: Cloud vendors have built AI/Machine Learning (ML)-specific modules for highly specialized functions such as omics analysis, high-performance computing (HPC) workload optimization, and knowledge graphs. Hyperscalers also offer a comprehensive suite of connectors and services to enable pharmaceutical companies to work with complex datasets
  • IT service providers: Leveraging industry expertise and domain knowledge, these providers offer consulting services, training, and support. They also develop and deploy GAI solutions to pharmaceutical enterprises and ISVs

These stakeholder groups are uniquely positioned to act as catalysts for Generative AI adoption. The exhibit below looks at the actions each group should take to move forward with GAI and the implications:

Picture3 2

Stay tuned for the second part of this blog series, where we will examine the most promising use cases in each area of the life sciences value chain, including their potential risks. We will also present a blueprint to successfully maximize the value of GAI-enabled solutions.

To discuss the future of Generative AI in life sciences, contact Kumar Dhwanit, or Rohit K.

Keep an eye out for our LinkedIn Live, The Possibilities for Generative AI in Sourcing.

Generative AI Heralds a New Era in Cybersecurity | Blog

In today’s ever-evolving threat landscape, generative Artificial Intelligence (GAI) is becoming an increasingly popular technology tool to defend against sophisticated cyberattacks. Read on to learn about the latest investments in GAI-powered security products, the potential benefits and drawbacks, and the ramifications for the cybersecurity workforce and industry. 

Learn about the latest pricing trends in cyber security in our webinar, Cyber Resiliency Strategy: Key Themes and Pricing Trends for 2023.

GAI has grabbed worldwide interest with its ability to create unique and realistic images, text, audio, code, simulations, and videos that previously were not thought to be possible. Lately, GAI has been applied in many industries, such as the creative arts, healthcare, entertainment, and advertising. Let’s explore the latest cybersecurity industry trends and how GAI can help security teams stay one step ahead of the latest threats.

Cybersecurity vendors are leaving no stone unturned to deliver the power of GAI

In recent years, advanced Artificial Intelligence (AI)- and Machine Learning (ML)-based technologies have been rapidly adopted across the cyber industry, providing intelligent automation capabilities and also augmenting human talent.

The vast use cases of AI/ML in cybersecurity include proactive threat detection, prevention, intelligence, user and entity behavior analytics (UEBA), anomaly detection, vulnerability management, automated incident investigation and response, and more.

With the release of ChatGPT (GPT-3.5/GPT-4), DALL-E, Midjourney AI, Stable Diffusion, and other developments, the hype around GAI is accelerating faster than ever, and vendors are racing to harness its power to develop new products and solutions leveraging this technology.

Key GAI vendor announcements

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Here are some examples of suppliers adopting GAI technology in the past four months alone:

  • SlashNext launched Generative HumanAI, an email security product aimed at combating business email compromise (BEC), in February
  • Microsoft introduced Security Copilot, a solution to help security professionals identify and respond to potential threats using OpenAI’s GPT-4 GAI and Microsoft’s proprietary security-specific model, in March
  • Flashpoint expanded its partnership with Google, incorporating GAI into its intelligence solutions for improved threat detection in April
  • Among other announcements last month, Recorded Future integrated OpenAI’s GPT model into its AI, Cohesity integrated with Microsoft’s Azure OpenAI for anomaly detection, and Veracode developed a tool utilizing GAI to address security code flaws

Generative AI captured massive attention at RSAC

At the recently concluded RSA Conference 2023 in San Francisco, GAI was a fascinating theme that was widely discussed and showcased in many innovative security products. These include SentinelOne’s announcement of Purple AI, which will leverage GAI and reinforcement learning capabilities to not just detect and thwart attacks but also autonomously remediate them.

Also at the event, Google Cloud launched its Security AI Workbench powered by a security-specific large language model (LLM), Sec-PaLM, aimed at addressing the top three security challenges – threat overload, toilsome tools, and the talent gap. The offering incorporates VirusTotal Code Insight and Mandiant Breach Analytics for Chronicle to augment efforts to analyze incidents and detect and respond to threats.

Foreseeable advantages stemming from GAI in the cybersecurity world

The advantages of using GAI for this industry can include:

  • Enhancing threat and vulnerability detection, response, and automated remediation

Its ability to analyze enormous amounts of data and insights from multiple sources enables GAI to detect malicious or anomalous patterns that otherwise might go unnoticed. This can lower alert fatigue and improve the mean time to detect or discover (MTTD), mean time to restore (MTTR), and threat coverage, and enhance overall risk management strategies while reducing total security operations costs. GAI can be employed for machine-speed triaging, predictive remediation, and automated response and action for low-risk incidents. Other potential applications are leveraging the technology to detect malicious URLs and websites and AI-powered phishing campaigns run against enterprises. Furthermore, it can be utilized in Infrastructure as a Code (IaaS) security for detecting and hardenings flaws and for auto-remediation of security misconfigurations and vulnerabilities in applications.

  • Bridging the cybersecurity talent gap

The cybersecurity skills shortage is widely recognized, with enterprises finding it daunting to hire and retain talent to effectively run internal programs. More than 3.4 million skilled cybersecurity professionals are currently required globally, according to the 2022 (ISC)² Cybersecurity Workforce Study.

GAI can create phishing/cyberattacks and stimulate threat environments or security awareness programs to test security professionals’ skills and knowledge, accelerating the learning curve and quickly upskilling and reskilling employees. The technology also can be applied to generate automated workflows, playbooks, use cases, and runbooks for enhanced security delivery capabilities.

  • Powering virtual assistance, enhanced collaboration, and knowledge sharing

GAI can lessen the burden on analysts of mundane tasks by analyzing, visualizing, and summarizing complex security data into comprehensive reports and charts that previously were created manually. GAI also can help build robust assistants for coding, chat, security, or investigation. It potentially can facilitate effective communication, and serve as a centralized knowledge repository, making it easy to share and manage data from one place. This can help enterprises augment knowledge management and foster a culture of continuous learning and engagement.

Watch out for offensive capabilities of GAI in cybersecurity

Major companies, including Apple, Samsung, Amazon, Accenture, Goldman Sachs, and Verizon, have either banned or restricted employees’ use of GAI-powered utilities to safeguard data confidentiality. Data breaches are a primary risk associated with GAI. Models use massive data sets for learning, and that data could contain enterprises’ sensitive information including Personal Identifiable Information (PII) and financial data. If carelessly handled, it could lead to unauthorized access, unintended disclosure, misuse, and even IP or copyright infringement. GAI also exposes enterprises to regulatory compliance risks, especially those subject to strict data protection laws like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), the California Consumer Privacy Act (CCPA), etc.

The use of GAI for malicious practices in social engineering, spear phishing, and other scams has been on the uptick. Another potential offensive aspect is that GAI can be employed to create advanced malware strains capable of evading signature-based detection measures.

Malicious actors could use GAI to create sophisticated exploits and other invasive codes to bypass security systems and exploit vulnerabilities in touchpoints. Considering its power to generate new content, brute-force attacks for password theft can be easily facilitated via GAI.

In addition, hackers can utilize deepfake technology to impersonate individuals, leading to identity theft, financial fraud, and the proliferation of misinformation. The efficiency and accuracy of an ML-based security system can be sabotaged if a hacker automates the creation of false positives, wasting analysts’ time and resources while ignoring the real threat.

GAI – A boon or bane?

In the words of Abraham Lincoln, “The best way to predict the future is to create it.” GAI is doing just that. The heavy investments in GAI are a double-edged sword. While the technology can strengthen enterprises’ cyber shield arsenal, adversaries can use it to thwart their defensive attempts. GAI is here to stay and its adoption will accelerate even with security threats, making it pressing for cyber leaders to quickly determine their response and adoption strategies.

Cyber leaders may find a path to expand their roles and become protectors of enterprises by actively taking actions to address GAI’s use. These proactive initiatives can include robust data loss prevention and governance; usage guidelines, policies, and frameworks; workforce education; thorough vulnerability and risk assessments; comprehensive identity and access management; and incident detection and response plans.

Everest Group will continue to follow this growth area. To discuss cybersecurity industry trends, please contact Prabhjyot Kaur and Kumar Avijit.

Continue learning about cybersecurity industry trends in the blog, Now is the Time to Protect Operational Technology Systems from Cyber Risks.

Exploring the Potential of Generative AI in the Life Sciences Commercial Technology Landscape | Blog

As the life sciences industry shifts from a traditional model to a hybrid commercial model, Generative Artificial Intelligence (GAI) can potentially be a valuable tool for commercial functions ranging from customer support to lead generation. Read on to learn about the investments providers are making in GAI and leaders’ viewpoints when it comes to embracing this technology.     

Watch the webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences, to learn more about how enterprises can leverage GAI to unlock business value.

Introduction of Generative AI in the life sciences commercial function

While Artificial Intelligence tools increasingly are being used across all industries to revolutionize customer engagement and drive business success, life sciences enterprises historically have been slow to adopt emerging technologies.

However, the life sciences industry is evaluating the potential impact of GAI for commercial functions. Let’s explore whether it will reshape the commercial technology landscape or if GAI will succumb to the inherent risks and challenges present in the life sciences industry.

The latest evolution in AI technology, GAI can create unique content in the form of text, images, audio, graphics, code, and more, in response to given prompts within seconds. Its versatile applications have captured widespread attention, with venture capitalists investing US $2.6 billion in 110 GAI-focused startups in the US last year alone.

One of the noteworthy demonstrations of GAI is the Chat Generative Pre-Trained Transformer (ChatGPT), launched by OpenAI, which has gained significant attention and received substantial investments, including a recent funding round of US$ 2 billion in January 2023.

The pandemic has transformed the life sciences industry’s commercial model, shifting from traditional in-person interactions to a hybrid approach that combines traditional and digital channels.

To attain a competitive edge, life sciences enterprises are prioritizing delivering hyper-personalized experiences to the end user. As a result, enterprises are prioritizing investments in data analytics and AI tools and are seeking domain-centric solutions over industry-agnostic solutions.

The commercial function serves as the customer-facing function for enterprises by engaging customers across multiple channels, potentially making GAI a highly valuable tool across the commercial value chain with a diverse range of applications and use cases.

Potential use cases of Generative AI in the life sciences commercial function

Enterprises’ primary focus is optimizing their commercial processes by leveraging AI tools to analyze large amounts of data, identify patterns, and generate actionable insights for the commercial function, thus driving business growth. However, given the strict industry regulations, human intervention/oversight remains essential for the overall usage of GAI.

Exhibit 1 illustrates the key use cases that enterprises are striving to unlock in the near term.

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Voice of the life sciences industry in adopting GAI tools in the commercial function

As enterprises explore the range of capabilities offered by GAI, the industry reaction is mixed. Some leaders express skepticism about the accuracy of the information generated, while others are optimistic about leveraging GAI tools to revolutionize customer engagement.

Most leaders believe the current state of GAI tools is not fully ready for adoption. But they anticipate it will play a pivotal role in the future in delivering a seamless omnichannel experience (integrating tools on chat, email, social media channels, etc.) and delivering personalized content (relevant content to customer persona). This will make GAI an integral tool to ease the ongoing transition to a hybrid commercial model.

Exhibit 2 highlights the various perspectives shared by industry leaders about using GAI tools in the commercial function of the life sciences industry.



Please note: As ChatGPT is the most utilized GAI tool, we observe enterprises discussing it more, and over time, we will see a clear distinction between GAI tools and ChatGPT.

Recent activities fostering the adoption of Generative AI in the life sciences

Despite concerns raised by industry leaders, GAI-based tools hold significant potential for delivering compelling commercial benefits in the near term. With continuous technological advancements and extensive training of tools on diverse and reliable life sciences data models, these tools can provide enhanced support for the commercial function.

Life science commercial technology providers and services providers have made the following investments to kick off their GAI journeys:

  • Salesforce launched Einstein GPT, a GAI Customer Relationship Management (CRM) technology that delivers AI-generated content across various interactions, including sales and marketing functions at large scale
  • Veeva has integrated a new AI tool specifically tailored for pharmaceutical sales representatives into its platform. This tool enables sales reps to obtain precise information about physicians or hospital practices, empowering them to personalize their pitches
  • Axtria integrated GPT models into its proprietary products. For example, Axtria DataMAx, a cloud-based commercial life sciences data management product, leverages GPT to drive efficiency and productivity
  • Doximity, an online networking service for medical professionals, introduced DocsGPT, which leverages GAI to streamline healthcare personnel’s communication by addressing product-related inquiries, aiming to reduce reliance on sales representatives
  • ZoomRx, a strategic healthcare consulting company, has developed the Ferma platform adopting GAI to analyze data from medical conferences, benefiting pharmaceutical companies’ medical affairs functions. Some of its clients include Amgen, AstraZeneca, Biogen, and Merck
  • Microsoft has integrated ChatGPT into Azure to develop new GAI-based tools. In this collaboration, Microsoft brings its expertise in areas such as natural language processing (NLP), computer vision, and reinforcement learning
  • Cognizant launched the Cognizant Neuro®️ AI platform to assist enterprises in deploying GAI at enterprise scale

Enterprises are widely adopting GAI tools with ongoing efforts to address and resolve concerns related to privacy, potential racial bias in training data, and regulatory compliance.

Stay tuned for the second part of this series on generative AI in life sciences, where we will delve into the challenges enterprises face in adopting GAI tools, analyze the supply landscape, and share Everest Group’s perspectives on the roadmap for tool adoption.

To discuss generative AI in life sciences, contact [email protected], [email protected], and [email protected].

Continue learning about GAI in the webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

Cognizant Conveys its Commitment to Growth to Analysts at Inaugural Event with New Leadership Team | Blog

At its first analyst event under the leadership of CEO Ravi Kumar, Cognizant openly discussed the company’s past problems, emphasized its renewed focus on relationship management, shared clients’ success stories, and previewed new products. Read on for reflections based on the Everest Group team’s interactions with Cognizant leaders at the event.  

Contact us directly for questions and or more information.

After a challenging past six years, the recent Cognizant event highlighted the company’s commitment to growth and improvement. The new leadership team demonstrated its awareness of issues that need repair and reinforced the company’s strong focus on bringing its core differentiator, relationship management, to the forefront. Compelling client success stories with renowned organizations like US Bank and Bristol Myers Squibb also were shared with the analysts and advisors who attended the event.

The context for the event was significant given the company’s struggles in recent years involving an activist investor followed by a slow growth period precipitated by misaligned priorities. Despite these issues, Cognizant ranked sixth in Everest Group’s latest version of the flagship leaderboard of global IT organizations – PEAK Matrix Service Provider of the Year 2023.

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Even with the remaining issues that need to be fixed, the company has sound fundamentals. Here are our takeaways from the main points we heard from Cognizant’s leadership at the event:

  • Self-awareness and commitment to improvement

The company’s self-awareness of the challenges it faced and its commitment to addressing them was a key theme that emerged from Cognizant’s analyst event. The provider acknowledged how several issues had impacted its performance and reputation in recent years. Rather than shying away from these concerns, it displayed a refreshingly transparent approach, recognizing the need for change and outlining specific actions to address the identified areas for improvement. This commitment to self-improvement demonstrates Cognizant’s dedication to delivering exceptional client experiences and driving sustainable growth.

  • Bringing relationship management to the forefront

Cognizant has long been recognized for its deep client relationships, which have been instrumental to its success over the years. The company emphasized the importance of relationship management as its core differentiator. Cognizant showcased a renewed focus on nurturing and strengthening these relationships, leveraging its vast expertise, industry knowledge, and client-centric approach. By reinforcing the significance of strong client partnerships, Cognizant appears to be picking the right battles.

  • Compelling client stories: US Bank and Bristol Myers Squibb

Cognizant shared inspiring client success stories that showcased its ability to drive innovation and create value for its clients. One notable example was its collaboration with US Bank, where Cognizant leveraged its digital transformation expertise to help the bank enhance its customer experience, streamline operations, and drive cost efficiencies. Cognizant’s partnership with Bristol Myers Squibb was another example shared. Cognizant supported the global biopharmaceutical company in leveraging advanced analytics and data-driven insights to accelerate drug discovery and development, leading to improved patient outcomes. These successes served as compelling examples of Cognizant’s ability to deliver tangible business results through technology-driven solutions.

  • Stability in leadership

A crucial factor contributing to the sense of stability and confidence at the analyst event was Cognizant’s leadership team. Along with Kumar, the other executives speaking at the event included Surya Gummadi, Prasad Sankaran, and Ganesh Ayyar. The leadership team’s steady guidance has played a pivotal role in steering Cognizant through transformation and growth. Analysts and attendees noted leadership’s openness in addressing concerns and the confidence they exuded in their ability to guide Cognizant.

Lastly, Cognizant gave the community a preview of recent offerings such as Cognizant Neuro AI, its new, enterprise-wide platform designed to provide enterprises with a comprehensive approach to accelerate the adoption of generative Artificial Intelligence (GAI) technology.

By acknowledging areas for improvement and demonstrating a transparent and determined approach, Cognizant conveyed its commitment to growth and delivering exceptional client experiences. The emphasis on relationship management as its key strength reinforced the company’s focus. As analysts, we will closely scrutinize Cognizant’s progress in these areas and offer insights to buyers and investors in IT and Business Process Services.

Everest Group was represented at this event by CEO Peter Bendor-Samuel, and Partners Abhishek Singh, Achint Arora, Manu Agarwal, Ronak Doshi, and Shirley Hung. Contact this team with questions about IT and BP services markets, enterprise buying trends, and the role of vendors. Reach out to contact us.


From left, Ronak Doshi, Shirley Hung, Abhishek Singh, Ravi Kumar, Achint Arora, Peter Bendor-Samuel, and Manu Aggarwal

Watch our webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences, to learn why leading providers are entering the market with significant investments.

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