HFS Horizons Report

Contents

1. Introduction, the HFS value chain, and market dynamics
2. Research methodology
3. Demographics and customer and partner data
4. Horizons results: Generative Enterprise™ services, 2023
5. Publicis Sapient profile: Generative Enterprise™ services, 2023
6. Further reading
7. HFS Research authors

1. Introduction, the HFS value chain, and market dynamics

Introduction

• This HFS Horizons: Generative Enterprise™ Services, 2023 report is the industry’s first competitive analysis of professional services firms and the value they create with enterprise clients adopting and experimenting with generative AI (GenAI) technologies.
• HFS’ Generative Enterprise™ articulates the pursuit of AI technologies based on large language models (LLMs) like ChatGPT and GPT-4 to reap huge business benefits for organizations in terms of continuously generating new ideas, redefining how work gets done, and disrupting business models steeped in decades of antiquated process and technology.
• The report examines and assesses 35 service providers. Our research evaluates the providers’ capabilities to understand the Why, What, How, and So What of their Generative Enterprise services offerings.
• This report also includes detailed profiles of each service provider, outlining their provider facts, strengths, and development opportunities.
• We based this Horizons research on briefings from each of the participants along with publicly available announcements, datapoints, and case studies. We began with an open call for participation, resulting in unprecedented participation levels. We requested each participant to provide customer and partner references, and we have used their responses and supplementary customer reference data to ensure the voice of the customer impacts the outcomes. Where a service provider chose not to brief us, we created profiles and scores from publicly available announcements, case studies, data, and our regular conversations with leaders across functions and industries.

Executive summary

1. The generative AI gold rush is on in pursuit of a $7 trillion prize
   We’ve never seen a technology adopted so quickly. Generative AI’s (GenAI) poster child, ChatGPT, reached 100 million users in two months. RPA took more than a decade to reach 15 million. Every boardroom is asking every CEO, “What are you doing with GenAI?” This bottom-up and top-down demand and the promise of a $7 trillion prize has prompted a gold rush among service providers as they hurry to organize and claim a piece of the action. In a matter of months, leading systems integrators and consultancies have conjured up new practices, divisions, platforms, and partnerships. They are scaling up, investing billions, training thousands of people, and recruiting thousands more—and this journey is only just beginning.
2. Point solutions dominate, but this is not where we will end up
   Already, we are witnessing a rapid diversion of AI budgets to GenAI projects. On average, this stands at 41% across the enterprises surveyed for this report, but we expect that to grow as enterprises move beyond their initial point solutions in POCs and pilots. Most are solving specific tasks. And as the next cycle of budget-making begins, we expect budgets to scale up to take GenAI deeper into end-to-end processes, shaping new ways of working. The next step will be harder but more rewarding, and if it doesn’t happen, there are going to be a lot of red faces among service provider leaders, many of whom have gone all-in on GenAI.
3. The disruption is coming first and fastest to CX, EX, and sales and marketing
   As part of our research for this report, we asked enterprise leaders the functions they are prioritizing for the application of GenAI. Customer experience (CX), employee experience (EX), and sales and marketing lead the way. This chimes with the case studies shared by service providers. Transforming code has been touted as a leading use case by many service providers, and it features prominently in their own internal use and in services they offer. But, in our own research, it has only appeared in around 10% of the case studies we’ve seen. A key thing to note regarding case studies to date is that many are proofs of concept and pilots, and few are coming with an ROI. At this stage in development, most enterprises are happy to see softer measures such as time-to-serve, CSAT, or time-to-market.
4. Knowing the tech is one thing; helping to transform with it is quite another
   Customers see a gap between how well their service providers deliver on tech implementation compared with their ability to transform business. It’s an important gap as enterprises seek help on their journey to the Generative Enterprise beyond the initial point solutions. Knowing the tech is one thing; helping transform ways of working because of the tech is another altogether. We think this gap will close as many service providers are going all-in on GenAI, focusing on proving the effectiveness of applying GenAI to their own ways of working first. The lessons they learn through self-transformation will give them the credentials to help enterprises shape their journeys.
5. This revolution is personal, and you need to get down and dirty with it
   Using GenAI tools is where your personal experience and understanding begin. This is your due diligence. The journey to the HFS Research Generative Enterprise is not easy, but it starts with your understanding. Leaders need to develop their GenAI muscle memory to begin seeing the future through today’s technology rather than persisting with a view constructed on their experience and knowledge of the technology of the past.

The new S-curve

• The Generative Enterprise inspires a new S-curve of value creation for the IT and business services industry. Traditional outsourced service areas are likely to lose momentum in favor of AI-led, data-driven services focused on driving growth and sustaining trust. Successful relationships cannot be effort-driven; they must be performance- and purpose-driven. Hybrid pricing with baked-in innovation funds will be critical to prove value. (see page 9)
• The GenAI gold rush is on: Leading IT and business services firms made 50+ GenAI-related announcements in just two months early in 2023. The primary focus of these was on expanding hyperscaler partnerships. (see page 10)
• The Generative Enterprise and its widespread application of GenAI and other forms of AI can successfully manage the Digital Dichotomy, balancing the macroeconomic Slowdown with the Big Hurry to innovate. (see page 15)
• Data and AI are like the stars of two galaxies colliding—the impact will destroy and create at an alarming rate. Without quality data, the benefits of AI will not scale, and to scale to the ecosystem, the data accessed will need to be increasingly private in nature. (see page 16)
• Stages of GenAI value creation for enterprises will be based on the nature of their data and the maturity of their technology. The impact of GenAI goes beyond productivity. (see pages 17-18)
• Roles GenAI will impact go beyond rote tasks, disrupting creativity and ideation.
• The road ahead is not easy. Page 20 lists 10 challenges every enterprise must consider.

Enterprises expect EX, CX, innovation, and productivity wins

• Employee experiences will improve — 56%
• Innovation velocity will increase — 53%
• Customer experiences will improve — 49%
• Overall productivity will increase — 48%
• Cost will decrease — 40%
• Revenue will increase — 36%
• Market valuation or share price will increase — 18%

The Generative Enterprise inspires a new S-curve of value creation

• Centralization and standardization
• Offshoring
• Nearshoring
• Lean and Six Sigma
• Tech augmentation
• DevOps
• Anywhere shoring
• RPA
• IDP
• Process mining
• ML
• Generative AI

• Operational scope
• IT infrastructure, ADM, contact center, and transaction processing
• 30%+ arbitrage-driven, upfront productivity
• 5%–10% year-over-year continuous improvements
• Improved business outcomes

• Additional 30%–70% productivity on operational IT and business scope
• Autonomous, data-driven decision making and exception processing
• Inclusion of creative activities enabling enterprise-wide end-to-end scope

End consumers’ ultra-rapid adoption of GenAI technologies drives business-to-business adoption

• Threads — 5 days
• ChatGPT — 2 months
  • Second-fastest ever to reach 100 million users
• TikTok — 9 months
• YouTube — 1.5 years
• Instagram — 2.5 years
• Facebook — 4.5 years
• Twitter — 5 years
• Spotify — 11 years
• Netflix — 18 years

• RPA users — approximately 15M by 2023
• ChatGPT users — 100M+ by 2023
• Timeline shown: 2012, 2022, 2023

Rapid consumer adoption makes GenAI a different disruption

• Rapid consumer adoption will drive B2B adoption — 59% strongly agree, 41% agree
• It democratizes innovation because billions of us can access it — 58% strongly agree, 42% agree
• It does not require huge up-front investments in exchange for a relatively quick ROI — 38% strongly agree, 48% agree, 14% disagree
• It is easy to use without special skills or training — 26% strongly agree, 61% agree, 13% disagree

Enterprise-grade and governed GenAI can transform the services that IT and business services can provide in this healthcare example

• Underwriting and product
  • Optimized benefits design
  • Dynamic, real-world, data-driven actuaries
• Sales and marketing and member management
• Provide data and network management
  • Personalized provider management
• Care and medical management
  • Chronic disease management
  • Care based on acuity and resource availability
  • Targeted service for under-served communities
• Claims and member services
  • Multi-modal services for members and providers
  • Provider appeals management
• Compliance and billing
  • Intuitive compliance planning and adherence

• Referrals and consultations
  • Enhanced diagnosis accuracy
• Pre-authorizations and admissions
• Ambulatory and acute care
  • Customized medical report
  • Personalized treatment plans
  • Enhanced surgical planning and practice
• Post-acute care and rehabilitation
  • Personalized post-acute-care support
  • Care based on acuity and resource availability
• Patient services and claims management
  • Multi-modal services for patients
  • Utilization reviews and appeals interactions with health plans
• Compliance and network management
  • Intuitive compliance planning and adherence

Leaders expect customer ops to create very high value in next 18 months

• Customer service, call center, or customer care — 47%
• Sales or business development — 43%
• Strategy setting (what to do) and implementation (how to get there) — 41%
• Supply chain — 33%
• Information technology — 26%
• Marketing, market research, communications, or PR — 24%
• Finance, accounting, treasury, or internal audit — 23%
• Manufacturing, production, operations, or service delivery — 23%
• R&D or new product and service development — 16%
• Sourcing and procurement — 9%
• Human resources, personnel, or benefits administration — 9%
• Legal, compliance, or regulatory affairs — 6%

Top three essential—yet lacking—skills for success in GenAI

• Problem solving — 47%
• Adaptability — 42%
• Critical thinking — 36%
• Communication and collaboration — 35%
• Understanding basic AI concepts — 34%
• Data security and privacy awareness — 31%
• Ethical awareness — 28%
• Data science, literacy in data analysis and visualization — 22%
• Domain knowledge (e.g., healthcare, finance, gaming) — 20%
• Development skills (e.g., software or prompt engineering) — 6%

The Generative Enterprise can successfully manage the Digital Dichotomy, balancing the macroeconomic Slowdown with the Big Hurry to innovate

• Enterprises face multiple macroeconomic headwinds and challenges assaulting their strategic goals.
• IT and operational budgets are tightening.
• The talent crunch will not improve unless we find a hidden continent under the ocean!

• Functional digital is Horizon 1. It is essential for survival.
• Enterprises realize the dire need to achieve their OneOffice™.
• Horizon 3, driven by OneEcosystem™, unfolds right before our eyes.
• The case for building a Generative Enterprise™ is stronger than ever.

Access to increasingly private data sets holds the key to redefining ecosystems, ways of working, and the value delivered

• Public
• Private enterprise
• Private individual

• Horizon 1: Functional digital
  • Additional productivity in operational IT and business scope
• Horizon 2: OneOffice™
  • Optimize enterprise value chains to drive superior experience
• Horizon 3: OneEcosystem™
  • Redefine ecosystems and change ways of working

• Many component parts
• Singular—sum of the parts
• (Text, speech, image, code, video)

GenAI could impact more than just productivity

• Current innovation focus — 9%
• Expected innovation focus in 2 years — 27%

• Current innovation focus — 29%
• Expected innovation focus in 2 years — 44%

• Current innovation focus — 63%
• Expected innovation focus in 2 years — 29%

Impact of GenAI across the three Horizons of enterprise innovation

• Exploit generative AI for competitive advantages and to potentially open new revenue streams
• GenAI is embedded into daily lives of consumers to influence and drive the way we live, work, and interact

• Integration of generative AI with traditional analytical AI and its application across the value chain
• Autonomous data-driven decision making and exception processing
• Inclusion of creative activities enabling enterprise-wide end-to-end scope

• Helpdesk operations (IT, employee, vendor, customer)
• Software development life cycle (coding, testing)
• Cybersecurity (threat hunting)

The role of data, OneOffice, and OneEcosystem in how GenAI will impact the enterprise beyond productivity

• Nature of data: Publicly available data
• Underlying technology: Modular generative AI (e.g., individual use cases across text, speech, image, code, video)
• Stakeholders: IT
• Type of impact:
  • Productivity from point solutions
  • Helpdesk operations (IT, employee, vendor, customer)
  • Software development life cycle (coding, testing)

• Nature of data: Publicly available data
• Underlying technology: Singular generative AI (e.g., integrated use cases across text, speech, image, code, video)
• Stakeholders: IT + business
• Type of impact:
  • Productivity from creative activities (e.g., writing, arts, visuals)

• Nature of data: Publicly available + Private enterprise data
• Underlying technology: GenAI integrated with other technologies
• Stakeholders: IT + business
• Type of impact:
  • Optimize enterprise value chains to drive superior experience
  • Autonomous data-driven decision-making and exception processing
  • Exploit GenAI for competitive advantages and potentially to open new revenue streams

• Nature of data: Publicly available + Private (enterprise + individual) data
• Underlying technology: GenAI integrated with other technologies (e.g., blockchain, quantum)
• Stakeholders: IT + business + strategy
• Type of impact:
  • Redefine ecosystems and change ways of working
  • GenAI is embedded into daily lives of consumers to influence and drive the way we live, work, and interact

An entire ecosystem around GenAI is unfolding right before our eyes

• Entertainment
• Productivity
• Other

• General productivity
• General and administrative
• Sales and customer support
• Marketing
• EPD, IT, security

• Law firms
• Creative
• Health
• Defense
• Agriculture and climate
• Construction

• Deploy and monitor
• Train and fine-tune models
• Use open-source models and frameworks
• Full-stack large language models
• Store and compute
• Label and process data
• Data warehouses or lakehouses
• Cloud service providers
• Hardware

The enterprise journey may be uncomfortable—10 reasons why

1. Most GenAI examples are not enterprise-centric, and only a handful of projects have reached production.
2. There is an arms race to build our foundational models with the crazy influx of capital. If the bubble bursts, the technology will be blamed.
3. More power is getting concentrated with hyperscalers. Enterprises are already frustrated with the oligopoly.
4. A brand-new ecosystem including Nvidia, Databricks, and start-ups is emerging. Enterprises don’t know how to navigate this.
5. Governance and integration will be key. Most data privacy laws are trying to block a black-box approach. Major legislation is looming, including the US AI Bill of Rights and the EU AI liability directive. Litigation is kicking in.
6. The FTC has opened an investigation into ChatGPT-maker OpenAI over potential harm it could cause and the company’s security practices.
7. Most GenAI use cases use public data. Getting enterprises to share private data will be challenging.
8. The singular focus on productivity is misleading.
9. Technology is continuing to evolve very rapidly, but enterprises are still struggling to adopt cloud!
10. The debate around AI’s carbon footprint is just getting started.

2. Research methodology

Service providers covered in this report

• Accenture
• Akkodis
• Amdocs
• Ascendion
• Bain & Company
• Brillio
• Capgemini
• Cigniti
• Ciklum
• Cognizant
• Deloitte
• Eviden
• EXL
• EY
• Genpact
• HCLTech
• Hitachi Vantara
• IBM
• Infosys
• KPMG
• LTIMindtree
• Movate
• Mphasis
• NTT Data
• Persistent
• Publicis Sapient
• PwC
• Sonata
• Sutherland
• TaskUs
• TCS
• Tech Mahindra
• UST
• Wipro
• WNS

Generative Enterprise services value chain (scope of services)

• C-suite education
• Employee education
• Employee training
• Workshops
• Hackathons
• Introduction to technology and interfaces
• Prompt training
• Centers of excellence
• Labs

• Use case generation
• Build pilots, proofs of concept
• Build production environments
• Rapid iterative design
• Build and test large language models
• Identify ecosystem partners
• Drive co-creation with ecosystem partners

• Policies and procedures
• Privacy and security
• Use case validation
• Process redesign
• Technology choices
• Vendor relationship management
• Technology selection and validation
• Governance
• Compliance

• Data strategy
• Data engineering
• Data analytics
• Large language model build
• Prompt engineering
• Retrieval augmented generation (RAG)
• Fine-tuning

• Ongoing technology innovations
• Platform implementation
• Platform management
• Ecosystem management

• Change management
• Function redesign
• Help enterprises understand the data, processes, and interactions to drive functional optimization
• Help enterprises break down the silos of data across the enterprise, continuously find patterns, and maintain robust governance across all decision points
• Enable the OneOffice to significantly improve decision making and stakeholder experience
• Ability to completely redefine how work is done (e.g., 30%–70% additional productivity, autonomous data-driven decision making, and the inclusion of creative activities, enabling enterprise-wide end-to-end scope)

The emerging Generative Enterprise technology ecosystem consists of many suppliers unknown to the enterprise a year ago

Sources of data

• Each participating vendor completed a detailed RFI.
• HFS conducted briefings with executives from most vendors.

• We conducted reference checks with 30+ active clients of the study participants and 40+ active partners via phone-based interviews and detailed surveys.

• Each year, HFS fields multiple demand-side surveys in which we include detailed vendor rating questions.

• Public information such as press releases, websites, etc.
• Ongoing interactions, briefings, virtual events, etc., with in-scope vendors and their clients and partners.

Assessment methodology

• How does your firm define the value of AI for your clients?
• What is your firm’s point of view on GenAI in terms of value creation potential? What will be the impact of GenAI for 1) clients and 2) your own firm?
• Why should enterprises choose you for their Generative Enterprise journey as described by HFS? What makes you different or stand out?

• Help enterprises understand the data, processes, and interactions needed to drive functional optimization

• Horizon 1+
• Ability to help enterprises break down data silos across the enterprise, continuously find patterns, and maintain robust governance across all decision points
• Enabling the OneOffice to significantly improve decision making, driving unmatched stakeholder experience

• Horizon 2+
• Ability to completely redefine how work gets done (e.g., 30%–70% additional productivity, autonomous data-driven decision making, inclusion of creative activities enabling enterprise-wide end-to-end scope)

• Please describe your AI-driven offering(s)? What industries and use cases are you targeting? Which of these are in production versus pilots?
• What is your technology roadmap for GenAI? Describe any proprietary IP, frameworks, tools, solutions, and accelerators that you have. Please share your current client experiences with GenAI.
• What other technologies (e.g., cloud, Web3, automation, metaverse) are important to deliver on the promise of AI?
• Please describe the current strength of your trained resources on AI technologies? How is this expected to change in the next two years?

• Strong machine learning capabilities
• Typically offshore-focused with strong technical skills

• Horizon 1+
• Strong machine learning, deep learning, natural language processing, and computer vision capabilities
• Offshore and nearshore capabilities with both technical and consulting skills
• Alliances with AI technology leaders
• Market-ready AI-driven proprietary tools, assets, and frameworks

• Horizon 2+
• Strong GenAI, LLMs, use cases, and capabilities
• Deep partnerships, including joint IP creation with AI technology leaders
• Strong frameworks for responsible and ethical AI
• Well-rounded capabilities across all value creation levers: talent, domain, technology, data, and change management

• How are you organized internally to develop your AI offerings and capabilities?
• Where are your main AI-related investments? (e.g., IP, partnerships, training, M&A)
• How are you making sure the use of AI is responsible and ethical?
• Please describe your commercial model for AI offerings. Include the approximate percentage of effort-based (e.g., FTE-based, T&M), performance-based (e.g., gain-sharing, innovation funds), and purpose-based (e.g., co-creation with clients) in your portfolio. How do you expect it to change in the next two years?
• Please describe your AI ecosystem of partners. How do you plan to augment it for GenAI?

• Primarily effort-based relationships

• Horizon 1+
• Increasing number of performance-based relationships in the portfolio

• Horizon 2+
• Driving co-creation with ecosystem partners
• Strong investments in GenAI
• Evidence of purpose-based (co-creation) partnerships with clients in addition to increasing number of performance-based relationships in the portfolio

• How are you organized to develop your Generative Enterprise offerings and capabilities—centralized, regional, or by vertical?
• Please share client experiences with Generative Enterprise.
• Voice of the customer

• Recognized as strong implementation vendors
• Referenceable and satisfied clients for ability to execute

• Horizon 1+
• Recognized as strategic partners by clients
• Referenceable and satisfied clients for ability to execute and innovate

• Horizon 2+
• Recognized as thought leaders by clients
• Referenceable and satisfied clients driving new business models with partnerships

3. Demographics and customer and partner data

The average shift in spending from AI to GenAI is already 41%

• We surveyed customer references provided by service providers to understand their current investments. GenAI budgets have primarily been carved out of AI budgets in 2023, and project scale remains small.
• Among enterprises surveyed, the average number of people involved in AI projects was just less than 20.
• The average amount of money spent on third parties to get projects done was about $560,000.

• Weighted average of spending that switched from AI to GenAI — 41%
• Ranges shown: 0%–20%, 21%–40%, 41%–60%, 61%–80%, 81%–100%

Service providers deliver on tech—less so on business change

• Tech implementation — 8.9
• Ability to transform business beyond tech implementation — 8.5

• Customers see a gap between how well their service providers deliver on tech implementation and their ability to transform business.
• Enterprises want this gap filled as they seek help on their journey to the Generative Enterprise. Knowing the tech is one thing; helping transform ways of working because of the tech is another altogether.

Partners see service providers through rose-tinted glasses

• Understanding GPT-4 or similar — 8.4
• Leveraging AI to redefine how work gets done — 7.8
• Enabling alignment across front, middle, and back office — 7.7
• Driving digital transformation — 8.2

• Helping clients understand GPT-4 or similar — 8.9
• Leveraging AI to redefine how work gets done — 9.1
• Enabling alignment across front, middle, and back office — 8.7
• Digital transformation — 9.3

• Partners think the service providers they work with are doing a much better job than customers give them credit for.
• For example, customers score service providers an average of 8.4/10 for their capabilities in helping them understand GPT-4 (and similar next iterations) of GenAI. Partners score them close to 9/10.
• There’s a gap across the four parameters we asked partners and customers to rate, indicating the need for service providers to question how well they are doing from the customers’ point of view.
• The starkest example is the gap between views on how well service providers are helping clients leverage AI to redefine how work gets done. This gap reiterates the gap customers identify between capabilities in tech implementation and the ability to drive change.

Partners and customers call out service provider talent issue

• Creative commercial models — 8.3
• Use of AI-specific partners — 8.4
• Attracting and retaining talent — 8.4
• Breadth and depth of industry-specific AI offerings — 8.5
• Identifiable investments in AI-related business and capabilities — 8.5
• Development of intellectual property or R&D — 8.6
• Co-innovation with clients and partners — 8.7
• Geographic coverage — 8.8
• Quality of service delivery — 8.8
• Expertise in AI — 8.8
• Use of best-of-breed partner technologies — 8.8

• Creative commercial models — 9.1
• Co-innovation with clients and partners — 9.3
• Use of best-available partner technologies — 9.3
• Development of IP and R&D — 9.1
• Geographic coverage — 9.5
• Attracting and retaining talent — 8.8
• Quality of delivery — 9.2

• Again, partners are happier to slap the backs of their service provider colleagues than service provider customers are.
• Partners and customers score service providers relatively low on attracting and retaining talent. It’s a talent war out there!
• Partners and customers want more creativity when it comes to commercial models. GenAI lends itself to outcome models, but we aren’t seeing the data reflect that yet.

Survey demographics (enterprise customer respondents)

• North America
• Europe
• Asia Pacific
• United Kingdom
• Latin America
• India
• Australia or New Zealand

• Digital
• Software engineering
• Architecture
• Data
• Infrastructure
• Cybersecurity
• Other

• IT
• Business
• Other

4. Horizons results: Generative Enterprise™ services, 2023

HFS Horizons—summary of Generative Enterprise service providers assessed in this report (1/2)

• Accenture — $3 billion investment to apply GenAI and AI to industry’s toughest challenges
• Akkodis — A data and ROI governance focus to drive innovation
• Amdocs — Taking GenAI deep into telco
• Ascendion — Becoming a Generative Enterprise to help clients become Generative Enterprises
• Bain — Helping enterprise leaders understand GenAI disruption to augment with it
• Brillio — Quick start with vertical solutions and outcome-based pricing
• Capgemini — Converting GenAI delivery experience into services for CX, strategy, software, and data privacy
• Cigniti — Pushing back against the hype of GenAI with an expectation-setting reality stick
• Ciklum — Agile engineering chops to get GenAI out of the blocks fast
• Cognizant — Enabling human-machine creative collaboration
• Deloitte — Value-led approach to integrating GenAI into AI strategy
• Eviden — Accelerating the GenAI journey with AI and cloud
• EXL — Data, AI, and domain chops for a vertical GenAI approach
• EY — Re-thinking how the enterprise works through AI and GenAI
• Genpact — Targeting the shift to outcome-driven processes
• HCLTech — Data-focused, experienced early adopter of all things AI
• Hitachi Vantara — Relatively slow starters must now prove considered approach will pay off with rapid market traction
• IBM — Serious AI chops show the way to the Generative Enterprise
• Infosys — Navigating from digital to an AI-first core infused with GenAI
• KPMG — Balancing and managing the GenAI risks and rewards

HFS Horizons—summary of Generative Enterprise service providers assessed in this report (2/2)

• LTIMindtree — Platform approach with prompts, ethics, and other controls to de-risk GenAI
• Movate — Helping customers learn the value of GenAI in CX
• Mphasis — Listening and responding to clients’ needs in customer service, developer productivity, and service desk transformation
• NTT Data — Embedded GenAI—beyond innovation for transformation
• Persistent — Data-led GenAI engineering expertise prepares the way ahead
• Publicis Sapient — GenAI walks hand in hand with digital business transformation for CX and EX
• PwC — Regulatory know-how to de-risk GenAI investments
• Sonata — Responsible-first approach focused on governance
• Sutherland — GenAI is just a tool to deliver better customer outcomes
• TaskUs — Deep LLM know-how focused on multiple CX cases
• TCS — Applying GenAI to level-up employees across the enterprise
• Tech Mahindra — Use-case-focused guidance for experimentation
• UST — Credible GenAI solutions built to keep bias out
• Wipro — Building digital-era AI-first intelligent enterprises
• WNS — Reduced-risk domain-focused GenAI through co-creation and outcome pricing

HFS Horizons for Generative Enterprise Services, 2023

• Horizon 2+
• Strong GenAI and LLM models, use cases, and capabilities
• Ability to completely redefine how work is done (e.g., 30%–70% additional productivity, autonomous data-driven decision making, inclusion of creative activities enabling enterprise-wide end-to-end scope)
• Strong frameworks for responsible and ethical AI
• Driving co-creation with ecosystem partners
• Well-rounded capabilities across all value creation levers: talent, domain, technology, data, and change management
• Deep partnerships including joint IP creation with AI technology leaders
• Strong investments in Generative AI
• Evidence of purpose-based (co-creation) partnerships with clients in addition to increasing number of performance-based relationships in the portfolio
• Recognized as thought leaders by clients
• Referenceable and satisfied clients driving new business models with partnerships

• Horizon 1+
• Strong ML, deep learning, natural language processing, and computer vision capabilities
• Ability to help enterprises break down the silos of data across the enterprise, continuously find patterns, and maintain robust governance across all decision points
• Enabling the OneOffice to significantly improve decision making, driving unmatched stakeholder experience
• Increasing number of performance-based relationships in the portfolio
• Market-ready AI-driven proprietary tools, assets, and frameworks
• Offshore and nearshore capabilities with both technical and consulting skills
• Alliances with AI technology leaders
• Recognized as strategic partners by clients
• Referenceable and satisfied clients for ability to execute and innovate

• Strong ML capabilities
• Help enterprises understand the data, processes, and interactions needed to drive functional optimization
• Primarily effort-based relationships
• Recognized as strong implementation vendors
• Typically offshore-focused with strong technical skills
• Referenceable and satisfied clients for ability to execute

• Accenture
• Ascendion
• Bain & Company
• Capgemini
• Cognizant
• Eviden
• EY
• Genpact
• IBM
• Infosys
• NTT Data
• Persistent
• Publicis Sapient
• Tech Mahindra
• Wipro

• Akkodis
• Brillio
• Deloitte
• EXL
• HCLTech
• KPMG
• LTIMindtree
• Mphasis
• PwC
• Sonata
• TCS
• UST
• WNS

• Amdocs
• Cigniti
• Ciklum
• Hitachi Vantara
• Movate
• Sutherland
• TaskUs

5. Publicis Sapient profile: Generative Enterprise™ services, 2023

Publicis Sapient: GenAI walks hand in hand with digital business transformation to amplify value for CX and EX

Strengths

• Value proposition: Publicis Sapient applies GenAI to amplify digital business transformation for enhanced growth in business model innovation, CX, EX, and enterprise enablement.
• Growth proof points: The company has a comprehensive use case library. Publicis Sapient also leads Publicis Groupe’s GenAI transformation. The Bodhi platform has used LLMs since 2020.
• Key differentiators: Publicis Sapient’s integrated SPEED (Strategy, Product, Experience, Engineering, Data and AI) capabilities cut time to market. GenAI value realization is integrated into Publicis Sapient’s Digital Business Transformation model. It focuses on enterprise enablers: skills, data quality, tooling standardization, ethics, governance, and partner strategy.
• Outcomes: It applied GPT-4 to improve substitutions for an online grocery retailer, enabled GenAI-customer support for a gardening society, and leveraged GenAI to reimagine the travel planning experience for a hospitality company.
• Customer kudos: Clients appreciate its customer-centricity, innovation, and pace.
• Partner kudos: Hyperscalers find its creatively innovative bent of mind in CX impressive. It is appreciated for its knowledge, consulting, and repeat revenue from big logos.

Development opportunities

• What we’d like to see more of: We like Publicis Sapient’s detailed, value-based approach to internal training.
• What we’d like to see less of: Much of how Publicis Sapient pitches its differentiators is echoed by many of its rivals. It needs to identify and amplify its differentiators to cut through in a noisy market.
• Customer critiques: Clients are mostly happy with the service but want Publicis Sapient to keep its quality consistent while maintaining growth.
• Partner critiques: Partners would like Publicis Sapient to continue expanding in growth markets (LATAM and Middle East), allowing partners to scale (geographies, customers, and maturity in GCP services.)

Key offerings

• Quick starts: GenAI Ignite workshop, GenAI hackathon, demos, and proofs-of-concept
• GenAI strategy: Business case prioritization, operating model, ethics, governance, and risk
• Industry-specific GenAI use cases: Blueprinting and implantation
• Cross-industry use cases: For example, GenAI product engineering, commerce, and content creation
• Enterprise GenAI implementation: Scaling with enterprise-grade solutions and platform accelerators

Mergers and acquisitions (2019–2023)

• PS AI Labs: Launched 2020, now wholly owned by Publicis Sapient (2023)
• Yieldify: Personalization platform for global brands (2023)
• Growth OS: AI and ML for media decisioning (2022)
• Bodhi: Enterprise AI platform (2022)
• Retargetly: Data and technology company (2022)
• CitrusAd: CDP and data monetization platform for global brands (2021)

Partnerships

• Microsoft, Amazon, Salesforce, Adobe, Google, Meta, Nvidia
• OpenAI, VertexAI, Anthropic, Humanloop
• Replicate, Hugging Face
• Databricks, Anyscale, Snowflake

Key clients

• Global bank
• International insurance company
• International travel and hospitality business
• UK retailer
• UK gardening society
• Global entertainment brand

Relevant global operations and resources

• Headcount growth locations: North America, LATAM, EMEA, APAC
• Targeted industries: Consumer products, energy and commodities, financial services, health, public sector, retail, telco, media and tech, transportation and mobility, and travel and hospitality
• AI labs: Dedicated data and AI center of excellence

Flagship internal IP

• Bodhi: Award-winning enterprise AI platform delivering LLM use cases with integrated industry accelerators since 2020
• PS Chat: Secure, scalable enterprise chat and LLM developer environment hosted on Azure, offering enterprise/tenant data boundaries, audit, custom policies, collaboration, data-masking, etc.

6. Further reading

Primers, advice, and guidance for the new world of GenAI

• The dos and don’ts and potential costs of GenAI
• Generative AI meets software development: the advent of generative coding
• Generative AI offers personalization and loyalty across the enterprise—if you get it right

Primers, advice, and guidance for new world of GenAI

• The bots ARE coming for your job after all
• How business leaders can take control of the GenAI conversation
• Keep your options open when it comes to public or private LLMs

You and your teams should be using GenAI daily

• Generative AI will bring more humanity into the workplace
• If you aren’t using GenAI daily, get out of the way!
• Research & Insights — Stop searching. Start asking.
  • We’ve trained a generative AI model, powered by Humata.ai, on two years of research, across industries, technologies, and change agents.
  • Go ahead. Ask a question.
  • “What are best practices in applications modernization?”
  • “Ask a question across more HFS documents”
  • “Go ahead. Ask a question. We’re revolutionizing the way you access business research.”

7. HFS Research authors

Phil Fersht, CEO and Chief Analyst, HFS

David Cushman, Exec Research Leader, HFS

HFS Research authors

About HFS