VIEWPOINT

2024

HUMAN-AIC O L L A B O R AT I O N : A N E W
ERA OF PRODUCTIVIT Y
IN SERVICE INDUSTRIESHow service companies can leverage AI
to overcome productivity challenges

AUTHORS
Marius Romanescu
Ronja Berger

Artificial intelligence (AI) can significantly enhance
productivity and performance in service industries,
as recent use cases demonstrate. To harness its
benefits, service companies must ensure that AI iscomplementing the way their employees work, notdisrupting it. Several challenges exist, including
ensuring quality results, navigating regulations, andimplementing AI at scale. In this Viewpoint, we sharesteps CxOs can take now to drive innovation andcompetitiveness through human-AI interaction.

Ronja Kallhammer
Max AdisARTHUR D. LITTLE

VIEWPOINTDRIVING PRODUC TIVIT Y& PERFORMANCE

To harness AI’s benefits, companies must ensure

In today’s dynamic business landscape, service

companies get started? This Viewpoint highlights

industries grapple with substantial challenges,

several use cases of early adopters in service

notably a prevailing labor shortage. As an

industries that have leveraged AI to support daily

example, the ifo Institute reported in August 2023

operations and unlock substantial productivity

that a striking 75.3% of legal and accounting firms

potential, as well as key challenges and ways toin Germany struggle to secure qualified staff as

overcome these when embarking on the AI journey.

that AI is complementing the way their employees
work, not disrupting it. But where can service

demographic shifts and other factors intensify
the dynamics of the labor market. At the sametime, 43.1% of German service companies face ashortage of skilled labor, underscoring the need

HOW FIRS T MOVERSADOP T AI

for streamlined processes to enable specialists

AI is not a distant vision; it’s a present reality for

to focus on high-value tasks. Similarly, the US

today’s service industries. The critical question

Chamber of Commerce reports that in the US,

is not whether to adopt AI but how to harness its“Even if every unemployed person with experiencecapabilities effectively. Pioneering companies

in the financial activities or professional andhave embraced AI, demonstrating its potential

business service sectors were employed, only

to revolutionize operations, boost productivity,

42% and 44% of the existing job vacancies inand drive growth through a diverse range ofthese industries would be filled, respectively.”

applications.

Rising customer expectations for quick responses,
digital interfaces, 24/7 service, and personalized
experiences further amplify the pressure. Amidthese challenges, AI offers the means to enhance
productivity and performance to navigate afiercely competitive marketplace.
Addressing current challenges
AI has evolved from conventional e-commerce

Example 1: Automated claims processing
in insurance
In the insurance industry, where efficiency andaccuracy are paramount, AI is reshaping theclaims processing landscape. Early adoptershave integrated AI and machine learning (ML)to streamline and enhance the speed of claims
processing.

applications to become an essential tool for

An innovative digital insurance company revolves

optimizing processes in service industries.

its system around AI-powered chatbots. Using

Recent breakthroughs in generative AI (GenAI)

natural language processing, these chatbots

and large language models (LLMs) have expanded

accurately extract and categorize key details

its applications, allowing automation to extend

from policyholders’ descriptions and incident

into creative and more complex tasks — andreports, enabling rapid processing of simple

to take over the sphere of conversations. Withclaims and ensuring quick payouts.

these technological advances, a Goldman Sachs
report estimates that up to 44%-46% of tasks

Benefits

in administrative and legal professions could

AI-driven systems improve accuracy in data

be automated in the future. This transformative

analysis, minimizing errors in claim evaluations.

potential empowers service organizations

This precision not only enhances the overall

to tackle industry challenges, providing aclaims process but also boosts customer

competitive edge by propelling productivity

satisfaction. Additionally, the automation reduces

and facilitating continuous improvement.

manual work, allowing productivity gains andenabling specialists to focus on value-adding
instead of transactional activities.

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VIEWPOINT

Example 2: Personalized customer

Example 3: Data-driven investment

engagement in financial services

decisions in financeIn the competitive landscapes of insurance andIn the complex world of finance, data-drivenbanking, personalization is a key differentiator.

decision-making is critical for success. A leading

AI is enabling early adopters to enhance customer

financial company is leveraging AI to revolutionize

engagement by tailoring insurance plans to meetinvestment decisions. It is incorporating AI

individual needs. Indeed, some global providers ofand ML to transform investment decision-

insurance and financial services are harnessing

making. Using AI-driven predictive analytics andAI-driven customer engagement to create aalternative data sources like satellite imagery andpersonalized experience. Analyzing customer

social media sentiment, the company can identify

data, including financial behavior, life events, andemerging trends to make informed decisions

risk profiles, AI algorithms recommend insurance

reacting to market movements. It also applies

plans aligned with individual customer needs. For

AI to algorithmic trading, executing trades atinstance, approaching retirement age prompts

optimal times based on real-time data analysis.

recommendations for retirement-focusedinsurance products.

Benefits
AI-driven investment decision-making based on

Benefits

real-time analytics offers substantial benefits,

As AI-driven personalization can enhancesuch as increased decision accuracy, improved

customer satisfaction by meeting individual

risk management, and streamlined portfolio

needs, it fosters long-term relationships andadjustments. Furthermore, tailoring investment

improves customer retention. Moreover, byoptions to individual clients enhances trust andanalyzing customer data, early adopters of AI

fosters strong client relationships.

gain insights for more informed decision-making
and targeted marketing strategies.

Figure 1. Key opportunities for AI in service industries
Figure 1. Key opportunities for AI in service industries

CUSTOMER INTERACTION

BUSINESS OPERATIONSDATA ANALYSIS & KNOWLEDGE

Customer service

Workforce management

Data gathering/surveying

Service personalization

IT development & testing

Predictive analytics& forecasting

Marketing & sales

Quality control

Fraud detection& prevention

Sentiment analysis

Process automation& decision support

Knowledge management& search

Source: Arthur D. Little

HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

Source: Arthur D. Little

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HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIESARTHUR D. LITTLE

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A wide spectrum of possibilities …

Another leading technical services provider
harnesses GenAI to revolutionize knowledge

These are just a few examples of many
opportunities for service companies to utilize AI toyield quantum leaps in productivity (see Figure 1).
The horizon of AI applications expands well beyond
the use cases highlighted here, with GenAI as apotent tool for accelerating the enhancements ofservice processes and their back-end operations.
Such future-oriented solutions demonstrate AI’s
versatility and potential impact.

management for intricate workflows. Bytransforming voluminous data and documents
into standardized, user-friendly work instructions,
including operating procedures and technicalillustrations, this approach establishes acomprehensive knowledge base for fieldtechnicians. AI-generated guides seamlessly
integrate with mobile SAP (Systems Applications
and Products) systems, offering a systematic

One such application involves conducting achecklist for service tasks. Technicians canmeta-analysis of customer interactions withleverage the interactive human-to-AI interface

sales representatives. AI, through transcribing

for instant access to detailed service guidance,

and analyzing extensive conversation data,

ensuring accuracy, optimizing performance, andidentifies recurring patterns in customer buying

adapting smoothly to evolving customer needs.

behavior. This strategic insight aids in optimizing
sales techniques and enhancing conversion rates,
fostering successful sales interactions.
In another example, a forward-thinking European
health insurance company is developing an
AI-based knowledge management support
system. Tailored for customer service
representatives, this system leverages anextensive knowledge base, intelligent search
options, and an intuitive interface to provide
guidance on complex care situations. The
outcome is a tangible improvement in efficiency

and employee and forcustomersatisfaction.
Figure 2. Main challenges
AI usage

CHALLENGES TO AI- DRIVEN
PRODUC TIVIT Y
Although the potential is great, many service
companies are encountering hurdles in realizing
AI benefits. The inherent complexities and limited
understanding of AI systems can erode trust,
limit a focused implementation, and — in theworst case — introduce new critical businessrisks. Therefore, companies must understandfour key challenges shown in Figure 2 and address
them early on to ensure a successful AI scaling tocreate maximum value:

Figure 2. Main challenges for AI usage

QUALITY OF
AI-BASED RESULTS

REGULATORYREQUIREMENTS
IMPLEMENTATIONSPEED & FOCUS

Ethical,moral &
social risk

Culture as roots
for embracing AI

Source: Arthur D. Little

Source: Arthur D. Little
HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

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1. Quality of AI-based results. AI, built on
extensive data, constantly improves decisionmaking and self-learning capabilities.
However, the more powerful AI becomes, theless transparent decision mechanisms may be
for humans. Establishing a balance between
decision transparency and output control is a
strategic, procedural, and technical challenge.
It requires ensuring high-quality and reliable

IT IS ESSENTIALF O R C O M PA N I E S T O
P R O A C T I V E LY D E F I N E
S U I TA B L E S T R AT E G I E S
FOCUSING ON AI,
INCLUDING HUMAN-AI
C O L L A B O R AT I O N

AI-driven decisions while accepting a certain
level of uncertainty to maximize AI’s benefits.
2. Regulatory requirements. In the evolving
landscape of AI, legal conditions are in
constant flux. Questions regarding ownershipand protection of copyrights and patentsarise as AI systems become more and morecapable of generating creative results.
Determining liability for AI system actions
can be challenging, as traditional legal
frameworks may not adequately address AI
decision-making complexities. Regulatory
frameworks, such as the EU‘s General Data
Protection Regulation (GDPR), add further
layers of complexity, impacting AI deployment
and data usage.
3. Implementation speed and focus. As withmost technological trends, initial ideas andpilots are swiftly implemented, but companies
often struggle with scaling AI to reap its full
benefits. Fragmented AI activities and a lackof overarching strategic view and governancemechanisms impede progress. Companies,
still building up internal resources andskills for AI, grapple with knowledge gaps

4. Ethic, moral, and social risks. As AI systems
are integrated into workplaces, ethicalconcerns related to fairness, transparency,
and bias emerge. In service industries, AI
systems may need to make ethically charged
decisions, such as treatment choices orresource allocations. AI, learning from existing
data, may inadvertently perpetuate biases,
leading to unfair treatment or discrimination.
Aligning AI-based decisions with human values
and moral frameworks therefore presents asignificant challenge. From a workplace healthand safety perspective, increased reliance on
AI technologies can furthermore affect mental
health and social relationships, potentially
isolating individuals.
To overcome these risks and challenges duringinitial AI implementations, it is essential forcompanies to proactively define suitablestrategies focusing on AI, including human-AI
collaboration. In the following section, we outline
six steps for CxOs to navigate these challenges
and embark on a successful AI journey.

and organizational uncertainty, hindering
widespread AI adaptation. Another limiting
factor may be the cultural challenge toadopt a willingness to “fail fast” in the agile
implementation of new AI solutions.

HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

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HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIESARTHUR D. LITTLE

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ARTHUR D. LITTLE

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GE T TING S TAR TED WITH AI

and assuring focus in situations where there

As the potential of AI continues to burgeon, CxOs

in Figure 4 serves as an example to evaluate

must take decisive steps to seamlessly integrate

potential use cases based on an organization’s

AI into their organizations. Here are six key

strategic priorities.

actions CxOs should take to begin their journeyof AI implementation (see Figure 3):

are scarce resources. The heatmap shown

3. Establish a quality data foundation. Ensure arobust data foundation with a clear, enterprise-

1. Define strategic alignment. Align AI

wide data management approach to ensure data

initiatives with your organization‘s overall

quality, readiness, and cleanliness. High-quality

strategic goals. Clearly define how AI will

data is essential for effective AI model training

support your business objectives to ensure

and preventing unreliable AI results. CxOs

a cohesive strategy that drives growth andshould invest in data governance practices that

innovation. This alignment ensures that AI

enhance the reliability and accuracy of the data

adoption is not an isolated endeavor but

used by AI algorithms.

an integral part of the organizational visionand roadmap.

4. Form cross-functional AI governance.
Establish a cross-functional AI governance2. Identify high-impact use cases. Conduct

approach encompassing a diverse “core team”

a thorough analysis of your organization’swith business, IT, and design expertise, as welloperations to identify areas where AI can

as integrating across existing company-wide

add significant value. Prioritize use cases

governance processes (e.g., business strategy

that are routine, data-intensive, and benefit

cycles, project portfolio management, resourcefrom automation. Focus on the potentials

planning). This promotes a holistic approachof augmenting human work with AI-driven

to AI integration and ensures that all aspects

support, which can increase workforceof the business are considered. External hiring

productivity while retaining key talent.

may be necessary to fill skill gaps and bring

Identifying high-impact use cases ensuresin specialized knowledge. Cross-functionala targeted and effective implementation

governance is crucial for overseeing AI initiatives

aligning them withAI
business goals.
Figure 3. Six steps for CxOs to successfullyandintroduce
leveraging synergies across business areas

Figure 3. Six steps for CxOs to successfully introduce AI

Define strategic alignment

Identify high-impact use cases

Establish a quality data foundation

Form cross-functional AI governanceSpecify regulatory adaptation

Ensure rapid deployment & scalability

Source: Arthur D. Little

HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

Source: Arthur D. Little

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ARTHUR D. LITTLE

VIEWPOINT

Figure 4. Value & impact assessment of AI capabilities acrossprocess domains of service companies

Figure 4. Value & impact assessment of AI capabilities across process domains of service companies

Value & impact assessment

DOMAINILLUSTRATIVE
Gaining & advising customers

AI CAPABILITIESNatural language
processing

Sales

Membership
mgmt.

Low

Shaping supply

Services
Tele./
Approval
Advicedigital via online
processes
in stores
consulting channels

Partner
mgmt.

High

Managing revenue

Premium PremiumAdd‘l.
mgmt. for mgmt. forproducts
corporate private& services
clients customers

Claims
mgmt.

….
…..

Information summarization
Information retrieval
Natural lang. understanding
Sentiment analysis
Image recognition

Computer vision

Audio recognition
Video recognition
Sensor fusion
Time-series forecasting

Predictive analytics

Recommender systems
Anomaly detection
Event classification
Regression

Autonomous
decision-making

Decision support
Motion planning
Path planning

Source: Arthur D. Little

Source: Arthur D. Little

5. Specify regulatory adaptation. Keep up-todate with the changing legal and regulatory
requirements related to AI. Develop a deep
understanding of the legal landscape,including data privacy, intellectual property,
and liability regulations. Define specificpolicies in line with company culture, ensuring
your organization remains compliant andavoids potential legal issues. CxOs should lead

R A P I D I M P L E M E N TAT I O N
IS CRITICAL TO
A C H I E V I N G FA S T
R E S U LT S A N D G A I N I N G
A COMPETITIVEA D VA N TA G E I N
THE MARKE TPL ACE

efforts now to establish ethical guidelines
and compliance frameworks to guide theirworkforce during the AI adoption.
6. Emphasize rapid deployment andscalability. Ensure speed and scalability in AI
implementation. Develop a clear strategy for

By taking these first steps, CxOs can successfully
integrate AI into their organizations to drive
productivity, performance, and innovation andthereby stay ahead of the competition.

rapid testing and scaling to ensure a nimble
approach to efficiently deploying AI solutions
across the organization. Rapid implementationis critical to achieving fast results and gaining
a competitive advantage in the marketplace.
CxOs should champion an agile approach withdedicated cross-functional AI teams thatallows their organization to pilot and “fail fast”
in order to adapt and scale AI solutions basedon real-time feedback and evolving needs.

HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

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CONCLUSIONACCEP TING THE AI CHALLENGEAI EMERGES AS AN OPPORTUNIT Y
T O E N H A N C E D A I LY O P E R AT I O N S A N D
RE VOLUTIONIZE CUSTOMER E XPERIENCE

In a world where service industries face unprecedented challenges,
AI emerges as an opportunity to enhance daily operations andrevolutionize customer experience. The goal is not workforce
replacement but capability augmentation, enabling growth,
innovation, and competitiveness. This Viewpoint explores
human-AI collaboration in service industries, highlighting thepotential benefits, practical applications, and challenges for AI
implementation. Service industry CxOs can harness AI’s power to:
1

Drive productivity and performance. In the face of labor
shortages and increased customer expectations, service
industries can turn to AI to optimize processes. Breakthroughsin AI, including GenAI and LLMs, offer transformative potential
to support service companies with both front-end and back-end
activities.

2

Meet high market expectations. Pioneering companies
showcase AI’s capabilities in personalizing customer
engagement, automating claims processing, andrevolutionizing data-driven investment decisions. These
examples highlight how AI not only improves efficiency
but also enhances accuracy and customer satisfaction.

3

Shape the future of the industry. From analyzing customer
interactions to developing AI-based knowledge management
systems, service industries should explore AI’s potential for
efficiency gains, employee satisfaction, and enhanced service
delivery.

HUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

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VIEWPOINTNOTESHUMAN-AI COLLABORATION: A NEW ERA OF PRODUCTIVITY IN SERVICE INDUSTRIES

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