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AI's Transformative Impact on Business and Employment (2025-2035) PDF Free Download

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AI’s Transformative

Impact on Business

and Employment

Skale Egenkapital

SEK Knowledge

Presented By :

AI Research Lab Team

(2025–2035)

List Of Contents
Transformative Impact on Business 03
Introduction 05
Manufacturing 06
Technology 10
Creative Industries 21
Education 25
Jobs & Skills 32
Conclusion 55
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Finance 14
Consulting 18
References 59
Contact 60

Transformative

Impact on Business

Manufacturing

3.78

Technology

3.10

Creative

1.85

Finance

1.15

Consulting

1.00

Education

0.11

Ai Research Lab

Sectoral AI Value by 2035 (Trillions USD)

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2026 2027 2028 2029 2030 2031 2032 2033 2034 2035

Strategic Transformations by Industry

Artificial Intelligence is becoming a foundational technology for

businesses across sectors. A recent global survey found that 78%

of organisations now use AI in at least one business function, up

from just 55% a year earlier. This surge in adoption indicates that

AI is transitioning from pilots to mainstream operations. For

CxOs and business leaders, the imperative is clear: leverage AI to

drive efficiency, innovation, and competitive advantage – or risk

falling behind. Analysts project that generative AI alone could

boost global GDP by ~7% (nearly $7 trillion) over the next decade,

by increasing productivity growth by around 1.5 percentage

points annually. However, capturing these gains requires

strategic integration of AI into business processes and models.

Below, we examine how AI is transforming key industries – and

what that means for business operations and strategy in each.

Projected CUmulative Global GDP Uplift from AI Adoption

(2026–2035)

By 2035, AI could drive up to 15% of global GDP growth. CxOs must treat AI

as core to business performance.

AI is no longer an experiment — it’s a growth engine

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Manufacturing

Business Transformation

Manufacturing has led the charge in automation for decades,

and AI is accelerating this evolution into what’s often called

Industry 4.0. Manufacturers are deploying AI-driven robots,

computer vision systems, and predictive analytics on the factory

floor. The results are higher efficiency, precision, and uptime. For

example, AI-powered predictive maintenance systems can

forecast equipment failures and schedule repairs before

breakdowns occur, dramatically reducing unplanned downtime.

One case study showed that an AI predictive maintenance

solution delivered a 30% reduction in unplanned downtime

within six months. AI-based quality control is another game-

changer: computer vision can inspect products for defects far

faster (and often more accurately) than human inspectors,

improving yield and reducing waste. Generative design

algorithms can even optimise part designs for performance and

Competitive Advantage

weight, producing innovations that human engineers might not

conceive. Companies embracing AI in manufacturing report

significant gains. Global automation spending is rising at ~9%

annually and is expected to exceed $300 billion in the next few

years, reflecting the rapid return on investment of these

investments. Factories that integrate AI and robotics can reach

near “lights-out” levels of automation – operating 24/7 with

minimal human oversight. While fully autonomous factories are

still rare, many are becoming hybrid human-machine operations.

In high-cost economies, AI-enabled automation allows firms to

restore or retain manufacturing by offsetting labour costs with

productivity. In Asia, which now leads the world in industrial

robot adoption, automation is viewed as essential to meet output

targets amid an ageing workforce. Global robot density (robots

per 10,000 manufacturing workers) more than doubled from

From Automation to “Smart Factories”

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2013 to 2022, reaching a new peak of 151 robots per 10,000

workers. Leading countries, such as South Korea, now have over

1,000 industrial robots per 10,000 employees in manufacturing –

a glimpse of where tech-driven efficiency can take us (see Figure

1).

Strategic Considerations

For manufacturing CxOs, AI adoption is as much about process

reengineering as about technology. Early movers have learned

that simply installing robots isn’t enough – they must redesign

workflows and retrain staff to work alongside AI. Companies like

Siemens and Bosch report that implementing AI at scale required

breaking jobs into tasks and automating the repetitive ones, while

elevating human workers to supervisory and problem-solving

roles. Crucially, AI allows for mass customisation and agility in

production. AI algorithms can instantly adjust a production line to

create varied product models or optimise supply chain logistics in

real-time by predicting demand and delays. These capabilities

transform manufacturing from a rigid, forecast-driven process to

a flexible, data-driven one. In sum, AI is pushing manufacturers

toward “smart factories” – highly efficient operations where data

from sensors, machines, and supply chains, AI analyses data to

continually improve output. The payoff is evident in success

stories: one Chinese electronics factory famously replaced 90% of

its assembly workers with robots, boosting productivity by 250%

and cutting defects by 80%. Such dramatic results underscore

why manufacturing firms view AI not just as a cost-saver but as

core to future competitiveness.

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0 200 400 600 800 1000 1200

South Korea

Singapore

China

Germany

Japan

USA

Density (robots/10k employees)

Country

Robot Density by Country & by Region

(2023, Robots per 10,000 Manufacturing Employees)

Europe

36.6%

North America

32.9%

Asia

30.4%

Figure 1 8

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Key Use Cases

Predictive maintenance (up to 25% cost reduction)

Computer vision for quality control

Supply chain optimization

Robotics and generative design

Productivity Impact

Up to 20–30% efficiency gains across plants

Trend

“Factory of the Future” driven by AI + IoT + digital twins

Manufacturing Data Trends

AI Value Creation

~$3.78 trillion by 2035

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Technology

Business Transformation

In the tech sector – software, IT services, and tech-driven

companies – AI is both a product and a productivity booster. Tech

companies are at the forefront of building AI solutions (from

machine learning platforms to AI APIs), and they are also early

adopters of AI to improve their operations. Perhaps the most

notable change is in software development itself. AI-powered

coding assistants (like GitHub Copilot or Amazon CodeWhisperer)

are transforming how developers write code. Developers can

auto-generate boilerplate code or even complete functions using

natural language prompts, with the AI trained on billions of lines

of code. Studies have shown that programmers using AI

assistants complete tasks significantly faster. GitHub research

found that developers were able to code up to 55% faster with

Copilot’s help. Such gains can compress development cycles and

allow engineers to focus more on design and problem-

solving rather than writing repetitive code. AI is also automating

software testing (auto-generating test cases), bug detection, and

deployment processes A(through AI in DevOps toolchains). In IT

operations, AI-driven monitoring systems (AIOps) can detect

anomalies in infrastructure or applications and even auto-

remediate issues, reducing downtime. In effect, AI is becoming a

co-worker for developers and IT teams, handling routine tasks

and optimising performance.

New Products & Business Models

For tech firms, embedding AI into products is a huge value driver.

Virtually all major software platforms – from office suites to CRM

systems – are gaining AI features (like smart assistants, predictive

analytics, or generative AI capabilities) that command premium

pricing. This opens fresh revenue streams. Goldman Sachs

estimates the total addressable market for generative AI

AI Everywhere, from Code to Cloud

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Strategic Considerations

Tech leaders must navigate a dual role: they need to be

innovators of AI and early adopters of AI for internal efficiency.

CxOs in tech should foster a culture where AI is utilised at all

levels – from engineers harnessing AI in coding to sales teams

using AI for customer insights, to support teams utilising chatbots

for basic queries. The organisations seeing the most significant

gains are those that redesign workflows around AI rather than

simply adding AI on top. McKinsey finds that workflow redesign is

one of the most

critical factors for achieving strong ROI from AI initiatives. For

instance, a software company might restructure its QA process to

rely on an AI bug-hunting tool as a first line, with human testers

focusing on complex scenarios. This requires retraining staff and

sometimes adjusting performance metrics (e.g., measuring

developers on how effectively they utilise AI tools, not just lines of

code). Another strategic aspect is talent: the tech sector faces

intense competition for AI talent, including data scientists,

machine learning engineers, and other specialised professionals.

While demand is high, there are indications it’s getting a bit easier

to hire these roles as more people train in AI. Nonetheless, tech

software is valued at approximately $150 billion (in addition to

the existing $685 billion software market). We’re seeing a race

where established tech giants and startups alike are launching AI-

enhanced offerings: e.g. cybersecurity tools that use AI to identify

threats, or cloud services that offer AI as a service (like pre-trained

vision or speech models via API). AI is also enabling entirely new

business models. For example, the rise of AI-driven platforms in

areas like autonomous driving software, AI-powered drug

discovery (by tech-biotech hybrids), or fintech solutions using AI

for algorithmic trading and credit scoring. Many tech companies

are now effectively “AI companies,” either by the products they

sell or by the AI-driven insights they use internally to make

decisions (such as product recommendations, user

personalisation, or ad targeting). According to one survey, more

than 75% of businesses plan to adopt big data, cloud, and AI

technologies by 2027, making these the top technology drivers of

future business transformation. The tech sector, unsurprisingly,

leads this charge, with IT and software functions being the most

common areas where companies have implemented AI so far.

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companies are investing heavily in upskilling their current

workforce on AI skills as technology continues to evolve. Finally,

tech companies must keep an eye on responsible AI practices, as

regulatory and ethical considerations (data privacy, bias,

explainability) are increasingly in focus. In summary, AI offers

tech companies a powerful lever to boost both top-line

innovation and bottom-line efficiency, and those who wield it

effectively are likely to dominate their markets in the coming

decade.

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Key Use Cases

Generative AI platforms (e.g., ChatGPT, GitHub Copilot)

Cloud-based ML infrastructure (AWS, Azure, Google Cloud)

AI chip development (e.g., Nvidia, AMD)

Developer productivity (up to 50% faster coding)

Trend

Massive R&D acceleration + new monetization models (AI-as-a-

Service)

Technology Data Trends

AI Value Creation

~$2.8–3.1 trillion by 2035

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Finance

Business Transformation

Finance has been a pioneer in adopting automation (think ATMs

or algorithmic trading), and AI is taking it to the next level. Banks,

insurers, and investment firms are deploying AI across a wide

array of functions, aiming for greater speed, accuracy, and

analytical power. In banking, one of the most visible changes is

the rise of AI-powered customer service – conversational chatbots

handling routine inquiries, loan applications being processed by

AI that analyses credit risk, and personalised financial advice

delivered via apps. For example, Bank of America’s chatbot “Erica”

has handled over 100 million customer requests ranging from

simple balance queries to assisting with transactions. The back-

office operations of banks are also being streamlined: AI

algorithms can flag fraudulent transactions in real-time by

detecting anomalies in spending patterns, far faster than

traditional rule-based systems. In lending, AI models assess

creditworthiness by evaluating a broader set of data (including

social and web data for small businesses), which can improve

loan approval speed and potentially inclusiveness, though it

raises fairness questions. On Wall Street, trading desks have been

transformed – many firms use AI-driven algorithms to execute

trades in milliseconds, optimise portfolios, or even engage in

news-based trading (where AI reads news feeds and executes

trades based on predicted market impact). Goldman Sachs

famously automated a significant portion of its equities trading;

what used to require hundreds of human traders now takes a

handful, supported by AI quants and engineers. In insurance, AI is

enabling instant underwriting by analysing photos for car

insurance claims or using machine learning to predict health risks

from various data points, which allows policies to be issued or

claims paid out much faster.

Automation with Assurance

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Data-Driven Decisions and Products

Finance is fundamentally about risk and information, and AI

provides an edge by finding patterns in vast data that humans

might miss. Investment managers use AI to sift through

alternative data (like satellite images of retail parking lots or

sentiment from social media) to inform their decisions. In fintech,

new services are emerging: robo-advisors that automatically

rebalance portfolios for investors, AI-driven budgeting apps that

give tailored tips, and even AI-based market makers in crypto

exchanges. These innovations can offer cost-effective solutions

to clients (for example, a robo-advisor charges lower fees than a

human financial advisor) and tap markets previously

uneconomical to serve. Even central banking and financial

regulators use AI – for instance, to monitor markets for systemic

risks or to detect money laundering patterns among millions of

transactions (RegTech). The efficiency gains from AI in finance are

quantifiable: one study estimated AI could increase banks’ profits

by 31% by 2025 through cost reductions and new opportunities.

Many banks report that processes like loan processing or

compliance checks that took days can be done in hours or

minutes with AI, improving customer satisfaction and

throughput. Moreover, AI helps reduce human error in number-

intensive work – a big boon in an industry where mistakes can be

extremely costly.

Strategic Considerations

For financial industry leaders, AI adoption carries high stakes due

to the sensitive nature of money and data. Trust is paramount,

so implementing AI requires robust validation and oversight.

Model risk management – ensuring the AI models are accurate,

unbiased, and secure – has become a crucial function. Many

banks now have AI governance committees and internal audit

processes in place for their algorithms. Another consideration is

the workforce (addressed in the next section in detail): as AI

takes over routine tasks, financial institutions are retraining

employees for more advisory and oversight roles. For example,

rather than manually reviewing loan documents, a banker might

spend time interpreting AI-generated recommendations and

building relationships with clients. This human-in-the-loop

approach often yields the best outcomes: AI provides analysis,

pulls ahead and humans provide judgment. 15

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We’ve also seen strategic partnerships in this sector, where banks

team up with AI startups or cloud providers to accelerate their AI

capabilities (such as JPMorgan’s multi-billion-dollar investment in

AI and cloud, or partnerships like Mastercard's acquisition of AI

firms to bolster fraud detection). Finally, customer acceptance is

key: while many customers appreciate faster service, some

segments may be uncomfortable with AI-driven advice or

decisions. Leading firms manage this by offering choices (e.g.,

easy access to human support) and being transparent about their

use of AI. In sum, finance executives should view AI as both a

competitive necessity and a strategic tool to enhance decision

quality. Those who use AI to create more innovative products (like

personalised insurance) or ultra-efficient operations will set the

industry benchmarks, while laggards risk eroding their market

share as agile fintech and tech-savvy banks

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Key Use Cases

Fraud detection and prevention (AI cuts fraud by up to 30%)

AI for credit scoring and underwriting

Conversational AI for customer service

Trend

Regulatory compliance powered by explainable AI (XAI)

Finance Data Trends

AI Value Creation

~$1.15 trillion by 2035

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Consulting

Business Transformation

Consulting might be thought of as a purely human, expertise-

driven field – yet it is being reshaped by AI as well. Top consulting

firms are leveraging AI tools to automate research, data analysis,

and even elements of strategy formulation. Tasks that used to

occupy junior consultants for days – compiling market data,

poring over financials, producing basic PowerPoint drafts – can

now be accelerated by AI in minutes. For instance, natural

language processing can scan thousands of documents or news

sources and extract key insights, providing consultants with a

fast, panoramic view of an industry or company. AI analytics

platforms can crunch client data to spot patterns (say, cost

overruns or customer churn drivers) far faster than any manual

Excel modelling. The result is that consultants can focus more

time on higher-value activities, such as interpreting insights,

crafting recommendations, and engaging with clients. One

analysis suggests that 30–50% of tasks in a typical consulting

project could be automated by generative AI, especially in the

research and analysis phases. Indeed, major consultancies are

starting to deploy AI copilots for their teams – Bain & Company,

for example, has partnered with OpenAI to embed GPT-4 into

their workflows, enabling consultants to generate initial strategy

documents or code for clients’ tools much faster.

New Service Offerings

Just as importantly, AI itself has become a hot consulting topic,

spawning new practices in AI strategy, data governance, and

digital transformation advisory. Firms like McKinsey and Deloitte

now have dedicated AI and analytics divisions. These groups not

only utilise AI internally but also develop AI solutions for clients or

provide guidance on AI adoption roadmaps. For instance,

consultants might deliver a custom AI model to a client as part of

Augmenting Expertise with AI

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that where a project might have had 5 team members, now 3

plus an AI toolkit can achieve the same results. Rather than

eliminating the need for consultants, AI augments their

capabilities. Successful consultants will be those who excel at

using AI as a partner, for instance, using a GPT-based tool to

generate an outline of a strategy and then relying on their

business acumen to refine and customise it. Consultancies

should invest in training their staff on these tools (as many are

doing – PwC is investing $1 billion to upskill all 65,000 of its U.S.

employees in AI and implement AI across its services). Moreover,

governance is key: ensuring the confidentiality and accuracy of AI

outputs is critical in client service. Leading firms are

implementing AI governance frameworks and oversight (e.g.

requiring human review of AI-generated client deliverables) to

maintain quality. Bottom line: consulting firms that harness AI

can deliver deeper insights faster and carve out new revenue

streams, but they must carefully manage talent and quality in this

transition.

an engagement (e.g. a retailer gets an AI-driven pricing tool built

by the consulting team). This represents a shift from purely

PowerPoint-based recommendations to more productized, tech-

enabled deliverables. It’s also a growth area: “AI and Machine

Learning Specialist” roles are among the fastest-growing jobs

globally (projected ~40% growth by 2027), and consultancies are

hiring these specialists to meet client demand. The consulting

business model is therefore evolving – blending traditional

domain expertise with technical prowess. Consultancies that

successfully integrate AI can perform projects faster and often

quantitatively demonstrate impact (e.g. showing an algorithm

improved a client’s efficiency by X%). This increases client ROI on

consulting expenditures and can serve as a competitive

differentiator.

Strategic Considerations

For consultancy leaders, AI offers a double benefit – internal

efficiency and market relevance – but requires change

management. The classic pyramid staffing model may become

more “diamond-shaped” as fewer entry-level analysts are needed

and more mid-level tech experts join. Already, some firms note 19

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Key Use Cases

Automated client insights and diagnostics

Legal/financial document summarisation

Custom AI strategy tools and platforms

Trend

High-margin services shifting from people-powered to hybrid

(AI+human)

Consulting Data Trends

AI Value Creation

~$0.7–1 trillion by 2035

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Creative Industries

Business Transformation

Creative industries – including media, marketing, design,

entertainment, and the arts – are experiencing a paradigm shift

as AI gains the ability to generate and remix creative content.

Generative AI models can now produce text, images, music, and

even video that in some cases pass for human-created. For

businesses in these fields, AI is a force multiplier for content

production and audience engagement. In marketing and

advertising, for example, AI tools can generate countless

variations of an ad copy or image tailored to different

demographics, something that was prohibitively slow or

expensive to do manually. Large ad agencies have started using

image generators like DALL·E or Midjourney to create concept art

and storyboards for campaigns in minutes. A notable case is

Coca-Cola’s 2023 “Create Real Magic” campaign, where the

company invited consumers and digital artists to use OpenAI’s

generative models to produce unique Coke-themed artwork –

blending user creativity with AI and driving massive brand

engagement. In media and publishing, AI is automating routine

content like sports recaps, financial reports, or basic news briefs.

The Associated Press has used AI for years to draft corporate

earnings stories from data feeds, freeing up journalists for more

in-depth reporting. Entertainment is also experimenting:

scriptwriters use AI for idea generation, and video game studios

employ AI to create vast virtual worlds or realistic character

dialogue on the fly. Even in filmmaking, AI is starting to be used

for special effects – for instance, “de-ageing” actors or generating

background scenes – which can significantly cut production time

and cost.

Content Generation and Personalization at Scale

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Strategic Considerations

For CxOs in creative fields, AI brings both excitement and

challenges. On one hand, embracing AI can slash production

costs and open up new creative possibilities (think interactive

content or personalized ads for every customer). On the other

hand, quality and authenticity must be carefully managed. AI-

generated content still benefits from human oversight – without

editorial judgment, AI can produce content that is off-brand or of

inconsistent quality. Leading organizations treat AI outputs as

first drafts. For instance, a design firm might generate 100 logo

ideas with AI, but human designers will curate and refine the best

one to ensure it meets the client’s vision. Another key strategy is

upskilling creative teams to work with AI. The role of a designer

or writer is evolving to include being a proficient “AI wrangler” –

knowing how to get the best results from tools through effective

prompts and then adding the human touch. Intellectual property

and ethics also come into play: businesses need to navigate

maintain consumer trust (e.g. disclosing when an image or article

was AI-assisted if appropriate). Those who get it right can achieve

a significant competitive edge. A marketing agency that pumps

Personalization and Engagement

Beyond content creation, AI is revolutionizing how creative

content is delivered and experienced. Streaming platforms and

social media use AI algorithms to personalize feeds, playlists, and

recommendations, which keeps users more engaged and drives

consumption. This data-driven personalization has become

essential for media businesses; for example, Netflix’s investment

in AI-driven recommendation systems is credited with

significantly higher viewer retention. In the realm of design and

user experience, AI can run multivariate tests to determine which

creative elements (layouts, colors, headlines) resonate best with

an audience, enabling real-time optimization of digital content.

Video game companies are leveraging AI to adjust game difficulty

and narrative paths based on player behavior, effectively

personalizing the gaming experience. All these applications share

a theme: scalability. AI allows creative industries to operate at an

unprecedented scale – a single copywriter or artist, augmented

by AI, can output the work equivalent of a whole team in certain

tasks. A survey of creative professionals found that 40% reported

higher efficiency and better results by using generative AI as a

creative aid. 22

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out high-performing content aided by AI will outcompete one

that relies solely on human throughput. In the coming years, we

can expect new creative formats (AI-generated interactive stories,

personalized comics, etc.) and deeper integration of AI in creative

workflows. The essence of creative industries – human

imagination and emotional connection – remains irreplaceable,

but AI is proving to be a powerful tool to amplify those human

talents.

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Key Use Cases

Generative content creation (text, image, video)

Hyper-personalized advertising

Creative concept prototyping

Productivity Boost

Up to 70% faster campaign execution

Trend

Human + AI co-creation, ethical concerns on IP and authenticity

Creative Industries Data Trends

AI Value Creation

$1.85 trillion by 2035

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Education

Business Transformation

Education – whether K-12 schools, universities, or corporate

training – has traditionally been a high-touch, face-to-face

domain. But AI is gradually making inroads, augmenting

educators and scaling personalized learning in ways previously

unattainable. The COVID-19 pandemic’s shift to online learning

accelerated the adoption of EdTech, including AI tutors and

learning platforms. One of the most powerful applications of AI in

education is the intelligent tutoring system. These AI tutors

provide students with practice problems, feedback, and hints

tailored to their learning pace. For instance, an AI math tutor can

detect that a particular student struggles with quadratic

equations and then present targeted exercises to strengthen that

skill. Early implementations in places like China (with systems

such as Squirrel AI) have shown remarkable results – in one rural

school, after just one month of using an AI-driven learning

platform, students’ grades, engagement, and confidence

improved significantly. Such platforms essentially bring one-on-

one tutoring to every student, something that schools with large

class sizes could never afford with human staff alone.

AI is also automating administrative and grading tasks. Routine

grading of multiple-choice tests has been done by machines for

years, but now AI can assist in grading essays and open-ended

responses. While not perfect, AI can flag areas in student essays

(grammar issues, off-topic sections) for teachers to review, cutting

grading time substantially. Administrative chatbots can answer

students’ frequently asked questions (like “When is the

application deadline?” or “How do I access my transcript?”), easing

the burden on school offices. In higher education and corporate

learning, AI tools can help create content – for example,

generating quizzes, flashcards, or even draft lecture notes on a

topic, which instructors can then refine. Universities have started

experimenting with AI teaching assistants: Georgia Tech famously

Personalized Learning at Scale

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used an AI TA (based on IBM Watson) in an online course to

answer students’ forum questions, and most students didn’t

realize their helper wasn’t human. This kind of support can

ensure that learners get timely help, even at odd hours

Personalization and Access

Perhaps the most transformative potential of AI in education is

personalized learning paths. Rather than a one-size-fits-all

curriculum, AI systems can adapt to each learner’s strengths,

weaknesses, and interests. A student strong in reading but weak

in math might get more math practice and fewer redundant

reading exercises, for example. AI can also provide enrichment

for advanced learners (offering harder challenges when it detects

mastery) and remediation for those behind (revisiting

prerequisites they haven’t grasped). This helps keep students in

their zone of proximal development – not too bored, not too

frustrated. For corporate training, AI can tailor reskilling

programs based on an employee’s current skill profile and career

goals, making workforce development more efficient.

Additionally, AI-driven analytics can predict which students are at

risk of falling behind or dropping out by tracking factors like login

frequency, assessment scores, or even sentiment in written

assignments. Educators and managers can then intervene early.

Overall, AI has the potential to dramatically widen access to

quality education, since one AI tutor can serve millions. UNESCO

projects a need for 44 million new teachers by 2030 to meet

global education goals – a gap that realistically cannot be met

through traditional methods alone. AI tutors and support tools

can help bridge this gap in under-resourced regions by

supplementing scarce human teachers.

Strategic Considerations

Education leaders (school administrators, university deans, Chief

Learning Officers in companies) should approach AI as an

augmenting tool rather than a replacement for educators. The

human element – mentorship, motivation, and social-emotional

support – remains critical in learning. Successful strategies pair

teachers with AI: teachers focus on higher-order teaching (e.g.

leading discussions, critical thinking exercises, project-based

learning) while offloading repetitive tasks (drilling practice

problems, grading) to AI. This can help reduce burnout and free

educators to do what they do best. A key step is training

educators to use AI systems effectively and to trust the data

insights they provide. Some resistance is natural, as teachers

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might fear being sidelined or doubt an AI’s educational judgment.

Change management and evidence of effectiveness (like pilot

programs demonstrating improved test scores) are important to

gain buy-in. Data privacy is another concern; student data used

by AI systems must be safeguarded and comply with regulations.

Equity is also a double-edged issue: AI in education can either

widen or close gaps. If only wealthy schools can afford AI,

disparities grow – but many AI learning tools run on basic

smartphones and are being offered at low cost or free (often

supported by government or NGO initiatives), which could

democratize access to personalized learning. From a business

perspective, the education sector is seeing a boom in EdTech

investments. Global spending on AI in education is projected to

grow from $1.8 billion in 2018 to $12.6 billion by 2025. For private

education companies and content publishers, integrating AI

features into their products (like adaptive test prep apps or AI-

driven language learning like Duolingo’s GPT -4-based tutor) will

be key to remaining relevant and competitive. The next decade

could see hybrid models like AI-assisted classrooms become the

norm, and CxOs in this space must envision how their

institutions can harness AI to improve outcomes while preserving

the human-centered mission of education.

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Key Use Cases

Adaptive learning and assessment (e.g., Squirrel AI, Khanmigo)

Virtual tutors and personalized learning paths

AI-generated content and simulations

Trend

Closing learning gaps and scaling low-cost education

Education Data Trends

AI Value Creation

$109 billion by 2035

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Manufacturing, technology, and finance will

see the largest AI windfalls. These sectors

alone will account for over $8 trillion in new

AI-generated value by 2035.

By 2030, companies in manufacturing and

technology will not be asking ‘Should we use AI?’

but ‘Why aren’t all of our systems self-learning

already?’

This curve allows executives to benchmark

themselves and prepare for transformation

AI Maturity

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The AI goldmine is not just in futuristic robots, it’s in optimising

boring but costly decisions: forecasting, fraud, maintenance.

Strategic Impact

Shifts AI discussion from ‘shiny’ to profit-generating use.

Prioritizes low-hanging fruit for ROI.

Encourages pragmatic rollout.

Top AI Applications

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EU US Asia LATAM

Talent Aviability

Investment

Policy/Ethics focus

Infra & Data Access

Strategic Impact

Helps multinationals plan where to locate talent or test AI

pilots.

Encourages policymakers to improve infrastructure or

governance.

Suggests collaboration opportunities (e.g., EU–LATAM

talent pipelines).

AI competitiveness is now geopolitical. Talent, trust, and

infrastructure define winners — not just tech

Regional Readiness for AI

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Jobs

& Skills

AI and the Workforce

While AI is transforming business operations, it is also reshaping

the workforce. For business leaders and policymakers, the crucial

questions are: What jobs will be lost or created? What skills will be

needed? And how do we manage this transition? AI will be a

“double-edged sword” for employment, automating some tasks

and roles while generating new ones and increasing demand in

other areas. The World Economic Forum projects a 23% job churn

by 2027, meaning that nearly a quarter of all jobs will change due

to the emergence of new roles or the redundancy of existing

ones. In absolute terms, 69 million new jobs may be created and

83 million eliminated, for a net loss of ~14 million jobs (about 2%

of current employment). It’s important to note this net figure

masks much creative disruption – tens of millions of individuals

will likely need to change jobs or skill sets. Historically,

technological revolutions (from steam engines to computers)

have ultimately created more jobs than they destroyed, albeit

with short-term disruption and pain. AI appears to be following a

similar pattern. Goldman Sachs' research suggests up to 300

million full-time jobs globally could be affected by AI automation

(to varying degrees), with most jobs seeing a portion of their tasks

(rather than the whole job) automated. Thus, the future is more

about task transformation than wholesale job extinction: AI will

handle specific routine or analytical tasks, allowing humans to

focus on more complex, interpersonal, or creative duties.

Across industries, jobs involving routine, repetitive work are the

most vulnerable, while those that require creative thinking,

strategic decision-making, or human empathy are more resilient

(often even in greater demand). At the same time, AI is giving rise

to entirely new roles – from AI model engineers to data ethicists

to automation coordinators – roles that barely existed a decade

ago. One study noted that 60% of today’s workers are in

occupations that did not exist in 1940, reflecting how technology

creates new work over time. The challenge for the workforce is

skills adaptation at scale. The half-life of skills is shrinking.

Impact on Jobs and Skills

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employers estimate that 44% of workers’ core skills will change

within five years. This puts a premium on continuous learning.

According to one estimate, by 2025, half of all employees

worldwide will require reskilling to meet the demands of new

technologies. Forward-looking organisations are already investing

heavily in upskilling: two-thirds of companies expect a return on

investment in retraining within a year as new skills boost

productivity or mobility. Now, we explore how AI is impacting the

workforce in specific sectors and discuss strategies that can help

manage the transition.

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Manufacturing

Job Displacement

Manufacturing has seen significant job losses from automation

over the past few decades, and AI-driven robotics will continue

this trend for certain roles. Repetitive assembly line jobs, basic

machine operation, and simple fabrication tasks are increasingly

done by robots or AI-controlled machines. Industrial robots today

can not only weld and paint (as they have for years in auto

factories) but also handle more dexterous tasks thanks to AI

vision systems – such as sorting random parts, assembling small

electronics, or packing goods in warehouses. Studies have

quantified these effects: each additional robot installed in

manufacturing is correlated with a net loss of about 1.6

manufacturing jobs on average. By one projection, up to 20

million manufacturing jobs globally could be displaced by robots

by 2030, about 8% of the sector’s workforce. These losses will be

felt most acutely in lower-skilled positions – e.g. assemblers,

welders, and machine operators – especially in advanced

economies with higher labour costs or in industries that can fully

standardize production. We’ve already seen dramatic examples

like the Chinese factory that went from 650 workers to 60 after

automating (mentioned earlier). In developed countries,

manufacturing employment as a share of total employment has

steadily declined in part due to automation. Regions and

communities dependent on traditional factory jobs may continue

to struggle unless new industries or roles emerge.

Job Creation and Evolution

On the flip side, manufacturing is not emptying – it’s shifting in

the skill profile. Modern “smart factories” need technicians,

engineers, and IT specialists to run and maintain automated

systems. Demand is rising for roles like robotics technicians,

industrial data scientists, and maintenance engineers who can

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work with predictive analytics. Many manufacturers report being

unable to fill skilled trade jobs even as unskilled roles are cut. A

study in the U.S. found a potential shortage of 2.1 million skilled

manufacturing workers by 2030 despite automation, due to the

gap between job requirements and workers’ skills. New roles such

as cobot coordinators (workers who manage collaborative robots

on the line) or additive manufacturing specialists (overseeing 3D

printing processes) are emerging. For workers, this means that

the quintessential factory job is becoming less physically intensive

but more technically demanding. Training programs – like

Germany’s apprenticeship system or new vocational tech

institutes – are crucial to prepare the existing manufacturing

workforce for these roles. In practice, many manufacturing firms

that automate also invest in retraining some of their displaced

workers for higher-tech roles. For example, a line worker might

be trained to program robots or interpret data from an AI quality

control system. When such transitions are managed, automation

need not result in mass layoffs, but rather gradual attrition of old

roles and growth of new ones. Still, not every displaced worker

finds it easy to transition; support in the form of accessible

training and sometimes relocation will be needed.

Management Strategies

Manufacturing leaders should approach AI workforce changes

proactively. Engaging employees early about planned automation

and providing pathways for them to move into new positions

fosters a culture of trust and agility. Many successful factories

implement a “centre of excellence” where a team is responsible

for continuous training in new tech. On the policy side,

partnerships between industry, government, and educational

institutions can create pipeline programs for the next generation

of manufacturing talent (e.g. community college courses on

industrial AI, and sponsored certification programs). It’s worth

noting that some manufacturing roles remain hard to automate

due to complexity or cost – for instance, custom fabrication,

certain assembly in small-batch production, or maintenance work

in unpredictable environments. Those jobs will persist, but even

they will likely incorporate AI tools (e.g. maintenance workers

using AR glasses with AI to diagnose issues). Thus, even workers

in “safe” roles will need digital skills. In summary, manufacturing

will continue to employ millions, but the mix of occupations will

shift: fewer line workers, more technicians and engineers.

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Companies and economies that manage to retrain and elevate

their manufacturing workforce will not only soften the blow of

automation but also gain a competitive edge through a more

skilled, versatile workforce.

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Technology

Job Displacement

In the tech sector itself (software engineers, IT admins, etc.), AI is

unlikely to cause mass unemployment, but it will change the job

content significantly. Certain roles or tasks are being partially

automated. For example, a junior software developer’s routine

coding tasks can be greatly sped up by AI code suggestions, which

means one developer can do more at the same time. If each

developer becomes, say, 20–30% more productive on average

thanks to AI, companies might slow the rate of adding headcount

in some areas. We might see fewer pure “coder” roles needed for

maintenance programming or simple apps, as those can

increasingly be generated from high-level specifications. IT

support roles are another area – AI chatbots and self-healing

systems can handle many Level 1 support tickets (like password

resets or basic troubleshooting). This could reduce the number of

helpdesk technicians needed. In one indicator, enterprise use of

AI for IT service management is growing rapidly, and anecdotal

reports suggest some firms have been able to repurpose a

portion of their support staff to other tasks after implementing

AI-based support. Additionally, quality assurance (QA) testing

roles might decline as AI generates and runs test cases

automatically. However, it’s important to note that technology

work often expands with automation – when efficiency increases,

companies often undertake more projects. So rather than seeing

widespread layoffs, tech companies might achieve more output

with the same number of people, or redirect staff to more

complex projects that were on the backlog.

Job Creation and Evolution

The tech sector is experiencing robust job growth driven by AI.

Essentially, AI is creating more tech jobs than it’s eliminating at

the moment. Demand for AI specialists is through the roof –

Upskilling for an AI-Native Tech Workforce

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in tech is a rising skill bar – the average tech job in 2030 will likely

require more AI familiarity than the average tech job in 2020.

Companies like IBM and Google have said they consider AI skills

essential for many roles and have incorporated AI questions into

their hiring or training programs.

Managing the Transition

For tech industry employers, the major focus is on continuous

learning and internal mobility. Many leading companies have

created AI training programs for their existing engineers (for

instance, Amazon’s Machine Learning University for employees).

They encourage software teams to integrate AI tools into their

daily work, sharing best practices on how to pair programs with

AI. The culture in tech generally embraces new tools, so developer

pushback is minimal when AI helps – although there can be initial

fears about “will AI take my job?”, these often dissipate once

developers use the tools and see them as aids. One interesting

outcome reported is improved job satisfaction: by automating

tedious coding, AI lets engineers concentrate on more rewarding

tasks, as evidenced by GitHub’s finding that 88% of developers felt

more productive and 75% enjoyed their work more with AI

roles like machine learning engineers, AI researchers, data

engineers, and AI product managers are among the fastest

growing. The World Economic Forum identified “AI and Machine

Learning Specialists” as the top growing job role, projecting a 40%

growth by 2027. Even beyond pure AI roles, most new software

products have an AI component, so software developers with AI

knowledge are highly sought. There are also new hybrid roles

coming up: prompt engineers (people who craft inputs to get the

best results from generative AI – though some argue this will itself

be short-lived as AI improves), AI ethicists (to ensure algorithms

are fair and compliant), and DevOps/MLOps specialists who

deploy and monitor AI models in production. Importantly, many

existing tech roles are upskilling rather than disappearing. A

backend developer might learn to incorporate AI APIs; a data

analyst might upskill to a data scientist who can build machine

learning models. There is evidence that tech professionals are

adapting: online learning platforms report surges in AI-related

course enrollment among software engineers. Moreover, entirely

new fields at the intersection of tech and other domains are

emerging, like biotech-software roles (for AI-driven drug

discovery), or AI in cybersecurity (creating demand for security

experts who understand AI threats and defenses). The net effect 39

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assistance. Nonetheless, companies must address potential skill

gaps – not every legacy programmer is well-versed in AI/ML, so

offering pathways (via courses, certifications, and mentorship) is

crucial to avoid leaving portions of the workforce behind. There is

also the challenge of recruiting for new roles when the talent

supply is limited – that’s why many firms invest in retraining their

current staff to fill AI roles instead of exclusively hiring externally.

From the perspective of an IT department in a non-tech company

(say the IT team in a bank or retailer), there’s a similar pattern:

some traditional admin roles might be reduced, but new roles in

AI integration and data science appear. In sum, for tech workers,

embracing AI is becoming part of the professional mandate.

Those who add AI to their skillset are highly valuable and can

drive exciting projects; those who don’t may find their

opportunities limited in the most innovative areas. The tech

workforce of 2035 will likely look different – more

multidisciplinary, more productive, and focused on overseeing

AI-infused systems – but it will be as vital as ever, with humans at

the helm to ensure technology truly serves business and society.

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Finance

Job Displacement

The financial sector has seen clear examples of AI-driven job

displacement, especially in roles heavy on routine data

processing. Bank branches have been slimming down for years –

ATMs and online banking reduced the need for as many tellers,

and now AI virtual assistants are further taking on customer

service queries. A startling case: one major investment bank

automated much of its equity trading operations, going from 600

human traders in 2000 to just 2 traders overseeing automated

systems by 2017. Those traders were effectively replaced by 200

software engineers keeping the algorithms running. Similar

trends are seen in back-office roles: credit underwriting, account

opening, and compliance checking – AI can process applications

and flag exceptions far faster, so fewer clerks or analysts are

required. In insurance, where armies of underwriters or claims

adjusters once pored over documents, AI can instantly evaluate

risk (using statistical models on big data) or assess damage from

uploaded photos. This doesn’t entirely remove humans – but

significantly reduces the headcount needed per case. The WEF

notes that record-keeping and administrative roles(like data entry

clerks, accounting clerks, and payroll clerks) are among those

expected to decline the most by 2027, with 26 million fewer such

jobs envisioned. Many of those are in finance or finance-related

functions, due largely to automation. Even customer-facing roles

like loan officers might decrease as loan platforms become AI-

driven; a customer can get a mortgage largely through an app

that instantaneously evaluates their financials. It’s worth noting

that while these specific roles shrink, overall financial sector

employment has often been stable or growing slightly – because

the sector evolves (e.g. fewer tellers but more financial product

salespeople). However, the composition is changing markedly.

Traditional mid-skill roles that involved a lot of repetitive

Redefining Roles and Skill Requirements

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paperwork are fading.

Job Creation and Evolution

Finance is simultaneously creating new jobs, particularly in the

realm of tech and data. As noted, Goldman’s “replacement” of

traders was a transformation of trader jobs into quantitative

engineer jobs. Banks, asset managers, and insurers are hiring

thousands of data scientists, AI modellers, and digital product

managers. For every process automated, there’s a need for

people to implement and monitor that automation. Roles like

algorithmic trading strategist, AI risk model validator, or FinTech

product developer are booming. Cybersecurity has also become

paramount in finance as operations digitalize – leading to a high

demand for security analysts and AI specialists in fraud

detection. The WEF’s list of fast-growing roles includes

Information Security Analysts and Business Intelligence Analysts,

which are heavily used in finance. Another growth area is

advisory roles that cannot be automated easily. While robo-

advisors manage basic investment portfolios, human financial

advisors focus on more holistic planning for clients (complex

financial planning, trust and estate considerations, etc.)

– these roles may even grow as the population ages and more

people seek personalized advice beyond what an algorithm can

provide. So the profile of financial sector jobs is tilting toward

high-skill roles: more STEM skills and analytical thinking required.

Even a typical banker or financial analyst today is expected to be

proficient in tools like Python or SQL to analyze data, something

that wasn’t the norm a generation ago. Also, new categories of

businesses (FinTech startups) are creating jobs that blend finance

and tech (think blockchain analysts, mobile payment product

designers, etc.). To illustrate, when mobile payments and peer-

to-peer lending took off, they spawned whole new departments

at banks and entirely new companies, employing people in roles

that didn’t exist before (like UX designer for a banking app or

compliance officer for crypto assets). Thus, while AI prunes away

some old roles, finance as a sector remains dynamic in

generating new opportunities.

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Adapting the Workforce

Finance industry leaders are actively managing the workforce

transition in a few ways. Reskilling programs are common,

especially in big banks – for example, a bank may retrain surplus

tellers as “universal bankers” who can do a mix of sales and

advisory, or train operations staff in data analytics to move into

risk analysis. Many financial firms have internal academies for

technology and analytics skills. There’s also a trend to recruit

differently: hiring more candidates with STEM backgrounds into

what were business-major roles. As of a couple years ago, about

one-third of Goldman Sachs’ hires were engineers – reflecting

how a “finance job” increasingly requires coding or quantitative

skills. Another tactic is human-AI teaming: rather than fully

automate a process and remove the human, some banks assign

employees to work alongside the AI, handling exceptions or

offering the personal touch for high-value clients. For example, AI

might draft a suspicious activity report, and a compliance officer

reviews and finalizes it – this improves efficiency but keeps the

officer in loop. Culturally, finance firms are trying to shift from a

traditional hierarchical approach to a more agile, tech-like culture

to retain talent. The influx of tech people means banks compete

with Silicon Valley for the same talent, so they have to offer

interesting projects (like working on cutting-edge AI models) and

flexible environments. From a societal perspective, the

contraction of certain clerical financial jobs is a concern, since

those have historically been stable middle-class jobs. Workforce

planners and governments may need to support those workers in

finding new careers (through adult education and placement

programs). Encouragingly, many displaced workers do find new

roles: e.g. someone leaving a bank’s back office might take their

numerical skills to a finance department in another industry, or

pivot into a customer-facing role that still requires human

empathy. In summary, the financial sector is in the midst of a

skills revolution – today’s finance professional needs a mix of

financial acumen, digital literacy, and strategic thinking. Those

who adapt will thrive in roles that are more interesting (less

paperwork, more analysis), and those firms that support their

workforce through the change will be better positioned to

innovate and perform.

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Consulting

Job Displacement

In consulting, AI’s impact on employment is subtler than in

sectors like manufacturing, but it’s poised to reshape the career

ladder and required skill sets. The traditional consulting model

heavily relies on fresh graduates/analysts to perform data

gathering, basic analysis, and slide preparation. As AI tools handle

more of that grunt work, firms may need fewer junior analysts for

a given project. For example, if a task like benchmarking a client

against industry data takes a team of 4 analysts a week, an AI

might do 80% of it in minutes, needing just one consultant to

verify and refine the output. This means the classic pyramid

(many juniors supporting each partner) could flatten somewhat.

We might see smaller teams where mid-level consultants directly

leverage AI, reducing the demand for large classes of entry-level

hires. Indeed, consultants themselves have noted that parts of

their job (like creating the first draft of a report or combing

through earnings transcripts for insights) are now much faster

with AI. It’s conceivable that over the next decade, consulting

firms will trim some roles that are essentially manual data

processors or research assistants. However, it’s important not to

overstate this – consulting engagements often expand to tackle

more problems when productivity increases, potentially

maintaining demand for talent. There isn’t clear data yet on

consulting job reductions due to AI, but we can expect gradual

erosion of certain support roles (like pure research analyst

positions or presentation specialists) as those tasks become

automated.

Job Creation and Evolution

Rather than a net loss of consulting jobs, we’re likely to witness

an evolution of roles. Consultants will increasingly be expected to

be “bilingual” – fluent in both business domain knowledge and AI

Changing Team Structures and New Roles

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and tech-augmented. The job of a consultant will be less about

crunching numbers manually and more about asking the right

questions, interpreting AI-generated outputs, and guiding clients

through change.

Workforce Strategy

For consulting companies, the key workforce strategy is upskilling

and role redesign. Many firms are already training all staff on the

basics of data science and AI tools, ensuring that even non-

technical consultants know how to use, say, a visualization tool or

a GPT assistant in their workflow. Some have created dual tracks

or “T-shaped” consultant models, where an individual might be a

generalist consultant but with deep expertise in an AI-related

field. The industry is also paying attention to retaining human-

centred skills. Clients ultimately pay for trusted advice, creativity,

and problem-solving – things AI alone can’t provide. So

consultants are doubling down on those areas: strengthening

skills like interpersonal communication, industry expertise, and

change management, which become even more important if AI

takes over the number-crunching. Consulting firms also face the

challenge of talent attraction in the age of AI – they now compete

tool usage. New roles are also emerging within firms, such as “AI

strategy consultant”, “analytics translator”, or “AI implementation

lead” who works on integrating AI solutions in client

organizations. The World Economic Forum reports high demand

growth for “Big Data and AI Specialists” across many industries,

and consulting is no exception – firms are aggressively hiring

data scientists and software engineers to work alongside MBAs.

These tech-savvy consultants may not have existed in a

traditional firm 15 years ago. Additionally, as consultancies

productize AI offerings, roles in product management and

technical sales within consulting firms could grow. Internal to the

firm, there will be teams maintaining the AI knowledge bases (for

example, some firms use internal GPT-style tools trained on

decades of their proprietary reports to support case teams –

those systems need curating by knowledge experts and

technologists). We also see consulting firms expanding into

managed services – e.g. running a client’s AI-driven marketing

analytics on an ongoing basis – which creates more continuous

roles rather than just project-based ones. At the senior level,

partners and managers will still be vital, but their profiles might

tilt more toward those who can harness advanced analytics for

insight. Ultimately, consulting jobs will become more specialized 45

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with tech firms for the best STEM graduates. However, many

recruits might find it attractive that at a consultancy they can

work on varied AI projects across industries rather than being

siloed in one tech company. In summary, consulting isn’t at risk

of disappearing – if anything, AI might increase demand for

advisory services as companies seek help navigating AI adoption.

But the nature of consulting work is shifting. The image of teams

burning the midnight oil to manually build Excel models is fading;

tomorrow’s consulting teams will leverage AI for instant insights,

focusing human effort on high-level analysis and client

collaboration. Firms that manage this transition well – by

blending human expertise with AI capabilities – will not only

improve productivity (some studies show AI-adopter firms in

consulting seeing higher profit growth) but also make the work

more engaging for their people.

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Creative Industries

Job Displacement

In the creative industries, the fear of “AI replacing artists” has

been a hot topic. The reality unfolding is that entry-level and

routine creative tasks are the most at risk, rather than the

complete eradication of creative jobs. For instance, graphic

design has many routine production tasks – resizing assets,

simple layout, background removal, and basic logo design – which

AI can now do quite well. A junior graphic designer who might

have spent hours making social media banner variations might

find that a tool like DALL·E can generate dozens of concepts in

seconds. This could mean companies hire fewer people for basic

design production, using AI tools managed by one senior

designer instead. In fields like advertising, we’re already seeing

agencies use AI to generate draft copy or imagery for campaigns,

reducing the need for large teams to develop initial ideas.

Journalism is another area: AI can write templated news (like

sports scores, weather reports, and financial summaries) without

human reporters. Outlets that have implemented these AI-written

pieces often repurpose reporters to more investigative work, but

it does mean fewer freelanced short article gigs. Administrative

creative jobs – e.g. photo editors who do simple touch-ups or

content moderators who select images – could also shrink as AI

handles those functions (automatic photo enhancement, AI

content curation). It’s telling that in WEF’s job forecast, roles like

“Bank Tellers, Clerks, and Data Entry” are the fastest declining

(largely due to automation), and similarly, we can imagine

“Publishing Proofreaders” or “Catalogue Illustrators” might be

among creative roles that see the decline. However, it’s worth

noting that creative work often doesn’t disappear but changes

form – the total output of content (images, videos, articles) in the

world is exploding, so even if each piece takes less human labour,

the demand for content is insatiable in the digital economy.

New Creative Roles and the Human Touch

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Job Creation and Evolution

Far from a dystopia of artist unemployment, we’re seeing many

new creative opportunities arising thanks to AI. One big area is

the need for AI-savvy creatives – individuals who blend artistic

skill with technical understanding to use the new tools effectively.

These are people like AI art directors, who know how to get the

best out of image generators and then refine the results, or

interactive narrative designers who incorporate AI to let

audiences influence a story. There are also roles in managing AI

content workflows: companies might have an “AI content curator”

who oversees the material generated by AI, ensuring it fits the

brand and legal guidelines. Furthermore, entirely new genres of

content are emerging. For example, in music, there are now “AI

DJs” that produce endless streams of custom music – a human DJ

or music producer might curate AI-generated beats and add their

flair. In gaming and VR, AI can create evolving storylines or

characters, so game designers are needed to set the rules and

coach the AI on desired styles. Another interesting development:

creative AI services have become a business in themselves – for

instance, startups offering AI-generated illustrations on demand

have designers and engineers working together. Those startups

hire traditional artists to help “train” AI or to create templates,

thus generating niche jobs. Additionally, as content creation

costs drop, we might see more localized and personalized

content production, employing creators to oversee those niche

productions (imagine hyper-local news bulletins auto-written by

AI but managed by a local editor). The creative fields may also

expand into new markets – for instance, small businesses that

couldn’t afford marketing can now do more marketing with AI

templates, possibly consulting with freelance creatives for final

touches, thereby increasing the overall pie of creative work.

Importantly, human creativity and cultural insight remain

inimitable: while AI can mash up existing patterns, humans lead

in defining new artistic directions, breaking the mold, and

imbuing work with meaning and emotion. The roles that

emphasize those human strengths will grow.

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Skills and Adaptation

For creative professionals, the message is to adapt and adopt.

Learning to use AI tools is becoming as necessary as learning the

latest software. Many designers are teaching themselves prompt

engineering for Midjourney, and writers are learning to edit AI

drafts rather than writing from scratch. Surveys indicate a

majority of creatives who use AI feel it enhances their work (e.g.

giving them more ideas or speeding up execution). Thus, creative

education programs are starting to include data literacy and AI

tool usage in the curriculum. At the same time, soft skills and

domain knowledge become more critical – understanding

consumer psychology, having a unique artistic style, or excellent

storytelling ability will set humans apart from AI’s generic

outputs. Another aspect is ethics and legal knowledge – creatives

need to navigate copyright in the era of AI (e.g. understanding

how training data might involve copyrighted art) and ensure fair

use. Some professionals may specialize in offering a “100%

human-made” brand as a selling point, especially for clients who

value that. Others will capitalize on AI to offer faster and cheaper

services and outcompete those who do not. Management in

creative industries should foster a culture of experimentation

with AI, giving teams the freedom to pilot new tools, while also

establishing guidelines (for example, to maintain originality and

avoid everyone converging on AI’s often formulaic outputs). In

conclusion, creative jobs are not going away – they’re evolving.

The drudgery in creative work (every field has some) is being

offloaded, which is largely positive. The challenge is ensuring that

upcoming artists and writers learn to carve a space where they

work with AI to amplify their unique creative voice. Companies

that harness the synergy of human creativity plus AI’s speed and

breadth will produce the most compelling content, whereas those

that try to rely on AI alone risk output that may be cheap and

quick, but soulless – and in the long run, creative industries thrive

on soul.

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Education

Job Displacement

In education, fears of AI “replacing teachers” periodically surface,

but the consensus is that AI will not replace teachers – it will

assist them. There are a few areas where roles might diminish.

One is in tutoring and test prep: human tutors who mainly help

students practice problems could face competition from always-

available AI tutor apps. If an AI can effectively coach a student in,

say, SAT math at a fraction of the cost of a human tutor, the

demand for human tutors could be reduced. Another area is

administrative and support staff in education: as mentioned,

things like scheduling, admissions processing, and basic advising

can be partly handled by AI chatbots or systems, potentially

meaning slightly leaner administrative teams at schools and

universities. Large online courses (MOOCs or big university

lectures) that used to require multiple teaching assistants to

grade and answer questions might use AI to handle some of that

load – so maybe fewer TAs are needed per class. However, these

adjustments are marginal in the grand scheme because

education globally actually suffers from a teacher shortage, not a

surplus. The United Nations estimates a need for tens of millions

more teachers by 2030, particularly in developing regions. AI

might help fill some of that gap, but it’s not subtracting existing

teachers en masse. In corporate training departments, perhaps

reliance on external trainers may decrease as companies use AI

learning platforms for standardized training (like onboarding

programs delivered via an AI tutor). But again, someone has to

create and oversee those learning materials

Teachers Augmented, Not Replaced

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Job Creation and Evolution

Education will likely see new roles emerge that didn’t exist

before. For example, learning engineers or education data

analysts who monitor student performance data coming out of AI

systems and help teachers intervene effectively. With more tech

in the classroom, schools might hire IT specialists or EdTech

integrators (often former teachers who retrained in tech) to

support teachers in using new tools – much like schools hired IT

coordinators when computers were introduced. We’re also

seeing the growth of online course facilitators – not quite

teachers, but people who moderate online discussions, or tutors

who hop on-demand when an AI tutor flags a student is really

stuck. There’s also content creation: AI can generate basic

content, but educational content still needs a lot of human

vetting and creativity, so instructional designers who can work

with AI to produce courses quickly will be in demand. An

interesting role is “AI curriculum specialist” – someone who

curates the best AI resources for a given curriculum and ensures

they align with learning objectives. Moreover, as lifelong learning

becomes essential, more people might engage in part-time

teaching or content creation, often mediated by platforms

(imagine a scenario where an expert in a company creates a

micro-course with the help of AI and shares it internally – that

employee has temporarily taken on an educator role). Teachers’

roles themselves are evolving: rather than pure lecturers, they

are becoming facilitators and mentors who use AI tools to

personalize instruction. Many teachers report that using AI for

routine tasks allows them to spend more time on one-on-one

student interactions, which is the most rewarding part of their

job. This “augmentation” could make teaching more attractive

and sustainable (reducing burnout from administrative overload

Skills and Training

For educators, adapting to AI is now part of the professional

development agenda. Training programs for teachers

increasingly include modules on using AI-driven educational

software, interpreting the data it provides, and maintaining

student engagement in tech-rich environments. Teachers also

need to develop skills to critically evaluate AI content – for

instance, if an AI tutor explains a math problem, the teacher

should verify it’s correct and pedagogically sound. This means a

strong grounding in their subject matter is as important as ever

(since AI might occasionally provide wrong or suboptimal

explanations, and the teacher must catch that). Soft skills like

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empathy, motivational ability, and classroom management will

never be automated and thus become even more defining

qualities of a great teacher. From a leadership perspective,

school administrators and education policymakers face the

challenge of ensuring equitable AI access. If only some

classrooms have the latest AI tools and others don’t (due to

budget or infrastructure), the digital divide could worsen

educational inequalities. So, investment in EdTech needs to be

distributed and coupled with teacher training in under-resourced

schools. There’s also likely to be increased collaboration between

educators and technologists – for example, teachers providing

feedback to EdTech developers to improve AI tutoring systems.

This opens up hybrid career paths where experienced teachers

might move into EdTech companies as advisors or product

designers. Overall, while AI will change the daily work of

educators, it amplifies the need for human connection in

learning. A future classroom might run on AI in the background –

diagnosing each child’s progress and suggesting content – but at

the front will still be a caring teacher orchestrating the

experience, providing mentorship, and inspiring students. The

employment outlook in education is thus more about quality and

capability of teaching jobs than quantity – the world needs more

educators, and empowering them with AI can hopefully draw

more talent into teaching and enabling better outcomes without

replacing the essential human element.

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High Medium Baseline

AI Talent & Skills Demand Forecast (2025–2035)

Machine Learning Ops

Prompt Engineering

Ethical AI/Governance

Data Science

AI Product Management

AI-UX Design

Reeskilling Trainers

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The real AI bottleneck isn't compute power. It’s people.

Strategic Impact

Executives should be aware of upcoming talent scarcity.

Investment in upskilling/reskilling must be promoted.

Universities and HR leaders must refocus programs.

Insights

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Conclusion

Leading in the Age of AI

Artificial Intelligence is to become as ubiquitous in business as

electricity – an ever-present utility and source of power. Its

impact on both business operations and employment will be

profound over the next decade. For business leaders (CxOs), the

key takeaway is that AI must be proactively and strategically

embraced. The case studies and data across various industries

demonstrate that AI can drive significant efficiency gains (from

reducing downtime by 30% in factories to handling 80% of

customer inquiries via chatbots in banking), enable new business

models (such as personalised products and on-demand services),

and ultimately boost financial performance. Companies

integrating AI at scale have seen measurable benefits – Skale

Egenkapital finds companies with extensive AI adoption are more

likely to report profit growth and productivity improvements than

their peers. However, realising these benefits isn’t just about

buying technology; it requires reworking processes, upskilling

people, and often a cultural shift toward data-driven decision-

making.

Leaders need to champion AI adoption from the top (in fact, one

survey indicates companies where the CEO personally drives AI

initiatives see better ROI). They should also set transparent

governance to address risks (bias, security, compliance) so that AI

is implemented responsibly, gaining the trust of customers,

employees, and regulators.

On the workforce front, the often-feared narrative that AI will

cause mass unemployment is too simplistic. Yes, AI will displace

specific roles, especially those that are heavily reliant on routine

tasks, and this transition can be painful for the individuals and

communities affected. But as history and current trends suggest,

AI will also create jobs and even whole new professions. The net

effect on society will depend on how well we navigate this

transition. This is a call to action for both business and

government: investing in education and retraining at an

unprecedented scale. The ability to learn and adapt is the

currency of the AI age. Encouragingly, many employers recognise

this – in a recent survey, 80% of companies plan to roll out

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workforce training in the next five years, and two-thirds expect a

return on such investment within a year. Strategies like job

rotation, where employees whose tasks are automated can move

into new, higher-value roles, will be vital. Social safety nets and

continuous learning credits might be needed to support mid-

career transitions in society at large. Importantly, human skills

will only become more valuable. Creativity, critical thinking,

emotional intelligence, complex problem-solving – these are hard

to codify into algorithms. As AI takes over routine work, the

relative demand for these uniquely human skills increases.

Empathy in healthcare, creativity in design, strategic thinking in

management, and mentorship in education – these define the

jobs that will thrive.

For organisations and their CxOs, a winning formula is emerging:

combine the strengths of AI with the strengths of people.

Businesses that use AI to augment their workforce – not replace

it, often see the most sustainable gains. For example, in call

centres, AI can assist human agents with real-time suggestions

and routing, improving service quality while humans handle the

nuanced interactions. In product development, AI can generate

options, and human experts make the final decision aligned with

the brand and strategy. This symbiosis often leads to higher

employee satisfaction as well – employees spend more time on

meaningful work and less on drudgery. It’s also critical for leaders

to communicate a vision of AI as a tool for empowerment to

alleviate employee anxieties. When workers see AI helping them

achieve more and advance their skills, they become champions of

transformation rather than resistors.

Looking ahead to 2035, we can expect AI to be even more capable

– some experts predict that around 60% of current work tasks

could be automated by then (up from ~34% today and ~42%

projected in 2027). That doesn’t mean 60% of jobs are gone, but

rather a dramatic shift in how work gets done. Work will likely be

more project-based and flexible, with AI handling many support

functions. Entirely new industries (perhaps around AI-driven

healthcare, climate tech, space, and beyond) will arise, and with

them new jobs we can barely imagine. The organizations that will

flourish are those that stay agile, continuously reskilling their

workforce, and redesigning work to integrate AI where it adds

value. As a leader, fostering a culture of innovation and learning is

key – encouraging teams to pilot AI ideas, rewarding

experimentation, and not punishing failures that yield insights.

In conclusion, the future of business and employment with AI is

not a zero-sum game but a path of co-evolution. AI will push

businesses to be more efficient and imaginative, and it will push

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humans to upgrade our skills and focus on what we excel at.

Rather than seeing AI as a threat, leading companies view it as a

catalyst, to streamline operations, and to unlock human potential

for higher-level work. This balanced perspective is crucial. As the

saying goes, “AI won’t replace managers, but managers who use AI

will replace those who don’t.” The same could be said for many

professions. By making strategic investments today, in

technology, people, and thoughtful change management,

organisations can ensure they are not just victims of AI

disruption, but architects of an AI-powered future where both

businesses and their employees can thrive.

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The insights and data in this report are drawn from a range of authoritative sources, including the World Economic Forum’s Future of Jobs 2023 report,

industry research by firms like McKinsey and Goldman Sachs, and numerous case examples from press and literature illustrating AI’s impact in real

organizations.

Accenture (2024) AI for Business: Seizing the Opportunity. [online] Available at: https://www.accenture.com [Accessed 12 Jun. 2025].

BCG (2024) Winning With AI in Financial Services. [online] Available at: https://www.bcg.com [Accessed 12 Jun. 2025].

Deloitte (2024) AI Adoption in Manufacturing and Supply Chain. [online] Available at: https://www2.deloitte.com[Accessed 12 Jun. 2025].

European Commission (2023) AI Watch: Artificial Intelligence in Public Services. [online] Available at: https://digital-strategy.ec.europa.eu/en/policies/ai-

watch [Accessed 12 Jun. 2025].

IBM (2024) Global AI Adoption Index 2024. [online] Available at: https://www.ibm.com/reports/global-ai-adoption-index-2024 [Accessed 12 Jun. 2025].

McKinsey & Company (2023) The State of AI in 2023: Generative AI’s Breakout Year. [online] Available at: https://www.mckinsey.com [Accessed 12 Jun.

2025].

OECD (2023) The Impact of AI on the Labour Market. [online] Available at: https://www.oecd.org/employment/impact-of-ai-labour-market/ [Accessed 12

Jun. 2025].

PwC (2023) AI and the Future of Work. [online] Available at: https://www.pwc.com [Accessed 12 Jun. 2025].

Stanford Institute for Human-Centered Artificial Intelligence (HAI) (2024) AI Index Report 2024. [online] Available at: https://aiindex.stanford.edu/report/

[Accessed 12 Jun. 2025].

World Economic Forum (2023) Future of Jobs Report 2023. [online] Available at: https://www.weforum.org/reports/the-future-of-jobs-report-2023/

[Accessed 12 Jun. 2025].

References

Contact Information

Phone Number

+ 45 4084 1200 / + 45 50861200

Our Website

www.SkaleEgenkapital.com

www.SkaleEgenkapital.dk

Email Address

seg@SkaleEgenkapital.com

Our Address

Klarup. Aalborg, Denmark

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AI’s Transformative

Impact on Business

and Employment

Skale Egenkapital

SEK Knowledge

Presented By :

AI Research Lab Team

(2025–2035)

List Of Contents
Transformative Impact on Business 03
Introduction 05
Manufacturing 06
Technology 10
Creative Industries 21
Education 25
Jobs & Skills 32
Conclusion 55
Skale Egenkapital
Finance 14
Consulting 18
References 59
Contact 60