APPEN LIMITED — AGM Information 2025

May 15, 2025

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ASX Release

16 May 2025

2025 Annual General Meeting – CEO address and presentation

Appen Limited (ASX: APX) provides the attached CEO address and presentation to be delivered at today’s Annual General Meeting commencing at 10.00am AEST.

Authorised for release by the CEO and Managing Director of Appen Limited.

For further information, please contact:

Investor Relations	Sam Wells
investorrelations@appen.com	NWR Communications
+612 9468 6300	sam@nwrcommunications.com.au
	+61 427 630 152

About Appen

Appen is a global market leader in data for the AI Lifecycle. With over 28 years of experience in data sourcing, data annotation, and model evaluation by humans, we enable organisations to launch the world’s most innovative artificial intelligence systems.

Our expertise includes a global crowd of more than 1 million skilled contractors who speak over 500 languages[1] , in over 200 countries[2] , as well as our advanced AI data platform. Our products and services give leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to launch world-class AI products.

Founded in 1996, Appen has customers and offices globally.

1 Self-reported. 2 Self-reported, includes territories.

Appen Limited, 9 Help Street, Chatswood, NSW 2067, Australia – ACN 138 878 298

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CEO Address 2025 Annual General Meeting 16 May 2025

Opening

Good morning everyone, and a warm welcome to Appen’s 2025 Annual General Meeting.

It’s my privilege to address you as we reflect on a year of strong progress and outline our vision for the future.

As a global leader in AI training data, Appen is at the forefront of a transformative industry, and I’m excited to share our achievements, our role in the evolving AI landscape, and our strategic priorities going forward.

Your presence here, whether in person or virtually, underscores your commitment to our journey, and I’m deeply grateful for your support as we continue to shape the future of AI.

Agenda

Let’s set the stage for today’s discussion with our agenda.

I’ll cover three key areas before handing back to Richard:

• first, I’ll share some insights into the AI market and Appen’s role within it;

• second, a recap of our 2024 performance, highlighting our financial and operational milestones; and

• third, our strategic priorities, 2025 guidance and our longer-term financial targets.

This structure is designed to provide a clear understanding of where we stand, how we’ve progressed, and the path we’re forging as a leader in AI data.

Market Overview and the Role of Appen

The AI industry is evolving at a rapid pace, driven by breakthroughs in models, data, and computational power.

At the heart of this revolution is data — high-quality, curated datasets that ensure AI systems are accurate, relevant, and impactful.

Appen is uniquely positioned to meet this need, delivering bespoke data that powers AI applications across industries, from smarter virtual assistants to safer autonomous vehicles and the exciting evolution from generative AI.

AI Driven by Model, Data, and Compute

To understand Appen’s critical role in the AI ecosystem, let’s examine the three pillars driving AI advancements: models, data, and compute.

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Model architectures, such as transformers and diffusion models, are the important foundation for AI development. However, these are increasingly open source and accessible to anyone, meaning the competitive edge no longer lies solely in the model itself.

Compute, powered by GPUs and TPUs, is largely about brute force — raw processing power that many organisations can access. Like model architectures, this has fast become commoditised and is largely a scale game.

The true differentiator of model performance is high-quality, bespoke data. Our customers are highly competitive, and the custom data that we provide for them is the key differentiator of their model performance.

Appen Powers the World’s Best AI

At Appen, our mission is to enable the world’s best AI through high-quality humanannotated data.

We achieve this by combining our global crowd workforce, advanced proprietary technology platforms, and over two decades of expertise.

Creating Bespoke Datasets

Our ability to create bespoke datasets is central to our value proposition, driven by the expert capability of our team and our proprietary software platforms.

Our AI data consultants engage with clients to understand their data requirements. This is a highly collaborative process where we work together with our clients to define the project scope and success criteria.

The next step is to configure our proprietary software platforms according to the project’s requirements. Our platforms are custom built to create high quality data at scale, supporting a wide array of use cases.

The connectivity with our global crowd workforce is a critical component of our operations. We rely on a broad network of contributors around the world to create data for our customers. The engagement with our workforce is highly automated through our software.

The output that we deliver to our customers is high-quality data. This data is then used to build and improve the performance of their AI models.

Next, I’ll explore our platforms in detail.

Scalable and Flexible Technology

Our technology ecosystem is composed of custom built software platforms.

Mercury, is our new workforce and project management platform. It streamlines the assignment and management of our global contributor network to client projects.

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ADAP, our data annotation platform, provides a highly configurable interface for contributors to produce accurate annotations, supported by AI quality validators. We have a similar annotation platform that has been custom built for our China clients called MatrixGo.

Our newly enhanced CrowdGen platform serves as a dynamic interface for our crowd workers, enabling them to explore earning opportunities, enrich their profiles by taking tests and quizzes to showcase their skills, and getting paid for their tasks.

We have made significant advances in our technology in the past year. Both Mercury and CrowdGen were launched in 2024, with a focus on improving the experience for our crowd, delivering internal operational efficiencies through automation, and better quality data for our customers.

The new Mercury and CrowdGen platforms are tightly integrated with our data annotation platform. This results in faster project setups and improved quality outcomes for our customers.

We continue to evolve our platforms with a very clear focus on enhancing data quality and speed while also improving the productivity of Appen’s internal workforce. This will ultimately support increased revenue and provide a pathway to elevated margins.

Supporting Our Customers

The depth of our expertise and versatility of our platforms enables us to support a wide variety of clients and use cases, from assisting social media companies with content categorization, to empowering enterprise SaaS AI agents, and supporting foundation model builders with supervised fine-tuning data.

We also enable innovation in AR/VR, robotics, and agentic AI.

By collaborating on these advanced projects, we adapt to the fast-evolving AI landscape, reinforcing our role as a trusted partner for our clients.

Data Requirements Vary Widely

The demand for AI data is incredibly diverse, reflecting the broad spectrum of customer needs we support.

There is not only variety in the tasks we perform, but also the duration of the projects. There are typically three varieties of projects we support, long-term continuous feeds of data, larger-scale one-off projects, and smaller-scale pilots.

Continuous data feed projects, running for 6 months or longer and addressing realworld changes or ongoing model improvements, made up 51% of our revenue in 2024. This forms a core part of our more-predictable revenues each year.

Larger one-off projects, typically exceeding $100,000 in revenue and focusing on specific model enhancements, contributed 40% to our revenue.

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Smaller pilots, generally under $100,000 and aligning with the important experimental nature of AI, accounted for 9% of our revenue. It’s worth noting that most of our large projects started as a small pilot.

Together, these project types drove our 2024 performance, demonstrating our ability to operate at scale while meeting the fast-evolving requirements of AI data.

Evolving Data Demands

Generative AI is an important driver of the market. We see three key trends emerging from generative AI related projects:

• First, tasks like reasoning and red-teaming require hours of iterative work involving a high cognitive load. For example, some annotation tasks can take over two hours to complete.

• Second, crowd workforce requirements are becoming more specific. Projects increasingly require nuanced domain expertise and higher educational levels. For example, we have seen a rapid increase in the number of projects requiring PhD level education.

• Third, we are seeing more projects that are experimentative and shorter in duration. This is due to the highly experimentative nature of generative AI model development. Supporting customers here is important, as these projects can scale to large opportunities very quickly.

As generative AI uses cases mature, we expect to see more longer-term data requirements to support ongoing model improvement.

Case Study – LLM Evaluation Project

A recent generative AI evaluation project highlights our capabilities.

A client needed domain-specific model responses assessed for relevance, accuracy, and harmlessness within four days.

Responding to this demand, Appen engaged over 500 contributors with diverse expertise in coding, math, and healthcare, leveraging our platforms to ramp the workforce within hours.

We subsequently delivered 100,000 annotations with exceptional quality, showcasing our ability to handle high-volume, time-sensitive tasks with precision.

Critical Areas for Long-Term Success

To capture further market growth, we’re focusing on three strategic pillars:

• First, an expert-led approach to sales and marketing, where our technical teams build deep, trusted relationships with AI researchers and product teams within our customers. Our customers are highly technical, and we are retooling our organisation to bring our expertise to the forefront of conversation with our customers.

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• Second, we are evolving our data offerings to address the rapidly changing needs of our clients, supported by our flexible technology infrastructure. The AI market is changing rapidly, and we are building robust and flexible platforms with the ability to evolve with the market.

• Third is technology-enabled efficiencies, leveraging AI-driven automation to enhance data quality and reduce costs to deliver. We utilise generative AI heavily throughout our operations, including how we validate the quality of our data and provide real time feedback to our contributors. As mentioned, data labelling tasks are becoming more complex, with higher quality requirements and shorter timeframe. Advanced technology is critical to be successful.

These priorities ensure we stay responsive to market trends and deliver consistent value to our clients.

2024 performance

Now, let’s turn to our 2024 performance — a year of resilience, growth, and transformation.

2024 Was a Pivotal Year

In 2024, we delivered 16% revenue growth, excluding Google.

Our China division grew 71%, reflecting our dominant position in the China market.

We successfully captured growth from generative AI, with 28% of revenue in H2 FY24 from LLM related projects

Our annotation platform became increasingly integral to project delivery for major clients.

We reduced operating expenses by 26% compared to 2023.

The combination of revenue growth and reduced costs led to a return to profitability, with $3.5 million underlying EBITDA before FX in 2024. This was a $23.9 million improvement vs. FY23

Revenue Growth Returning

Excluding Google, Q4 2024 revenue grew 37% year-on-year, reaching $66.7 million.

Growth was driven by large technology clients in the US and China.

China Success Continues

Our China division was a standout performer, growing 71% in 2024 compared to 2023.

We partnered with leading LLM builders, tech companies, and automotive clients, delivering in-facility annotation projects that have more predictable revenue streams.

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Generative AI Major Driver

Generative AI was a major driver of our growth, contributing 28% of our H2 2024 revenue.

While non-LLM segments grew steadily at 2% half-on-half, the growth in generative AI projects reflects our ability to pivot to high-demand areas of the market.

Global Product Growth

Global Product revenue represents projects for our largest customers that are delivered on Appen’s data annotation platform

The complexity of generative AI projects is increasing, and therefore customers are utilising our highly flexible ADAP platform rather than their internal data annotation platforms.

Opex Reduced by 37%

Cost management was a cornerstone of our 2024 strategy.

We reduced operating expenses by 37% from H1 2023 to H2 2024, completing a $13.5 million cost reduction program.

By leveraging automation and optimizing our operations, we’ve created a leaner, more efficient organisation without compromising our ability to deliver for clients. This discipline sets the stage for sustainable profitability.

EBITDA Improving

Our financial turnaround is evident in our EBITDA performance.

In Q4 2024, we achieved an underlying EBITDA (before FX) of $4.7 million, marking a return to profitability.

Our focus on profitability remains steadfast, and we’re committed to maintaining this momentum as we scale our operations and pursue new opportunities in 2025.

2025 Strategy and Outlook

Looking to 2025, our strategy centres on delivering value through enhanced quality, speed, and diversification.

I’ll now outline our operational strategy, 2025 guidance, and longer-term targets.

Near-Term Strategy

Our strategy for 2025 is designed to strengthen Appen’s core business and drive sustainable growth by focusing on all six critical areas outlined in our plan.

• First, we’re highly focussed on growth, particularly in LLM related projects. We are appointing new sales leader and bolstering our go-to-market teams with greater technical expertise. Note that we have already signed a new sales leader from a very high-profile AI services and software company who will be starting in mid-June.

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• Second, we’re driving operational efficiency and automation by utilising AI across our operations. This includes further incorporating generative AI into our platforms for higher quality data delivery.

• Third, we’re accelerating technology innovation by utilizing AI across our software development teams. We are also running a high velocity innovation prototyping approach, where we rapidly test new ideas before building into our platforms.

• Fourth, we’re growing our people, particularly by expanding our technical expertise in our project delivery team. Investment in talent ensures we are bringing delivery excellence and thought leadership to our customers.

• Fifth, following the launch of CrowdGen we are evolving our data workforce. This includes utilising generative AI in the interview process and how we match contributor skills to projects.

• Finally, we’re committed to prudent cost management, including leveraging automation to maintain our cost base as we scale.

By executing in these six areas, we’re positioning Appen to deliver superior data quality, speed, and diversification, ensuring we meet the evolving needs of our clients while driving profitable growth.

FY25 Guidance

I’ll now provide a 2025 guidance statement, before commenting on our longer-term targets.

Notwithstanding some of the inherent uncertainties within our business this information reflects Appen’s commitment to providing greater transparency. In doing so, we hope to provide investors and shareholders a clearer view of our strategic direction and financial expectations.

We are optimistic about our FY25 revenue opportunity. This is supported by a few drivers.

• First is the more predictable project work from large US tech customers, typically H2-skewed. We are delivering very high-quality work for our large customers, and in some instances, quality is at an all-time high. This lays a great foundation for growth.

• Second is sustained growth in China. The market opportunity is significant, and we are very well placed to continue to grow in China.

• And finally, work not yet included in pipeline, including less predictable generative AI related work, which is typically awarded directly with short notice. This is a very dynamic part of the market, and we have strong capabilities that are highly valued by our customers.

Considering this, Appen provides the following FY25 guidance:

• Revenue between $235 million and $260 million, and

• Positive underlying EBITDA[1] for 2025.

1 Underlying EBITDA excludes FX gains/losses, restructure costs, transaction costs, and acquisitionrelated and one-time share-based payment expense.

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Longer-term targets

In relation to Appen’s longer-term targets we continue to be backed by solid market fundamentals and tailwinds.

The opportunity for Appen is driven by large-scale investment in the development and utilization of generative AI, and reinforced by robust estimates for market growth[2] .

Our strategy to capture long term growth remains focussed on:

• Driving growth, via strengthened sales, marketing and client engagement;

• Evolution of offerings to support changing customer needs; and

• Delivering operational efficiencies via innovative technology investment.

Considering this, Appen is targeting sustainable and profitable growth, reflected by:

• greater than 20% 3-year revenue compounded annual growth rate to FY27; and

• circa 10% Underlying EBITDA[1] margin by FY27.

Closing

In closing, 2024 was a year of resilience, growth, and transformation for Appen. We navigated challenges, seized opportunities in generative AI, and strengthened our financial foundation.

Looking forward, there remains a significant opportunity for Appen to capture material value from the AI data market. We are only at the beginning of the impact of AI, and the role we play in creating high quality data remains a critical ingredient.

Thank you for your continued support and belief in our vision. I look forward to engaging with your questions and insights.

I’ll now hand back to Richard.

2 Grand View Research: Data Collection And Labelling Market Trends.

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CEO Address Ryan Kolln

© 2024 proprietary & confidential

Agenda

1. Market Overview (incl. role of Appen)

2. 2024 Performance Recap

3. 2025 Guidance and Longer-term targets

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Market Overview (incl. role of Appen)

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AI remains driven by a combination of model, compute & data

Model Compute Data Deep learning (Convolutional Hardware: GPUs / TPUs Public data (e.g, internet Neural Networks) scraping) Generative AI (Transformers, Proprietary data including diffusion network, large bespoke datasets language models)

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Appen enables the world's best AI through high quality human data

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Appen creates bespoke AI training data for leading model builders

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Client
requirements
Appen AI data consultants
Description of task
Workforce
requirement Workforce and project management Bespoke AI
Data annotation platform: ADAP
training data
platform: Mercury
Data quality rubriks
Volume of data
required
Crowd interface: Crowdgen
Task duration
Global data annotation workforce
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Scalable and flexible technology underpins Appen’s operations

Proprietary technology platforms that enable high quality data at scale

Appen AI data consultants

Mercury ADAP + MatrixGo[1] Crowd and project management platform Data annotation platform

CrowdGen Crowd onboarding and interface

Team of experts to manage AI data projects Highly consultative engagement with clients Leverages 20+ years of Setup and management of Configuration of annotation Primary interface for Appen expertise workforce assignment to tasks contributors projects Interface for contributors to Highly automated approach AI based automations for complete tasks to assess applicants contributor project Built-in AI quality assurance assignment and management Offered as a SaaS platform and is used for Appen managed projects

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1. Previously known as China A9.

Examples of how Appen supports its customers

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Evaluation of LLM
Ad evaluations Image annotation
responses
Foundation
Social Misinformation Domain specific AI first Embodied AI
model
media categorization model reasoning companies agents (robotics)
builders
AR/VR & wearable Agentic AI training Multimodal data
device data data annotation
Product Evaluation of LLM 3D Lidar
classification annotation
responses
Customer support Enterprise Multimodal data In-cabin image
Ecommerce Automotive
chatbot data SaaS annotation data collection
Audio data for
Multi-modal data Data for speech
speech
analysis recognition
recognition
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Data requirements range from continuous feed, large scale and finite, to small and experimentative

% of Appen Project characteristics Examples of customer requirements revenue in 2024 Continuous data feed • Solving for real-world changes >6 months duration • 51%51% projects Ongoing improvements to AI models • Data required for specific model One-off projects, larger 0-6 months / improvement 40% 40% • scale >$100k Often involves multiple one-off projects for same model 9% • Experimentation to understand how data can 0-6 months / Smaller pilots and improve model performance 9% <$100k experiments • Typical pathway to larger projects

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Generative AI data demands are evolving rapidly, with a trend to more complex and shorter project durations

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Workforce Projects are more
Task complexity is
requirements are experimentative and
increasing
more specific shorter in duration
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• •

• Reasoning and red-teaming tasks can Level of expertise in the workforce is take multiple hours to complete and increasing rapidly are highly iterative

• Project setup times are short

• Many projects are experiments that may lead to larger opportunities

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Case study of recent LLM evaluation project High volumes with short delivery timeframes

Project overview: large scale evals with ra id scalin p g

Client request was to evaluate domain-specific model responses

• Multi-dimensional evaluation criteria (relevance, accuracy, harmlessness, etc.)

• Rapid project turnaround (~4 days)

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550 contributors matched Rapid ramp within hours
across 10+ domains of ro ect launch
p j
Units of data delivered in 4 days
100,000
80,000
Coding, 92 Math, 75
60,000
Biology, 63 Finance, 57
40,000
Automotive, 69
20,000
0
Healthcare, 63 Legal, 55 Other, 50 Tech, 36
0 12 24 36 48 60 72 84
Hours since project Launch
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Appen sees three critical areas for long-term success

Expert-led approach Evolved data Technology enabled to sales and offerings efficiencies marketing

• Appen customers are at the forefront of AI model development

• The data needs of customers is evolving rapidly as AI advances

• Creating high quality data increasingly relies on a highly automated workflow

• Building deep relationships with customers requires a highly technical approach to sales and marketing

• Appen continues to enhance the technical expertise in go-to-market teams

• Appen continues to refine data offerings to meet customer needs

• Appen’s highly flexible underlying technology infrastructure is critical for success

• AI enabled workforce management is proving to deliver improved quality

• Appen continues to invest in its platform development as a key differentiator

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2024 Performance Recap

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2024 was a pivotal year for Appen – balancing cost and growth

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Broke into LLM market, with 28%
16% revenue growth for the full
Grew China revenue by 71% of revenue in H2 FY24 from LLM
year, excluding Google
projects
Returned to profitability, with
Appen technology playing a
Reduced full-year OPEX by 26%, $3.5 million underlying EBITDA
more important role in project
compared to FY23 before FX (+$23.9 million
delivery for major clients
improvement vs. FY23)
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Revenue growth returned

Group revenue

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80.0
70.0
19.1
60.0 23.3
18.3
21.9 13.4
50.0
40.0
66.7
30.0
54.3 55.0 54.1
47.2 48.6
45.0
20.0 40.2
10.0
0.0
Q1 FY23 Q2 FY23 Q3 FY23 Q4 FY23 Q1 FY24 Q2 FY24 Q3 FY24 Q4 FY24
Total excluding Google Google
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Commentary

• Excluding impact of Google, Appen experienced a return to revenue growth largely due to the rise of generative AI

• Excluding Google, revenue for Q4 FY24 grew 37% on pcp (from $48.6 million in Q4 FY23)

• Growth continues to be driven by large technology customers in US and China

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China success continued

China division revenue

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20.0
18.0
16.0
14.0
12.0
10.0
17.7
8.0 15.8
13.4
6.0 12.0
11.1
4.0 8.1 8.1
7.1
2.0
0.0
Q1 FY23 Q2 FY23 Q3 FY23 Q4 FY23 Q1 FY24 Q2 FY24 Q3 FY24 Q4 FY24
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Commentary

• 71% growth in FY24 compared to FY23

• China customers include leading LLM model builders, along with leading technology and auto customers

• Projects are mostly for in-facility annotation, resulting in a more predictable revenue profile

• Appen has the unique advantage of partnering closely with both Western and Chinese technology companies

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Generative AI major driver of revenue growth

28% of group revenue from generative AI-related projects[1] in H2 FY24

Non-generative AI projects growing ~2% HoH

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30%
28%
20%
15%
10%
6%
0%
H2 FY23 H1 FY24 H2 FY24
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140
120
33.6
100
14.7
5.3
80
60
40 83.5 85.3 87.2
20
0
H2 FY23 H1 FY24 H2 FY24
Other revenue LLM related revenue
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1.Excludes Google

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Global product growth reflects reliance on Appen’s technology

Global Product revenue

Commentary

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16.0
14.9
14.0
12.0
10.0
8.0 8.6
6.0
5.6
4.0
3.4
2.0 2.7 2.5
2.3
1.1
0.0
Q1 FY23 Q2 FY23 Q3 FY23 Q4 FY23 Q1 FY24 Q2 FY24 Q3 FY24 Q4 FY24
Global Product
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• Global Product revenue represents projects that are delivered on Appen’s data platform

• For Appen’s largest US technology customers, most work traditionally delivered on their internal platforms

• With the rise of generative AI, Appen’s seen an increasing reliance on Appen technology to support projects

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Opex reduced by 37% H1 FY23 to H2 FY24

Opex[1,2]

Commentary

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80.0
60.0
20.0
40.0 17.6
15.8
14.4
45.9
20.0
31.9
28.2 26.9
0.0
H1 FY23 H2 FY23 H1 FY24 H2 FY24
Employee expenses All other expenses
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• Employee expenses reduced by 41% and all other expenses by 28% compared to H1 FY23

• Significantly reduced product and engineering spend achieved by establishing a hub in Hyderabad

• Consolidation of business units and rationalisation of delivery resources contributed to the reduction in employee expenses

• Rationalisation of corporate overhead and tight cost controls on all other expenses also a key driver of the reduction

• Focus remains on managing the cost base in line with the revenue opportunity

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1.Employee expenses per management reporting. Excludes share-based payment expense and direct project workers included in gross margin calculation (i.e. crowd expenses).

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2.All other expenses included in underlying EBITDA before FX.

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EBITDA improving as costs remain in control

Underlying EBITDA before FX[1]

Commentary

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8.0
6.0
4.0 4.7
2.0 2.8
0.6
1.0
0.0
-2.0
-2.9
-4.0
-6.0
-7.2
-7.5
-8.0 -8.5
-10.0
Q1 FY23 Q2 FY23 Q3 FY23 Q4 FY23 Q1 FY24 Q2 FY24 Q3 FY24 Q4 FY24
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• Underlying EBITDA[1] (before FX) for Q4 FY24 was a profit of $4.7 million, continuing the return to profitability

• Acted swiftly to control costs following the Google announcement and completed $13.5 million cost reduction

• Revenue growth in China and improved gross margins resulted in positive EBITDA contribution in Q2

• Profitability remains a key focus for Appen

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1.Underlying EBITDA excludes restructure costs, transaction costs, impairment loss, earn-out adjustment, and acquisition-related and one-time share-based payment expense.

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2025 Guidance and Longer-term targets

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Near-term strategy

Phase 1 of Appen’s 12-month strategy focusses on delivering improved data quality and speed (unit economics), further bolstering Appen’s core business and greater revenue diversification

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Enhanced focus on LLM growth

• New sales leadership

• More technical expertise in GTM team

• Expand ex-customers in talent pool

Operational efficiency & automation

• Next phase of automations in our platforms

• LLM agents in support operations

Accelerate technology innovation

• LLMs utilised throughout software development lifecycle

• Rapid innovation prototyping

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Grow our team

• Increased technical expertise in project delivery teams

• Expansion of technical and operations resources in India and Philippines

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Evolve data workforce

• LLM interviewer to assess new contributor quality

• Enhanced AI matching of crowd to tasks

Prudent cost management

• Productivity benefit captured through automations

• Leveraging AI to maintain cost base while scaling

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FY25 Guidance

The Company is optimistic about its FY25 revenue opportunity, supported by:

• More-predictable project work from large US tech customers, typically H2-skewed;

• Sustained growth in China; and

• Work not yet included in pipeline, including less predictable generative AI related work, typically awarded directly with short notice.

Considering this, Appen provides the following FY25 guidance: Revenue between $235 million and $260 million; and . Positive underlying EBITDA[1]

1. Underlying EBITDA excludes FX gains/losses, restructure costs, transaction costs, and acquisition-related and one-time share-based payment expense.

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Longer-term targets

In relation to Appen’s longer-term targets (FY27+), the Company continues to be backed by solid market fundamentals and tailwinds.

The opportunity is driven by large-scale investment in the development and utilization of generative AI, and reinforced by robust estimates for market growth¹.

Appen’s near-term strategy focus remains on:

1. Driving growth via strengthened sales, marketing and client engagement;

2. Evolution of offerings to support changing customer needs; and

3. Delivering operational efficiencies via innovative technology investment.

Considering this, Appen is targeting sustainable and profitable growth, reflected by:

20% 3-year revenue CAGR², to FY27; and

~10% Underlying EBITDA[3] margin by FY27.

1. Grand View Research: Data Collection And Labelling Market Trends.

2. Compound annual growth rate (CAGR).

3. Underlying EBITDA excludes FX gains/losses, restructure costs, transaction costs, and acquisition-related and one-time share-based payment expense.

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Thank you

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