While the network is exploring and advancing the question of leveraging AI, this hub has been co-created with the support of various WWF offices to enable collective learning & resource sharing. Here, you’ll find an overview of AI, practical tools, AI governance resources, and various case studies of AI applications in WWF’s conservation work.

Key Topic 1

AI basic knowledge
For users new to AI concepts; provides foundational knowledge.

Key Topic 2

Tools & Resources
For users looking for practical tools, training courses, and workshops.

Key Topic 3

AI in WWF
For those interested in real-world applications and case studies within WWF.

Key Topic 4

Policy & Governance
Dedicated space to explore our guiding Global WWF AI principles, AI policies and the governance framework examples.

This image has been generated using DALL-E, a generative imagery AI that turns prompts into images

The WWF International AI Working Group has approved and launched WWF's very own AI guiding principles. Click here to access them

Innovation in Action: Our AI Community & Governance

Meet the AI Community of Practice

The Innovation@WWF team convenes the AI Community of Practice (AI CoP) at WWF. It is a community of WWF offices which meets every quarter and engages in shared learning, collaboration, and knowledge exchange on AI across our network. It was launched as a small informal group in 2024, has now been formally recognised as AI CoP for shared learning by AI Working Group (AIWG) at WWF International.

Meet the AI Working Group (AIWG)

The cross organizational AI Working Group (AIWG) for WWF is established by the Network Executive Team (NET) to support the adoption and leveraging of Artificial Intelligence (AI) as a strategic capability within the organization to advance our conservation mission. The AIWG will conduct groundwork, gather insights, and provide recommendations to guide the organization in effectively integrating AI to further our global conservation mission. The AIWG will report to the Global Operations Committee (GOC) with Executive sponsorship and oversight by the Chief Operating Officer, WWF International.

For more detailed information about AIWG, please refer to the Terms of Reference (TOR)

WWF International's
Artificial Intelligence Policy

While applicable to WWF International and its Country Offices, this policy is a foundational step in establishing ethical, sustainable, and transparent AI governance across our organization. It is being shared with the entire WWF Network to serve as a template or source of inspiration for offices that are developing their own AI policies.

When it becomes operational (January 2026), the policy will apply to all WWF International staff, contractors, partners, and relevant third parties, ensuring consistent and responsible AI practices across regions and projects. WWF has vetted Microsoft AI tools and platforms, such as Copilot Web and Copilot Chat as the primary tools to be used for staff productivity, for secure, responsible use. Copilot Web, now available to all WWF International staff as a direct benefit of the MARA migration, offers enterprise-grade data protection, operates entirely within our secure Microsoft environment, and supports productivity through features like document summarization, task automation, and multilingual assistance. Staff can expect whitelists of approved tools to be available by the time the policy is enforced in January 2026. AI is transforming how we work and deliver impact. At WWF, we recognize its dual potential: to enhance internal operations through Productivity AI and to drive innovation in conservation through Impact AI. This policy provides tailored guidance for both domains.

Access WWF International's AI Policy

Artificial Intelligence (or A.I.) refers to the simulation of human intelligence
in machines to perform tasks that typically require human intelligence.

These machines can do everything from recognizing speech and patterns, processing large sets of data such as numbers and documents, planning and executing complex multi-step tasks, generating images or videos, etc. in minutes or even seconds. Today, AI is everywhere around us, from the virtual assistants in our smartphones to the recommendation engines that suggest what to watch next on our favorite streaming services.

THE TAXONOMY OF AI

Despite there not being a universally agreed definition of AI, through collating a series of definitions we can explain the taxonomy of AI and hopefully an understanding of the different terms associated with it.

A – Computer Science - focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.

B – AI (Artificial Intelligence) - is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines.

C – Machine Learning – considered part of AI – involves developing algorithms that enable computers to learn from and make predictions or decisions based on data. Multi Language (ML) models can improve their performance over time without being explicitly programmed, making them suitable for tasks such as data analysis, pattern recognition, and predictive modeling.

D – Deep Learning is part of machine learning. The ‘depth’ deep learning refers to the complexity of a mathematical model.

E – Data Science – needs computer science and AI. However, it also involves a lot of statistics, business, law, and other application domains, so it is usually not considered to be a part of computer science.

GENERATIVE AI (GenAI)

You probably heard the term "GenAI” as it’s becoming increasingly popular in the last few years. The “Gen” refers to Generative AI, a type of AI that can understand, learn, and apply its intelligence across a wide range of tasks to generate a desired result for you. Sometimes this can be done much faster than a human could potentially do it, like executing big analysis of data sets in seconds versus hours or even days for a human. The buzz around Gen AI stems from its potential to revolutionize industries by providing solutions that were previously thought to be within the exclusive realm of human capability.

HOW WAS GENERATIVE AI CREATED?

Artificial Intelligence (AI) began its journey in the 1950s, marked by the development of algorithms and theories to simulate aspects of human intelligence. Progress in computing power and data analysis techniques over the years has advanced AI from simple automated tasks to complex machine learning and neural networks, enabling practical applications in many fields.

What we are seeing with this ‘Generative AI’ explosion, is that AI technology is advancing at an unprecedented pace. Language and image recognition capacities of AI systems have improved so rapidly over the last 5 years that some AI systems perform better than humans in language and image recognition in some tests. This capability is set to double every 6 months.

Investment in AI development is doubling year on year, AI patents are increasing 30x year on year and AI related jobs are popping up in every sector. As AI technology improves further it will only increase the pace of change. ‘One reason why artificial intelligence is such an important innovation is that intelligence is the main driver of innovation itself. This fast-paced technological change could speed up even more if it’s not only driven by humanity’s intelligence, but artificial intelligence too. If this happens, the change that is currently stretched out over the course of decades might happen within very brief time spans of just a year. Possibly even faster.’ (Source: Our World in Data, Dec 2022)

Couple the pace of AI technology development, with an acceleration of adoption rates, we are looking at a technological breakthrough which could transform how we operate. ChatGPT is the most popular large language model–based chatbot, which was developed by OpenAI and launched on November 30, 2022. It reached a 100 million users in 2 months.

BREAKING DOWN AI FURTHER

Narrow vs General AI
Narrow AI is AI trained and focused to perform specific tasks. Narrow AI drives most of the AI that surrounds us today. E.g. Apple Siri, Amazon Alexa, Autonomous Vehicles. General AI is a theoretical form of AI where a machine would have an intelligence equal to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. While General AI i AI is still entirely theoretical with no practical examples in use today, that 't mean AI researchers aren't also exploring its development. (Source, IBM).

Generative Models
Generative AI refers to deep-learning models that can take raw data — say, all of Wikipedia or the collected works of Rembrandt — and “learn” to generate statistically probable outputs when prompted (Source, IBM). These generative models may also use Natural Language Processing (NLP), to understand, interpret, and generate human language. NLP has applications in chatbots, sentiment analysis, language translation, and content generation.

3 main technologies that led to the development of GenAI:

GENERATIVE AI IN
THE NON-PROFIT SPACE

In the non-profit space, GenAI has the potential of being a game-changer, enhancing operational efficiency, enabling data-driven decision-making, fast iteration of prototypes and scenarios and opening new avenues for addressing complex global challenges.

ChatGPT crossed one million user mark in just five days
after it was made public in November 2022

“AI won’t replace innovators. But innovators who
use AI will definitely replace those who don’t.”

Philippe de Ridder - CEO of Board of Innovation

HISTORICALLY, THE NON-PROFIT SECTOR HAS BEEN SLOW IN
UNDERSTANDING AND ADOPTING NEW TECHNOLOGIES.

This hesitancy can hinder organizational growth and limit the impact of charitable efforts. Embracing and leveraging AI represents an opportunity to overcome this inertia, embrace a culture of innovation and optimize resource allocation, and make data-driven decisions, providing a competitive edge.

Part of the systemic sluggishness to adopt can also be overcome by understanding AI now, and proactively address ethical concerns related to AI applications. Ethical AI principles can be integrated into AI strategy, ensuring that AI is used responsibly and transparently, and crucially can be agile and adapt as the technology and legislation changes.

By integrating AI, non-profits like WWF could potentially amplify our impact, reaching conservation goals previously deemed unattainable, while ensuring we sustain both the planet and our well-being without burning out in the process.

AI's rapid development often sparks skepticism and even fear;
particularly regarding its impact on jobs, copyright and climate impact.

We acknowledge these challenges and the need of transparent communication, ethical AI development practices, and education on AI's benefits and limitations. AI is not black and white and the more we know about it, the more responsibly we can approach it with.

JOB CONCERNS

The automation potential of AI raises concerns about job displacement, with routine tasks being more susceptible to automation. However, AI also creates new job opportunities and demands for skills in AI management, oversight, and ethical considerations, emphasizing the importance of adaptive workforce development.

AI's ability to generate content that mimics human creativity poses copyright challenges, necessitating clear guidelines and innovative copyright laws that recognize both AI-generated works and the rights of original creators, ensuring fair use and attribution.

CLIMATE IMPACT CONCERNS

The environmental footprint of AI, especially the energy-intensive nature of training large models, underscores the need for sustainable AI practices. Prioritizing energy efficiency in AI research and application can mitigate its climate impact, aligning with conservation goals.

MISINFORMATION CONCERNS

AI-generated realistic content, such as deepfakes, poses risks of spreading misinformation. This necessitates enhanced verification tools and ethical guidelines to combat fake news and ensure responsible AI use, emphasizing the critical role of digital literacy in distinguishing genuine from synthetic media.

Embarking on the AI journey within conservation begins with understanding the basics of AI and identifying specific challenges where AI can offer solutions. Here’s an overview of how people are starting to experiment with AI.

WHAT ARE THE MAIN GENERATIVE
AI TOOLS PEOPLE ARE USING RIGHT NOW?

Two notes on this:
1- Note-taking apps can be helpful while also raise concerns when they dial into calls without people present and without any agreements made around this.

2- Be careful which account you are using to create Gen AI accounts - if using your WWF account check beforehand that there are no other procedures such as using your private email.

RESOURCES

NEW! AI at WWF: A Learning Journey Course

A flexible self-paced course designed to empower WWF Staff with shared knowledge and resources to understand and explore AI

Access the Course

GenAI tools

Commonly used Generative AI tools, such as Microsoft Co-Pilot ChatGPT, Claude, and Google LLM, empower non-profits by enhancing communication, streamlining workflows, and optimising content creation. Automating routine tasks, these advanced models enable organisations to focus on mission-critical initiatives, drive innovation, and maximise impact while reducing costs and enhancing collaboration for operational efficiency and accountability.

Assessment tools

With collaboration across WWF offices and our involvement in the Innovation for Impact Network, we have access to some sheet-based assessment tools to assess the mainstreaming of AI in your office, mapping high potential cases and assessing risk. Feel free to have a look at them and make a copy for your own office/team/project.

Learning & Workshops

Courses (NEW!)

Workshops & Webinars

AI for Non-Profits Workshop by the Center of Humane Technology

GEN AI Workshop Recording_1.mp4

Key Takeaways from AI for Nonprofits (cover).pdf

The Nature Code

AI-Powered Nonprofits

Resources & Reports

QUESTIONS TO DISCUSS WITH YOUR TEAM ON AI

Use this discussion guide to help your team explore the applications and implications of AI within your team. Be as specific or general as you like. At this stage anything is possible so explore it’s use now and in the future.

One way to think about AI is how it will impact your work internally at the WWF (e.g. systems, processes, tasks etc) and then externally (e.g. data protection, misinformation, audience needs etc)

WWF offices are already experimenting and advancing on the uptake of Gen AI for our work. We don't want to reinvent the wheel, and instead start by learning from the first movers and share the lessons obtained so far.

6 EXAMPLES WHERE WWF IS ALREADY
USING AI IN CONSERVATION

WWF UK
Using AI to create a powerful sea ice forecasting system and its application on conservation planning

WWF UK's science team has been working with the Alan Turing Institute and the British Antarctic Survey's AI Lab to develop IceNet AI, a sea ice AI model to predict the extent of sea ice in the Arctic and Antarctic. Rod Downie from WWF UK shares in the video their work on the IceNet AI sea Ice Model and the Caribou feasibility study in Antarctica using IceNet to predict the timing and location of caribou migration over the Coronation Golf.

HOW ARE WWF OFFICES WORKING WITH AI?

Many WWF offices are already approaching AI in their own different ways. While some have not used AI at all, others have restricted the use of AI tools and others are already engaging them in their work and exploring other ways it can be used, including the creation of AI policies for their offices.

As AI technologies mature, their adoption rates are soaring across industries. Early adopters are gaining a significant advantage in efficiency, cost savings, and innovation. Delaying AI adoption may result in WWF losing ground to competitors and missing out on valuable opportunities.

Here are some examples of actions taken by WWF offices:

Global AI Principles:

WWF International AI Working Group (AIWG) has now established the global AI guiding principles. The Guiding Principles for AI usage across the WWF Network document outlines how WWF aims to leverage AI to advance its mission of creating a world where both people and nature thrive. These principles emphasize the transformative potential of AI while acknowledging the need to mitigate associated risks, including environmental impacts. The principles are grounded in WWF's core values of courage, integrity, respect, and collaboration. Here are the guiding principles.

The key takeaway from this document is that WWF will adopt, develop, and use AI in ways that support its mission through productivity gains and conservation impact, while ensuring ethical, transparent, secure, equitable, and accountable AI practices. AI should enhance human capacity, align with ethical standards, and promote fairness and inclusivity.

WWF US:

"Moreover, to further our commitment to responsible AI use, WWF US has taken a proactive step by initiating an AI Governance Council. This council's primary objective is to develop a comprehensive set of guiding principles that will steer the organization's approach to AI. These principles will ensure that our use of AI aligns with our values and supports our mission. You can find below the drafts for the Council's Charter as well as the one for a Policy on the Ethical, Responsible, and Effective Use of AI for Operational and Programmatic Purposes within the organisation."

‘When you invent a new technology, you uncover a new class of responsibilities. If the technology confers power, it starts a race. And if you do not coordinate, the race will end in tragedy.’

Institute for Human Technology

THINKING AHEAD

What skills do you need to have to work with AI?

First, the knowledge

To properly work with AI, a general understanding of how it works is fundamental. This combined with a culture of continuous learning, experimentation and skills development is crucial to fully take advantage of AI.

As this is an area of constant evolution, we must remain aware of what is happening in topics such as prompt engineering, data literacy, ethical and AI implementation to ensure WWF teams are up to date and equipped to leverage AI technologies effectively for conservation.

Second, what tools do we need?

While many of the available AI tools are free, most of the powerful ones required paid subscriptions to access them.

We recommend that once you and your team have identified which kind of needs you have, you can then think about how to experiment meeting those needs with AI tools.

Start with free options to fully understand if the tool is actually what you need before investing funds and energy on paid versions. For example, you can start experimenting with prompt engineering with AIs such as Gemini and ChatGPT 3.5 to get you or your team familiar, before jumping straight into a paid subscription.

When starting with Gen AI tools we highly recommend to initiate conversations on guidelines and policies for usage to minimise potential risks.

POSSIBLE APPLICATIONS FOR WWF
(MOST EXAMPLES WERE SHARED BY WWF UK)

Giving everyone access to a personal assistant.

AI not only enhances external-facing aspects of an organization like conservation efforts, fundraising, and advocacy, which we will come onto discuss, but it can also significantly improve internal operations. Tools such as ChatGPT can serve as an always-on personal assistant, ready to assist in administrative tasks, freeing up employees' time for more strategic and value-added activities.

Zoom has a plug-in which records and transcribes your meetings, Slack has integrated the tech to allow you to summarize messages so you can save time, ask questions, and instantly find answers and draft messages in seconds with their AI writing assistant. Microsoft acquired OpenAI and has already integrated basic GPT models into Outlook to help you write emails, Word to improve your writing style and PowerPoint and Excel to generate slide designs, graphs, and graphics.

Microsoft is launching Microsoft 365 Copilot in November 2023, its AI assistant feature, which integrates with existing Microsoft Apps to allow more creatively and productivity and includes Business Chat. Business Chat works across the LLM, the Microsoft 365 apps, and your data—your calendar, emails, chats, documents, meetings, and contacts—to do things you’ve never been able to do before. You can give it natural language prompts like “tell my team how we updated the product strategy” and it will generate a status update based on the morning’s meetings, emails, and chat threads.

Example 1: Take raw data and use prompt – ‘What trends are we seeing in this month’s sales data’ – to generate analysis and summaries in Excel

Example 2: Draft a product release based on data from document X, presentation Y and my notes documents to create a product announcement

Source: Microsoft

Creative and writing support.

As well as administrative tasks, there is a new wave of ‘Generative AI tools, which can support with more creative tasks, from instruction guide writing to creative writing and content generation.

Guides, Processes, and Instructions - Although on the lower end of the creative spectrum, process, or instruction guide writing, would have usually taken hours, can now be drafted in minutes using tools such as ChatGPT, and although they will still require human oversight, they can massively reduce the time spent on them, freeing up time for other added value activities. e.g., we need a new instruction guide for amending a person's record on our database. Simply ask ChatGPT for a step-by-step guide, review the guide, make any tweaks, and save in a document. A task that might take a few hours can now take less than 15 minutes.

Creative Writing and proofing – AI generative tools can assist with writing blogs or thought leadership pieces. You may just want a structure to follow or some key talking points, or you can ask it to write you the entire piece for you to then tweak. It can also be useful for spell checking pieces where someone else doesn’t have time to review, and it can also work as a useful tool to ensure you’re using accessible and age-appropriate language. E.g., ensuring a 500-word website piece explaining net zero policies and their impacts, is accessible to teenagers.

Content Generation - Image generation tools such as Midjourney or DALLE allow you to create realistic images and art from a text based description. Similar tools are also appearing allowing you to develop video and audio content from a text description. Tools such as Flair allow you to create stunning product shoots in seconds. These generative tools are allowing anyone, regardless of budget to create high quality unique content.

Optimizing our marketing.

There are also multiple tools to help improve marketing effectiveness, through both creative and design improvements, but also through creating predictive models for next actions and personalized campaigns to ensure we are being hyper personalized across our campaigns and platforms.

Creative evaluation - Our digital marketing team has already been working on an AI project which uses years of historic data to predict the performance of our digital creatives. Results have been strong so far and they are looking to roll this model out across all new paid digital creatives going forwards. There are other tools which provide similar analysis and recommendations for website and email design.

Personalized Campaigns - By integrating AI tools into our database we can analyze donor data and behavior to create personalized fundraising campaigns. This information can then be used to create tailored messages and appeals to individual preferences, which would potentially increase donor engagement and contributions. Within Apteco PeopleStage there are tools which we can use which could in theory mean we could be sending tailored and personalized appeals and campaigns to every single supporter.

Donor Behavior Prediction – In a similar way to creating personalized campaign and appeals, we can use Machine learning models to predict donor churn and identify potential major donors. This insight could inform targeted retention strategies for regular giving and cultivation efforts for our mid and high value donors. Currently this is being done on a small scale on an ad hoc basis, but again we have predictive analysis tools within Apteco which we should look to utilise and scale up over the coming year.

Sentiment Analysis – we already currently monitor our online sentiment, as well as the key topics and terms the public is interested in and actively searching for content on. AI tools can further support this through analysing large sets of data from multiple sources to allow us to proactively tailor our messages and content to capitalise on opportunities and address issues.

In the current economic climate, we know that revenue growth is going to be challenging. Where we can unlock growth is through optimization and efficiencies in how we acquire and retain supporters, but this can only be done through implementing the appropriate AI technology solutions, rather than relying on manual human capacity to deliver.

Customer service and experience.

It's important to note that while AI can greatly enhance customer service and experience in a charity, it should complement, not replace, the human touch. Building trust and personal connections are vital in our sector, and AI should be used to augment the capabilities of our customer service teams.

Our chatbot already serves as an example of how we can use AI to assist our supports in answering FAQs or amending records, payment methods or contact preference. The more we can improve this feature and look to implement it across other channels such as email, the more we can free up customer service teams for more value-add tasks.

The customer service teams are an incredible source of data and insight into the quality of the experience supporters have with the WWF and the pain and friction points they might experience. At the moment that data is difficult to analyse. The team could benefit from AI tools which allow them to run predictive and sentiment analysis, where they can evaluate large amounts of data to gauge support sentiment towards the charity, issues within product offerings and common issues within customer journeys, which can then be fed back to specific teams and the wider organization to drive improvements.

Advocacy.

AI is poised to significantly enhance the capabilities of advocacy teams by streamlining processes and increasing engagement.

Automated Content Generation: AI-driven content generation tools can alleviate the burden of manual writing tasks for advocacy teams. By automating the creation of reports, articles, and briefs, people can devote more time to crafting effective strategies and engaging with stakeholders. This not only accelerates the pace of advocacy efforts but also ensures that content remains up-to-date and relevant.

Policy Insights - AI can analyze vast quantities of policy documents and legislative texts to provide insights into environmental policies and regulations. This information can guide our advocacy efforts and help shape policy discussions.

Predictive Analytics for Campaign Success: AI empowers advocacy teams with predictive analytics, enabling data-driven decision-making. By analyzing historical campaign data and external variables, AI can forecast the potential success of advocacy initiatives. This predictive capability allows for more efficient allocation of resources, helping people focus their efforts on campaigns with higher chances of achieving desired outcomes. As a result, advocacy teams can optimize their strategies and increase their impact in driving environmental change.

Personalized Constituent Engagement: AI-driven personalization tools enable advocacy teams to tailor their messages and content to individual constituents' preferences and interests. This level of personalization fosters deeper engagement and support for conservation initiatives. Teams can deliver targeted communications that resonate with each recipient, ultimately mobilizing a more effective and responsive advocacy network.

In summary, AI's integration into advocacy work enhances efficiency, strategic decision-making, and stakeholder engagement. Automated content generation, predictive analytics, and personalized engagement tools empower advocacy teams to work smarter and more effectively in advancing our mission. However, this should be explored further by advocacy and campaigning experts and those with knowledge of the application of these AI tools within an advocacy setting.

Conservation.

AI enhances WWF’s conservation efforts through species monitoring using camera traps and drones with computer vision for species identification, aids in proactive poaching detection by analyzing audio and visual data and optimizes habitat restoration through historical and climate data analysis, improving resource allocation for reforestation and rehabilitation. We have several examples of AI in action, monitoring changes to habitats and ecosystems such as coral reefs and sea ice formations in real time, to help inform our teams and local communities work in those areas.

We should expect the use of this technology to accelerate over the coming years but should ensure that our conversations teams have the necessary expertise to capitalise as this technology rapidly improves and develops.

Transformative work for certain industries such as Retail, Food, etc.

- Using Gen AI to assess data and create rankings and reports

- Industry and company research for transformational partnerships

- Due dilligence in partnerships

AI will continue to change the world and we believe that instead of avoiding interacting with it or using it without enough preparation, WWF can lead by example.

Implementing AI solutions that are inclusive, informed and ethically grounded,
setting a standard for a responsible AI approach across the NGO sector.

anchor

Sources

Microsoft AI Integration - https://www.theverge.com/2023/2/10/23593980/microsoft-bing-chatgpt-ai-teams-outlook-integration
Chat GPT and Fundraising - https://fundraising.co.uk/2023/03/15/ai-for-fundraising-why-chatgpt-matters-to-fundraisers/
Guardian - Websites blocking ChatGPT
AI in charts - https://hai.stanford.edu/news/state-ai-9-charts
Brief History of AI - https://ourworldindata.org/brief-history-of-AI
Artificial Intelligence - https://ourworldindata.org/artificial-intelligence
IBM - What is AI? - https://www.ibm.com/topics/artificial-intelligence
Government Report - Impact of AI on UK Job Market
AI Standards Hub - https://aistandardshub.org/
OECD Employment Outlook 2023: Artificial Intelligence and the Labor Market
IPOS – AI Impact on Qualitative Research
Microsoft Co Pilot – A Whole new way to work
Forbes - AI Will Change Your Work Significantly. Here’s How To Respond

Reuters - ChatGPT sets record for fastest-growing user base - analyst note
contextual.ai - Plotting Progress in AI

Microsoft AI Integration - https://www.theverge.com/2023/2/10/23593980/microsoft-bing-chatgpt-ai-teams-outlook-integration
Chat GPT and Fundraising - https://fundraising.co.uk/2023/03/15/ai-for-fundraising-why-chatgpt-matters-to-fundraisers/
Guardian - Websites blocking ChatGPT
AI in charts - https://hai.stanford.edu/news/state-ai-9-charts
Brief History of AI - https://ourworldindata.org/brief-history-of-AI
Artificial Intelligence - https://ourworldindata.org/artificial-intelligence
IBM - What is AI? - https://www.ibm.com/topics/artificial-intelligence
Government Report - Impact of AI on UK Job Market
AI Standards Hub - https://aistandardshub.org/
OECD Employment Outlook 2023: Artificial Intelligence and the Labor Market
IPOS – AI Impact on Qualitative Research
Microsoft Co Pilot – A Whole new way to work
Forbes - AI Will Change Your Work Significantly. Here’s How To Respond

Reuters - ChatGPT sets record for fastest-growing user base - analyst note
contextual.ai - Plotting Progress in AI

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Innovation in Action: Our AI Community & Governance

WWF International's Artificial Intelligence Policy

Artificial Intelligence (or A.I.) refers to the simulation of human intelligencein machines to perform tasks that typically require human intelligence.

AI's rapid development often sparks skepticism and even fear;particularly regarding its impact on jobs, copyright and climate impact.