# **Organisational Adoption of Open and Responsible AI: A Case Study from The Turing Way Practitioners Hub**

2023 was the year that artificial intelligence (AI) went mainstream. Even if you are not an AI expert yourself, you have almost certainly heard of – and most likely used – an AI tool. We used apps like ChatGPT[^1] to draft emails, plan holidays, edit photographs and ask for music recommendations. While we were happily involving virtual home assistants, search engines and chatbots in our day-to-day lives, businesses across the board explored how AI can enhance efficiency and drive innovation in their respective sectors. Such was the scale of the media coverage and the pace of adoption, AI was named ‘word of the year’[^2] by Collins Dictionaries.

Popular large language models (LLMs)[^3] and Generative AI[^4] technologies like [GPT](https://openai.com/gpt-4) and [DALL-E](https://openai.com/dall-e-2) are effortlessly unleashing our creativity and generating enormous amounts of content, but is that sufficient to trust them to help build responsible AI tools? How can commercial organisations across different sectors take advantage of AI beyond Generative AI and LLMs? How can these tools assist in ethical decision-making while mitigating the risks of potential biases and harms? How can startups and Small and Medium Enterprises (SMEs) join the party with limited resources and experience?

The answer, like the technology itself, is complex.

## BridgeAI to Bridge the AI Divide

“There is a lot of hype around tools like ChatGPT, but not necessarily a lot of understanding about the technology itself or how, specifically, AI can help individual businesses to solve problems or make improvements in their companies,” says Dr Michalis Smyrnakis, AI group leader at the [Hartree Centre](https://www.hartree.stfc.ac.uk/). Hartree, founded in 2012 to help UK organisations adopt advanced technologies, is a partner in [Innovate UK’s](https://www.ukri.org/councils/innovate-uk/) [BridgeAI](https://iuk.ktn-uk.org/programme/bridgeai/) programme. BridgeAI empowers businesses to navigate the complexities and unlock the potential of AI. Focusing on four key, high-growth-potential sectors – agriculture, construction, creativity and transport – BridgeAI provides funding and support to enterprises seeking to boost their productivity and competitiveness through the responsible adoption of AI.

Dr Claire Morris leads Innovate UK’s involvement in Bridge AI. “In the UK we have a thriving research sector around AI and related technologies,” says Claire, “but our success in translating them into economic impact has not been as consistent.” The aim of BridgeAI, as the name suggests, is to bridge the gap between developer communities and adoption in high-growth-potential sectors. “BridgeAI,” says Claire, “is helping AI developers understand business requirements and businesses understand where the opportunities are in terms of productivity and return on investment.”

“Take agriculture,” adds Michalis. “If the AgriTech[^5] Industry can provide solutions to help predict climatic conditions, analyse soil quality and monitor crops more efficiently, a farmer’s output and productivity could be greatly enhanced. Companies may decide that AI can help them solve a problem but they might not know how to develop and implement the technology, or what type of people they need to hire to achieve that. At Hartree, we create roadmaps to show what stage of their AI adoption journey companies have reached and the steps they need to take to adopt AI-based products and see the benefits for their organisation and sectors.”

### The Turing Institute’s Role in Training, Upskilling and Enabling AI Adoption

[The Alan Turing Institute](https://www.turing.ac.uk/), the national institutecenter for data science and AI in the UK is a BridgeAI delivery partner. The Turing has been creating new opportunities for upskilling, training and expert consultation to build up data science and AI capabilities across the target sectors. [AI skills and training](https://www.turing.ac.uk/skills) have been curated and made available in the Turing’s online learningtraining platform for on-demand access and live training workshops are offered regularly for cohorts of companies. “Industries want to use AI technologies to improve their productivity – from automating tasks to sparking creative ideas and developing impactful tools. There are thousands of potential use cases across the four BridgeAI sectors,” says Dr Nico Guernion, Director of Partnerships at the Turing. He adds: “One of the challenges around AI adoption is that there aren’t currently enough people with the right set of skills – not just technical, but sociotechnical – in data science, data engineering and tech innovation to help companies take advantage, responsibly, of the opportunities that AI presents.”

An [AI Skills for Business Framework](https://www.turing.ac.uk/skills/collaborate/ai-skills-business-framework) is under developmentwas developed in 2023, a project led by Dr Matt Forshaw, Senior Advisor for Skills at The Alan Turing Institute in collaboration withand supported by the [Office for Artificial Intelligence](https://www.gov.uk/government/organisations/office-for-artificial-intelligence) within the Department for Science, Innovation and Technology (DSIT)[^6]. It is a step towards fulfilling the [National AI Strategy](https://www.gov.uk/government/publications/national-ai-strategy/national-ai-strategy-html-version) commitment to share guidance on skills for enabling employees to use AI in a business setting, and more resources will be developed to support the adoption of AI maximise the impact of data and AI skills across BridgeAI priority sectors. An [independent scientific advisory (ISA) panel](https://www.turing.ac.uk/work-turing/innovate-uk-bridgeai-independent-scientific-advisors-role-profile), recently onboarded at the Turing, will also play a key role in advising companies on their data and AI needs, drawing both scientific and sociotechnical expertise from within the Turing and other BridgeAI delivery partners. Notably, two of the Turing’s flagship projects are involved in the BridgeAI partnership: _[The Turing Way](https://the-turing-way.netlify.app/index.html) _Practitioners Hub and the [AI Standards Hub](https://aistandardshub.org/).

#### Integrating Open Practices for Responsible and Inclusive AI

_[The Turing Way](https://the-turing-way.netlify.app/index.html)_ is an open source, community-driven project on data science. Known for its high-quality online handbook, to date, 450+ diverse contributors from international organisations have collaborated to develop 350+ chapters and resources, covering best practices under the guides for reproducibility, project design, communication, collaboration, ethics and community. “_The Turing Way_ started as part of the £38.8 million AI for Science and Government programme, supported by the [UKRI Strategic Priorities Fund](https://www.ukri.org/what-we-do/strategic-priorities-fund/)<span style="text-decoration:underline;">,</span>” says Kirstie Whitaker, Director of [Tools, Practices and Systems](https://www.turing.ac.uk/research/research-programmes/tools-practices-and-systems) (TPS) programme at the Turing and founder of _The Turing Way_. “I drew on my experiences of inefficiencies in academic research, the reproducibility crisis, and how lonely my colleagues and I felt trying to learn software development, data management and statistical design skills without any specific support or guidance. In _The Turing Way_ we demonstrate inclusive collaboration among researchers and practitioners in the co-creation of resources that they want to use. The book is a living artefact: as our knowledge of AI changes, the book must also evolve. We welcome practitioners and experts from all domains to use _The Turing Way _resources as well as keep the guidance up to date for themselves and others.”

Since its launch in 2019, _The Turing Way _has informed the UK government analysis function’s [strategy for Reproducible Analytical Pipeline](https://analysisfunction.civilservice.gov.uk/policy-store/reproducible-analytical-pipelines-strategy/), open working and reproducibility in health as recommended in the [Goldacre Review](https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067053/goldacre-review-using-health-data-for-research-and-analysis.pdf) for the Department for Health and Care[^7] and the Mayor of London’s [Emerging Technology Charter](https://www.london.gov.uk/publications/emerging-technology-charter-london). Some notable collaborators and users of _The Turing Way_ are the NASA-Transform to Open Science programme[^8], eScience Center Netherlands[^9], The Health Foundation[^10], the Office for National Statistics[^11] and the Wellcome-funded Data Science Without Borders project. _The Turing Way _has been used worldwide, with over 7000 monthly visitors of the book and over 1000 members engaging on the Slack workspace.

_[The Turing Way Practitioners Hub](https://www.turing.ac.uk/turing-way-practitioners-hub)_, an extension of the project_,_ is funded by BridgeAI to support direct engagement and collaboration with industry experts from organisations adopting practices from _The Turing Way_, advancing their efforts to promote data science and AI as openly as possible. Hosted under the TPS Research Programme, the project draws expertise from the Turing’s Research Community Management, Research Application Management, Academic Skills and Research Engineering teams, as well as the broader open source and open science network of _The Turing Way._ Led by Dr Malvika Sharan, TPS Senior Researcher for Open Research and a co-lead investigator of The Turing Way, the Practitioners Hub serves as a platform for cross-sector collaboration, knowledge exchange and strategic partnerships, spanning various organisations and data science initiatives, including the BridgeAI network. By leveraging a cohort-based approach, the programme brings together practitioners from partner organisations as Experts in Residence (EiRs) and supports their efforts in advancing good practices in their networks. Cohort activities are designed to build a shared understanding of open source, open data, cross-sector collaboration, computational reproducibility, and ethical considerations in the context of data science and AI. EiRs provide a forum for improving and enabling the effective adoption of practices that enhance the quality, viability and real-world impact of data science and AI technologies in their sectors.

#### Navigating AI Standards for Governance and Regulation

Championing and embedding another set of important practices in AI standards, governance and regulation is the AI Governance team leveraging expertise and insights from their involvement in the AI Standards Hub. The National AI Strategy, launched in 2021 by the UK Government, aimed to construct a regulatory framework for AI to foster economic growth, instilling public confidence and establish the UK as a key actor in the global AI landscape. The [AI Standards Hub](https://aistandardshub.org/) has been integral in advancing this strategy. Shakir Laher, the Research Application Manager from the AI governance team and TPS member, says: “AI standards are set to play a pivotal role, serving as a foundational element that empowers organisations to achieve excellence and quality, while effectively mitigating associated risks through seamless integration into their assurance systems. They can be further utilised as tools for operationalising regulation, providing organisations with a means to showcase not only their adherence to safety, ethical and legal standards but also their commitment to elevating their reputation and cultivating public trust.”

Noting some of the most significant AI moments in 2023, such as the release of an AI regulation [white paper](https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper), the hosting of an [AI Safety Summit](https://www.gov.uk/government/publications/ai-safety-summit-introduction) and the introduction of an [Artificial Intelligence Safety Institute (AISI)](https://www.gov.uk/government/publications/ai-safety-institute-overview/introducing-the-ai-safety-institute) in the UK, Shakir adds: “With the fast-moving activity in the AI governance landscape, our team is providing much-needed knowledge and clarity for UK businesses. The AI Standards Hub’s training for the BridgeAI community will help companies understand best practice in AI governance and operationalise those practices to remain competitive in the UK and in international markets.”

A key pillar of the BridgeAI programme is the responsible adoption of technologies. “We want companies to think carefully about ethics, privacy and transparency before deploying new technologies,” says Jimmy Jarvis, Senior Policy, Research and Engagement Manager at [Digital Catapult](https://www.digicatapult.org.uk/), a BridgeAI partner. “Programmes like Digital Catapult’s Futurescope Accelerator[^12], _The Turing Way_ Practitioners Hub and the AI Standards Hub can certainly help organisations and practitioners consider those important aspects in deploying responsible AI solutions.” Nico adds: “These initiatives tie in very nicely with one another by sharing resources, teaching and showcasing best practices, and providing communities for practitioners and experts to learn from each other.”

## Cross-Sector Exchange on Industry Best Practices

In 2023, the Practitioners Hub project brought together the [inaugural cohort of Practitioners Hub EiRs](https://www.turing.ac.uk/turing-way-practitioners-hub/eirs) representing five organisations: [Office for National Statistics](https://www.ons.gov.uk/), [Genomics England](https://www.genomicsengland.co.uk/), [British Antarctic Survey,](https://www.bas.ac.uk/) [Energy Systems Catapult](https://es.catapult.org.uk/) and [DigiHaul](https://digihaul.com/). With an overarching goal to accelerate the adoption of best practices, the Practitioners Hub led by Malvika and coordinated by Research Project Manager Alexandra Araujo Alvarez, delivered a six-month curriculum designed for peer-based knowledge sharing, skill building and adoption of community practices in EiRs’ work.

Each EiR engaged in a range of activities between June 2023 and December 2023 to develop plans and strategies for their product development and/or team collaboration in the context of data science and AI projects. Specialised workshops and training sessions included stakeholder mapping for community engagement, user journey mapping for responsible system design, skills mapping and pedagogy for technical skill building, systems mapping for identifying ‘leverage points’, and finally, road mapping for implementing best practices in their work in the long-term. In addition, each EiR was offered personal coaching, consultation and customised support in advancing the impact of their work as open source/science, reproducibility and _The Turing Way_ champions.

During their participation in the cohort, the EiRs developed case studies exploring the state of best practices within their respective organisations across selected data science and AI projects and teams. Five [case studies from the first cohort](https://zenodo.org/communities/the-turing-way-practitioners) highlighted: 1) collaboration in open source energy projects; 2) AI for decarbonising road networks; 3) open practices for sensitive data; 4) promoting open working in government; and 5) intersection of data science and open practices.

We were struck by the common themes across the application areas, and the nuances of incorporating best practices in each specific organisation. Next, we share some key lessons developed from our engagement with the first cohort of experts in residence.

### Open Working in AI to Tackle Important Challenges

The benefits of open working have been a key theme of discussion for the first cohort of _The Turing Way_ Practitioners Hub organisations. According to data science practitioners at the Energy Systems Catapult – including EiR Dr Stephen Haben – openness offers several benefits, including: reducing duplication of effort and enabling more efficient allocation of resources; aiding reproducibility, increasing the confidence we can have in our collective knowledge; driving innovation through visibility that encourages others to build on the work and develop new products; and making it easier for people with common interests to work together.

Damon Roberts, a Data Consultant at the Energy Systems Catapult, has worked with a host of SMEs in the private sector to help them deliver open source projects in the energy sector. “One of my favourite projects – and a hugely successful one – involved a company called [Heatweb](https://www.heatweb.co.uk/),” says Damon. “Heatweb produced open hardware controllers for heating and cooling units using Raspberry Pi devices[^13]. The goal was to give people access to data about their systems and allow them to make minimal, commonsense improvements that could have massive impacts.” With Heatweb, clients will happily pay for deep expertise and knowledge of the product as well as the hardware or software itself. Damon adds: “Developing a successful business model for an open source project involves a different way of thinking. In my experience, companies that are flexible are well placed to take advantage of the opportunities; companies that are more set in their ways often struggle.”

Energy Systems Catapult helps translate innovation into tangible impact in the energy sector. One of its projects is the [Catalogue of Projects on Energy Data (CoPED)](https://coped.energy/) – an open source directory of metadata and energy projects in the UK that not only highlights the key players and partnerships in the sector, but promotes openness, transparency and collaboration among them. Samuel Young, Practice Manager for Data Science & AI, says: “With CoPED, we aimed to set it up to drive enduring impact by applying approaches that any impactful open source project requires: 1) designing with a diverse set of end users in mind; 2) building an inclusive, active community of stakeholders; 3) identifying an ongoing model for support; and 4) getting the basics right, such as choosing appropriate licences.”

“I think a lot of sectors will be looking towards open data and open source methods – particularly smaller companies who may not be able to invest in their own developers,” adds Claire. “A strong driver for us is to make sure businesses across different sectors are able to take advantage of open ways of working in a secure and responsible manner.”

One of those sectors is transportation – a traditional industry still in the early stages of digitisation and technological adoption but with high potential for growth. Dr Wenjia Tang, an EiR at the Practitioners Hub, is Head of Data at [DigiHaul](https://www.digihaul.com/), a digital transportation startup. Wenjia’s role at DigiHaul is to optimise the company’s use of data, making sure transportation efficiencies and customer service levels are as high as possible. “Data is our currency at DigiHaul,” says Wenjia. “Our priority has been to gather the right types of data and develop our platform, but now we are thinking about how advanced data science and techniques like machine learning and AI can help us improve operational efficiency and deliver an even better service.” People working on a shipment often manually check and input information, without any standard system for checking the accuracy of the human-supplied data, which can introduce errors. Wenjia says: “We are exploring how we can use Generative AI to verify new data against previously collected information. Can we look at information about the same type of vehicle, goods, customer or route and spot when something is wrong? Could we develop a generative AI tool to answer employees’ queries about shipments or shipment carriers? This is the type of project that will really demonstrate the potential of data science and get people at DigiHaul engaged with the topic.”

“When it comes to responsible adoption of AI, some of the UK’s government departments are in the process of setting a good standard,” says Nico. “For example, the [Department for Transport (DfT)](https://www.gov.uk/government/organisations/department-for-transport), having received independent scientific advice from the Turing, gathered public opinion on its [plan to use AI for analysing consultation responses](https://assets.publishing.service.gov.uk/media/654e6f078a2ed4000d720d12/using-ai-in-consultations-and-correspondence.pdf), before deciding whether to implement the use of AI for this type of application. This is a good way of doing things: identify a specific AI use case, with significant potential benefits, and possible drawbacks, and engage the public in the conversation before making decisions about applications of these technologies.” The [Transport Data Strategy](https://www.gov.uk/government/publications/transport-data-strategy-innovation-through-data) by DfT sets out targeted interventions to work with and support the transport sector to harness the benefits of data, grow the economy, reduce environmental impacts and improve transport for the user.

One of Digihaul’s key aims is to take carbon out of the UK’s transport network. Carbon emissions from the transportation sector are influenced by a number of different factors, including the weight of the vehicle and the shipment sizes. Wenjia says: “Of the hundreds of thousands of haulage journeys made in the UK each year, around 20% are estimated to be empty. We are using algorithms to try to understand where efficiencies could be made, such as by introducing better data practices. For example, we are exploring the potential of data sharing to work with the industry as a whole – if other companies made their own databases visible, we could be looking at efficiency for one big transportation network rather than lots of small networks working in silos. This step would make a big impact in reducing wastage and emissions.” As per the Transport Data Strategy, a strong example in support for open data is by Transport for London, which is predicted to generate up to [£130m of annual economic benefits](https://content.tfl.gov.uk/deloitte-report-tfl-open-data.pdf) for Londoners, tourists and TfL as a body. Pointing to more such evidence, the strategy encourages the ‘open by default’ approach to sharing and combining data, maximising the value of the data economy.

“There are challenges, such as discoverability, lack of data literacy and privacy concerns, but it’s definitely worth exploring the potential of open source and open data in the transportation sector,” adds Wenjia. “Being part of the Practitioners Hub has been really helpful: I have had lots of inspiring conversations and we know we are not alone at DigiHaul in what we are trying to do with AI and data science.”

### Determining the Level of Openness

One of the questions organisations have to consider when adopting AI-based tools is how ‘open’ they wish to be with their technology – whether they are developing their own software or making use of existing open source respurces. Nico says: “There is often a degree of resistance to adopting open approaches when it comes to AI and machine learning. Organisations can have fears around competitive advantage or data privacy. But when you speak to practitioners who are actually working with data, or working with machine learning or other types of AI methods, they’ll tell you there is a huge benefit in taking a collaborative approach and considering open source for their particular use case.”

As government-adjacent organisations dealing with potentially sensitive data, the question of ‘how open?’ is particularly pressing for Genomics England (GEL) and the Office for National Statistics (ONS) – two participants in the first cohort of _The Turing Way_ Practitioners Hub.

Dr Natalie Banner is GEL’s Director of Ethics, responsible for helping the organisation navigate the tricky ethical and regulatory waters of cutting-edge genomic medicine and sensitive patient data. In the context of open science and open source, that means striking a balance between safeguarding participants’ genomic data and maximising the research potential of that data. And when it comes to open tools and resources, developers need to think carefully about whether they could be used in unforeseen ways that exacerbate rather than alleviate existing inequalities. Natalie says: “I don’t think we have a sophisticated enough understanding yet of what’s possible in terms of open science and open source – partly because of the focus on stringency around access to data and the need to protect it. There is an unavoidable ‘closedness’ to what we do, but I think we could really reap the benefits of working openly if we had a more nuanced view of what it means. That could be greater use of open-source code, better showcasing of our huge amounts of available data, fostering collaborations through our [Genomics England Research Network](https://www.genomicsengland.co.uk/research/academic) or programmes like [Diverse Data](https://www.genomicsengland.co.uk/initiatives/diverse-data), or – from my perspective – more transparency for participants around how data could potentially be used by researchers. Of course, all of that needs to be done while fully respecting the privacy and confidentiality of our participants. But in this field there is no alternative to open science: researchers are often looking for the proverbial needle in a haystack, and therefore we need to work ‘as open as possible’ on an international scale.”

Rowan Hemsi, Practitioners Hub EiR from ONS, highlights that there are many guidelines available on what individuals in the civil service should be doing to maintain code quality, data security and an appropriate level of transparency. “These resources are really helpful, but it is quite a difficult transition to move from never having heard of a practice like open source to implementing it in your own work,” says Rowan. Alluding to the main responsibilities of ONS in collecting, analysing and disseminating statistics about the UK's economy, society and population, Rowan adds: “It’s really important as a civil service body that we are transparent and share what we are doing and how we’re doing it. The work we do is not just about technology: it’s about people. It’s about how we can build people’s skills and how we can change culture – not just by solving technical problems but also by addressing ‘people problems' or, rather, what we should call ‘people opportunities’.”

“ONS is on a journey,” says Ross Bowen, dissemination lead for the Integrated Data Service at ONS. “We’re increasing our use of things like [reproducible analytical pipelines (RAPs)](https://analysisfunction.civilservice.gov.uk/support/reproducible-analytical-pipelines/) and trying to train analysts in using software engineering tools. I think we’re on the path to having much of our analysis openly published on GitHub[^14]. Going forward we’ll need to make it easier for people to pick up the skills to shape their data into a format that’s easier for users to work with – and we’ll need to show analysts that if they invest in this, it will be better for everyone.” Reflecting on the growing interest in open source from people at ONS, Dan S., co-lead of RAP support at ONS, adds: “I think we’re trending in the right direction. Part of my role is about persuading people to open up their repositories, or helping people to write better-quality code as a first step to publishing it openly. We could be doing more in terms of showing technical leadership in this area and offering incentives or recognition for people who open source their work – [NHS England](https://www.england.nhs.uk/) (previously NHS Digital) is a good example of an organisation that does that well. The general direction of travel across industries is towards more openness, so it’s important that we don’t fall behind.”

Reflecting on their participation in the Practitioners Hub, Rowan says: “The most valuable thing for me was getting to meet people from beyond the public sector. It was really interesting to learn about practices outside government organisations and realise that while we all have slightly different circumstances, we are actually facing similar challenges. It’s nice to see that you’re not alone in facing these problems, and hopefully we can continue to try to communicate across sectors and address them collaboratively.”

### Engaging the Right Stakeholders for AI Adoption at Different Levels

Practitioners Hub EiR Lucy Stephenson works within the UK Polar Data Centre at British Antarctic Survey (BAS), one of the Practitioners Hub organisations. Lucy says: “Researchers and data scientists at BAS are already making good use of AI in their work. Particularly, our AI Lab is using AI and machine learning techniques to help tackle everything from sea ice forecasting to monitoring wildlife. We’ve done really well at adopting AI to replace some of the manual tasks associated with lab and field work, and there is still lots of potential for growth in the area of automation, which will improve reproducibility. What’s been critical is that we have institutional buy-in at BAS to adopt new technologies and open ways of working. These concepts are embedded in our organisational strategy.”

Lucy adds: “Research areas that have high growth potential are traditionally those that have fewer standard computational techniques. Working with external experts and contractors does help with this aspect of the work in the short-term, but perhaps the more sustainable way of approaching it will be to invest in early-career data practitioners who can be upskilled in combining their technical skillsets with existing domain knowledge.” One high-profile success story within BAS is the [Wildlife from Space](https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.turing.ac.uk%2Fturing-way-practitioners-hub&data=05%7C02%7Cmsharan%40turing.ac.uk%7Ce6f208a0bf8c48e014a408dc3c49586c%7C4395f4a7e4554f958a9f1fbaef6384f9%7C0%7C0%7C638451532917730786%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=6%2BuppFm9TfWtCBNXhChhLtM8j%2B2A3jD%2BnerWCuA1nSk%3D&reserved=0) project led by Dr Peter Fretwell, which uses super-high-resolution satellite imagery to monitor animals such as walrus, penguins, seals and whales that can be difficult to study on the ground (or in the sea) in remote places. Lucy says: “The project uses AI to automate counting of wildlife from satellite imagery.[^15] Researchers invested in postdocs and PhD students who were both wildlife biologists and computer scientists.”

When it comes to adoption of new technologies for business, senior buy-in and building understanding among leaders is key. Claire says: “There’s definitely a skills gap among the early-career workforce from a technical point of view, but there’s also work to be done to empower decision-makers at executive level. Commercial and legal teams should be engaged in understanding the implications – both opportunity and risk – of technological adoption. Some of it is about knowing the technology, but some of it is the cultural aspects of business change management, particularly in less mature sectors.”

Delivered by Innovate UK and strategic partners, BridgeAI connects businesses in priority sectors with AI experts, facilitates the co-creation of technologies between the supply and demand sides of the AI ecosystem and provides access to scientific expertise. Emphasising outcomes and impact, Claire says: “One thing I’d like to see more of is assisting technical experts, such as product developers, in understanding how to communicate with businesses about technologies like AI in a language they can understand. Our key sectors are largely tech-agnostic: they are more interested in the improvements to their outcomes than the technology itself.”

## Learning from Sectors with Proven Success with AI

High-growth-potential sectors, although entering the AI adoption journey later, have the advantage of learning from AI-driven or AI-pioneering sectors, such as health, energy, education and technology. Building on their knowledge, these emerging sectors can develop, deploy and iteratively improve more informed and innovative AI solutions that serve their user community, as well as effectively integrate with and improve business processes. The co-adoption of AI with open source, open data and industry best practices, in collaboration with other sectors, will be key to building a collaborative environment that maximises the positive impact of AI for businesses, the economy and society at large. In this broader context, the role of _The Turing Way_ Practitioners Hub within BridgeAI and the open source/science communities is to continue to offer this platform for cross-sector collaboration, strengthening a collective commitment to transparency, responsibility and ethical considerations in the adoption of AI.

### What’s Next for The Turing Way Practitioners Hub?

The inaugural cohort of the Practitioners Hub has served as an invaluable source of insights, offering perspectives from EiRs on what best practices mean in the context of data science and AI for their respective sectors. Feedback from them has been incredibly important in reinforcing the need and value of fostering this collaborative forum for future cohorts.

To engage with the broader network of BridgeAI, the Practitioners Hub aims to bring together the next cohort of practitioners from the target sector along with experts from AI-driven sectors represented by EiRs. Our EiRs, already involved in decision-making and strategy formulation for advancing technology, skills and data practices in their organisations, will be well-placed to fully benefit from _The Turing Way_. Through the resources and activities provided by the Practitioners Hub, they will be supported to incorporate systems-level, collaborative and community-minded approaches into developing long-term strategies for their teams and data/AI projects.

The goal is to openly engage with practitioners from various sectors and leverage the transferable nature of data skills and processes to harvest the benefits of AI. “My recommendation to anyone joining Practitioners Hub is to generally embrace the parts of your own perspective that are different from everyone else's - that are unique and, hence, useful for everyone,” says Lucy. Pointing to the diverse range of opportunities provided by the Practitioners Hub, she adds, “You may come from a niche and, therefore, think that certain topics or practices aren't relevant for you within the Practitioners Hub or The Turing Way, but keep an open mind: you'll be surprised. One of my big takeaways from the experience was to think about the place for open-source in environmental science, beyond open-data and open-code, which wasn't even something on my radar going in.”

_The second cohort of The Turing Way Practitioners Hub will begin in September 2024. For details, please visit our website: [https://www.turing.ac.uk/turing-way-practitioners-hub](https://www.turing.ac.uk/turing-way-practitioners-hub). _

## Acknowledgements

This case study is published under _[The Turing Way Practitioners Hub](https://www.turing.ac.uk/turing-way-practitioners-hub)_ Cohort 1 - case study series. The Practitioners Hub is a _[The Turing Way](https://the-turing-way.netlify.app/welcome)_ project that works with experts from partnering organisations to promote data science best practices. This is a meta case study, drawing key lessons from five case studies published by the first cohort of EiRs. This case study provides an overview of common understanding, challenges, successful examples, and trends related to best practices, specifically in open source, open data, and collaboration across different sectors. All case studies are published on Zenodo and linked to our website under CC-BY 4.0 license:

* "The power of collaboration: creating maximum value from open source energy projects." Zenodo, 2023, doi:[10.5281/zenodo.10338376](https://zenodo.org/records/10338376).
* "AI in transportation: DigiHaul's vision for decarbonizing the UK's road network." Zenodo, 2023, doi:[10.5281/zenodo.10338551](https://zenodo.org/records/10338551).
* "Genomics England and The Turing Way: a path to open science in a sensitive data landscape." Zenodo, 2023, doi:[10.5281/zenodo.10338456](https://zenodo.org/records/10338456).
* "Meet the ONS open sourcers: promoting practices for working in the open." Zenodo, 2023, doi:[10.5281/zenodo.10338293](https://zenodo.org/records/10338293).
* "Exploring the intersection of data science and open practices at British Antarctic Survey." Zenodo, 2023, doi:[10.5281/zenodo.10337801](https://zenodo.org/records/10337801).

This work is supported by[ Innovate UK BridgeAI](https://iuk.ktn-uk.org/programme/bridgeai/). The Practitioners Hub has also received funding and support from the Ecosystem Leadership Award under the EPSRC Grant EP/X03870X/1 and [The Alan Turing Institute](https://www.turing.ac.uk/).

The inaugural cohort of The Turing Way Practitioners Hub has been designed and led by Dr Malvika Sharan. The Research Project Manager is Alexandra Araujo Alvarez. Stuart Gillespie is the technical writer for this case study, and others in the series. Led by Dr. Kirstie Whitaker, Programme Director of the Tools, Practices, and Systems research program, The Turing Way was launched in 2019. The Turing Way Practitioners Hub, established in 2023, aims to accelerate the adoption of best practices. Through a six-month cohort-based program, the Hub facilitates knowledge sharing, skill exchange, case study co-creation, and the adoption of open science practices. It also fosters a network of 'Experts in Residence' across partnering organisations. For any comments, questions or collaboration with _The Turing Way_, please email: [[e-mail]](mailto:[e-mail]).

<!-- Footnotes themselves at the bottom. -->
## Notes
[^1]:
[https://openai.com/blog/chatgpt](https://openai.com/blog/chatgpt)
[^2]:
[https://www.collinsdictionary.com/woty](https://www.collinsdictionary.com/woty)
[^3]:
[https://en.wikipedia.org/wiki/Large_language_model](https://en.wikipedia.org/wiki/Large_language_model)

[^4]:
[https://en.wikipedia.org/wiki/Generative_artificial_intelligence](https://en.wikipedia.org/wiki/Generative_artificial_intelligence)

[^5]:
[https://en.wikipedia.org/wiki/Agricultural_technology](https://en.wikipedia.org/wiki/Agricultural_technology)

[^6]:
[https://www.gov.uk/government/organisations/department-for-science-innovation-and-technology](https://www.gov.uk/government/organisations/department-for-science-innovation-and-technology)

[^7]:
[https://www.gov.uk/government/organisations/department-of-health-and-social-care](https://www.gov.uk/government/organisations/department-of-health-and-social-care)

[^8]:
[https://nasa.github.io/Transform-to-Open-Science/](https://nasa.github.io/Transform-to-Open-Science/)

[^9]:
[https://www.esciencecenter.nl/](https://www.esciencecenter.nl/)

[^10]:
[https://www.health.org.uk/](https://www.health.org.uk/)

[^11]:
[https://www.ons.gov.uk/](https://www.ons.gov.uk/)

[^12]:
[https://iuk.ktn-uk.org/opportunities/futurescope-bridgeai-acceleration-programme-digital-catapult/](https://iuk.ktn-uk.org/opportunities/futurescope-bridgeai-acceleration-programme-digital-catapult/)

[^13]:
[https://www.raspberrypi.com/](https://www.raspberrypi.com/)

[^14]:
[https://github.com/](https://github.com/)

[^15]:
[https://en.wikipedia.org/wiki/Satellite_imagery](https://en.wikipedia.org/wiki/Satellite_imagery)