Artificial intelligence (AI) is ever evolving and, with the increasing adoption of AI in business, it is vital to consider its true impact and any risk factors to ensure AI is performing for your organisation.

Often organisations find themselves shying away from AI adoption amid concerns over what it entails. On the other hand, some organisations fear being left behind and rush to adopt AI without considering appropriate use cases and the potential downsides of poorly considered implementation. There is an ongoing debate about whether investing in AI is truly worthwhile and when, or even if, it will deliver a return on investment.

There is a double constraint when it comes to adopting too fast or being slow to uptake appropriate AI use. The most effective way to address both scenarios, and the key factor you cannot afford to overlook, is the implementation of proper AI governance.

ai adoption challenges

“AI starts with data, however few organisations are in full control of their data. Being in a position where the organisation and its people can trust that their data is accurate, timely, and secure seems a luxury to most, but is critical to fully realise the benefits of AI. If an organisation does not feel in control of its data, starting with a proper data maturity assessment is a great start to establish a baseline on which to develop a data strategy and a roadmap for change. Data governance has gone from curse word to the holy grail of AI innovation and will be a key lever for organisations to future proof themselves in an AI-driven world.”

Simon de Timary, Head of Data and AI at BJSS

With the European Union (EU) AI Act now in effect as of 1 August 2024, it's more important than ever to ensure you're adhering to AI best practices. The EU AI Act is currently the most comprehensive AI law in the world. It exists so organisations that make AI systems respect people’s wellbeing and rights as well as to support AI investment and innovation with a legal framework.

AI readiness: Overcoming barriers to successful implementation

 

“For your organisation to be AI-ready, it’s vital to identify and test use cases, and make sure you have good quality, relevant data for those. Getting support for AI use within the organisation is an essential step too. Along with having the time and resources to prepare for using AI. It's also important to have Responsible AI policies and AI risk management processes in place. These will need to cover your organisation’s needs, risks, and uses of AI. Finally, it's crucial to provide staff training on what AI systems are good at, and not so good at – and how to use them responsibly."

Laura Musgrave – Responsible AI Lead at BJSS

Modernise your legacy IT estate and create an evolved data strategy. Create a short, medium, and long-term view of your data and business strategy. Consider what information you need to capture, to what level of detail, and how you can store and access this for actionable insights.

Determine what facets of your data strategy are the most important to create the intended outcome. Consider the following questions: What data do we need to capture? What insights and trends do I need to be aware of? What kind of experience am I trying to create? It comes back to understanding the signals users give you, building the right models to action those signals, and tweaking algorithms for continuous improvement.

Start small and scale fast. A hypothesis-based approach is key. Brainstorm use cases which you think will provide the most value to your business. Discuss with cross-cutting functions to obtain a clear picture of the wider considerations, such as clear governance and security requirements. Once established, build out your use case to quickly demonstrate value back to the business.

Don’t feel comfortable because others are being complacent. A lack of action in fixing existing data challenges presents a significant risk of losingai adoption challenges out to the competition.

“Developing a robust approach to identifying AI opportunities, building solid governance processes that enable them to widely spread the use of AI across most functions, training their staff and adopting new ways of working; organisations will be able to identify new revenue streams, develop products that are more suitable and tailored in a more affordable way and overall reducing operating costs through gained efficiencies. Leaders will use these cost savings to further power innovation and AI adoption thus increasing their lead over their competition.”

Simon de Timary, Head of Data and AI at BJSS

Opportunities created by AI: Insights and case studies 

“The opportunities and risks of using AI can vary across different industries and use cases. For example, the consequences of AI incidents in healthcare, finance, or manufacturing could be greater than some industries. This is because they provide essential services, and/or need safety or privacy considerations. It's important to look carefully at use cases and risk management, to make the most of the technology.”

Laura Musgrave, Responsible AI Lead at BJSS

The benefits of AI implementation will vary based on the specific industry, use case, and the extent of integration within a company’s operations.

Some benefits that can arise from the successful implementation of AI include

  • Automation and efficiency
  • Improved decision making
  • Enhanced customer experience
  • Advanced analytics and insights
  • Cost reduction and resource optimisation
  • Enhanced productivity and innovation
  • Risk mitigation and fraud detection
  • Enhanced employee satisfaction
  • Scalability and adaptability

ai adoption

One example of an appropriate AI use case is the work that BJSS collaborated with Care Fertility on, to enhance embryo selection during IVF. By analysing time-lapse images, AI algorithms identify key developmental stages, automating embryo annotation with accuracy comparable to manual methods. This innovation drastically enhances reproducibility, reduces embryologists' workload and turnover, and ultimately improves patient treatment outcomes, marking a significant advancement in IVF technology. 

When AI governance is carried out correctly it becomes a key driver for enabling innovation. For example, Project SEEKER was developed by BJSS in collaboration with Heathrow Airport, Microsoft, UK Border Force and Smiths Detection. Here, AI is used to automatically detect illegal wildlife items in luggage and cargo at borders. Using AI algorithms trained on CT scans, the solution alerts enforcement agencies when contraband is detected, aiding in the fight against illegal wildlife trafficking with over 70% accuracy.

Steps for succesful adoption

“AI disruption amongst organisations is already there to a certain extent but it is certainly not too late to get started. The best way to prepare is to start adopting AI in a controlled manner. This can be achieved in a number of ways but combining practical experiments with the development of a strategy and governance is an excellent way to show value rapidly while setting up for long term success. Selecting the right use case is probably the first place to start. There are several factors to consider when selecting a use case but to build momentum, measurable impact is probably the most important one. Constraints such as lack of access to quality data are likely to be the biggest blocker.”

Simon de Timary, Head of Data and AI at BJSS

What is often overlooked is how ready your organisation is to adopt AI. It is crucial that you assess and understand not just the opportunities that AI can bring to your organisation, but the potential pitfalls, too. BJSS offers an AI readiness assessment that evaluates an organisation’s readiness and effectiveness regarding the use of AI and provides tailored recommendations to fully leverage AI as a value driver. 

Choosing appropriate use cases for AI in your organisation is key. Your AI strategy should act as a handbook for all stakeholders to have access to for a full understanding of each step of the strategy and how AI is set to be part of the organisation. 

AI governance is crucial for organisations to assess and manage risks and ensure overall confidence in AI use. It involves establishing policies, procedures and controls to mitigate ethical, legal and operational risks associated with AI deployment. By implementing robust governance frameworks, organisations can promote transparency, accountability, and responsible AI practices. 

The BJSS eBook, Responsible AI: A comprehensive guide to governance, provides a thorough overview of AI governance, policies, frameworks and practices that should guide AI development at ideation and beyond.

The future of AI in organisations

“Given the pace of change it is quite hard to project too far in the future. However given the fact that AI is already really pervasive in the workplace and the current trajectory, I expect AI will become more transparently omnipresent. In the enterprise, tasks that are completed entirely by humans will become scarce and people will have learned to coordinate with AI agents to boost productivity.”

Simon de Timary, Head of Data and AI at BJSS

Rushing into AI adoption without proper governance can lead to suboptimal outcomes, while hesitancy can result in missed opportunities. Awareness, training, planning and governance are essential for responsible AI use. By finding this equilibrium, organisations can harness AI's potential while mitigating risks, fostering innovation and maximising benefits for all stakeholders.