Guest blog: from game-changing to everyday AI - there’s an AI flavour for every appetite
AI is opening up a plethora of opportunities for organisations of all sizes and is poised to become a key differentiator in today’s competitive landscape.
How can AI adoption unlock opportunities for diverse organisations and help to improve efficiency, drive innovation, and enable data-driven decision-making? And how can organisations and governments work together to address the barriers to AI adoption and create an environment where innovation thrives?
The path to AI adoption for organisations begins with clearly defining their AI ambition—enhancing existing operations or pursuing transformative innovation. With the proper support, even the most minor organisations can leverage AI to create value, drive growth, and remain competitive in an increasingly digital economy.
AI for every organisation: from game-changing to everyday AI
AI’s potential can be divided into two broad categories: game-changing AI and everyday AI.
Game-changing AI
Game-changing AI has the potential to revolutionise industries and society with breakthrough technologies like autonomous vehicles and advanced medical diagnostics. It’s high cost and high risk, but offers the potential of ground-breaking long term rewards.
Everyday AI
In contrast, Everyday AI focuses on incremental improvements, optimising routine tasks and enhancing existing operations. There are already a host of accessible, well-established AI tools that are lower cost and easy to implement. And there are growing numbers of solutions focused on improving everyday operations that can have just as big of an impact as game-changing AI, especially if it’s tied to an organisation’s specific business objectives.
Whilst larger enterprises might be in a better position to invest in cutting-edge AI research and development, smaller businesses can achieve quick wins by adopting accessible AI tools. For example, small retail businesses can use AI-driven analytics for better inventory management, while local government bodies can automate elements of customer service with chatbots. The key lies in matching AI solutions to the specific needs and scale of the organisation.
Examples of AI innovation across sectors
AI innovation has proven to be successful across multiple sectors, from manufacturing and logistics to healthcare and public services. In the public sector, AI is transforming service delivery by automating administrative tasks, enabling predictive maintenance for infrastructure, and enhancing decision-making through data analytics.
At Modular Data, we’ve been lucky to be involved with some game-changing work with His Majesty’s Prison and Probation Service (HMPPS). With AI-driven data products, we’re helping HMPPS gain deeper insights into operational efficiency, recidivism trends, and rehabilitation outcomes. This initiative promotes how AI can transform public services and deliver significant societal benefits.
In the private sector, manufacturers are using AI to optimise supply chains, improve production processes, and predict equipment failures. A good example is the phenomenal work Replan, an AI-driven supply-chain optimisation business, is doing with manufacturers like Britvic and Premier Foods. The telecommunications sector is increasingly adopting AI for network management, service automation, and customer engagement.
Overcoming the barriers for successful AI adoption
AI’s ability to process large datasets and provide actionable insights allows businesses to reduce costs, improve customer satisfaction, and stay competitive. But if we’re to move into the age of an AI revolution, with AI accessible to organisations of all sizes, we need to recognise and overcome the barriers to AI adoption.
Poor data quality - rubbish in, rubbish out
To be confident in the outputs of AI initiatives, we need to trust the inputs. Poor quality and siloed data are significant barriers for successful AI. Most organisations will often need help with fragmented, inconsistent, or poor-quality data, which limits the effectiveness of AI models. The UK Government can play a critical role in facilitating better access to high-quality datasets, especially for smaller organisations that lack the resources to collect and maintain such data.
AI skills gap
Whilst large organisations have the budget and resources to hire top AI talent, small and medium-sized enterprises (SMEs) may need to take a different approach and nurture inhouse talent into industry experts. Investing in AI skills development, promoting training programmes, and encouraging collaboration between academia and industry can help bridge this gap. The recent UK Government AI Upskilling fund has been a brilliant resource for SMEs. Hopefully after this initial pilot, it will be rolled out on a wider scale, and will include public sector AI upskilling to help the UK workforce prepare for an AI-driven future.
AI infrastructure
Large-scale AI projects need significant computing power, which can be prohibitively expensive for smaller organisations. While cloud services have made AI more accessible, there is still a need for public investment in shared AI infrastructure. This could be an opportunity for the UK Government to support the creation of centralised AI hubs that provide affordable access to compute resources. This would make AI accessible to organisations of all sizes and bring innovative ideas to life.
Moreover, developing AI ecosystems, such as those found in technology clusters, can foster collaboration between businesses, researchers, and government. These ecosystems can act as innovation accelerators, allowing organisations to share knowledge, access funding, and rapidly develop AI solutions.
Ethical concerns
As AI adoption grows, it’s putting the spotlight on the ethical considerations for using AI. Issues such as algorithmic bias, data privacy, and transparency around data use need careful consideration. At Modular Data, we build transparency and governance in all our AI solutions, to ensure that the models deployed are ethical, compliant, and aligned with organisational goals. But on a wider industry level, it would be great for the UK Government to provide guidance and frameworks to help organisations implement AI responsibly.
Additionally, initiatives like AI TRiSM (Trust, Risk, and Security Management) can help businesses integrate ethical considerations into AI projects from the outset. Organisations can build stakeholder trust, enhance user acceptance, and avoid reputational risks by embedding ethical principles into AI strategies.
A roadmap for AI adoption across sectors
AI presents vast opportunities, and organisations of all sizes and sectors can benefit. However, to realise these opportunities, it’s crucial to address the barriers to AI adoption. By investing in infrastructure, skills, and ethical governance and facilitating access to high-quality data, the UK Government can help create an environment where AI thrives.
The path to AI adoption starts with understanding the ambition for how AI will add the most value, whether that’s through enhancing existing operations or pursuing transformative innovation. With the proper support, even the most minor organisations can leverage AI to create value, drive growth, and remain competitive in an increasingly digital economy.
In this evolving landscape, Modular Data stands at the forefront of delivering scalable AI solutions that empower organisations across sectors. By focusing on immediate gains and long-term innovation, Modular Data is helping its clients navigate the complexities of AI, ensuring they unlock the full potential of this transformative technology.
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