As we stand on the brink of remarkable technological advancements, the next decade promises to transform digital landscapes in ways we’ve only begun to imagine. This article explores the cutting-edge technologies that will drive this transformation, including artificial intelligence (AI), blockchain, and edge computing, each poised to redefine industries and profoundly impact our daily lives.
Experts from various sectors share insights into how AI can revolutionise healthcare by enhancing diagnostic accuracy and streamlining finance through automation while playing a pivotal role in strengthening cybersecurity measures. Edge computing emerges as a game-changer, enabling decentralised data processing that reduces latency and enhances real-time decision-making, which is especially crucial in finance and healthcare. Additionally, blockchain technology is highlighted for its potential to safeguard data integrity and privacy, offering new levels of trust and security.
By examining the implications of these advancements, this article offers a comprehensive view of how emerging technologies will shape the future, guiding businesses to embrace innovation thoughtfully and balancing opportunity with ethical responsibility.
Kyle Hill, Chief Technology Officer at ANS.
Mayra Hurtado is the CEO and founder of the Femtech startup Hormony.
David Primor, Co-Founder and CEO at Cynomi.
Dominic Trott, Director of Strategy and Alliances, Orange Cyberdefense.
Pavel Goldman-Klaydin, Head of AI at Sumsub
Matthew Hill, General Manager, Gotonomi.
Michael Cade, Global Field CTO & Lead Technologist Cloud-Native & OSS, Veeam.
Jeff Watkins, CreateFuture‘s Chief Technology Officer.
Mathieu Duperre, founder and CEO of Edgegap.
Marc Overton, Managing Director Euronorth, Dassault Systemes.
Przemysław Krokosz, Edge and Embedded Technology Solutions expert, Mobica (a Cognizant company).
How do you envision artificial intelligence evolving in the next decade, and what specific industries do you think will benefit the most from its advancements?
Kyle Hill, Chief Technology Officer at ANS.
“Over the next decade, businesses will learn more about AI’s capabilities and determine what is and isn’t appropriate for their operations. The pace of AI adoption is set to continue picking up, with global AI adoption by organisations set to expand at a CAGR of 38.1% between 2022 to 2030.
“Microsoft Copilot for M365 is an AI tool which primarily integrates AI capabilities across Microsoft platforms. It’s the first of its kind to adopt specific business tools to make short work of tasks such as data processing and day-to-day account management. But there’s definitely lots more to come for AI technology in the workplace. The wave of AI technologies will continue for a while and will only become more advanced over the next decade.
“The healthcare industry is one sector that AI will undoubtedly revolutionise. From MRI scans to health professional technology platforms, AI will touch every part of the industry. These innovations will save healthcare workers time and alleviate pressure by providing accurate results quickly.
“With the power to enable acceleration, processing, and efficiency, AI will also change the game for the finance industry. Banks, insurance companies, and more will benefit from AI. The technology can work in the background on a wide range of tasks, such as automating complex processes like fraud detection and regulatory compliance to enable proactive decision-making and improve cybersecurity.
Mayra Hurtado is the CEO and founder of the Femtech startup Hormony.
“In the next decade, artificial intelligence (AI) will significantly enhance healthcare by supporting doctors in making faster and more informed decisions, especially in time-sensitive or resource-constrained environments. One of the primary benefits of AI is that it can assist doctors who are often limited by available information, time, and stress. AI systems, by contrast, can analyse large datasets continuously, helping doctors focus on the patient experience and personalised care.
“According to a report by Accenture, AI could save the US healthcare system up to $150 billion annually in 2026 by improving diagnostics, streamlining administrative workflows, and optimising operational efficiencies. This becomes especially relevant in fields like perimenopause and menopause, where there is a growing scarcity of specialists. As specialists retire, there is expected to be only one OB-GYN for every 9,000 patients, making it difficult for many women to access care.
“Furthermore, AI’s collaboration with doctors has been shown to improve diagnostic accuracy. AI-assisted diagnostics can reduce human error, providing up to 30-40% improvements in certain areas like image analysis. This improves areas where OB-GYNs are facing burnout, with nearly 60% of OB-GYNs reporting burnout from the pressures of high patient volumes and administrative burdens.”
David Primor, Co-Founder and CEO at Cynomi.
“In the next decade, AI will evolve into an indispensable tool for industries grappling with increasing complexity, particularly cybersecurity. As businesses continue their digital transformation, the attack surface expands, giving rise to sophisticated cyber threats like advanced phishing attacks and identity fraud. AI’s ability to process vast amounts of data in real time will be crucial for identifying high-priority vulnerabilities and predicting emerging threats with unprecedented accuracy.
“Machine learning, a subset of AI, will play a pivotal role by continuously refining its understanding of potential attack patterns, evolving alongside the threat landscape. By leveraging AI, businesses can prioritise security gaps more efficiently and respond to threats in real-time, reducing the risk of breaches.
“Additionally, the integration of blockchain technology will strengthen data integrity, ensuring that transactional data remains secure and immutable. Combined with AI’s adaptive learning capabilities, these technologies will enable businesses to stay ahead of cybercriminals, safeguarding both operations and customer trust. To succeed, companies must ensure that their data strategies are both agile and robust, capable of supporting the swift adoption of emerging innovations while addressing evolving risks.”
As digital transformation accelerates, cybersecurity risks are becoming more complex. How can technologies like AI and blockchain help counter these threats?
Kyle Hill, Chief Technology Officer at ANS.
“Businesses continue to become more and more tech-enabled. But all too often, cyber security is treated as an afterthought. Cyber attacks aren’t slowing down any time soon, with ransomware attacks rising by 14% in August to a total of 450 attacks. This demonstrates the necessity for businesses to secure their operations against cyber risks.
“Over the next decade, businesses will need to ensure they are protected against more sophisticated attacks. AI will play a crucial role in improving organisations’ cybersecurity measures. AI can handle large amounts of data and often predict potential threats and vulnerabilities before humans. So, businesses will be better protected from increasingly sophisticated cybercriminals.
Dominic Trott, Director of Strategy and Alliances, Orange Cyberdefense.
“Research from Orange Cyberdefense’s Cy-Xplorer 2024 found that the number of cyber-extortion victims rose by 77% over the past twelve months, undeniably showing that the cyber threat landscape is becoming more dangerous.
“While the data found that AI – specifically GenAI – isn’t significantly impacting the volume of attacks, it is helping threat groups globalise by breaking down language and cultural barriers that have likely shielded some economies. This is because GenAI allows threat actors to produce credible phishing emails and malicious websites more quickly, expanding their reach with a wider ‘addressable market.’
“Businesses must use all the tools and techniques to combat these increasingly sophisticated attacks. In response, many are integrating GenAI to make their security operations more agile. GenAI can support elements of security analysts’ workloads, such as aggregating log data and recommending and automating responses. Significant efficiencies can be gained regarding the speed of response to security threats, but there are also benefits in helping security analysts prioritise the most impactful engagements and reducing the risk of burnout by automating lower-value and repetitive tasks.
“However, GenAI must be integrated with the proper ‘guardrails’ in place to facilitate and safeguard adoption meaning businesses can use it to combat and respond faster to threats. They must not only rely on the use of the technology itself to generate efficiencies in security operations; but rather must make sure they take the correct technical architecture and policy steps to ensure that GenAI use doesn’t itself expose the organisation to risk.”
Pavel Goldman-Klaydin, Head of AI at Sumsub
“The number and quality of deepfakes are increasing and evolving daily worldwide. Even with the most advanced technology, differentiating between a deepfake and reality is getting much more challenging. Our data has shown a massive growth of deepfakes from 2023 to 2024 – with upwards of a 245% YoY increase worldwide.
“AI-powered solutions can be instrumental in helping businesses distinguish between fact and fiction online. Cutting-edge deepfake solutions can use several AI models to detect artefacts within an image in real time to verify whether a video is a deepfake.
“Beyond this, AI can help businesses better understand cybersecurity threats across their customer journey. This technology can monitor hundreds of thousands of users automatically and in real-time, identifying clusters of activity that could be associated with bots or fraudulent activity – which can prevent threats before they happen.
“These tools are vital in a world where cyber fraud is all too common. New data from Deloitte showed that surveyed executives experienced at least one (15.1%) or multiple (10.8%) deepfake financial fraud incidents in 2023.”
What are the ethical implications of some of these emerging technologies, and how can companies balance innovation with responsible use to avoid unintended societal consequences?
Kyle Hill, Chief Technology Officer at ANS.
“Responsible adoption should be at the forefront of business’s minds when adopting new tech. Otherwise, they risk falling short of maximising their potential and facing unintentional consequences. AI adoption is a pivotal aspect of a business’ journey towards transformation, but it must be done responsibly with the right building blocks to draw out all of its capabilities.
“Having the right cloud, data, and security measures in place is essential when building the foundations of responsible AI. This will enable efficiency and security from the ground up, ethical use, and long-term success for organisations, both large and small.
“Another way to protect businesses against consequences from AI adoption is to ensure your data is as clean as possible and without duplicates or inaccuracies. The output that AI can generate is only as good as the data you can feed into it.
“While organisations may have ethical concerns around AI, such as the risks of exposing sensitive data, having these foundations in place will be pivotal to maximise innovation in a safe and responsible way.”
Mayra Hurtado is the CEO and founder of the Femtech startup Hormony.
“The ethical implications of emerging technologies like AI and blockchain are particularly significant in healthcare, where they can significantly impact patient care and access. AI-powered healthcare systems risk exacerbating inequalities if they aren’t designed carefully. As Yuval Noah Harari warns, AI can centralise power, potentially leading to biased decision-making. This concern is especially relevant in women’s midlife health, such as perimenopause and menopause, where data is often scarce. If the data fed into AI models is biased or complete, it could lead to fair and accurate treatment recommendations.
“For areas like perimenopause, it is essential that the data used is clean and unbiased to ensure accurate care for all women, particularly in an area historically underserved. Companies like Harmony, which leverages AI to provide personalised hormone monitoring, must prioritise gathering representative data to avoid perpetuating biases and ensure that care is inclusive and equitable.
“Similarly, blockchain can decentralise control over patient data, but it needs careful implementation to ensure privacy while maintaining collaborative care. Balancing innovation with responsibility involves ensuring transparency and developing explainable AI systems, so patients understand how decisions about their care are made, minimising the risk of bias or misuse.”
How do you see the role of autonomous systems—like drones, self-driving cars, and robots—in shaping future economies and transforming everyday life?
Matthew Hill, General Manager, Gotonomi.
“As drone technology advances and the regulatory landscape is clarified, autonomous UAVs are going to become more prominent. People will already be familiar with drones being used in defence applications and, to a limited degree, in logistics applications, and they are now becoming more common in other market sectors, too.
“UAVs – especially when drones need to travel beyond visual line of sight (BVLoS) – will become commonplace in industries where inspection and surveillance are crucial, such as power, pipe and rail lines, or offshore wind farms. This is because the inspection and surveillance of this infrastructure commonly uses human-in-the-loop models to tackle hard-to-reach installations where the risk and cost of human interaction are high. As environmental incidents are on the rise, the use of UAVs to ensure that the UK’s critical infrastructure remains operational will become more prominent.
“UAVs will also be used more within the logistics and delivery sectors in the coming years. As costs come down and autonomous systems enable one pilot to control multiple drones at the same time, we will move into mass adoption. There are already companies offering commercial delivery services in localised areas, and we can see examples of logistics operations in areas where the risk profile is low. For example, clusters of islands like those off the coasts of Scotland or Greece offer reasonable proving grounds, as in these cases, replacing crewed aircraft provides a clear business case, and the majority of the operation is over open water.
“Key technologies required to achieve true autonomous flight are a robust command and control (C2) link, electronic conspicuity and detect-and-avoid, which allow a drone to sense objects within its vicinity during operation. Each of these is on the roadmap for regulators, and technology providers are working with OEMs and operators to build the best systems for each application.”
The concept of edge computing has gained traction recently. How do you see this technology transforming industries, and what challenges do businesses face when adopting it?
Michael Cade, Global Field CTO & Lead Technologist Cloud-Native & OSS, Veeam.
“As we explore technologies poised to revolutionise digital transformation in the next decade, edge computing stands out as a game-changer, particularly in enhancing data resilience. By processing data closer to its source, edge computing minimises reliance on centralised data centres, thereby reducing single points of failure and improving operational continuity.
“The ability to perform real-time data recovery and analytics locally is invaluable, especially in critical sectors like healthcare and finance. This localised processing reduces latency and supports more immediate and informed decision-making.
“However, the transition to edge computing is challenging. Security remains a top concern, as decentralising data increases potential vulnerabilities. Robust data management strategies are essential to handle the complexity of distributed data environments. Additionally, ensuring compatibility with existing IT infrastructure and maintaining the reliability of edge devices are crucial to fully leveraging the benefits of this technology.
“Edge computing’s potential to enhance data resilience makes it a pivotal element in the future of digital transformation. Addressing the associated challenges will be key to unlocking its full potential and ensuring secure, efficient, and continuous operations across industries.”
Jeff Watkins, CreateFuture’s Chief Technology Officer.
“Edge computing, by its definition, aims to move the execution of computing tasks closer to the consumer. Anything requiring a fast, distributed workload will no doubt benefit from the edge computing revolution. AI will benefit the most from this, allowing for lower latency and, thus, more seamless end-user experiences. In reality, edge computing will bring the world closer by reducing latency and providing more overall computing power, especially in larger cities.
“Businesses implementing edge computing based services will likely find that there’s an industry skills gap of those who can design and implement it well. Many organisations are only just moving to the cloud, to make that jump again from the cloud to, well, a mist of computing resources, that’s going to be a challenge to get buy in for. Security is also going to be a challenge when you step outside of one or two cloud data-centres into a much more distributed network – although one would expect that resilience would improve, due to that distributed nature. Fully leveraging edge computing will also require organisations to design distributed systems, which is a much more sophisticated approach than more traditional designs.”
Mathieu Duperre, founder and CEO of Edgegap.
“Edge computing brings computing power closer to where it’s needed most. This inherently leads to a highly distributed infrastructure, as the resources you need to reach are also distributed. Positioning computing power closer to the end users enables handling more intensive workloads with minimal latency. (Latency, the time it takes for communication to occur, is reduced when the distance is shorter, resulting in faster response times.)
“Multiple industries need this combination of low latency and substantial computing power right now. The explosion in generative AI platforms is placing enormous demands on the current cloud infrastructure, so reducing the amount of data that needs to be backhauled to a centralised location will save both processing time and money.
“Video games are another huge global industry where Edge computing is making inroads thanks to its ability to improve players’ experiences as they play online with others. Delivering a better multiplayer experience reduces churn, increases engagement, and ultimately boosts revenue for game studios—which is crucial for success when a game can cost hundreds of millions of dollars to create.”
With the explosion of data, what role will machine learning play in helping companies make better decisions, and how can businesses ensure that their data strategies keep pace with innovation?
Marc Overton, Managing Director Euronorth, Dassault Systemes.
“Machine learning will play a crucial role in enabling businesses to process large volumes of data quickly and accurately, leading to more informed decision-making across all business areas. However, as the volume of data grows, so do the risks associated with managing it. To stay alert, companies must stay abreast of any innovations or new technologies being integrated into their operations and relevant regulatory requirements.
“To build a robust data strategy, it’s essential to involve stakeholders from various departments, ensuring that data security efforts align with overall business goals. Cross-department collaboration is key to embedding security practices seamlessly into workflows and processes, helping businesses manage data effectively while mitigating potential risks.”
Przemysław Krokosz, Edge and Embedded Technology Solutions expert, Mobica (a Cognizant company).
“For years, many IT industry leaders have said that “data is king.” Now, with the rise of Generative AI and Large Language Models, this is more true than ever. However, one must make a distinction between “data” and “information”. Turning vast amounts of data into valuable or actionable information is challenging, but this is where Artificial Intelligence and Machine Learning techniques come in handy.
“Still, Machine Learning is not a turnkey solution. It needs to be supported by sources of good quality data; the more, the better. In an industrial setting, that often requires the implementation of a significant underlying data-gathering technical infrastructure, which will include such things as connected sensors and actuators, various forms of connectivity and localisation infrastructure (5G, Wi-Fi and RFID), along with industrial IoT platforms, Cloud storage solutions, dashboards and much more.
“Besides the tech, businesses must also employ data analysts to train and maintain their models. This is necessary to monitor performance, investigate anomalies and protect against data drift and bias.
“There is clearly huge potential in machine learning, but a lot of thought needs to be given to the underlying data. If companies don’t have a clear strategy and processes in place to support this, they will struggle to reap the benefits.”
With quantum computing on the horizon, what kind of real-world problems will it solve first, and how will it disrupt the current digital infrastructure?
Jeff Watkins, CreateFuture‘s Chief Technology Officer.
“Quantum computing is expected to be able to solve problems that are not currently practically solvable with traditional computing. It has seemed tantalisingly close for some time, and claims made by researchers on the topic are often quickly refuted or proven to be solvable with traditional computing. However, material sciences and chemistry stand to benefit from the quantum revolution, able to find new innovative solutions in a fraction of the time of more traditional computing.
“If (…and it is an if) we do see a major leap in quantum computing in the next decade, encryption is likely the first thing that will need to change. Not all encryption algorithms are quantum sensitive, as matrix encryption is quantum resistant, but systems would need to move to using it. The term crypto-agility was coined as a concept to help people move their encryption algorithms through configuration rather than rebuilding entire systems, but this would require wide-spread adoption in order for it to provide the potential benefits. The technology, even if it is made available for use, will be expensive, and could become an arms race, so countries will need to make sovereign investments into it to keep up, and also learn to live in a world where quantum and traditional computing coexist.”
As we look ahead to the next decade, it’s clear that rapid advancements in technologies like artificial intelligence, blockchain, and edge computing will define digital transformation. These innovations promise to reshape industries, drive efficiency, and introduce unprecedented capabilities across sectors from healthcare to finance and beyond. These technologies will empower organisations to navigate a complex and fast-evolving digital landscape by automating processes, enhancing decision-making, and improving data security.
However, with great potential comes a responsibility to adopt these tools thoughtfully and ethically. Embracing AI and blockchain requires robust data strategies and a commitment to transparency, ensuring that these powerful technologies are used responsibly. Additionally, the rapid growth of edge computing and autonomous systems will demand an upskilling workforce and careful consideration of security measures.
Businesses that recognise and address these challenges will be well-positioned to leverage emerging technologies for success. By aligning innovation with ethical principles, companies can build a resilient and adaptable foundation for the future. In a world where technology is advancing at an unprecedented pace, those who harness the power of these tools responsibly will thrive and shape the path forward in a digitally connected world.
