Tech trends to watch in 2022
Gartner has made its annual predictions on the tech trends to watch over the next 12 months. Digital Bulletin spoke to a number of experts to find out more about the technologies tipped to have a breakout year
Hyperautomation
Luis Huerta, VP and intelligent automation practice head, Europe, Firstsource
I don’t think it’s a surprise that Gartner named hyperautomation a top strategic technology trend of 2022. The pandemic has only exacerbated the need to remove the dependency on staff being able to undertake business operations in a fixed location. Consumer demand is also evolving rapidly, and customers expect the ability to self-serve, use their digital channel of choice, and obtain answers quickly. Organisations can only serve this demand by automating their back offices, and the elements of the front office that are relevant, freeing up the time of expert staff to deal with the critical interactions that matter and require a human touch.
Leaders can certainly leverage hyperautomation to create competitive advantage in their business in several ways. One way is by improving customer experiences (faster resolution and fewer errors), so organisations can increase market share and reduce churn. Another, is by deploying machine learning to analyse customer behaviour better, anticipate problems, and forecast better, so organisations can align better to their changing markets. Furthermore, by increasing staff satisfaction when low-value tasks are removed from their working day, we allow them to serve customers better. And finally, by reducing costs in the back office, we can release capital that can be deployed in driving other strategic priorities.
AI Engineering
Andre Azevedo, CEO, Ancoris
Artificial Intelligence (AI) is unquestionably the decade’s favourite buzzword. Everyone in the technology sector is talking about it and the benefits that it brings in its path to becoming one of the most transformative tools in history. AI is becoming more and more mainstream, but businesses are still not making the most of it. In fact, recent research from Accenture uncovered that despite their best efforts, nearly 80% of enterprises failed to scale their AI deployments across their organisations last year.
It’s clear that some companies are struggling to keep up with the ever-changing AI developments, as they wrestle with how to collect, use and manage data in a cost-effective way. The biggest challenge here is that organisations with traditional business models assume AI is only applicable for larger, more established corporations, so it’s becoming increasingly critical for businesses to better understand how they can adopt these tools, what the return on investment is and how best to manage AI in the long-term.
Ultimately, business leaders are not seeing enough real-life examples of AI success and best practices that will help them advance in their field and be better than their competition. AI will continue to play an important role in 2022 and businesses that make the most of it will be the ultimate winners in their markets. Whether they use it to improve customer experience or the bottom line.
In 2022, we’ll see the continuing democratisation of AI which will help businesses to develop new and exciting offerings in line with their transformation efforts. Whilst we can predict a new wave of technological advancements, the industry needs to demystify the preconception that AI is just for large businesses, and demonstrate that the technology is suitable and scalable for companies of all sizes. With this, organisations will be able to provide a better customer experience, optimise resources, develop new products and offerings, to name a few benefits.
Cloud-native platforms
Scott Riley, Director, Cloud Nexus
Cloud adoption is no new phenomena but with the sudden surge of remote working experienced in early 2020, ‘the cloud’ has been thrust back into the spotlight. In the modern-day business world, we’re no longer seeing such a large dependence on retrofitted legacy applications and instead, businesses are becoming aware of the need to migrate to the cloud.
The issue is, those just joining the party are falling behind with an old hat ‘lift and shift’ approach to cloud-based operation — focusing only on creating a digital version of their on-premises network. Not only is this limiting how far modern architecture can be leveraged, but it is increasing their security risks and worse, prohibiting scaling at an appropriate level.
Aside from the obvious risks, the reality is that cloud migration has moved mountains since it first became a thing and as Gartner points out in his 2022 predictions, the future is all about placing CNPs at the heart of digital initiatives — something which can be done by the rapid adoption of re-platforming. Re-platforming, i.e., migrating the server’s core function (but NOT the server) to a native Cloud Platform which is built specifically for that purpose, is the best IT decision a business can make as we head towards a cloud future dominated by CNPs.
Question is, where might one start?
If the current day is anything to judge on, I’d say there are a few trends we can expect to emerge in 2022. Virtual machines, especially for traditional roles will soon be replaced by serverless technology — web, database and app can instead run on the cloud provider’s fabric and businesses will begin to recognise this. What’s more, it’s a simpler way to operate as it provides the core function without having an operating system underneath in constant need of being patched, managed, and updated. It’s also distributed over multiple server nodes thus enhancing performance and availability. Serverless technology also has fewer security risks due to the dedicated nature and the removal of the general-purpose operating system, and with the number of data breaches and hacks we’ve seen this year, there’s no denying businesses will be on the hunt for a safer way to operate.
The biggest risk for businesses headed into 2022 is being paralysed by the fear of change and staying where they are, exposing themselves to the risk of a serious breach.
Privacy Enhancing Computation
Dr Ellison Anne Williams, founder and CEO of Enveil
Data is the backbone of the digital economy. It can inform our decision making, increase our efficiency, and shape our behaviours. So naturally, most businesses are on a constant quest for better data — and more of it. At the same time, they also increasingly recognise that this data will not come from sources or inputs they own or control.
Organisations need to be able to leverage and collaborate with third-party data resources while respecting privacy boundaries and protecting business objectives. 2022 will be the year that we see a substantial shift towards using Privacy Enhancing Technologies (PETs) to solve big-picture privacy challenges like secure data sharing and collaboration. With Gartner predicting 60% of large organisations will use these techniques by 2026, we expect more companies to begin exploring and implementing PETs-powered solutions to leverage data and extract value while still meeting compliance requirements.
PETs has come into its own as a category for its ability to deliver both business-enabling and privacy-preserving capabilities that allow sensitive data assets to be shared and analysed without compromising security or inhibiting use. While PETs are not new, the category has gained visibility recently thanks to the growing prioritisation of privacy on a global scale as well as advances that have made the technologies computationally practical for broad commercial use. For example, PETs can be used to uncover untapped revenue streams by allowing organisations to securely and ethically monetise data assets without fear of exposure by allowing computations to occur in the encrypted or ciphertext domain.
As businesses transition out of pandemic survival mode and find their way back to innovative initiatives, we will see market leaders implementing PETs not only in pilot environments, but for practical use at scale. This will be especially prevalent in industries that handle vast quantities of sensitive data such as financial services, healthcare, and government.
Data Fabric
Dr. John Morris, Senior Vice President and Chief Technology Officer, Seagate
‘Data fabric’ sounds pretty technical, but this technology holds a huge amount of potential for enterprises. To understand why you need to think big.
The amount of data we produce is staggering, and it’s rising rapidly. We worked with IDC on projections that estimated the world’s enterprise data to grow by 42.2% annually over the next two years, and by 2025 all the world’s data will total 179 bn zettabytes (ZB). To put that in context, just 1ZB is equal to 1 billion terabytes (TB).
But here’s the problem. Only 32% of business data that is generated is actually used by companies to create value, according to IDC research published in our Rethink Data report. At the same time, our reliance on emerging technologies like AI is increasing — with widespread applications from healthcare to banking. These technologies require massive amounts of high-quality data to learn and carry out processes.
So, why are we letting so much data go to waste? One of the biggest reasons is the inability of businesses to cost-effectively store and manage that data, and by extension, filter out useful information from the bad.
This is where data fabric steps in. Data fabric acts as an extra layer on top of IT infrastructure that standardises data running through it, in real-time. It pulls together disparate data from across an organisation — including private, public, and hybrid cloud environments, as well as endpoints, edge, and core systems — into one electronic environment.
Anything under the ‘fabric’ is standardised, simplifying the complexity many businesses struggle with and helping them get more value out of their data.
We’re expecting data fabric adoption to accelerate over the coming years, but one example of it in use today is Compute Express Link, an open standard collaboration between leading AI companies such as Google, Microsoft, and Alibaba. Compute Express Link connects the high-speed central processing units that power AI with other devices or memory storage in the data centre and relies on data fabric to do so.
Next year will likely bring some major leaps forward in the development of data-intensive technologies like AI, AVs, and quantum computing. These have exciting future applications but will need vast quantities of high-quality data. Data fabric will be a vital enabler if we are to maximise the potential of these technologies.
Generative AI
Dr Jai Ganesh, Senior Vice President and Head of Innovation, Mphasis
Gartner predicts that generative AI will account for 10% of all data produced by 2025. I agree that it will emerge as one of the leading and most powerful technological trends, and its impact can be monumental.
Processing a range of content such as images, audio and text, machine learning algorithms can identify nuanced patterns and interconnections to create new content in a way that transcends what was possible before. With improvements in hardware such as GPU, TPU as well as specialised AI chips, we should in the future be able to train the models with larger and more representative data sets.
The technology is game-changing to the business landscape because of its wide applicability to varied use cases — heralding exciting new opportunities across industries, not only for media but also healthcare, software, manufacturing and many more. It can open the door to new markets, services and products faster.
I do also agree that, as with any cutting-edge development, there are the usual risks to take into consideration, such as the potential for misuse for fraudulent purposes. With fast-moving innovation in the field of generative AI, it’s vital to ensure the right risk management, responsible conduct and data safeguarding measures are firmly in place. And to ensure that companies harnessing this technology have the right digital skill sets on board to apply it. In the future, generative AI technology could embrace the principles of responsible AI by identifying bias in data sets and negating their usage, as well as provide explanations of the results produced.
Looking ahead, I strongly believe that generative AI will advance in tandem with the evolution of large language models as well as large multi-media models, as they play a critical role in the training of a vast range of AI content being generated.
Composable Applications
Chris Harris, Vice President, Global Field Engineering at Couchbase:
In 2022, we expect organisations will realise that technology composability needs to become a major priority, as they seek increased agility and simplicity in meeting business goals via on-premises, multi-cloud and edge deployments. Instead of relying on monolithic architectures, businesses will move towards an approach where new digital applications are made using component parts with well-defined interfaces.
This could mean enterprises no longer have to reconfigure their server, storage and connectivity systems or manage underlying infrastructure. Instead, composability will expose all data and digital capabilities, enabling all services to be controlled from one single unified control plane that spans across all environments.
Not only will this enable greater simplicity, but for many organisations, adopting composability could be the difference as to whether they can navigate safely through changing business environments or not. Digital applications will also become easier to create and integrate, as businesses look to extend composability throughout their entire technology stack. As a result, we expect organisations will be able to drive quicker digital transformation initiatives.
That’s because we think enterprises will be able to become more ambitious without needing to make extra investments. For instance, they’ll be able to create new services by making use of their existing skillset and digital capabilities. With reusable elements, applications will no longer need to be built from scratch meaning less reliance on specialist knowledge and resources. In addition, most of the technology we’re seeing businesses turn to as they look to embrace a composable mindset, doesn’t require additional training — again meaning faster transformations, that use less resources.
Those with complex legacy infrastructures may look at this idea as an impossible dream. However, capabilities such as legacy offloading and the ease of adopting cloud-based databases as a service, enable you to break out individual services from even the most complex applications quickly and effectively.
These are the reasons why we expect organisations worldwide will look to adopt composability. With demand for seamless digital experiences rising, and digital transformation budgets being squeezed, we think composability will allow enterprises to go the extra mile, without needing to expend extra resources or effort.
Distributed Enterprise
Callum Adamson, CEO and Co-founder, Distributed
Gartner is late to the party this year in saying the ‘Distributed Enterprise’ will be the next big revenue driver for organisations, because its impact is already being felt as the most important global business trend. Although the pandemic forced most businesses to be ‘distributed’ in one way or another, the reality of being a distributed enterprise in the long-term is setting in. One of the key considerations here is the impact on building and maintaining a workforce.
By embracing a world in which their workforce is far more dispersed, every business has access to a much wider, potentially global, talent pool. It’s hard to believe that the world’s best talent sits within the M25. That’s because it doesn’t. There are skilled workers all around the world. Organisations need to take advantage of the opportunity to broaden their horizons and see the talent available, unencumbered by geographical boundaries. Those that do will be in a better position to take a lead in the talent war. That’s because talent is global, but skills gaps are local.
If that’s not enough, it’s clear that remote working, which enables distributed enterprises, is also what the employees want. It’s actually good for them and their career progression. After all, proximity to colleagues and managers in an office doesn’t automatically lead to valuable time spent together. When implemented properly, working remotely from wherever you are in the world can allow for better 1:1 time with teams and managers, because it must be planned. Meetings are more focused and structured, which ensures that everyone has an opportunity to participate.
However, the benefits of distributed enterprises extend far beyond the business, like more efficient asynchronous working. Cultivating teams that are distributed around a country, or beyond, means each member spends their money in the location they’re based in, which contributes to better wealth distribution. Our study with USC Marshall found that on average, every British pound paid to a distributed team member creates the equivalent of £2.14 in purchasing power across the world. Investing in the global economy, rather than concentrated urban centres, can also improve local infrastructure such as education and healthcare. The solution to solving global inequality is of course far more complicated, but we cannot ignore the potential.
Taking all this into account, it’s hard to believe it has taken this long for businesses to realise the possibilities and benefits offered by the distributed enterprise. If they haven’t already, organisations should seriously consider how embracing a distributed enterprise model could drive future success, not just for the company itself but for their employees and global economies too.
Decision Intelligence
Russell Haworth, CEO NBS
Decision Intelligence, Gartner is predicting that this is a buzzword that’s going to be big in 2022. But what is it?
Simply put, it’s all about business choices. Decision Intelligence (DI) is the discipline which aims to improve an organisation’s decision-making by applying machine learning at scale. Today, DI unlocks value in an organisation’s data, putting Artificial Intelligence (AI) in the hands of commercial decision makers for the first time. This allows organisations to go beyond human limitations to analyse huge amounts of data, quickly use it for insight that will inform decisions, and be able to monitor the impact. The use of AI allows decision making to be faster, more consistent and higher quality, as machines don’t need to rest or have off days.
There are exciting case studies of businesses using DI to help manage their inventory, demand planning and some forecasting tasks.
You may already have seen an element of technology informing behaviour, if you’ve made a film choice using Netflix’s suggestions or chosen a book based on Amazon’s steer. These recommendation engines are powered by huge amounts of data and use analytics to make suggestions. This is truly DI in action as people are being helped to make better decisions.
You can imagine how frontline employees are keen to have appropriate information to help them do their jobs better. There’s a raft of new approaches to information design being created to help people synthesise complex information quickly and reach an informed decision. It goes beyond internal data and done well includes customer sentiment i.e. social listening to incorporate into near real-time analysis of data on which to optimise decisions.
There are some potential pitfalls to be mindful of. We’re talking about data, so as ever the quality of data that gets fed into the AI system is paramount. You get out what you put in, so you’re probably going to have to spend some time getting the data in shape. There’s a real move to remove bias from datasets and to ensure that you’re being as equitable as possible in your AI endeavours. This is obviously the morally correct thing to do. It’s also going to lead to competitive advantage if you’re analysing data that others aren’t!
As ever with a new technology coming into being, it can be an anxiety provoking time for employees. However, the general consensus is that AI will lead to more, and better-quality jobs, as humans and machines work together and the AI picks up the boring, repetitive work that people aren’t very good at (in comparison to machines).
While you’re probably going to see a lot more articles written about DI, practical adoption of the technology is likely to be a case of phased evolution rather than a paradigm shift in the next year.
Total Experience
Professor Luciano Nardo
Successful Total Experience will be a fundamental of global, digital platforms servicing medical sectors as virtual healthcare continues to grow post-pandemic.
Shifting to a digital platform will be particularly beneficial to fertility practitioners and patients: The traditional model of individual clinics operating in silos has disadvantages for both clinics and patients.
For patients, fertility treatment is emotionally and physically exhausting with waiting lists for the best clinics, often followed by long journeys for each consultation or investigation as treatment progresses. Additionally, there is little access to advice and support outside of in-person appointments and normal clinic business hours.
Meanwhile, clinic profitability relies on constant lead generation, while recruiting patients from a relatively small geographic location; clinics are also limited by the number of patients they can physically accommodate each day.
A successful TX for fertility services — and other healthcare services in the future, will be typified by a digital portal run by experienced physicians, nurses and counsellors offering 24/7, personalised and supportive consultancy and care.
Underpinning platforms will be Artificial Intelligence (AI) and Machine Learning (ML). AI will assess, measure and tailor patient engagement, as well as being a tool for case triage and allowing clinicians to define clinical protocols for better and more consistent outcomes. ML will demonstrate and educate patients about treatment strategies and the building of various models to analyse and use data to optimise outcome delivery.
Patients will be matched with the best-performing ‘partner’ IVF clinics based on their location and therapeutic needs, while investigations, ultrasound scans and blood tests will be performed close to the patients’ homes by an approved satellite clinic.
This digital, patient-centred, always-on approach empowers the patient to be in charge of their own treatment cycle, effectively reducing the time, stress and costs involved in fertility treatment.
Clinics working in partnership with a digital portal will reap the benefits of acquiring and sharing specialist knowledge, while more easily being able to keep patients abreast of their treatment journey: Improved communication reduces the potential for errors and complaints.
Compared with traditional practice, clinics will also see improved margins and an expanded geographical reach, while reducing their financial commitment to marketing and advertising.
In turn, clinicians will benefit from data-driven, clinical decision support and a digital platform that fosters greater interaction with both patients, and other healthcare professionals.
In summary, the 360-degree nature of successful Total Experience will continue to drive better outcomes for each stakeholder in the fertility journey.
