NavNavNavNav

Digital trends in a post-pandemic world

We are used to change in the digital age, but few of us could have predicted the changes and upheaval that the global pandemic has brought. The pandemic has completely changed how many of us live and work and some of these changes may be permanent.

What are the key themes?

Image

People

People are our most precious assets. Many people are now working remotely, across business either individually or in teams. The people theme explores people’s behaviours, experience and privacy.

 

Take me there

Image

Location independence

New ways of working mean that employees, customers, suppliers and everyone across the business ecosystem can be located anywhere. The location theme addresses the technology shifts that are driving a distributed cloud structure that facilitates anywhere operations in business.

Take me there

Image

Resilient delivery

This theme is about having the ability to adapt or pivot. Business must be flexible using sophisticated technologies such as AI to respond to a data-driven world.

 

 

Take me there

People

Internet of behaviours 

Total experience 

Privacy-enhancing computation

Internet of behaviours

The IoB consists of multiple approaches to capture, analyse, understand and respond to all kinds of digital representations of behaviours. A range of public- and private-sector organisations will seek to use the IoB’s digital capture ability to affect or influence the behaviours of individuals or collective demographic groups.

The IoB combines multiple sources of intelligence such as commercial customer data, citizen data processed by public-sector and government agencies, social media, public domain deployments of facial recognition and location tracking. Additional sources can include things such as temperature and other physical measurements in both private and public domains. From the analysis of data in these myriad resources, it is possible to tag an increasingly broad array of people’s behaviour as an ‘event’.

Emerging technology innovations and algorithm developments enable more precise monitoring and interpretation of behaviours. The IoB combines existing technologies that focus on the individual directly (e.g. facial recognition, location tracking and big data) and connects the resulting data to other indirectly identifiable information (e.g. cash purchases, automotive telemetry, vacuum bot layout data and device usage data). Thus, the IoB is partly based on the Internet of Things (IoT). In the IoT, physical things are ‘instructed’ to perform certain actions under certain conditions. In the IoB, people’s behaviours are monitored and incentives or disincentives are applied to influence them to perform toward a desired set of operating parameters. A program can apply value judgments to behavioural events based on the behaviour desired by the program’s deployer.

In response to the pandemic, organisations are deploying additional behaviour intelligence sources at a faster pace. Examples include temperature measurements, face recognition deployments, contact tracing and location-tracking systems. The focus is on combining physical and digital behaviour data to influence behaviours that will reduce the spread of infection.

Total experience

Total experience (TX) is a strategy that creates superior shared experiences by interlinking the multi-experience (MX), customer experience (CX), employee experience (EX) and user experience (UX).

Organisations need a TX strategy because they must continuously enhance their CXs and EXs, especially as these interactions have become more mobile, virtual and distributed, mainly because of COVID-19. TX is about more than improving the experience of one constituent — it improves experiences at the intersection of multiple constituents. These intersected experiences require organizations to rethink how they change behaviour and technologies by addressing the feelings, emotions and memories that make up the CX and EX, as well as the experience of partners and other constituents.

Twenty years into the experience economy, expectations and demands from customers and employees continue to change. That is because CXs and EXs are constantly evolving, driven by new interactions. This leads to participation, then to engagement, and on to satisfaction, loyalty and advocacy. UX is about the usability and design of apps and products to reduce effort, increase engagement and drive satisfaction. MX is about the technical implementation across a wide range of devices, touchpoints and modalities of interaction. The pandemic has increased the need to transform the digital experience, moving from keyboards and screens to multiple modalities using conversational, immersive and touchless environments. The business moments in which experiences between customers, employees, partners and ‘things’ are inextricably interlinked are particularly important.

Privacy-enhancing computation

Privacy-enhancing computation comprises three types of technologies that protect data while it is being used to enable secure data processing and data analytics:

  • The first provides a trusted environment in which sensitive data can be processed or analysed. It includes trusted third parties and hardware-trusted execution environments (also called confidential computing).
     
  • The second performs processing and analytics in a decentralised manner. It includes federated machine learning and privacy-aware machine learning.
     
  • The third transforms data and algorithms before processing or analytics. It includes differential privacy, homomorphic encryption, secure multiparty computation, zero-knowledge proofs, private set intersection and private information retrieval.

Each technology provides specific secrecy and privacy guarantees and some can be combined for greater efficacy.

Global data protection legislation is maturing and, with the unstoppable pervasiveness of personal data, every organisation that processes personal data faces ever-higher privacy and non-compliance risks.

At the same time, organisations now realise the economic potential of their data repositories. The demand for processing data in untrusted environments and performing multiparty data sharing and analytics is rapidly growing.

Location independence

Distributed cloud

Anywhere operations

Cybersecurity mesh

Distributed cloud

Distributed cloud is the distribution of public cloud services to different physical locations while operation, governance and evolution of the services remain the responsibility of the public cloud provider.

Distributed cloud addresses the need for customers to have cloud computing resources closer to the physical location where data and business activities happen. This could be, for example, in an enterprise data centre, a 5G network or even on a manufacturing floor.

As distributed cloud evolves, we predict a time when customers will ask a cloud provider for cloud resources that comply with certain general performance requirements.

Interest in hybrid cloud computing is rising. The main reason for this is that many customers need to deal with technologies they already own and operate, often within their own data centres.

These customers cannot abandon existing technologies in favour of complete and immediate migration to the public cloud. Sunk costs, latency requirements, regulatory and data residency requirements, and even the need for integration with non-cloud, on-premises systems hold them back. Instead, they use a combination of private cloud-inspired and public cloud styles of computing. This creates a hybrid IT environment.

Anywhere operations

Anywhere operations describe a business operating model designed to reach customers anywhere, enable employees anywhere and use digital technologies to deliver business services anywhere. It challenges the conventional wisdom that it is necessary to be in a specific location, interacting face-to-face to maximise value and efficiency. It drives a next normal in which employees, contractors, business partners, customers and end consumers will be remote from each other.

A digital-first, location-independent mindset is a prerequisite to anywhere operations. Providing a seamless and scalable digital experience requires changes in the technology infrastructure, management practices, security, governance policies and employee and customer engagement models.

Organisations that emerge successfully from the COVID-19 pandemic will have an anywhere operations foundation. Organisational preparedness and resilience to cope with the crisis depend largely on the maturity and readiness of digital capabilities.

Anywhere operations is not only about remote working as there are practical needs that demand physical proximity to people and equipment. In such cases, anywhere operations use contactless interactions but preserve the unique value of personal interaction.

The workplaces of the future will evolve in response to the pandemic regardless of whether they are offices. The changes will include the adoption of remote support technologies using mobile devices, wearables and augmented reality headsets.

Contactless and passwordless interactions using IoT sensors, smart cards enabled by near-field communication and wearables will become the norm for mundane transactions such as unlocking physical access to restricted spaces and operating elevators or vending machines.

Cybersecurity mesh

The cybersecurity mesh is a distributed architectural approach to scalable, flexible and reliable cybersecurity control. Most assets and devices are now located outside the old ‘walls’ of the organisation. The cybersecurity mesh enables any person or thing to securely access and use any digital asset, no matter where either is located, while providing the necessary level of security.

The COVID-19 pandemic has accelerated the process of turning the digital enterprise inside out. We have passed an inflection point — most organisational cyber assets are now outside the traditional physical and logical security perimeters. However, as the extended enterprise weakens our ability to control access to our critical digital assets, citizen and consumer expectations about data protection continue to grow.

The cybersecurity mesh is a convergence of new need and new security services. Although security leaders understood this architectural model 20 years ago, they lacked incentive and a convenient set of technologies. It was too much work for too little benefit. That has changed. Now, with security services, the cloud can protect the cloud.

As anywhere operations continue to evolve, the cybersecurity mesh will become the most practical approach to ensure secure access to, and use of, cloud-located applications and distributed data from uncontrolled devices.

Resilience delivery

Intelligent composable business

Hyperautomation 

AI engineering

Intelligent composable business

Digital business needs to quickly make decisions in support of business moments, informed by relevant data. They must then adapt their capabilities to act on those decisions in near real-time. To do this, organisations must rethink how they make decisions.

But these newly reengineered decisions require a new platform for development, assembly and deployment. Current application and app development environments are not helping. DevOps and other new operational practices are helping, but a new architecture is needed. Composable applications provide the building blocks to assemble needed packaged business capabilities to help execute decisions. They provide the flexibility to adapt as information emerges or assumptions change.

Decision making is not about dashboards or reports; it is about increasing autonomy, enrichment and augmentation of how decisions are made (by human or machine) at every level of the organisation. Intelligent business will unleash creativity and innovation. It will also reduce costs while driving the discovery of new business value and business models founded on data value and on being data-driven. Data and analytics, including AI and applications, will merge at the point of a business moment, turned into value for all stakeholders.

Hyperautomation

Business-driven hyperautomation is a disciplined approach that organisations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms. Examples of these include AI, machine learning, event-driven software architecture, robotic process automation (RPA), intelligent BPM suite, integration platform as a service, low-code tools and other types of decision, process and task automation tools.

Hyperautomation has been trending at an unrelenting pace over the past few years, mainly because of the pent-up demand for operationally resilient business processes.

The collective impact of these business and IT realities is the launch of many initiatives (often disparate and siloed) aimed at applying automation across knowledge work for either efficiency, efficacy or business agility.

The organisational zeal for using hyperautomation has led to many new offerings, vendors and commercial models across an extensive number of technology markets.

Hyperautomation is irreversible and inevitable. Everything that can be automated will be automated. Competitive pressures for efficiency, efficacy and business agility are forcing organisations to address back-, middle- and front-office operations. Organisations that resist the pressures will struggle to remain competitive or to differentiate.

AI engineering

AI engineering is a discipline focused on the governance and life cycle management of a wide range of operationalised AI and decision models.

These include machine learning, knowledge graphs, rules, optimisation and linguistic and agent-based models. AI engineering methods also enable the governance and procedures for retuning, reusing, retraining, interpreting or rebuilding AI models. AI engineering also deals with the combination of AI techniques (or composite AI). Organisations are combining different AI techniques to:

  • Improve the efficiency of learning
     
  • Broaden the level of knowledge representations
     
  • Solve a wider range of business problems more efficiently

As AI techniques do not only solve problems but are starting to creatively generate solutions, generative AI techniques are also coming under the purview of the AI engineering discipline. Organisations can use generative AI algorithms to create models of things that do not exist in the real world. Generative models have the potential to affect many creative activities.

In the longer term, generative AI techniques will fundamentally change industries including manufacturing, architecture, aerospace, pharmaceuticals and the media. The benefits to society are potentially significant; for example, AI research is underway to support the formulation of new drugs for pandemics.

A robust AI engineering strategy will facilitate the performance, scalability, interpretability and reliability of AI models, while delivering the full value of AI investments.

CloseClose