Get to grips with transformative technology
Your guide to implementing transformative technologies such as Cloud, AI, Digital Engineering and IoT.
Get to grips with transformative technology
Change is coming. Cloud is your chance to transform fast
Vice President and Markets Leader, Cloud Infrastructure and Security services, Cognizant
Cognizant’s The End of the Beginning study states that 77% of its respondents have made significant cloud investments, making cloud top across all technologies surveyed over two years.
More than 70% are already seeing ROI, but the report notes that cloud may no longer be a differentiator, as both digital leaders and laggards in digital have already deployed it.
This article examines the opportunities, challenges and pitfalls in scaling enterprise cloud deployments, based on our experience in the field.
Why cloud scaling appeals
Not every enterprise has yet made significant investments in cloud, with some more focused on a legacy simplification agenda. ‘Cloud’ is a broad term, however, and if we include SaaS applications in its definition then cloud encompasses a huge part of the enterprise technology stack. Notably, there’s a renewed push to move from dabbling with cloud services and trying POCs to leveraging cloud for real heavy-lifting.
Why? Because business models continue to remain under relentless stress as a result of changing customer behaviours and competition from digital natives. They’re putting pressure on traditional IT architectures and old modes of working. Legacy platforms can’t deliver agility, lower the cost of operations or speed up time to market, so companies are looking at making fundamental technology refreshes. Cloud is at the heart of such refreshes, along with automation, user experience, security and more.
The End of the Beginning calls cloud a “low-risk bet” but, in a complex organisation, sorting out the mess and losing the weight of technical debt through an enterprise-wide cloud implementation is a huge challenge – particularly if the previous technology is bolted on or aggregated via M&A. Many have tried to progress cloud transformations and struggled.
For enterprises, cloud- or digital-enabled changes tend to take two forms.
- Companies want to become digital-first, so almost all the money and energy goes to new platforms that replace legacy ecosystems. That’s viable if you have a clear vision, but it’s harder where there’s lots of legacy.
- The second form is a two-pronged, bimodal model. Here, you refresh legacy to unlock cloud-like attributes and do digital-native work in parallel. You regard legacy as remaining important for the foreseeable future for the business.
Some companies rely on brand and customer loyalty to delay change, but even quasi-monopolies have come under pressure to act. It’s become a matter of survival: customer expectations of user experience and product lifecycle innovation have shifted dramatically, and the mentality of lean startups means they are positioned to catch up quickly. Speed to innovation is the most important attribute for any organisation – and speed to innovation can only be enabled by cloud and digital technologies.
This is the reason why enterprise is moving from hobbies and POCs on cloud to wholesale change. Everyone must go down that path eventually, but it’s not always seamless.
Multiple clouds, myriad challenges
The challenges associated with cloud adoption and migration are the classic business transformation challenges. It’s not about the technology, stupid, but the wider aspects of organisational change, governance, communications and executive support, security and velocity of the programme.
Moreover, multi-cloud has become a reality in large organisations, because cloud is a business-led programme and business chooses the technologies and cloud hyper-scalers it wants to use for competitive advantage. That means multiple cloud platforms are often at play.
IT needs to meet this challenge on two levels.
- Service governance. The IT organisation may have lost the plot either because of shadow IT or because the adoption of new platforms means they are dealing with governance beyond their direct control. Areas such as service operations, cost management, security and remediation universality, dealing with legacy degradation and associated migration and modernisation tasks become ever more important.
- Platform deployment. Multi-cloud is becoming common because, for example, the analytics guy loves Google and the infrastructure department loves AWS or Azure. This issue is being addressed as microservices, containers and service mesh become mainstream and as tools such as Google Anthos, Kubernetes and VMware ‘Project Pacific’ help companies deal with portability, deployment and orchestration.
Building blocks – rise of serverless
Enterprise greenfield projects and start-ups are also addressing the time to market and transformation challenge by using managed platforms (such as AWS and Microsoft Azure) with service integration through a Lego-like approach.
Netflix runs on this serverless model, and it’s amazing how powerful it is: a complex, global web platform that offers failover, user authentication, global media distribution and management, security, choice of database and more.
If you want to be state-of-the-art and have the right environment for it, such an approach is highly attractive. Indeed, it’s a matter of time before it becomes standard for mainstream companies. Only those with complex enterprise needs and recalcitrant legacy will want to build their own infrastructure. It’s a replay of the old argument: ‘Why do bespoke development when there’s an off-the-shelf package?’
Use case example
Two of Cognizant clients - a publishing firm and a utilities giant - represent two different approaches to cloud transformation. The publisher went all in to become cloud-first, throttling all investment in legacy. The utility company delineated legacy transformation and digital innovation areas via clear demarcation lines to improve key operations without stifling progress on a digital first agenda.
Cloud is a major opportunity to enable and automate enterprises today but as companies scale up cloud efforts, the change mandate can’t be overstated. Recognising that fact early will help companies to prepare for faster, more efficient ways of doing business.
In a nutshell
Cloud is at the heart of how companies automate and digitise operations for flexibility and speed. But it’s harder to deploy for companies with significant legacy assets
Change management is a major challenge. It has to cover organisational change, governance, communications and executive support, security and velocity
Dealing with multi-cloud poses new questions but tools and managed serverless cloud platforms can help to simplify IT. Even complex business needs can be assembled in Lego-like fashion
AI: From preparing data to re-engineering your organisation
Vice President, Cognizant AI & Analytics, Europe
Ninety-nine per cent of Cognizant clients have already made a move into AI. They’re now thinking about their next stage, seeking to apply it more broadly to data and analytics, swerve potential mishaps and scale for sustainable success.
Why does this matter? Because Cognizant’s The End of the Beginning report suggests that there is a strong correlation between AI leaders and commercial success. Generally, the greater the investment, the greater the expected return, and that includes data management and data analytics.
But how do you industrialise AI, and where are the risks? Matt O'Kane, Head of Analytics in Europe for Cognizant, discusses in the following article and in this short podcast...
Getting data ready for AI… using AI
AI is usually presented as the ultimate way to probe information assets and identify and act upon patterns. The mantra goes:
- Get your data house in order manually
- Apply machine learning to a data lake or other centralised repository
- Deploy the resulting models into production
In fact, you can also use AI for step one, too. Today, a large organisation might have hundreds of people cleaning, standardising and integrating data, but you can deploy AI for the same task, and port it from on-premises to cloud – all faster, and with fewer errors than manual methods. This is a case of the snake eating its own tail: AI being used to satisfy its own needs.
Building infrastructure for AI
Data management is a critical aspect of AI, but few clients have prepared data for AI or advanced analytics. They use data for reporting, business intelligence and powering downstream apps. But AI needs to consume data differently to normal methods. Every company has multiple data lakes and ways of doing things, but for effective use of AI they need to be built to handle large volumes of historical data whilst delivering results in real-time – and that’s not easy.
Data scientists currently spend 80 per cent of their time wrangling data, putting it into shared formats and finding other ways to make it useful. But data scientists want to spend their time working on business problems so data models built for machine learning – often called feature stores – are becoming more common.
It’s a common first step to apply AI to a single use case, then show it off as a successful case study. Yes, you can solve individual cases – but the key is to build ‘muscle memory’ for AI by applying it more broadly across lines of business. The good news is that the data science boom, which saw the Harvard Business Review dub ‘Data Scientist’ “the sexiest job of the 21st century”, has created an influx of graduates. At the same time, ‘off-the-shelf’ machine learning routines mean we can enjoy a fast start with AI, reducing the impact of the current data scientist shortage.
However, it’s important to recognise that getting the most from data and AI is a collective effort. AI teams can’t sit in an ivory tower. Brownfield (involving legacy systems and processes) and greenfield teams must work in tandem, and AI needs to be assimilated with the rest of IT and business. Digital leaders are embracing this: Google now sees itself as a “machine learning-first” company, democratising machine learning skills across its software engineers.
Too often, AI is treated as ‘pixie dust’ – a magical fix for all ailments. So be realistic. You needn’t transform the entire organisation in one ‘big bang’ but should look to do it slice by slice. Quick wins will help you build faith across the business, but you need scalability. Ask a business unit what they need from IT, and they will all have lists of 50-100 items. Think about those use cases in lists of 10s, and for each list, ask ‘what data and what infrastructure do we need for 10 machine learning models?’
A framework for ethics and compliance must also be in place to mitigate the risks of algorithms that have inherent bias, as was seen in the case of the Apple credit card launch, where men and women were offered different credit limits. AI makes questions of ethics and privacy more complex but rules such as GDPR have helped prepare us. We’re already seeing more appointments in roles such as Chief Privacy Officer, and the CIO needs to be aware of these trends.
CEOs want to advertise that their companies are investing in the AI space, so there’s already buy-in and momentum. But where that’s lacking, smart IT leaders need to draw on business case studies. You need to talk the language of the business and not lapse into buzzwords.
Machine Learning today is reminiscent of what happened in Business Process Re-engineering 30 years ago. Then, inspired by writers such as Michael Hammer, organisations exploited new, technologically-enabled workflows and processes. Today, AI’s promise is unleashed when people think about changing how they build products, serve customers and reduce costs.
Every time you book an Uber ride, hundreds of Machine Learning models run, honing calculations for estimated times of arrival, routing and diagnostics. So, think big, think scalability, and think about how AI and data can reinvent your organisation.
Bumps in the road:
- Ignoring data management needs
Treating AI as a silo
Ethics and governance
Looking beyond AI
Data, analytics and AI form a powerful triumvirate. But it’s not just those components that solve business problems.
For example, if you’re a retailer, online purchase returns is an endemic problem. UK consumers send back almost half of online clothing purchases, according to Barclays. The result: huge costs and logistical hassles. So, retailers must think not just about data manipulation and querying but also the customer experience. They need to talk to customers and understand why they’re returning products.
You need digital engineering to automate the supply chain, and the legacy applications in retail must be modernised. Design Thinking and ethnographers will help solve your customers’ real problems.
Use case example
Network Rail monitors criteria such as signalling, weather, track defects and maintenance works, so it can predict component failures, overrunning engineering works and passenger delays.
In a nutshell
AI leaders expect high ROI and are typically digital and commercial leaders
Most companies have tried AI but few have scaled up
Industrialising AI requires preparing data in new ways – and AI can help to do that too
Treat AI as a way to re-engineer the organisation
Putting people at the heart of software development
Director, Cognizant Digital Engineering
Cognizant’s The End of the Beginning report stresses that the advantage traditional enterprise have over digital age companies is the data provided over years of serving customers. But today, too many companies aren’t leveraging that data, and are thinking about building products and services without adequate user research on how they will use them or what they want from them.
Digital Engineering (DE) resolves such issues, and has become the common theme across modern software development. It incorporates:
- Product-based thinking, focused on business outcomes
- A platform-based approach, where development tools aid integrated outcomes
- Full stack, cross-functional teams where individuals have specialist skills and work together
- A proven execution framework that enables digital at scale
DE helps organisations transform to build human-centred products and services while embracing operational excellence. This article examines best practices for making DE the bedrock of your development while warning about potential pitfalls.
Every organisation has ‘run’ and ‘change’ imperatives. ‘Run-the-business’ is where the money comes from, and it pays for everything. ‘Change’ is for new opportunities a business wants to capitalise on. For change projects, as our The End of the Beginning report says, if you’re investing less than 10% of revenues in new technologies, then “best of luck.”
A great way to start any change initiative is to engage with end users to understand the needs and, where that isn’t possible, to leverage data to find out. But whichever type of project it is, you need to ask questions such as, ‘Who are the users?’, ‘How does the new product(s) deliver value to the users?’ and ‘what processes, teams and applications make up the product value chain?'.
Map the answers to those questions to product planning and consider the possibilities. For example, in retail the possibilities may include channels, inventory, shipping and product lifecycles. Ask, ‘How do I make that product quicker, optimise value and improve feedback?’ and ‘Does our delivery model support new product and services?’
Build platforms and teams
The next question to ask is ‘Do I have the right platform capability for the software I’m building?’ You need a rich set of features a developer can use to be productive, rapidly developing and integrating with the upstream/downstream applications using self-service features provided by the platform.
The right platform will help get to desired outcomes fast rather than squandering time worrying about changes that don’t differentiate the resultant product.
Getting teams right for your development project is also essential. Cross-functional teams need T-shaped (deep skills plus ability to collaborate broadly) and E-shaped (combining experience, execution, exploration and expertise) individuals. Having them work side-by-side in small pods will provide speed and focus while anthropologists, ethnologists and focus groups will offer further human-centric insights. Gamification can help with peer- and pod-level recognition, aiding the creation of outstanding communities of practice.
Velocity is also key, delivering focus and reduced friction upfront. Use a six- to eight-week discovery phase to build your vision and target architecture, working closely with the business and IT, rather than stretching it over six months.
With any software development project, scalability needs to be factored in at the start. If, for example, you build a platform for payments, you can’t think about it being for just a single use case. It must be self-sufficient and generate revenue to pay for itself; features such as multitenancy and high levels of configurability will make it sustainable for years.
Implementing Digital Engineering
Digital Engineering (DE) is the glue that brings together human-centred design, data, analytics, software engineering and platform thinking. But a DE framework also needs long-term thinking and the understanding that people and circumstances will change over time. Having a scientific basis will help make plans fit for the future so use market research helping to predict tomorrow’s world of possible game-changing advances. Only about 20 per cent of companies get this aspect right.
Successfully implementing DE requires cultural change, which happens via seeding thoughts across teams and showing the art of the possible. A greenfield, next-generation customer platform won’t be optimal if the back-end infrastructure is left behind. The latter will often require core modernisation and legacy transformation without adding any new value. Therefore, DE must align people across the product value chain with desired outcomes and identify new roles, skillsets and ways of working to adopt. You need a vision, definitions and objectives.
Not everyone will appreciate the new ways of working and not everyone will believe in the set goals. Communicating a shared vision is therefore essential. Explain that change must happen, so everyone must know their part in the plan and be confident that the technology aspect is achievable.
Celebrating successes, persuading and communication are important aspects of sustainable development success. You need to put collaboration to the fore but also create an environment where individuals can benefit personally. It needs to be an enjoyable workplace with few Chinese walls and offer the chance to evolve through new skills and roles and where culture and buy-in must be nurtured over time.
Bumps in the road:
Not communicating a clear and consistent vision
Siloes and people who don’t buy into the new way of working
Not building in scope for future changes such as scientific advances
Use case example
Cognizant worked with Severn Trent Water, using Agile ways of working and encouraging collaboration and out-of-the-box thinking with new developer tools to revamp STW’s software engineering capability. The application release cycle was reduced from three months to four weeks and the company freed up 20 per cent more time to focus on innovation.
In a nutshell
Digital Engineering is the glue that helps organisations to transform to human centred products and services whilst embracing operational excellence
Product based thinking and platform-based approach can not only help in organisation cultural transformation but also significantly improve time to market
Communicating a powerful vision for change that’s simple, consistent and authentic that every team member can relate to are essential aspects
How to win at customer experience in the age of the consumer
Head of Consumer Goods Portfolio, Cognizant
Cognizant’s The End of the Beginning report says that over the next three years, almost 80% of respondents will be involved in the implementation, maturing or advanced stages of customer experience. When we looked at companies that are successfully building shareholder value, 52% said their number-one lesson was to begin with human insight. That human lens is more important than ever.
Matt Simpson, Head of Consumer Goods Portfolio for Cognizant examines the challenges and opportunities that come with scaling to interact better with customers, and to deliver powerful new experiences in the following article and in this short podcast...
The age of the customer
We’re living in the age of customer control. S/he can compare a price against online retailers from a shop aisle, view similar products, read reviews, even inspect product provenance and the retailer’s sustainability record. Digital has also transformed the expectations customers have of their interactions with businesses: they want their experiences to be seamless, connected and rewarding – and will compare those experiences to the best-in-class across any sector.
The consequences of getting customer experience (CX) wrong can be catastrophic – but get it right and the value is incredible. Research from eConsultancy says 51% of customers will never do business with a brand again after one bad experience, and 42% will take revenge online.
On the other hand, millennials will pay 21% more to companies that excel at customer experience. Forrester reports that CAGR revenue growth is 17% for CX leaders versus 3% for laggards.
The ‘interrupt’ era is over
Brands have traditionally marketed by interrupting buyer activities such as watching TV or reading magazines, but in the digital world there are so many more touchpoints available, and customers are able to curate their own experiences through them, filtering out the things they don’t want to see.
The old ‘interrupt’ model is dead. Instead, businesses can connect with customers by making their activities more rewarding, or creating new experiences they’ll value. Mauro Porcini, chief design officer of PepsiCo, says “We compete with the latest song of Beyoncé.” Businesses have no divine right to attention… and you’re competing with everyone.
Follow the customer
Very broadly, legacy businesses are struggling to deliver the same levels of customer focus as newer, digital-born companies. Investment in the technology, data and content capabilities to deliver customer experiences is critical, but businesses won’t succeed without accompanying cultural and organisational change. The business itself must become customer-centric. In every meeting, you must ask, ‘what does this mean for our customers?’
Businesses must put themselves in the shoes of their customers: assess the journeys, the experiences and identify where they break down. It’s not that complex: we are all customers, and we all have experience of what makes a good and bad interaction with a business.
To genuinely embed and embrace customer-centricity, businesses need to set big, hairy audacious goals as John F. Kennedy did when he said: “This nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to earth.” Businesses need to be ambitious about CX, and that ambition needs to be supported from the C-suite down.
It will not be enough to set up a small innovation team tasked with transforming the customer experience. Innovation in a sandbox will not deliver the CX transformation needed. CX must cut across every team, with clear responsibilities and outcomes for each.
Goals must also be practical and precise. What are you measuring? NPS? Customer advocacy? Customer satisfaction? Whatever it is, count it.
Where’s the content?
Technology and data have unlocked incredible capabilities for marketers to deliver experiences. They can now continuously listen to customer signals and identify those micro-moments that matter to each and every customer.
But without the right content expertise to deliver on that information, the investment in the technology and the data can go to waste. Often businesses that have implemented enterprise solutions such as the Adobe Experience Cloud or Salesforce Marketing Cloud are using only a fraction of their capabilities to deliver experiences. It’s the marketing equivalent of spending millions building a massive, state-of-the-art football stadium and then only allowing amateur teams to play there. (And to stretch the analogy: dealing with the problem of an empty stadium by repeatedly upgrading the facilities.)
But if a business can combine innovative systems thinking with creative storytelling it can start to deliver the type of experiences their customers will value. That means content creators (journalists, designers, copywriters, videographers, front-end developers, VR artists) working hand in hand with technology architects, data scientists and strategists. And it all needs to add up to something coherent and credible – experiences that while informed by customer data, also feel distinct and unique to the brand and its view of the world. This is where a clear content strategy and governance is required: consistency in purpose, tone and quality, and transforming the internal processes of content creation.
Becoming a leader
Customer experience leaders need:
Listen, communicate pain points, provide the right content.
The ability to think up, investigate and create new solutions.
The know-how and nimbleness to act on ever-changing preferences, build innovation into culture and scale.
You must disrupt… and you are being disrupted.
Bumps in the road:
Investing in technology without the content expertise to deliver value from it
Failing to align with customer needs along their journeys
Treating CX as a project or a campaign rather than embedding it everywhere
Use case example
We developed a content strategy for a global sportswear retailer to support aggressive growth targets on their e-commerce platform. The remit was to help deliver premium experiences for customers to improve love for the brand and desire for the products. With the strategy in place we established a “Future Content Laboratory”: an agile data, insights and creative team that identified, tested and disseminated new ways of planning, creating and distributing content to deliver on the vision. Results included a 5% increase in conversion and a 7% increase in ‘add to cart’ on the targeted areas of the website.
Many legacy businesses are losing market share to digitally-born new entrants who have customer-centricity built into the DNA. To continue to compete, those established businesses need to not only invest in the technology and data capabilities to deliver valuable customer experiences, but also in the content expertise and cultural transformation required to become truly customer-first.
Legacy businesses need to move fast and learn fast. The journey isn’t easy, but the destination is luminous.
In a nutshell
Today, customers rule. They have unprecedented knowledge and control over what they purchase
To compete, brands need to combine innovative systems thinking around their technology and data with creative storytelling to deliver rewarding customer experiences
Becoming customer-centric requires organisational and cultural change, and leaders need to walk the walk
Get ready for IoT and the new world
Head of Europe, Engineering Services and IoT, Cognizant
Cognizant’s The End of the Beginning report found that over three-quarters of respondents have already made significant investments in cloud, mobile and cybersecurity, but fewer are investing in Internet of Things (IoT).
Many respondents are already well into what the World Economic Forum calls the Fourth Industrial Revolution (“a fusion of technologies that is blurring the lines between the physical, digital and biological spheres”). But we still have a long way to go.
This article focuses on what digital leaders must do to scale IoT, the relevant macro factors impacting its deployment, and pitfalls to avoid in IoT projects.
The big picture
Businesses are facing large-scale disruption, and there are at least four big driving forces behind it. First is the changing role of technology. Machines used to be designed around what humans can’t do, but as technology becomes more and more intelligent, tomorrow’s jobs will be designed around what machines can do, with humans paid to do what they can’t. Predictable physical work or tasks involving sensors, data gathering or pattern recognition, like that of a radiologist, will be increasingly performed by machines. Unpredictable physical work like forestry or construction, or jobs needing social skills or empathy, will be human-centric for a long time.
The second big driving force is the changing behaviour of tomorrow’s consumers and employees: Generation Z is known for its focus on quality of living and sustainability, as well as its comfort with the digital world. Markets are changing from products and ownership to services and sharing, because this is what young people demand. For example, General Motors says it’s not a car company but a mobility company. Clothing, lodging, healthcare and jobs are moving the same way.
The third driving force is declining GDP growth and productivity worldwide. Aging infrastructure and increasing marginal cost of energy production suggests we will have declining productivity for decades to come.
The final and perhaps most important force is climate change. What led us here is industrial activity, and unless companies figure out a way to make money through sustainable means, no government action or policies will be able to reverse the damage done so far.
The fourth industrial revolution is humanity’s next big bet to deal with those four forces. History tells us that when there is step change in how we power our economy (energy production), how we manage our economy (communications) and how we move our economic assets (transportation), a paradigm shift in economic activity occurs. Energy is becoming cheaper thanks to renewables: the wind and the sun never send us a bill and tomorrow’s internet of energy will see every building decked with solar panels and equipped for energy trading.
Communications, with advanced wireless protocols such as 5G connecting billions of devices, will be easy, reliable and cheap. Mobility will be cheaper, connected, shared, autonomous and electric.
The combination of these phenomena will see marginal costs of production fall. Everyone will become producer-consumers, making goods by downloading designs and 3D printing them, for example..
Cut costs, hike revenue, find new markets
In this changing world, you need to map the future: what if you are in the paper business and in future paper isn’t made from trees? Or think like a rival: how would you disrupt yourself?
IoT is about one thing: economic value creation, either as a result of reducing costs and keeping revenue the same, or increasing revenue and keeping costs the same. IoT can reduce costs by boosting asset performance via sensors that generate valuable data, and by streamlining operations, by means such as reducing field visits and enabling self-service.
IoT can also increase revenues from new business models or by providing invaluable insights about consumption, superior customer service, or by providing the ability to serve adjacent markets. But companies investing in IoT must also transform their cores if they are to drive new outcomes.
Once a company has considered macro factors and set goals for its IoT deployment, it must swerve the common obstacles, such as:
1. Standardising platforms
There are many point solutions that promise to solve a particular problem very well, but companies must think of the larger picture and journey, and standardize on a set of standard platforms to avoid risks associated with interoperability and maintenance.
2. Finding ways to scale fast
If customers don’t have business objectives clearly defined, financial planning to realize ROI and change processes defined, they get caught in a cycle of PoCs. Gartner estimates it takes three years to get payback on IoT, so having a structured capital allocation program, a gate process to measure and quantify success or detect failure early, is critical.
3. Prioritising the right use cases
Identify the low-hanging fruit: the IoT projects with reasonably good ROI and shorter time to market, and those which can co-exist with the current core. This is critical to building positive momentum.
4. Complex development needs
As most IoT programs fundamentally change how an organization operates or makes money, the delivery also requires a new approach. The new diversity of technologies results in a skills gap. Further, the lack of modern processes to enable adoption and deployment in an agile way puts the CIO’s team under stress.
5. Digital know-how
In today’s world it’s essential to have technology-savvy business executives and business-savvy technology executives.
6. Change management
IoT programs often force various functional departments to work together to achieve ROI. For example, R&D, production and after-sales all must work together on new IoT-enabled services if they are to determine the right business models and achieve fast time to market. Creating a specific digital unit almost always fails because it’s seen as distinct from surrounding teams. But a ‘centre of excellence’ model can build value propositions, attract leaders, create new operating models, improve skill sets, and formalize financial and programme management.
7. Dealing with legacy
Don’t put your focus solely on either legacy modernization or the new stuff – work instead to create balance. Modernize the elements needed for transformation. For example, network traffic may quadruple when you connect sensor devices, requiring an upgrade.
A Gartner poll has found that only 9% of companies have achieved measurable benefits from IoT programs, while about 60% are still in strategy definition and experimentation phase. IoT programs fundamentally change the way a company operates, and so they require the highest level of sponsorship, including the buy-in of the board. Most companies which have not yet started on this journey are disturbed by the prospect of spending capital on disrupting a well-run business.
But if you can’t see that the new world is going to be fundamentally different, your business is at risk. There is a significant first-mover advantage in digital: the winner takes it all.
Bumps in the road:
Failing to build momentum by not balancing larger projects with quick wins
Not balancing legacy modernization with greenfield work
Not having the necessary skills for the post-change, IoT-equipped world
Use case example
One of the world’s leading pump manufacturers recognized that its customers were buying fewer and fewer pumps. The pumps were now smarter, better and longer-lasting, which meant it was facing a shrinking sales opportunity. It started building subscription-based vertical applications for various industries that it used to sell pumps to, using IoT sensors, AI and AR to enable its customers to see faults and act accordingly, reducing the need for field visits.
Today, 70 per cent of world’s water is used in agriculture, and 70 per cent of that water can be saved via the precision farming applications that sit on top of the company’s pumps. This is a great example of a business which has not only adopted and scaled an IoT program, but has also figured out a way to create a new, regular revenue stream while contributing to its sustainability goals.
In a nutshell
The world is changing, thanks to new technologies and industry models, consumer and employee needs, and economic and ecological pressures.
IoT can help companies increase efficiency, reduce costs and add new value, but it’s vital to find the right opportunities to develop strategically, set goals and ensure your IT core is ready for the change
The road to successful IoT adoption is littered with challenges: prioritize the low-hanging fruit, onboard the right skills, build a basis for collaboration and be ready to scale at pace