I’ve seen a lot of companies embark on these projects and programmes only to fail. With the speed at which organisations are being pressured to change right now, they almost find themselves in a perfect storm to repeat the mistakes, which end up being expensive and time-consuming.
There’s clearly a lot of scope for many of our businesses to improve how we work and how we serve customers, be they B2B or B2C. Some of our organisations have no choice. They’re rooted in brick and mortar constructs now defunct in a novel coronavirus world.
Thomas Siebel is a billionaire businessman and technologist who authored a book called Digital Transformation: Survive and Thrive in an Era of Mass Extinction. In it he tells us where to start transforming our businesses.
...today, the availability of inexpensive sensors (under $1) and credit card-sized AI supercomputers interconnected by fast networks provide the infrastructure to dramatically transform organisations into real-time adaptive enterprises. Cloud computing, big data, AI, and IoT converge to unlock business value…. A key challenge for organisations is how to bring together and leverage these technologies to create meaningful value and positive return on investment.He's right that the challenge lies in uniting the technologies that transform our businesses into real-time, adaptive enterprises. That’s precisely where I’ve seen the failures occur here in South Africa.
Sometimes IT departments, tempted by the plethora of open source solutions, test them in isolation and find they work, in isolation. But the complexities of integrating them into the organisation’s broader IT ecosystem as well as the management overhead of many different systems and software become a bridge too far. They’re trying to go it alone because they want to save money or think they’ll be able to do it a lot faster. Both are good goals, but they represent a short-sightedness that causes failure further along the road.
In many cases, our IT departments are stocked with good people skilled at putting the usual IT tools to work for the business. That’s what they trained to do, it’s what they’ve been doing in businesses for years, and that is why you cannot reasonably expect them to suddenly become experts in whole new fields. Any reasonable person would consult the experts, particularly when you consider projects or programmes of this nature and how deeply and widely they potentially affect the core of the business.
Our IT people are being asked to go beyond simply replicating existing business processes using new technologies. We’re asking them to use the technologies in ways that create exponential advantage if we want to compete. That requires specialist knowledge and skills.
Cloud computing, for example, changes how businesses provide servers, storage and applications. You can provision a server running a business system and a database or a software service within hours instead of weeks. Much of it can be automatic so it happens within seconds or minutes instead of hours. That’s a significant change because it’s quick, cheap, and effective. It means our businesses can quickly change what we do or how we do it.
One of the challenges of big data has been the cost to store it and the capability to quickly scale processing it. In the past, it was often more cost-effective to store subsets of data and have experts interpret it. But now it’s often cost-effective to store big volumes of data and process it in parallel in the cloud.
IoT is about connecting sensors, machines and anything that can provide data to the Internet to share its state, receive information then act on it, or both. It may be simple but it can create enormous potential value, particularly when you combine it with cloud data storage, big data to organise it into information, and AI and ML to use it knowledgeably and effectively.
AI, in essence, means making smart machines and software that learn to solve problems and do the work of people. Much of that today focuses on recognising patterns such as fraud, machine and equipment failure, essentially making decisions and acting based on analytics. ML can learn by example. Feed it a few thousand images labelled “damaged” or “undamaged” and it can thereafter determine, quite precisely, quality control of parts coming off the assembly line. It’s much faster and more accurate than people and it’s truly transformative.
Digitalisation makes work easier, produces more accurate results, and enables automation. But it’s just the first step of the journey. To transform our businesses means extracting deeper value in future from the tools and materials many of us desperately crave right now because of the global pandemic. We can only achieve that with sound advice and expert guidance to ensure we build the foundations of real-time, adaptive enterprises.
