1. What is Digital Transformation and how it is different from the IT/ICT transformation that is happening since for than four decades?
2. Why is AI a critical part of this transformation?
Let me take the first question.
Digital Transformation is actually a cognitive revolution. It is a more humanlike way of making sense of the world around. And this is our clue to the most important difference between IT and Digital-
‘IT systems are not humanlike. They don’t try to make sense of the world around them. They create a small world of their own and everyone follows the rules of that world. The input has to be given in the way they demand and output will be available in forms that they prefer. You better fall in line.
Digital, or Cognitive systems would want to fit into the world they find themselves. To understand this, see how we humans manage our operations. We take in all the signals from the real world. We see, hear, feel, smell and then speak, show, move and push at the world around us. That’s what Digital systems aim to do. They want to process all the available inputs and then interact in a natural way. That’s why they hope to solve problems much closer to our life.’
Now let’s turn to the second question, why AI is so critical in this transformation.
I think the above distinction must have given you an intuitive answer. Because we are aiming for a humanlike way of interacting with the world, we need AI to do the processing. We also need sensors (IoT) and motors (Robotics), displays (VR) and so on. You can see how well the various Digital Technologies fit in this paradigm.
But even before AI, there is a big big factor that drives this whole transformation, and that factor is Data.
Since we are talking about real world data, we are talking huge huge volumes in forms that we never processed before. Vision, audio, waveforms, text, handwriting and many more types of data need to be captured (again IoT), stored and processed (Cloud, Big Data) and managed (Blockchain). But if you are the one responsible for planning and implementing systems, this central importance of data means a fundamental change for you.
When you plan classical IT systems, you start with the objectives of the company. Accordingly, you define the requirements. Analysis and design are further carried out on the requirements. Data is an outcome of this process.
Fig 1: Planning for classical IT systems
In Digital systems, however, data drives the system planning. This is again somewhat like like humans. We cannot demand more data from the world. We work out our affairs in such a way that we can manage with whatever data the world gives us. And we have become very good at extracting as much meaning from that data as possible.
So the new paradigm will be:
Fig 2: Planning for Digital systems
As we can see, we are now thrown in a more uncertain and complex world. The existing data indicates possibilities of what can be done. These possibilities have to be mapped on the organizational objectives to decide the Digital Transformation plan.
There are more complexities in the Digital paradigm that shown in the diagram. But for the purpose of this article I would like to present a simple view, leaving the complexities for a future and longer article.
This immediately leads us to the four most important points that can help us to get started with AI. This framework does not consider AI in isolation, but the whole Digital Transformation.
The four hints are:
1. The data that you have: Identify all the data that your company owns. The data can be put into three major buckets:
- Structured Data: This is the easiest data to identify. It will be found in all the IT systems that you have implemented so far. Also look for the countless excel sheets that your employees have created.
- Unstructured data: This will be typically text that has no fixed format. Emails, proposals, invoices, challans, vouchers and so on. Hint- even if you might have some of this data in your IT system, there may be ‘left-over’ data. For example, while some part of an invoice is entered in your AP system, there may be some that is not entered. This left-over might contain interesting possibilities.
- Dark data: This is that part of your data that you never really thought about. In fact, it may not be being captured today. Photos that can take, vibrations that you can record, video that you can capture – these possibilities are endless. No wonder that IBM put the volume of dark data at 80% of all data.
2. Objectives of your company: There is a whole load of literature on how to identify objectives of your organization and I am definitely not qualified to comment on it, but broadly they will fall in two buckets:
- Aspirations: what you would like to happen – improvements, new initiatives, lead over competitors and so on.
- Pain points: what you would like to remove – delays, leakages, inefficiencies.
There will be various areas of your business that will have their own objectives – customer experience, process efficiency, employee satisfaction, innovation, leadership and so on. I will write no more – you are the best judge.
3. Applications (or Possibilities): The applications of data are of course endless, but since we are developing a framework, let’s again put them in the biggest buckets:
- Automation: Essentially replacing human efforts by machine. This not only saves cost, but improves accuracy and speed. In most cases, human effort can be diverted to higher cognitive tasks, giving further advantage to the organization.
- Analytics: Again lots of material is available on this topic, generally categorized in four types:
The most valuable use of analytics is in decision support, for which it has to be combined with Knowledge (see below).
- Knowledge: The third and often ignored use of data, is also the most difficult to achieve. Combined with automation and analytics this can give rise to spectacular applications. The currently popular use case of chatbots is an example of the Knowledge possibility.
4. Existing systems: The fourth hint for getting started with AI is studying your existing IT systems. Most of the above possibilities give best results when interfaced with one of the existing system.
I will now try and put these four things in the framework together with help of a simple example.
Let’s say you have an invoice management module in your existing Accounts Payable (AP) system. The scanned copies of incoming invoices are entered in the system by your employees. These scanned documents are the unstructured data.
The possibility this data presents is that of process automation, along with many others. Now, your company has a defined objective of improving process efficiency, and automation of invoice management fits well with that. Since you already have a AP system in place, the forth criterion is also met.
It seems that automation of invoice management is the right problem statement for your company to get started with AI. There will be a few startups and experienced companies who will be able to help you to get started.
The article was first published on the author’s linkedin pulse page, its been re-published here with the author’s permission.