Much has been written and reported about the drive for businesses to digitise their offerings, and the described successes, models and plans have almost become an end in themselves. I’ve often felt confused about the goals being described by a multitude of role players, advisors and suppliers of solutions. I believe that is that it is important to take a checkpoint, think about the target state, and assess the leverage opportunities for beating the competition in getting there.
For the sake of this dialogue digitization simply refers to the use of technology and processes that make use of intelligent systems and processes not previously available in the industrial age set, (to make the distinction with automation). That’s it. There are concomitant technologies that have given this more impetus such as network speeds, cloud infrastructure and in-memory databases but these, although useful are secondary.
Why should digitization matter
It matters simply because digitization offers the opportunity for a business to use technology to deal with a myriad of business problems. These are typified by tasks which currently require deep experience and knowledge, and are supported by decision support technology, which when modified (digitized), in a way that removes the human bias, is able to improve through learning to levels unattainable by teams of very skilled people, at a speed and accuracy impossible for humans. These technologies use speed and self-learning power to constantly raise the technology threshold for work at any level of virtually any organisation or industry. The capabilities are scalable and transferrable across a business.
This means that systems can take much of the knowledge and expertise of an expert (s), and apply them while constantly improving on the scope of the learned data to deliver faster and faster decisions that defy human effort, with more accuracy, at a dramatically improved price point. That is why the early business cases of bots have been so successful at mimicking experts with standard questions to deliver extremely reliable and rapid outcomes, whilst constantly expanding the scope of knowledge, reliability and execution of the bots, in an ‘always on’ scenario. This has the effect of pushing the skills and execution level of an organisation up constantly.
In my experience organisations are full of people who know things about the organisations customers and processes that they cannot implement due to lack of tools, inflexible and dated applications. Digitization provides the ability to codify this knowledge and to use technology to learn and refine without resorting to recoding, analysis and recoding, testing and, implementation. We know that it is possible, using the capability of software platforms, to build systems which will create and deliver an offer to a customer, convert it, and create all of the systems of record (core account/transaction) as well as the compliance – without a single human intervention, and this while the business practitioners are sleeping.
These technologies will also constantly refine preferences based on behaviour (data), regardless of industry:
- Does a customer prefer a bundle or direct price?
- What time of the day and in which location is most likely to result in a positive offer to a customer?
- Which delivery vehicle is best, mobile, internet, telephone call and which terminology should be used?
- Does this response look fraudulent or inconsistent with a customers historic behaviour?
Winning the race
It is obvious that the need to digitise offers a set of tools to help to win the competitive race. In thinking about a future strategy, it is clear that the above capabilities will be manifest in the winners strategies because they offer price, focus and performance benefits, regardless of business model, which cannot be achieved in any other way. Arbitrary solutions don’t help. Essentially a digital organisation geared to beat the competitors will (over time), have the following competence:
- The ability to integrate internal and external customer data into a virtual data model, deliver and create insights which generate customised (unique) offers in real time
- A mobile platform capable of processing these offers and assimilating the relationship close to the customers heart (alerts, confirmations)
- A core system which can convert the customers agreement into the required contracts, terms and GL postings at the same speed as the rest of the environment, i.e. in real time.
What’s the test?
Test 1: Is the proposed development consistent with the target state – it may be an interesting project and give marketing kudos but is really automation, and doesn’t contribute toward a refined technology driven, cost reducing outcome which competitors are rapidly building?
Test 2: Does the proposed development build competence in data insights(data model), delivery and core process which is scalable and builds on internal knowledge and competence which competitors don’t have?
Finally..
It goes without saying that integrated technologies deliver value more quickly, with lower levels of complexity and more power than those that are not. This is because integration increases complexity since each proprietary system (best of breed), requires all of the business logic between systems to be coded by the client, and managed and changed through every innovation/upgrade. High levels of custom proprietary software inevitably lead to poorer class performance, or alternative inflexible innovation and agility capacity. In my experience this cost and time overhead is seldom included in the proposed business cases.
