Before embarking on this journey to the ‘new normal’, it is important for companies to first define the business outcomes they are hoping to achieve. This will enable them to identify what is currently not working, and the changes they have to subsequently make to survive in an increasingly unstable market.
Ensuring success through change requires a thorough understanding of the existing business and the right setting for technology to thrive. Artificial Intelligence (AI) and other technologies require the business context if they are to learn the nuances and subtleties impacting business goals, to sense friction, and to take intelligent action.
Identify the bottlenecks in your business
We all know that getting the right information at the right time is key to good decision-making – but most organisations lack the required visibility to make these critical decisions effectively. Transactional systems are a great resource, but too often data is siloed or incomplete for the task at hand and by the time someone has inputted it into a report, it’s outdated.
Without understanding data within the context that generated it, business leaders can’t spot inefficiencies or subsequently fix them. This leaves them unable to make truly informed decisions in this (or any) scenario. Decisions will therefore be made based on skewed data, while the operational staff are left bogged down with too many manual steps to keep broken processes running. Middle managers are caught, well, in the middle – wasting their time manually compiling status reports that are simply too little, too late. Whenever change occurs, teams often default to offline ways of working to override system-based processes.
Not all technology is created equal
To overcome this problem, the knee-jerk reaction for most companies is to invest in the latest and buzziest technology. But what many fail to reconcile is understanding how the technology actually contributes to their business goals. For instance, Business Intelligence (BI) promises actionable insights, but leaves companies hanging when it comes to actually taking action based on these insights. Robotic Process Automation (RPA) enables action but isn’t very smart – apply it without insight and you’ll just be automating inefficiency at scale. AI generally lacks the necessary business context to truly make intelligent decisions.
According to McKinsey, only 5% of CFOs say they’ve seen a substantial ROI from digitisation and automation initiatives. This is not surprising because ultimately none of these technologies by themselves can truly understand how to drive your processes towards the outcomes that matter.
Creating a system for success
So what does the right technology look like? It has to be focused on the business needs and goals, while understanding the business context of the existing processes and work. It needs to be flexible and adjust in real-time to switch around priorities as circumstances inevitably change: a solution that provides recommendations based on business outcomes and clearly shows the impact executive decisions will have on the team and its KPIs – by taking into account end-to-end interdependencies. This solution also needs to ensure that information aligns across teams, so that knowledge can flow throughout the organisation in a truly frictionless manner.
Such an outcome-driven solution can be created through a combination of process mining technology, automation and AI. The underlying process mining foundation provides the business context the AI requires to learn the nuances and subtleties impacting business goals, to sense friction, and to take intelligent action. It aligns every level of the organisation behind the same priorities, eliminating repetitive tasks and decisions. Executives can then set business goals and track performance, empowered by real-time data that enables them to make better decisions. Continuous real-time analysis automatically surfaces process anomalies by impact – and their recommended action or fix – so middle managers can act more proactively.
Anomalies can have positive or negative consequences, but the important thing is to be able to tackle them fast either way and to understand why they are happening in the first place.
Operational staff will now find their work prioritised for them, with next-best-action recommendations guiding them towards the tasks and decisions that will have the greatest impact on business outcomes. At the same time, many repetitive tasks — such as updating master data in transaction systems — can be fully automated, so employees can focus on high-value work. Any change in priority is also immediately reflected at every level, so the entire team is constantly working towards the same goals.
Taking advantage of such a solution, companies can identify gaps in the market more easily and pivot their businesses to meet these requirements. For example, a clothing manufacturer experiencing a slump in sales may find that it can turn a profit by using its resources to produce PPE equipment instead. Rather than making blind decisions and arbitrary moves to pivot the business, the organisation now knows exactly what processes to tweak and what technology to invest in. This will allow them to stay ahead of the curve, by reacting to changing customer behaviour and business conditions quickly.
The days of making decisions without the benefit of the right insights at the right time are finally behind us. No more siloed processes that can’t be adjusted in real-time. No more toiling away on unnecessary manual steps. Sensing operational friction and acting decisively to eliminate repetitive tasks is the first step for any company aiming to pivot their business model. Change cannot be achieved overnight but by setting clear targets and getting the whole organisation onboard companies can improve their chances of not only surviving, but thriving through the current crisis.