When in hibernation, organizations need to look through a lens primarily of what work should be paused, and where people can be redeployed. The longer they can retain capacity, the more likely they’ll be able to adopt other responses.
Data should be used to drive modelling of scenarios. While modelling inputs and assumptions may be far from precise in response to COVID-19, the modelling process is still valuable. Modelling socialises assumptions and exposes sensitivities, allowing organizations to collaboratively explore multiple potential futures, as demonstrated in this Australian electricity sector model.
Data-driven discovery, such as market clustering analysis, can reveal potential pivots, especially when paired with qualitative techniques like user research. Machine Learning and AI solutions can be used to quickly adapt to new market conditions. Here, adapting means reorganising the basic building blocks of producing goods or services, in order to meet the changes in demand for a new market.
People are flexible and machines are scalable, so adapting means finding the right mix of automation, augmentation, and enablement: automation of high-volume or high-velocity judgement tasks (e.g. decisions involved in approving a loan or claim), augmentation for scaling high-value judgement tasks (e.g. decisions involved in providing great customer service), and enablement to share best practices. For example, we have worked with clients to augment teams with machine-vision-based quality assurance solutions. Starting simple and evolving solutions ensures the best fit and earliest value realization.
Using data to assess high volume and high-touch interactions has never been more important. Being able to quickly ascertain what type of demand is being sought will enable organizations to understand how they can service these volumes quickly and potentially reduce non-value creating tasks through proactive automation. For example, banks can proactively identify customers who may be approaching financial distress and in turn proactively offer services to help customers through this time.
Chatbots are increasingly used to augment front-line staff while providing a natural interface for common queries. This in turn frees up people to focus on higher value work that requires more complex judgement.
The call to ‘flatten the curve’ applies here too. Electricity markets are a mature example of managing sometimes volatile fluctuations in demand. The combination of many data sources helps predict and understand which interventions will help avoid or smoothen the surges. At the limits of demand management, electricity markets also respond with additional capacity. Again, consider Machine Learning and AI solutions to create more capacity to respond.
It seems like every household right now is participating in some form of remote delivery. Children taking remote dance classes and karate lessons, people purchasing items online in volumes never before seen, and even socialising over video-conferencing parties.
It's certainly easier for organizations with developed digital channels to quickly respond to current conditions, but there is good news for those with underdeveloped channels, as mature partner ecosystems are available: social media channels, digital shopfronts and delivery services. Data will help you maintain your customer relationships through these new channels of engagement and fulfillment, and ensure your physical customer experience translates faithfully to digital.
When organizations are experiencing a new and sustained pattern of demand, this is a great opportunity for further investment in the resilience and scalability of underlying systems. Assessing current technical architectures and making plans to move to modern data architectures may provide a step change in both data integration capabilities as well as an opportunity to double down on advanced analytics capabilities.
Scaling advanced analytics capabilities will enable more parts of your organization to derive insight from complex data and, with real time data agility, take meaningful and timely action for your customers. Supporting practices like Continuous Delivery for ML models will help create an ecosystem for sustained growth beyond our current conditions.
We’re seeing a wide range of effects across organizations, driven by different dimensions of changing customer demand. The common factor, however, is that every organization is dealing with unprecedented changes. We have no choice but to be resourceful, think on our feet, and use the environment available to us, much like the pioneers of old.
Organizations need to be able to respond quickly, make decisions at lightning speed, and execute in ways they have never had to before. Data and ML/AI solutions need to be part of the plan in order to respond, decide and execute in the now, in order to be ready for the future.
Solutions that can be built iteratively and in a connected fashion across the various demand change scenarios, will help organizations move fast and effectively and arrive on the other side in good shape.
Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.