ith the pandemic crisis, there have been significant changes that affect human behavior, like physical distancing and remote work. Three key developments have been forced to accelerate and exemplify different ways to leverage emerging technologies like Machine Learning (ML) in business:
-Maintaining Customer Communications during the crisis, e.g., upgrade channels for bi-directional conversations, monitor and analyze changing behavior, personalize content and messaging on all channels.
-Flexible Production and Supply Chain, e.g., explore sourcing alternatives, improve forecasting & planning, leverage real-time event information.
-Operational Resilience, e.g. assess current vulnerability levels, increase security, accelerate and prioritize reaction.
Before the pandemic, decision-makers often argued why it was really hard to do the above and with a lack of pressure, business cases were often shaped unattractively. But now that some of those were forced upon us, was it really that hard?
After an initial, quite short shock phase when COVID-19 came quickly, followed by survival considerations (and death for weaker setups), businesses had to decide quickly how to maintain their customer interactions, particularly through digital channels. This included having a feedback or bi-directional communication path to mimic physical interaction and more complex exchanges like negotiation and settling. To differentiate vs. the vast number of online offers with blurred frontiers, organizations had to quickly leverage intelligence to further personalize interactions and messaging with their prospects, customers, and partners.
Established customer experience tools, including machine learning, could not only provide this out-of-the-box but also recommend, e.g., the next best action towards a specific customer or segment. Similarly, mature algorithms and tools already exist today for financial and asset management, supply chain improvements and flexibility, and attracting, retaining, and developing talent.
Organizations had to digitalize quickly across most internal and external processes, but there were many tools already available that might not have been on their radars before the pandemic. Several large IT solution providers and niche providers had already built them into their large business software suites.
And while many businesses might not feel like they can control what happens next, they can always choose how they respond. When looking at technology and related processes specifically, a pandemic shines a spotlight on the weakest links across systems. Machine Learning can help focus on the biggest potential threats (by helping to estimate probability and impact) and accelerate reaction to those events. A field where some industries are already advanced is AI-enhanced IT security. Identifying normal thresholds and alerting when something happens outside of those is a well-advanced application.
Organizations everywhere are rebuilding and recovering from this disruption by reconfiguring their supply chains, moving customer and employee engagements to a virtual world, and closing their financial books remotely. A strong recommendation is not to try to build the underlying systems from scratch as the businesses should focus on their goals rather than the underlying technology. There are strong established IT vendors and with the Cloud, the required flexibility has increased a lot over the last few months and years.
I see two particular categories that can be supported by emerging technologies and that some organizations are currently overlooking a bit due to recent turbulence:
Targeted innovation. We all need to be prepared to manage all sorts of changes whenever and however they occur. Machine Learning can help identify and visualize actions with a high impact on the business model, quickly adapting to recent changes that are not yet well understood by human experts. IDC and others have recently shown that focus on ROI is back high on the decision maker’s priority list. Assessing a pragmatic case and providing the confidence to kill too fancy endeavors in the short term can be well supported by ML.
Modern user experience. Let’s face it, Data Scientists are still a scarce resource for many organizations. IT providers have been putting a lot of focus to create user interfaces for emerging technologies that “citizen scientists” can use. E.g., a salesperson can do simple analyses on markets and segments or a finance expert can play with smart scenarios by dragging and dropping, while the software chooses the best-suited machine learning algorithm in the background. Same, for example, in Human Resources. This combines the knowledge of the respective expert with the power of AI technology. The user interface plays a key role here and if done well, it supports evolving organizational structures and changing roles and responsibilities. Besides, the user experience can nowadays often be personalized to fit culture and priorities—as humans will continue to adapt technology to their needs and follow the path of least resistance.
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Rapid Digitalization Was Easier Than We Expected
November 5, 2020
W
ith the pandemic crisis, there have been significant changes that affect human behavior, like physical distancing and remote work. Three key developments have been forced to accelerate and exemplify different ways to leverage emerging technologies like Machine Learning (ML) in business:
-Maintaining Customer Communications during the crisis, e.g., upgrade channels for bi-directional conversations, monitor and analyze changing behavior, personalize content and messaging on all channels.
-Flexible Production and Supply Chain, e.g., explore sourcing alternatives, improve forecasting & planning, leverage real-time event information.
-Operational Resilience, e.g. assess current vulnerability levels, increase security, accelerate and prioritize reaction.
Before the pandemic, decision-makers often argued why it was really hard to do the above and with a lack of pressure, business cases were often shaped unattractively. But now that some of those were forced upon us, was it really that hard?
After an initial, quite short shock phase when COVID-19 came quickly, followed by survival considerations (and death for weaker setups), businesses had to decide quickly how to maintain their customer interactions, particularly through digital channels. This included having a feedback or bi-directional communication path to mimic physical interaction and more complex exchanges like negotiation and settling. To differentiate vs. the vast number of online offers with blurred frontiers, organizations had to quickly leverage intelligence to further personalize interactions and messaging with their prospects, customers, and partners.
Established customer experience tools, including machine learning, could not only provide this out-of-the-box but also recommend, e.g., the next best action towards a specific customer or segment. Similarly, mature algorithms and tools already exist today for financial and asset management, supply chain improvements and flexibility, and attracting, retaining, and developing talent.
Organizations had to digitalize quickly across most internal and external processes, but there were many tools already available that might not have been on their radars before the pandemic. Several large IT solution providers and niche providers had already built them into their large business software suites.
And while many businesses might not feel like they can control what happens next, they can always choose how they respond. When looking at technology and related processes specifically, a pandemic shines a spotlight on the weakest links across systems. Machine Learning can help focus on the biggest potential threats (by helping to estimate probability and impact) and accelerate reaction to those events. A field where some industries are already advanced is AI-enhanced IT security. Identifying normal thresholds and alerting when something happens outside of those is a well-advanced application.
Organizations everywhere are rebuilding and recovering from this disruption by reconfiguring their supply chains, moving customer and employee engagements to a virtual world, and closing their financial books remotely. A strong recommendation is not to try to build the underlying systems from scratch as the businesses should focus on their goals rather than the underlying technology. There are strong established IT vendors and with the Cloud, the required flexibility has increased a lot over the last few months and years.
I see two particular categories that can be supported by emerging technologies and that some organizations are currently overlooking a bit due to recent turbulence:
Targeted innovation. We all need to be prepared to manage all sorts of changes whenever and however they occur. Machine Learning can help identify and visualize actions with a high impact on the business model, quickly adapting to recent changes that are not yet well understood by human experts. IDC and others have recently shown that focus on ROI is back high on the decision maker’s priority list. Assessing a pragmatic case and providing the confidence to kill too fancy endeavors in the short term can be well supported by ML.
Modern user experience. Let’s face it, Data Scientists are still a scarce resource for many organizations. IT providers have been putting a lot of focus to create user interfaces for emerging technologies that “citizen scientists” can use. E.g., a salesperson can do simple analyses on markets and segments or a finance expert can play with smart scenarios by dragging and dropping, while the software chooses the best-suited machine learning algorithm in the background. Same, for example, in Human Resources. This combines the knowledge of the respective expert with the power of AI technology. The user interface plays a key role here and if done well, it supports evolving organizational structures and changing roles and responsibilities. Besides, the user experience can nowadays often be personalized to fit culture and priorities—as humans will continue to adapt technology to their needs and follow the path of least resistance.
