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AI-powered Integrated Business Planning is crucial to post-Covid recovery

AI
(Image credit: Shutterstock / metamorworks)

Artificial Intelligence (AI) has moved out of the lab into the boardroom. From analyzing and predicting customer behavior to powering operational automation, the benefits of using AI in global enterprises are now well-proven. Indeed, adoption is rising quickly. A recent study by McKinsey has found that businesses plan to invest even more in AI in response to the Covid-19 pandemic, and the acceleration of all things digital.

But while companies are rushing to AI, not everyone is seeing the payoff. Indeed, even for the trailblazers, there’s work to be done. For many organizations, a tendency to think of AI as a discrete business tool, rather than a driver of holistic change, is still limiting the return on investment. And many are still not able to harness the power of AI to fundamentally improve decision-making. 

So, how can companies do better and reap the rewards of developments in AI and Machine Learning?

Helping supply chain management evolve and adapt

During the pandemic’s first wave and the lengthy lockdowns, many global enterprises struggled to make supply chain decisions. Supply chain, commercial and financial teams often work using legacy systems – relying on tools like Microsoft Excel to gather and analyze data. It is well known that historical data isn’t always a good indicator of what’s happening at any given moment. This proved particularly problematic in the midst of a global pandemic when change was happening fast, and real-time information was needed.

This served as a wake-up call, and has prompted the most forward-looking organizations to look to technology partners to help them make smarter decisions based on better, and more up to date, information. Using the power of AI and Machine Learning, companies are able to add “drivers” to historical data, such as publicly available data sets such as weather information and events, etc. and help create better forecasting models. By turning that data into knowledge, companies can make more accurate demand predictions and improve their overall planning.

Powering the recovery of post-pandemic retail

The Covid-19 pandemic sent shockwaves through the retail sector, as all non-essential shops were forced to close their doors to the public, and retailers needed to quickly pivot online. For many, AI-powered this move – helping retailers to understand and map customer behavior, preferences and habits, enabling them to target, attract and retain online shoppers – and manage stock, supply chains and logistics accordingly.

Today, AI is helping smooth the transition “back to normal”. As retailers reopen, it’s critical for them to understand where and when store traffic will increase again in order to allocate the right supply to the right channel and balance online and offline operations.

AI-powered customer data analysis will play a vital role in helping retail companies understand customer behavior in a post-Covid world. We know that customers may be more hesitant to return to stores, perhaps wanting to shop at different times, or in different ways than before. We know that behavior differs based on many factors such as geography, or even news on the progress or decline of the virus, and AI can show us what is happening now, and predict what is most likely to come.

Introducing the “Digital Brain” 

AI is powering better and greater data use. But data is only as good as what companies are able to do with it. If you can’t access data quickly enough, or it’s fragmented or incomplete, it ceases to be as useful as it could be.

Many organizations lack a “digital brain” - a continuous, automated process of learning from data across all business units, departments, as well as from outside of the enterprise. Yet such capabilities are crucial in making an organization’s digital transformation plans come to life. There are solutions available that enable end-to-end connectivity, building a digital representation of the enterprise and turning data into knowledge, facilitating better and faster decision making. They help customers improve their end-to-end visibility across all the nodes in the enterprise – then turn data into actionable insights. They also power “post-game” analysis so companies can look at what they did, what worked and what didn’t, helping them to flex, adapt, grow and learn.

A digital brain that can access and analyze company-wide, real-time data enables businesses to make better decisions and is a key capability for moving towards a more digital operating model. For example, one of the largest retailers in the world uses these tools to improve its on-shelf availability, dynamically checking stock and ensuring fast replenishment. The huge size of this global business meant that manual or even automated stock takes are simply too unwieldy. Instead, the team needed AI to understand the full end-to-end network and allow it to make data-driven decisions when constraints occurred.

Technologies like AI and Machine Learning are powering the better understanding of customers, the ability to better manage supply chains and more, and are helping many navigate the challenges posed by the Covid-19 pandemic. However, the value of that data is lost when decision-making isn’t fast enough.

What has made digital giants like Google, Amazon and Netflix so successful is their ability to connect the dots between data from across their businesses, and do it fast. This is a critical piece of the digital transformation puzzle, and also the one where companies most often fall short. It’s also here where companies must improve.

Closing comments

It is clear that AI can deliver tangible results and business value fast. If companies are able to harness the power of a digital brain, bringing together real-time, comprehensive data from across the business and beyond and using it to take action and improve decision making. One of the largest retailers in the world uses these capabilities to improve its on-shelf availability, dynamically checking stocks, and ensuring fast replenishment. The huge size of this global business meant that manual or even half-automated solutions would not scale. Instead, the team needed AI to understand the full end-to-end network and allow it to make data-driven decisions when constraints occurred.

With a digital brain, organizations are armed with greater self-awareness and interdisciplinary thinking to drive radically new business models and reap the rewards of digital transformation.

Igor Rikalo, COO, o9 Solutions