What Problems are AI to Solve?

What Problems are AI to Solve?

Many businesses across the Nordic region are looking to capitalize on emerging opportunities in AI. However successfully implementing AI solutions, both into company operations and stakeholder interactions, first requires a deeper understanding of the problems at hand. This should be followed by rewiring the whole thought process you take towards tackling those problems.

In today’s environment, businesses will typically have access to rich sources of information; many previously untapped. Making meaning out of it requires the right infrastructure in place as well as aligning business challenges with the data that can help address them. Understanding how AI can drive sustained, incremental progress across your business operations is critical in realizing the large-scale transformation that AI applications can bring. 

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Human behavior and AI
Small data points alone provide little insight. Instead, the picture is brought into greater clarity when a collection of disparate data sets is pulled together to build a fuller representation of the situation. An example of where this approach has been successfully applied is a large ocean-based supply chain provider, for which we created a modern data platform

A shipping company with a rich history in the industry, the company wanted to ensure that they continued to maintain its leading position moving into the digital era. More specifically, the company wanted to gain greater control over the sustainability of its operations.

Working in partnership, we were able to install an AI platform that helped to consolidate and analyze data from across a fleet of 55 vessels. Using sensor technology, the onshore data platform helped to oversee performance data from various sources including fuel consumption and cooling operation, and pinpoint opportunities for improved performance and sustainability.

By orienting the platform towards a clear business imperative (reducing carbon emission), the company was able to gain accurate insight into which parts of its operations were the biggest contributors to waste issues and at what times of day. In turn, this enabled better decision-making leading to fundamental – potentially multi-generational – changes in the way it operates. 

A relationship built on collaboration
Businesses may hold reservations over incorporating AI into their wider strategies as they feel ill-equipped to draw the divide between human and machine operations. The problem with this perspective is that it’s inherently antagonistic. In its place, AI should be viewed in terms of augmenting human work, rather than outright replacing it. 

AI applications need to be developed for employees, optimizing pre-existing systems and processes while streamlining the means of employee collaboration. Such applications provide the capabilities to handle low-value, repetitive tasks more quickly and reliably than human workers would be able to do, while also tackling higher-value tasks such as data collection and real-time analysis. These applications then deliver insight to employees on a granular level to support better internal decision-making and the next best action for customer interactions.

This is the approach we’ve taken with an agriculture business with the ambition of advancing from a labor-intensive business towards becoming knowledge based and connected in its approach. The creation of an AI-based operations center enabled automation at scale across farming sites, freeing up resource for more creative and cerebral tasks and, in effect, liberating rather than limiting human involvement in the processes.

AI-fueled customer-centricity
The planning, implementation and application of AI systems should, directly or indirectly, further the goal of becoming a more customer-centric organization and continually offering a higher quality of service. 

A popular consumer goods company understood the importance of organizing data around the customer. The company operated a fragmented set of data systems, with more than 50 different ERP systems across different areas of its operations, including supply chain, production and sales. 

We consolidated these systems and improved collaboration across its 108 factories. Now, with advanced capabilities such as Computer Vision and IoT embedded across its value chain, the company is better positioned to deliver on the needs and preferences of its end customers.

Taking this type of approach lets you focus on what is truly important to your customers and be able to refine individual processes based on collected data. This means that you will be able to see both macro- and micro-improvements in relation to your customer interactions in a continuous feedback loop.

Mapping AI with specific problems
Gaining oversight and control of the granular data sources available to you can lead to massive business benefits over a longer time frame, but only when properly planned, understood and accurately implemented. 

The Nordic businesses that view AI as a specific solution to a specific problem generate the most success. AI is about activating the assets you have available to you and making them work in a more intelligent, efficient manner. By identifying and defining the problems that exist in your organization, and how these problems are hindering the outcomes you desire for your end customers, you can start to see big rewards from relatively small and incremental changes.

For more information about how AI can help your business and the solutions potentially available to you, please take a look at the eBook we have created here.