Four ways AI can create new opportunities across companies’ value chains
There is no single way in which AI brings value, which can make it hard for companies to choose how they will implement AI.
From cost reduction to a generation of new revenue, Artificial Intelligence (AI) is more and more generating new value for companies. There is no single way in which AI brings value, which can make it hard for companies to choose how they will implement AI.
When analyzing the value chains of companies, we can identify four ways AI can create value:
Real-time forecasting and anticipating events
As proven in a previous article we wrote, AI can improve your predictive capabilities and better manage your business. Machine Learning, an application of AI that provides systems with the ability to automatically learn and improve from data gives companies the power to better make forecasts. For example, the prices of raw materials in the chemical industry are very volatile and hard to predict, but thanks to Machine Learning we succeed with 98% accuracy to predict the price of 1 chemical 3 months in advance. This gives our client a considerable competitive advantage in the procurement negotiations (such as arbitrage) and for supply chain optimization. Another example is the planning of demand. For customers in retail and wholesale, we have developed models that can predict how the demand will evolve, with an accuracy higher than existing forecasts.
Operate at higher productivity, a lower cost and higher efficiency.
AI can help increase the productivity of operations at a lower cost and higher efficiency. People easily associate AI with automation and of course, this is one-way companies could benefit from AI. AI can be introduced as a tool to remove the routine tasks out of people’s job so they can focus on the more creative part of their tasks. But it could help your operations better prevent the impact of a human error or mechanical failure, for example in fast processes A typical example is Robotics Process Automation (RPA) is a disruptive technology that allows organisations to automate business processes in a controlled, flexible and scalable way. Chief financial officers are increasingly automating their invoice approval & control process through RPA, using technologies such as invoice parsing. Another example is predictive maintenance of machines or other assets (think office equipment, company car fleets...). Predictive maintenance uses data from different sources like historical maintenance records, sensor data from machines, or even weather data to determine when a machine will need to be serviced. As a result, operators anticipate when to make a repair, and companies can even avoid damage by performing the right servicing to avoid asset breakdown.
Promote products and services tailored to your target market.
The third way AI can add value to your value chain is by promoting very precisely your products and services to your target market. Within the sales and marketing department, one of the objectives is to know your target markets and clients to the best of your ability. But still, a lot of valuable insights get lost either due to not holding records or due to not knowing there are insights to be found behind particular data. AI is of considerable help to sales and marketing professionals because it can process data at a much higher rate and can also give back insights it found based on the data set.
For example, targeted promotions or product recommendations. As marketers, it is hard to reach every client with the right message. This is why marketing departments benefit a lot from the recommendation generated by AI. AI models recommend products based on the data about customers, their purchasing patterns, their purchasing behaviour, etc. AI has been historically used by sales and marketing departments because of the volumes of data held, and the ROI. Amazon generates ⅓ of its retail revenue thanks to targeted product recommendations. The more a company knows about its target market, the better they could serve them, but in order to do so they need to transform data into actions, and this is where Machine Learning strives.
Provide tailored and more personal customer service.
Customer service is the front desk of your company and has, therefore, a big impact on customer success and customer retention. Being able to have a tailored customer service for every client based on their preferences and feedback is an unreachable dream for many companies, and AI makes companies closer to this objective.
The major impact of AI is that it can interpret customers signals and recommend actions from it. For instance, detect a negative sentiment in an email to flag it to the customer service as high priority email. Or rank incoming calls to customer service depending on the value of a customer for a company (customer lifetime value). Or read an email content and send it directly to the right department to enable faster handling of customer request. Chatbots are another example. AI-based chatbots enable real-time feedback to a customer so to decrease customer service costs and also give customers fast feedback and higher satisfaction.