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We’ve reached a moment in time when AI has truly captured the zeitgeist. It dominates discussions on everything from its role in creativity to its potential impact on the job market.
But how can AI-based solutions drive value for global companies?
Before we understand how AI can play a part in business development, we must step back and look at what it is: a tool for innovation and problem-solving.
I firmly believe that innovation based on real-world needs will always be valuable. Companies that can provide genuine solutions to existing problems will undoubtedly succeed. Those businesses that can bring AI-based solutions to the market are likely to achieve better financial results for their investors and stakeholders.
This is why AI is currently the most critical topic of discussion within companies. By analyzing complex data, identifying patterns and trends, and generating insights that help decision-making, AI is becoming increasingly relevant in the business world.
However, not every company is successfully integrating AI into its decision-making and management processes.
Successful cases of risk management and customer relationship solutions are on the rise, especially in anticipating demand, optimizing logistical routes, and detecting and preventing flaws in the retail process. But AI is constantly evolving, with new breakthroughs and advancements emerging regularly.
As such, companies must stay abreast of the latest developments in AI and explore innovative ways to implement them in their business strategies, solving problems that add value to the stakeholders. Only then can they stay ahead of the curve and gain a competitive advantage in the market.
However, finding the right balance between staying ahead of the curve and remaining aligned with stakeholders’ expectations can be tricky. Leaders must ensure that they make data-based decisions that benefit their business and customers. AI is not just a tool for innovation – it’s a tool for business agility.
Machine learning can be used to generate more accurate sales forecasts, which can then be segmented by item or category and grouped or personalized by sales channel and store. In addition to sales history, market data can also be incorporated to create more robust models. Even external data – from weather to social media trends – can be applied to gain new insights.
All pricing stages can be effectively planned and managed by applying predictive analytics and optimization features. This technology can provide improved pricing, promotion planning and management throughout the merchandise lifecycle. If we take the example of price sensitivity analysis, AI can be used to analyze how customers respond to changes in price, enabling businesses to identify an optimal price point for their products or services.
By using flexible AI solutions, we can improve industrial processes. For example, with predictive maintenance, AI algorithms can be used to predict when machinery is likely to fail, enabling maintenance teams to fix equipment before it breaks down, thus reducing downtime and maintenance costs, and increasing productivity
By using AI to estimate the probability of an employee leaving the organization, interpretability techniques can be used to identify organizational drivers that are correlated with higher attrition rates. Everything from exit interviews to sentiment analysis can be used to identify patterns. This information can drive the creation of effective retention policies.
Image of Viviane Martins, CEO of Falconi. Source: Falconi
The above examples address some of the daily challenges that global companies face.
However, to fully explore AI, companies need to establish a clear goal of what they want to achieve, plan and execute well, and ensure that the company’s goals are reflected in the teams. Otherwise, AI can ultimately cause a systemic imbalance. Without the right expectations and without traceability of AI production, companies will be wasting their time and resources.
Moreover, I believe that companies like Falconi must lead the way in connecting management excellence with disruptive innovation in order to generate real value.
Falconi has been working to develop and solve challenges in various sectors of the economy, including retail, manufacturing, services, people, logistics, finance and agribusiness.
One common factor across these sectors is that our performance is always focused on generating sustainable value and concrete results in a balanced way within the company’s systemic vision.
An AI solution cannot produce true impacts without establishing this management infrastructure.
For companies just starting to explore the possibilities of artificial intelligence, I advise that – before venturing into AI – they focus their attention on the issue of data.
Companies should enable a data culture within their teams, encouraging them to capture, process and manipulate this data. This should happen on a day-to-day basis, whether through business intelligence or an analytics platform.
Once you allow your teams to do this work, it’s possible to start gaining some insights. Only then does it become realistic to think about developing an AI solution to help the company look further ahead – or to think about building something automated or personalized.
The golden rule is that companies must find the balance between staying ahead of the curve and remaining aligned with stakeholders’ expectations. Otherwise, facing the changes of a new economy and enduring with sustainable results can be daunting.
Main image of a robot hand reaching through a laptop: Blue Planet Studio/Shutterstock
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