“The changes brought by artificial intelligence and machine learning will help the companies that embrace them and create barriers for those that do not,” – Jeff Bezos
Today, artificial intelligence (AI) is not just a technological trend; it is a necessity for businesses striving for efficiency, scalability, and a competitive edge. According to PWC, 54% of company executives report that AI has already significantly boosted their business productivity. And this is just the beginning – it is projected that from 2023 to 2030, the average annual growth rate of the AI industry will reach 37.3%.
However, just like a hammer that can help a skilled craftsman build a sturdy structure but remains just a piece of metal in untrained hands, AI only works when managed correctly. The primary “building material” for AI is data. The accuracy, relevance, and quality of that data determine whether AI will benefit the business or, conversely, lead to false predictions and poor decisions. Flawed or incomplete data is like crooked nails – even with the best tools, the outcome will fall short of expectations. That is why companies leveraging AI must focus not only on algorithms but also on strategies for collecting, analyzing, and structuring data.
But why should businesses optimize data systems that were already working “well enough”? Is it worth risking process changes for the sake of artificial intelligence? Let’s take a closer look.
The Value of AI Solutions for Business: Concrete Benefits
In a world where data volumes are growing exponentially, businesses can no longer rely solely on experience and human resources. Even if you gather an entire team of analysts, they won’t be able to process and analyze Big Data as quickly and accurately as AI. But that’s not the main point – the real value lies in not wasting human resources on processes that can be automated. Artificial intelligence doesn’t replace people; it complements them by taking over routine tasks and allowing employees to focus on strategic decisions.
Proper use of AI in business offers numerous benefits: improved customer interaction, higher operational efficiency, enhanced security, innovative products and services, and more. Companies that actively adopt AI are shaping the future of their industries, becoming market leaders, strengthening their positions, and increasing their value not only in the eyes of consumers but also among potential employees and partners.
It might seem that AI is only effective for large IT companies, but this is a myth. Artificial intelligence has already adapted to most business processes, offering unique features for different industries. For example:
- Recommendation Systems: AI analyzes users’ past preferences and generates further recommendations based on them.
- Chatbots and Virtual Assistants: AI automates customer service, providing quick answers to common questions.
- Demand Forecasting: AI helps create more accurate demand forecasts, enabling better planning and resource allocation.
- Manufacturing and Logistics Processes: AI improves inventory and supply management, ensures product quality control, and optimizes delivery routes, considering external factors.
- Personalized Advertising: AI personalizes customer experience by identifying audience preferences, helping create the most relevant ads for each consumer.
- Financial Analysis: AI quickly detects fraudulent activities in banks and other financial institutions and automates stock market trading.
- Healthcare: AI is increasingly used to diagnose diseases and forecast demand for medical services.
- HR and Recruitment: AI is effectively used to analyze resumes, rank candidates, and conduct initial interviews, etc.
Before selecting an AI solution, it’s important to determine exactly what you expect from it – in other words, what benefits you want for your business and which pain points AI should address. This will help you understand which type of artificial intelligence best suits your needs, as AI, like any other science, has its own branches. Currently, the most common types are cognitive services and machine learning.
Cognitive services aim to simulate human brain activity – for example, voice recognition, image recognition, natural language processing (NLP), sentiment analysis, and content generation.
Free generative tools such as ChatGPT and Microsoft Copilot are already widely used to optimize business processes. ChatGPT can indeed automate some aspects of a company’s operations by handling routine tasks. However, for precise processes like demand forecasting, such a tool may not be the best solution. When using free generative tools, it’s essential to remember that their algorithms are stochastic and unpredictable – you will never get the same response twice, even if you don’t change the prompt. There is also the risk of AI “hallucinations” – cases where the tool generates information that isn’t backed by real facts. Additionally, these tools do not guarantee the security of your confidential data, as the information you input is openly used for further model training.
At present, more complex solutions based on machine learning algorithms provide the greatest value for businesses. One example is SMART Decision HUB, which offers tools tailored to company needs, such as demand forecasting, price optimization, workforce management, marketing process optimization, and personalized customer interaction. If you are unsure how to choose the most relevant solution for your business, it is worth consulting with vendors. By collaborating with experts, you can make your data work for you rather than the other way around. Vendors can not only implement the required tool but also help prepare your existing data for its use. In some cases, they can even advise on which data you are missing to fully leverage the desired AI solution.
One of the key advantages of AI-based solutions is that they enable businesses to act proactively rather than reactively. However, for any AI solution to work correctly and deliver the most accurate results, it needs up-to-date, well-structured data – something that an ERP system can effectively provide.
ERP Systems as a Data Source for AI: How It Works
Before implementing artificial intelligence (AI) in your business processes, it’s crucial to remember one thing: AI doesn’t create; it reproduces results based on the information it receives. Like any science, AI is built on mathematical algorithms and precise calculations. The more accurate the input data you provide, the more accurate the results will be. If AI receives unstructured or incorrect data, its output will also be flawed, causing chaos in processes instead of optimizing them.
However, it is possible to automate chaos – but only after structuring it. That’s where ERP (Enterprise Resource Planning) systems come in. They organize and consolidate information about all business processes in a company: finances, inventory, production, supply chain, and more. ERP systems, such as Dynamics 365 Business Central, provide a single access point to relevant and reliable data, which can be effectively used to train and enhance AI models.
Using ERP system data efficiently with AI tools allows businesses to analyze large volumes of structured information, uncover hidden patterns, and provide predictive insights to optimize operations. Let’s explore how structured ERP data can help improve AI performance:
- Financial Data: ERP systems handle accounting, expense management, budgeting, and financial reporting. AI can analyze this data to forecast cash flows, detect financial risks, predict fraud, and optimize expenses.
- Pricing: ERP systems store information about company sales, promotions, budgets, and marketing strategies. Based on this data, AI-integrated solutions – such as SMART Price Insights – can automate and optimize business pricing strategies using a demand-driven strategy.
- Production Data: ERP systems consolidate information on production planning and control, product or service quality, and sales data. Based on these insights, AI tools like SMART Demand Forecast can improve production efficiency by accurately forecasting demand.
- Logistics Data: ERP systems contain information about supply chain management, warehousing, and product distribution. AI can analyze this data to optimize delivery routes, manage inventory, and forecast demand.
- Sales and Marketing Data: ERP systems process customer orders, sales history, and marketing campaign performance. AI solutions like SMART Personal Engagement can personalize customer offers, predict market trends, and assess the effectiveness of marketing strategies.
- HR Data: ERP systems manage personnel information, including employee performance and training. AI can utilize this data to optimize work schedules and forecast staffing needs. For example, SMART Flexi Scheduler efficiently manages team working hours by evenly distributing the workload.
When using ERP systems as a data source for AI, it is essential to understand that data structure is critical to the effective application of AI. But how can this structure be ensured? SMART business has chosen to implement a universal data structure, which has significantly improved the efficiency of its AI-based solutions. So, what makes this structure unique?
A Universal Data Structure (UDS) is an approach to organizing information that ensures consistency, standardization, and accessibility for different systems and users. It involves using uniform formats, structures, and standards for storing and processing data, enabling the integration of diverse information sources into a cohesive whole.
As mentioned earlier, AI operates on mathematical algorithms. Using data that hasn’t been adapted to UDS is like trying to solve the equation a + b = c with variables x and y. It may be possible, but it resembles walking from Germany to France when you could have taken a plane or at least a train. Unstructured data is almost impossible to use for prompts in standard AI-based solutions – even more so because crucial information can get lost simply due to its lack of adherence to a standard structure. That is why a crucial step in preparing to implement AI into business processes is the complete structuring and adaptation of existing data – a task where ERP systems and vendors can be of great help. Vendors, such as the experts at SMART business, can provide guidance on the best ways to structure available information, while the ERP system serves as a source of essential data for implementing AI-based solutions. The more meticulous and detailed the preliminary work on data is, the faster and more effective the results of using artificial intelligence will be.
How Dynamics 365 Business Central and SMART Demand Forecast Enhance Forecast Accuracy – A Practical Example of ERP System Integration with an AI-Based Solution
Modern companies striving to stay at the top of the market are not just competing for customer loyalty – they aim to anticipate customer needs. Demand forecasting plays a crucial role in this, serving as the key to effective business management across production, procurement, logistics, and marketing. However, globalization and the growing demand for personalization lead to a rapid increase in the volume of data businesses must analyze to achieve accurate demand forecasting. Manual methods are no longer sufficient – they are slow, error-prone, and incapable of efficiently processing large data volumes. AI-based solutions are changing the game by quickly analyzing data and identifying patterns. They help not only in forecasting demand for new products based on analogs but also in smoothing out anomalies, improving the accuracy of calculations. Demand forecasting powered by artificial intelligence allows companies to respond quickly to changes, optimize processes, and manage resources effectively – a critical factor for success throughout the entire supply chain.
Accurate demand forecasting has a greater impact on a company’s operations than it might initially seem, as all business processes are closely interconnected. For example, a single failure in demand forecasting can easily lead to issues in the HR department. How so? An inaccurate demand forecast may result in a shortage or surplus of products, directly affecting production. If production runs out of raw materials or equipment due to incorrect calculations, delays will occur, which in turn may cause overloading or shortages of staff, creating problems for HR. Subsequently, this can impact financial performance, logistics, and the level of customer service.
These challenges can be overcome with SMART Demand Forecast. This machine learning-based system helps to:
- Improve forecast accuracy: The artificial intelligence and machine learning algorithms integrated into the solution take into account an optimal set of factors that influence forecast precision.
- Reduce inventory levels and optimize availability: Advanced analytics algorithms enable accurate demand forecasting, ensuring product availability when needed without overstocking. This reduces write-offs, optimizes inventory, increases profit, improves service, and enhances business reputation.
- Lower labor costs: SMART Demand Forecast optimizes planning and analytics, reducing the workload on teams, minimizing human error, and shifting the focus from routine tasks to strategic development.
- Enable prompt business decision-making: Power BI’s analytical capabilities support timely management decisions based on up-to-date data. SMART Demand Forecast allows retrospective sales analysis, quality assessment of input data, promo sets, and compensated sales.
One of the key advantages of the SMART Demand Forecast solution is its ability to perform scenario forecasting based on a company’s specific ERP system data. This approach allows businesses to theoretically review potential outcomes of various strategies without investing time and resources upfront. Based on the results of different scenarios, company leaders can make the most beneficial management decisions regarding future development strategies.
When forecasting demand, the artificial intelligence algorithms in SMART Demand Forecast consider all company-specific details. For example, for McDonald’s Georgia, a SMART business client, forecast aggregation was adjusted to a weekly basis for each retail outlet instead of the previous approach, which aggregated forecasts for the entire network. In practice, this adjustment helped achieve 83% sales forecast accuracy for each location based on four weeks of aggregated weekly data.
According to research, accurate demand forecasting can lead to:
- A 15% increase in sales volume
- A 20% improvement in customer satisfaction
- A 7% reduction in debt risk
- A 15% boost in competitiveness
- A 7% optimization of production planning
- A 15% increase in marketing efficiency
- A 25% growth in profit
* Data provided by McKinsey & Company, Gartner, IDC, Ipsos, and Dr. Steve Morlidge – “The Little Book of Operational Forecasting”.
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