If you follow the news, it can seem like artificial intelligence (AI) is added to pretty much everything these days. Yet, it’s worth looking into the details because AI tools can impact the overall processes around it, leading to new business models and processes.
As a trusted partner of clients in many industries, we at the TM Group have followed the ERP landscape for decades, including the various ways in which vendors add AI technology to their products. If you’re curious about implementing AI-enabled ERP solutions in your business or unsure if you can trust them, you’re in the right spot.
How is AI changing the notion of ERP software right now?
As the tools keep evolving, AI will be to ERP software what ERP is to manual processes. Not all implementations of AI will have the same impact on the user experience, though.
For instance, many vendors are embedding AI chatbots or AI-based voice commands into their solutions. While these will certainly deliver a level of greater intractability with ERP system’s user interfaces, especially for less technically experienced teams or those simply needing to operate hands-free, they merely add an assistive layer to the user experience, rather than changing the fundamental interaction with the ERP software system.
In other cases, AI-embedded ERP tools already offered in systems allow users to leverage various features immediately. As an example, Microsoft Dynamics 365 Business Central already includes AI functionality. Microsoft, which has invested $13 billion in its partnership with OpenAI, brings the power of AI to small and medium-sized businesses with AI-powered Microsoft Dynamics 365 Copilot and their Business Central ERP application, enable companies to work smarter, adapt faster, and perform better. With the AI tools in Business Central, clients can automate repetitive tasks. Improve customer service, anticipate business challenges, and enhance management’s decision-making.
At their core, these new features often aim to automate repetitive tasks such as data entry and inventory management. Sometimes, they’re used to mine data at a depth that simply wouldn’t be feasible for human employees, allowing an ERP system to provide real-time, insightful analytics.
However, it’s essential to keep in mind that an AI-powered ERP system still has its limitations and that most tasks should only be automated with human oversight.
A few examples will show where to draw that line. At this point, you can already automate your invoice processing with a precision that most human workers could never achieve. That’s because it’s a fairly predictable business process. In fact, its predictable nature may even be the reason why it’s often attached to human error.
On the other hand, AI may technically be able to match candidates with your most recent job postings or deal with complaints in customer service. However, it’s probably not advisable to completely outsource this task to conversational AI, given that you often won’t have full insight into the decision-making process. A biased protocol might lead to ethically questionable decisions at best and make you liable at worst.
So yes, AI will significantly change our notion of what ERP is and does for years to come, but it doesn’t mean we can put everything on autopilot.
Does AI-enabled ERP make sense?
Due to the vast amount of choices in ERP with varied AI capabilities, it’s only natural that you’ll face the decision of how and where to leverage AI at some point. Should you choose an AI provider that seamlessly integrates with your ERP or rather use AI parallel to your ERP solution?
While there are additional ethical and legal questions to consider when implementing AI tools, implementing an AI-enabled ERP platform is always advisable due to both AI and ERP’s transformative natures. Think of AI as an internal combustion engine. It can generate power outside of a car, but you’ll see the biggest advantages as part of it. Similarly, AI will augment the capabilities of a traditional ERP platform, providing your business with a competitive edge over those relying on manual processes.
Let’s say retail chain A uses AI algorithms for demand forecasting, and retailer B doesn’t.
Retailer A could then employ machine learning algorithms to analyze historical sales data, considering various parameters like seasonality, trends and correlations with external factors. Since they wouldn’t depend on manual data analysis, they could continuously integrate real-time data sources like weather forecasts, social media trends and economic indicators to adjust predictive models.
By more accurately predicting demand, the model could suggest optimal inventory levels for each product, ensuring the right products are available at the right locations and times.
Retailer B would also analyze sales data, but due to the limits of every human employee, they might miss subtle patterns or correlations between various data points. It’s also likely that they would use statistical models like moving averages to forecast future demand.
However, these methods might struggle to capture complex relationships between variables or adapt quickly to changing external factors. As a result, factors like weather or economic conditions might be considered a luxury and will not be factored into their models. Their less accurate forecasts make them more prone to overstocking and stockouts, leading to potential losses and missed sales opportunities.
Not to mention, AI models don’t need to share each company’s confidential information to keep learning. That means they’ll be able to suit increasingly specific requirements as developers move into smaller industry niches. And while you should always ensure that each model’s output is accurate and suits your business needs, AI models, as a rule, can handle larger data volumes than any human could fathom, allowing them to scale with your operations as you add features or even train a proprietary model.
It certainly makes sense to consider user access and permission rights to the model, as you would with any new software. That said, AI will provide a level of performance optimization and simultaneous customization that we haven’t encountered before. If you don’t join early, you’ll probably end up losing to competitors who do.
What are some of the benefits of incorporating AI and machine learning into ERP systems?
Time is money, and AI helps you make the most of both. With AI-driven data analysis, decision makers don’t have to wait on the next monthly report to assess financial risks or tailor marketing strategies to recent trends. Instead, they can simply generate automatic reports, whether that’s on anomalies in transactions that might suggest fraudulent activities or on predictive maintenance schedules based on equipment sensors and production planning.
Strategies like these can safeguard your business against legal claims, downtime and security risks – all while improving your bottom line.
Since AI can also be integrated with CRM and marketing automation tools, it can serve to personalize customer experiences. Once you’ve consolidated all systems, these recommendations can bridge the gap between recent market data or customer feedback and your marketing and inventory strategies.
By leveraging AI in inventory management, you’ll realize significant cost savings by minimizing excess inventory holding costs and improving supply chain efficiency through streamlined operations and reduced wastage.
Ultimately, what those benefits will look like strongly depends on your business model, software choices and KPIs. Whenever you choose to incorporate AI and machine learning into your operations, it’s worth determining goals early on and tracking them as you move forward.
Which challenges and limitations do you need to be aware of using AI within your ERP?
Implementing AI functionality with any ERP system presents a promising avenue for efficiency and innovation, yet it’s not without its challenges and limitations. Chief among these are the inherent risks associated with handling sensitive data within AI-driven processes. Data breaches and privacy concerns loom large, demanding robust strategies to mitigate these risks. Encryption, strict access controls and ongoing monitoring are vital components of safeguarding sensitive information.
Another hurdle lies in the need for employees to cultivate AI-related skills and knowledge. Investing in comprehensive training programs or hiring AI expertise can bridge this gap, ensuring that the workforce is equipped to harness the full potential of AI within the ERP environment.
The implementation process itself can pose its own set of challenges, especially when meshing AI capabilities with ERP capabilities and data sets. Selecting the right AI-embedded ERP solutions demands meticulous evaluation, considering factors like compatibility, scalability, and customization options. A seamless implementation process calls for a strategic roadmap and close collaboration between IT teams and business stakeholders.
However, cost implications can’t be overlooked. While the upfront costs for AI-embedded ERP might seem daunting, the long-term benefits often outweigh these initial expenses. Improved accuracy, streamlined processes, and data-driven insights contribute significantly to operational efficiency and competitive advantages.
Speak with an expert today to learn more about AI ERP implementation
Navigating the terrain of AI as well as ERP necessitates a balanced approach. It demands proactive measures to mitigate risks, investment in skill development, careful selection of business solutions, and a thorough assessment of costs versus benefits. With diligent planning and strategic execution, harnessing AI’s potential within ERP systems can pave the way for transformative growth and enhanced performance.
If you’re curious about the benefits your enterprise could reap from automation, get in touch with our expert team to discuss a customized solution tailored to your needs.
