Humanity Elevated Future Proofing Your Career - ERP Systems, Digital Transformation and Change Management in AI Era

Episode Date: January 12, 2025

MIT CISR’s research on digital culture (2023) emphasizes four key elements that organizations must cultivate to support successful digital transformations:Innovation Mindset: being comfortable with experimentation, having tolerance for risk, being willing to learn, and being able to adaptData-Driven Decision Making: using analytics, making choices based on evidence, using performance measurement, and continually improvingAgile Ways of Working: using an iterative approach, collaborating across functions, making quick decisions, and responding flexiblyCustomer Centricity: focusing on experience, creating value, integrating feedback, and adapting continuallyBy fostering these traits, companies can create an environment that is more open to change and better able to use digital technologies. In addition, these companies can better respond to market changes and customer needs.

All right. So let's dive into this AI powered ERP thing. You've been sending over some pretty intense research. MIT, Harvard, even Michigan State. You're serious about this, huh? Yeah. Well, you know, it got to stay on top of things, right? Absolutely. And the big question you keep coming back to are robots about to like take over finance and leave us humans out of a job. Well, that's the million dollar question, isn't it? Yeah. I mean, the robots aren't quite ready to run the show just yet. But what we are seeing is a big shift from just managing resources to systems that can learn, adapt and make their own decisions, intelligent decisions. OK, so not just spreadsheets that crunch numbers faster, but like a whole different way of thinking about the work. Exactly. And one of the coolest things to me is how enterprise

intelligence has evolved. Think about it. We went from basic reporting, like just looking at what already happened, to trying to predict what's coming next with predictive modeling. And now we've got these cognitive systems. It's like having a co-pilot in the cockpit that can not only analyze the data, but actually suggest the best course of action. Hold on. Cognitive systems. Now that sounds like something out of Star Trek. What does that actually mean for someone like me working in finance? So think of it this way. Imagine your ERP system could understand patterns and learn from experience. It can even make judgments based on tons of data. That's what these cognitive systems are starting to do. Whoa. It's pretty wild. And it's powered by all kinds of AI capabilities, like computer vision that can actually see and understand images, like for quality control. And then there's natural language processing.

So you can talk to these systems like you're sending a text. No more fighting with those clunky interfaces. I can get behind that. But let's get down to brass tacks. What kind of routine finance work is AI actually taking over? Yeah. Well, that's where the lines get a little blurry. You know, RPA, robotic process automation.

It's great for all that data entry, invoice processing kind of stuff, but it's going way beyond that now. Decision intelligence can analyze market trends and even flag potential fraud before it happens. Wait a minute. So is this going to make a lot of finance jobs like obsolete? That's the question, right? And that's where the human element comes in. Every report we looked at emphasized this. It's not about replacing people. It's about giving them superpowers.

Superpowers, huh? Yeah, think about it. Like Michigan State's research. They actually said that companies might fall into a trap if they just focus on the tech and not the people using it. So just buying fancy software doesn't magically solve everything. Exactly. It's like if you suddenly had all these incredible insights and predictions, but your team didn't know how to use them, what's the point?

You need things like change management and upskilling, even redesigning workspaces to support this new way of working with AI. So AI is giving finance professionals superpowers, but they need training to use them responsibly. Exactly. Instead of being stuck doing boring routine tasks, they can focus on the big picture strategic thinking and analysis. They'll be the ones interpreting the AI's insights, making recommendations and driving those big strategic decisions. It's like moving from number crunching to becoming a strategic advisor. OK, let me just make sure I'm getting this right. You're saying AI will change what finance people

do not necessarily eliminate their jobs. Exactly. And the real challenge is how quickly can organizations and the people in them adapt to this whole new world? And how do we make sure people who are used to doing things a certain way don't get left behind? That's a great point. So with all these AI systems getting so smart,

how far away are we from like a totally automated back office? Are we all about to be replaced by robots? Not so fast. Even the most futuristic research from MIT, everyone says there's still a need for human oversight, especially when you're dealing with ethics and compliance. There's tricky situations where experience and judgment are critical. So it's not about eliminating the back office.

It's about making it smarter, more efficient, freeing up humans to focus on being human. Exactly. Think of it as a partnership, not a takeover. And that's where the real excitement is. It's not just about managing resources anymore. It's about using AI insights to drive strategic growth and innovation. Okay, so that's the big picture. Now let's get a little more specific.

You mentioned two ways to approach this, the 90-day quick start and the enterprise scale rollout. What's the difference? Like, how do I even know where to start? Well, it all depends on what the organization is trying to achieve and what kind of resources they have. The 90-day quick start is like dipping your toe in the water. It's about finding those areas where AI can make a difference right away and then implementing a solution quickly, you know, just to prove it works. A little pilot project to get everyone excited. Exactly.

But if you're looking for a complete digital transformation, then you need to go for the enterprise scale rollout. That's a much bigger commitment. It involves a company-wide strategy, a phased implementation plan, and a long-term commitment to change. Like renovating your entire house instead of just repainting one room.

Yeah, exactly. And that means a lot more planning buy-in from leadership and maybe a little hand-holding along the way. And speaking of hand-holding, the research also talked a lot about training, not just learning how to use the new systems, but like understanding how AI works and what it means for your job. just learning how to use the new systems, but like understanding how AI works and what it means for your job. Absolutely. It's like you said before, it's about giving people superpowers, but they need to know how to use them. It's not just about clicking buttons. Right. It's like if your AI can suddenly analyze mountains of data and see trends you might miss. You got to know how to interpret

those insights, ask the right questions, and then make the best decisions for your company. Exactly. It's like learning a whole new language, the language of AI-powered finance. And like any language, it takes time, effort, and the right teacher. For sure. We've covered a lot already. The evolution of ERP, how AI works, the importance of the human element, and these two different approaches. But before we move on, I'm curious. With all this talk about data and algorithms, are we losing the human touch? That's a great question. And it's

something the Harvard research brought up, especially when it comes to customer interactions. AI can definitely personalize things and make them more efficient, but people still want that human connection, you know, especially when it comes to complex or sensitive issues. So it's all about finding the right balance between automation and human interaction. Exactly. AI can handle those routine, repetitive tasks, freeing up people to focus on building relationships, providing empathy and offering that personal touch that only a human can provide. It's kind of funny, actually. You know, we've been talking about how AI is changing the way finance people work.

But the tech behind the ERP systems themselves has also gone through this massive transformation. Remember how we talked about moving from those old systems to the intelligent enterprise? Well, that shift is reflected in the way these platforms are built. So it's not just software updates. It's like a whole new way of building these systems from the ground up. Yeah, exactly. The old way of doing things, that big monolithic architecture where everything's kind of jammed together, it's being replaced by something

way more flexible and adaptable. You know, one of the key concepts here is microservices, which honestly sounds way more complicated than it is. Okay, so break it down for me. What are microservices? And why should someone who spends their day in spreadsheets even care? Imagine you're building a house.

Instead of having this one giant structure where everything's connected, you create these separate modules, like a kitchen module, a bathroom module, a living room module. Each one is self-contained, but they can still, like, talk to each other. That's basically what microservices do for ERP systems. They break down those complex functions into smaller, more manageable pieces. So instead of one giant program, you have a bunch of smaller ones working together. What's the advantage of that? Well, for one thing, it makes the system way more agile. Each microservice can be developed,

updated, and even scaled independently without affecting the whole system. It's like being able to renovate your kitchen without having to tear down the whole house. Okay, I get that. And I'm guessing it also makes the system more resilient. If one part goes down, the whole thing doesn't crash. Exactly. Plus, it's way easier to manage and update. You can add new features or fix bugs in one microservice without worrying about breaking something else in the system.

It's a game changer for keeping up with how fast technology is moving. The research also mentioned something called serverless computing and edge capabilities. Those sound pretty futuristic. What are those all about? So serverless computing is like having an invisible IT department in the cloud. Instead of managing your own servers, you basically rent computing power from these providers like Amazon or Google. It frees up your team to focus on developing and improving the actual applications without getting bogged down in the technical stuff.

So it's like outsourcing the infrastructure so you can focus on what really matters, the functionality and the user experience. Exactly. And edge capabilities are all about bringing the processing power closer to where the data is being generated. You know, think about sensors on a factory floor

collecting data in real time. Instead of sending all that information back to a central server, you can process it right there on the edge of the network, which makes things faster, more efficient, and it opens up all sorts of possibilities for like real-time decision-making and predictive maintenance.

Wow, it sounds like the way these modern ERP systems are built is all about flexibility, scalability, and speed. Yeah. But even with all these separate modules, they still need to be able to talk to each other. Oh, absolutely. And that's where APIs come in application programming interfaces. They're basically the messengers that allow different systems to communicate and share data seamlessly. So APIs are like the glue that holds this whole microservices world together. That's a great way to put it. And the research

really emphasized this API-first approach to building these systems. It means thinking about integration from the very beginning, making sure everything can connect and share data effortlessly. It makes sense. You wouldn't want to build a house and then realize you forgot to put in doors between the rooms. Exactly. And this API-first approach is crucial for creating that interconnected ecosystem we were talking about earlier, where your ERP system can talk to your CRM, your e-commerce platform, your supply chain management tools, everything.

It's about breaking down those silos and letting data flow freely throughout the organization. Okay. I think I'm starting to see the big picture here, but I'm also a very practical person. We talked about the 90-day quick start and the enterprise scale rollout. What are the actual steps involved in implementing one of these systems? And how long does it all take? Well, it depends on which approach you choose and how complex your organization's needs are. But generally speaking, there are a few key phases, planning, configuration, data migration, testing, training, and then finally going live. Okay, that sounds pretty straightforward.

But I'm sure there are like a million details we're not talking about. What are some of the biggest challenges people run into during implementation? Oh, you're not wrong about those million details. One of the biggest huddles is usually data migration. Getting all your existing data cleaned up, formatted correctly, and moved into the new system can be a huge undertaking. And if you're not careful, it can cause all sorts of errors and problems down the line. So it's not just about plugging in a new system and hitting go. Definitely not. It takes meticulous planning, careful execution, and lots of testing to make

sure everything is working the way it should. And then there's the human element, getting everyone on board trained up and comfortable with the new system. That's often the most overlooked and the most important aspect of a successful implementation. It's like a complex dance, coordinating the technology, the data, and the people. Exactly. And it requires strong leadership, clear communication, and a whole lot of patience. But the rewards can be huge. When it's done right, a well-implemented AI-powered ERP system can completely transform an organization, making it more efficient, more agile, and ultimately more competitive.

That all sounds great in theory. But let's be realistic. Even with the best planning and execution, things can still go wrong. What are some of the biggest risks involved in implementing these systems? And how can organizations mitigate them? You're absolutely right. There are always risks involved in any big IT project. One of the most common is scope creep. That's where the project starts to grow beyond its

original goals and it leads to delays and budget overruns. That's why it's so important to define a clear scope upfront and stick to it as much as possible. So discipline is key. What about resistance to change? I imagine some people might be hesitant to embrace a new system, especially one that involves AI. Oh, absolutely. Change is never easy, especially when it comes to how people do their jobs. That's why communication is so crucial. You need to explain why this new system is a good thing, address people's concerns, and provide enough training and support. It's about bringing everyone along and making sure no one feels left behind. Okay, that makes sense. Now, before we wrap up this part, there's one more

thing I'm curious about performance optimization. What does that mean in the context of AI powered ERP? And why is it so important? So performance optimization is all about making sure your system runs smoothly, efficiently and responsibly. It's about making sure people have a good experience with the system and that your team can work productively. It's like tuning up a high performance car. You know, you got to make sure everything is running at peak efficiency. So it's not just about having the fanciest technology. It's about making sure it actually works well in the real world. Exactly. And there are so many things that can affect performance from the underlying infrastructure to the application code and how the database is designed.

You need to look at everything when you're optimizing the system. And I'm guessing that with AI involved, things get even more complicated. Absolutely. AI algorithms can be really resource intensive, so you need to make sure your system can handle them. You also have to think about data quality and how the AI models are trained. If your data is bad or your models aren't trained properly, it can really hurt performance. So it sounds like performance optimization is an ongoing thing, not just a one-time fix. You got it. You need to constantly monitor your system, identify bottlenecks, and make adjustments as needed. It's all about continuously improving how efficient and responsive your system is to make sure it's meeting your organization's needs. This has been a fascinating look at how

these systems are implemented and optimized. But with all this technology and data floating around, I can't help but think about security. How do we keep all of this sensitive information safe, especially in a world where everything's connected? That's a great question and something we definitely need to talk about. When we come back for part three, we'll dive into the critical world of security and compliance in AI-powered ERP. All right, so we're back. And we've been talking all about this AI-powered ERP world, exploring how it's changing things for finance professionals and how to actually implement these systems.

But there's one big piece we haven't really touched on yet, security. With all this data flying around and these interconnected systems, how do we make sure it's all safe? How do we protect ourselves from cyber attacks and data breaches? You're absolutely right. That's a critical piece of the puzzle. Security is paramount, especially when we're talking about sensitive financial information and these increasingly complex systems. It's like we're building this high-tech fortress around our organization's most valuable assets. A fortress, huh? So how do we even begin to build something like that? The research mentioned a multi-layered approach. What does that mean, practically speaking? Well, think of it like layers of an onion. Each layer adds another level of protection. You've got your network layer with things like firewalls and intrusion detection systems.

That's your first line of defense. Then there's the application layer, making sure the software itself is secure and doesn't have any vulnerabilities. And of course, there's the data layer where encryption and access controls are essential. But there's another layer that often gets overlooked. The human layer. Ah, yes, the human factor. We've all seen those click-to-slink emails that can cause so much trouble. So how do we tackle that in the context of AI-powered ERP?

Security awareness training is key. Everyone in the organization needs to understand the basics of cybersecurity, things like phishing scams, strong passwords, and how to handle sensitive information responsibly. It's about creating a culture where everyone feels responsible for security, not just the IT department. And the research also mentioned this concept of zero trust architecture. What is that exactly and why is it becoming so important? So zero trust is a security mindset. It basically says you shouldn't trust anything or anyone by default. Imagine you're trying to get into a high security building. Even if you have a badge, you still have to go through checkpoints, verify your identity, and prove you have clearance to access certain areas. That's the idea behind zero trust. You're constantly verifying no matter who

or what is trying to access your systems. Sounds pretty intense. But I guess in a world where everyone's working remotely and accessing data from all kinds of devices, that level of security makes sense. Exactly. It's not just about keeping the bad guys out. It's also about protecting against accidental data leaks or someone inside the organization accessing things they shouldn't. So we've got security covered, but what about compliance? There are so many rules and regulations out there. How do companies keep up, especially with the added complexity of AI? Yeah, compliance can feel like a minefield, but it's essential for maintaining trust and avoiding those big fines. The key is to understand exactly which regulations apply to your industry and where you're located.

Whether it's GDPR for data privacy, CCPA in California, or HIPAA for healthcare, you need to build compliance into your systems and processes right from the start. So it's not just a box to check. It's about making compliance part of the foundation of your organization. Exactly. And with AI, it becomes even more important to make sure your algorithms and how you handle data align with ethical and legal requirements. Transparency and accountability are essential. Okay, that makes sense. Now let's talk about actually defending against cyber attacks. What are some of the key strategies organizations should be thinking about? The research emphasized a proactive approach to threat management. It's like playing chess. You have to be thinking several steps ahead, anticipating what your opponent might do. That means staying informed about the latest threats, doing regular vulnerability assessments,

and having strong intrusion detection systems that can spot suspicious activity right away. So it's not enough to just build walls. You need a security team that's constantly on guard, monitoring for threats and ready to respond quickly. Exactly. And having a well-rehearsed plan for what to do if something does happen is crucial. If there's a breach, you need to know how to contain the damage, recover your data and communicate openly with everyone involved. Like a fire drill for cyber attacks. Everyone knows their role and is ready to act fast. Now, before we wrap things up, I'm curious about third-party risk management.

With all these interconnected systems, how do you make sure your partners and vendors are just as secure as you are? That's a great point. You're only as strong as your weakest link, right? You need to be just as careful about vetting your third-party vendors and partners as you are about That's a great point. You're only as strong as your weakest link, right? You need to be just as careful about vetting your third party vendors and partners as you are about your own systems. That means doing thorough security assessments, having clear agreements about data sharing, and constantly monitoring to make sure they're meeting

your security standards. So security is an ongoing process, not a one time fix. Absolutely. It's constantly evolving, and organizations have to be proactive, vigilant, and ready to adapt to stay ahead of the game. Well, I think we've covered a ton of ground in this deep dive. We've talked about how ERP is evolving the power of AI, the challenges of implementing these systems, the importance of performance optimization, and the crucial role of security and compliance. It's clear that this is a complex and rapidly changing world, but it's also a world full of incredible possibilities. I couldn't agree more. We're seeing a fundamental shift in the way businesses operate,

and those who embrace these changes and approach them strategically will definitely succeed. So for someone like you who's been thinking about this so much, what's the biggest takeaway? What's the one thing you want people to remember from all of this? To me, it's the combination of human ingenuity and technological innovation. We're not talking about robots taking over. We're talking about a powerful partnership between humans and AI that can unlock amazing levels of efficiency insight and strategic decision making. The key is to approach this change with excitement, but also with a healthy dose of caution. Technology is a tool, and it's up to us to use it wisely. That's a great point and a perfect note to end on. Thanks for joining us on this deep dive into

the world of AI-powered ERP. We hope you found it insightful and maybe even a little inspiring. Until next time, keep exploring, keep learning, and keep pushing the boundaries of what's possible in this exciting new era.