ERP transformations still miss the mark
Enterprise resource planning (ERP) has been around for decades, and yet, most ERP transformation efforts still underdeliver. You’ve seen it happen. Massive investments go in, yet agility, intelligence, and measurable results often don’t come out. According to Bain & Company’s 2025 Technology Maturity Assessment Benchmarking Survey, more than 80% of ERP transformations fail to meet expectations for budget, timeline, or value. That’s not a small oversight. It speaks to a deeper structural issue.
The problem doesn’t lie with ERP platforms alone. It’s how companies implement them. Too often, businesses over-customize in an attempt to meet every edge-case requirement. This creates systems that are tough to maintain and nearly impossible to upgrade. What should be a harmonized digital backbone ends up as a sprawling, fragmented infrastructure. The result is burdensome tech instead of streamlined operations. Intelligence becomes significantly harder to extract, because the system isn’t standardized or ready to scale.
For C-level leaders, the takeaway is simple but important: complexity must be the enemy. The goal is not to design a system that does everything today. It’s to build a system that can adapt and deliver value tomorrow. Executives need to recognize that real transformation isn’t about adding layers; it’s about removing friction. The value of ERP should come from simplification. Clean architecture. Clear workflows. Reliable data.
Agentic AI can actually fix this
Now here’s the part to pay attention to, agentic AI. This is where the world starts tilting in the right direction.
Agentic AI isn’t just another line of code. It shifts how you operate. These are autonomous, event-driven systems that make decisions and take action on their own. They don’t wait for human input. They don’t look for approval loops. They just execute, based on the data they receive and the parameters they’re given. That fundamentally changes what ERP is for.
You stop relying on the system as a reporting or data storage tool. Instead, the system becomes a real-time, intelligent actor. It reads what’s going on in your operation and takes action, in procurement, finance, supply chain, or wherever you choose to apply it with structure. This creates a feedback loop of efficiency. Fewer bottlenecks. Less human latency. Greater responsiveness.
And the future’s already moving here. Bain’s 2025 study shows 78% of IT leaders expect some ERP functions to be replaced or enhanced by agentic AI within the next three years. That’s not a fringe prediction. That’s where the competitive floor is about to move.
For you, this means looking at ERP not as a finished asset, but as a platform ready for evolution. Agentic systems can be lightweight, fast, and scalable, if deployed with purpose. The key is to start with high-impact areas where decisions follow predictable rules and where fast execution drives tangible business results. Think about finance, planning, and procurement first. Then scale from there.
The capability is real. The architecture is capable. The only question now is how fast you want to move.
Scaling still fails because most companies stay in pilot mode
The tools are getting better. The opportunity is real. But most companies are still stuck running pilot programs with agentic AI, small trials that never evolve. It’s not a capacity issue. It’s not a lack of use cases. It’s usually structural friction and decision paralysis.
There are five main blockers. First, the operating model isn’t clear. Most businesses haven’t defined how humans and AI agents should interact. Who’s responsible for what outcome? Where does decision autonomy start and stop? If that’s not mapped out, you don’t get traction. Second, internal talent is missing. The skills to design, deploy, and manage agentic workflows just aren’t in place at most organizations.
Third, the tech is still early-stage. Most agentic frameworks lack mature orchestration tools. Until recently, deploying even moderately complex AI agents required stitching together legacy systems and writing custom integration logic. That’s shifting, but not fast enough for companies without deep development capacity. Fourth, data quality is a pain point. When information is siloed, outdated, or missing governance, AI agents can’t make smart decisions. And last, leadership hesitates. There’s fear of vendor lock-in. ROI remains hard to quantify. And without strong executive support, budgets stall.
So what do you do? Push through. These aren’t new problems. They’re just new in this specific context. CIOs that succeed here move decisively. They fix data first. They train or hire people who understand agent-based execution. And they don’t wait for perfect tech. They adopt and iterate. Scale will always be uncomfortable, but staying in pilot mode costs more over time.
Bain’s 2025 survey shows where the market is going: 78% of tech leaders believe agentic AI is going to augment or replace core ERP functions within three years. You don’t want to be playing catch-up when that shift locks in.
Agentic AI delivers early wins in finance and planning, start there
You don’t need to transform every part of your ERP system at once. In fact, that’s the wrong approach. Start with where agentic AI naturally fits: finance and planning. These functions operate with structured data, repeatable workflows, and time-sensitive outputs. That makes them ideal for automation and autonomous execution.
In Bain’s survey, leaders consistently pointed to areas like procure-to-pay, record-to-report, and forecast-to-plan as the starting points. Why? Because that’s where the business impact is most immediate. These are areas with measurable throughput. You can track turnaround times, errors, compliance cost, and decision quality, all of which improve with reliable agent deployment.
This gives CIOs and CFOs an advantage. Together, they can prioritize a contained, high-value rollout that delivers results. Once the organization sees performance improvements, lower costs, higher velocity, better accuracy, you build trust in the model. That makes expanding into adjacent processes easier.
You should lead this shift with clarity. Don’t frame agentic AI as a lab experiment. It’s a business capability with short-term ROI and long-term scalability. It changes how work is done, not just how it’s recorded. Start with finance and planning. Measure everything. And be ready to scale fast once the early systems prove reliable.
Scaling agentic AI comes down to four questions
To move agentic AI from trial to transformation, you need clear answers to four basic questions: What’s required? How will you deliver it? Who will you partner with? And what happens if you don’t?
Let’s start with what’s required. Every organization has dozens of potential AI use cases, but only a few will have immediate business value. Start by prioritizing three to five use cases with the highest ROI. Then rethink the surrounding process architecture to be agent-first. That means designing for automation from the ground up, decisions, data flows, and performance guardrails all need to work without manual intervention. Don’t just bolt AI onto old workflows. Redesign them.
Next, how will you deliver? Build, buy, or partner, that’s the choice. The right answer depends on seven factors, including strategic importance, vendor fit, total cost, integration complexity, internal technical talent, regulatory risk, and long-term flexibility. For standardized, low-risk tasks, outsourced or vendor-led models work well. For areas core to competitive advantage, in-house builds can justify the cost and complexity. There’s no one-size-fits-all here. Choose based on use case, not convenience.
Partnerships matter. Early in your journey, stick close to your platform provider. Most are offering agentic tools, templates, and low-code environments to accelerate deployment. As you move into more complex use cases, business integrators help with redesign, ROI modeling, and change management. And as agentic tooling matures, system integrators will play a bigger role in deployment at scale. These roles will evolve, so should your approach to who you bring in and when.
And lastly, what’s the cost of doing nothing? According to Bain’s research, companies that have scaled AI across critical workflows are already seeing EBITDA gains of 10%–25%. If your organization stays in observation mode, you lose the opportunity to shape the technology, influence vendor roadmaps, and build internal capability. The longer you delay, the more likely you are to get locked into inflexible ecosystems that you didn’t design. None of that plays to your advantage.
Inaction is the real risk
While some companies are waiting to make a move, others are already scaling agentic AI, and they’re capturing the benefits. That gap will grow fast. If you don’t act, you’re not standing still, you’re falling behind. The math supports it. Bain’s 2025 research shows leading adopters of AI are already driving 10% to 25% gains in EBITDA. That compounds over time.
But there’s more to consider. As platform providers expand their ecosystems, they’re becoming orchestration hubs, managing not just agents but inter-agent coordination. If you’re late in developing your internal capability, you become a passive consumer of someone else’s roadmap. That means less control over system behavior, slower adaptation, and more exposure to platform lock-in.
Delaying also drives up transformation costs. If your agentic AI efforts run separately from ERP modernization, you’ll end up reworking the same process twice, and paying for it each time. Integration becomes harder. ROI gets diluted.
And there’s the workforce to think about. Skilled professionals increasingly want to work with teams deploying real AI at scale. If your roadmap is slow or unclear, don’t be surprised if top talent opts for faster-moving organizations. This isn’t about hype, it’s about visibility and relevance. People want to build the future. Show them you’re doing that.
Inaction may feel like risk reduction, but over the next 12 to 24 months, it becomes the risk itself. The technology is ready. The frameworks are emerging. Waiting isn’t the safer option, it’s the costlier one.
The orchestration layer is where strategic control will be won or lost
Agentic AI isn’t just about what agents can do individually, it’s about how they work together. That’s where orchestration comes in. The orchestration layer governs coordination between agents, manages workflows, and ensures that AI-driven actions are aligned with business objectives. This layer is becoming central to strategic control, and platform providers know it.
Enterprise platform vendors are moving fast to position themselves as hub providers for agent orchestration. They want to control the infrastructure that manages how agents communicate, prioritize tasks, and comply with governance policies. On the surface, they offer interoperability. In practice, they’re building ecosystems that lean toward proprietary lock-in. This is deliberate. It gives them tighter control over standards, updates, and future integrations.
For CIOs and CTOs, the issue is simple: if you don’t own this layer, or at least influence it heavily, you lose flexibility. You put long-term innovation in the hands of a third party. Vendor roadmaps begin to define your capabilities. Technical dependencies grow. And extracting yourself later becomes expensive and disruptive.
This is more than a technical conversation. It’s a strategic one. As your enterprise becomes more AI-reliant, your ability to control how agents interact, to define logic, priorities, security, and compliance, becomes a competitive differentiator. Architectural decisions you make now will determine how agile and open your AI environment remains over the next five to ten years.
Get in front of this. Choose tools that allow composability and interoperability. Invest in tech leadership that understands not just how systems run, but how they scale in complexity. Maintain optionality. That’s how you stay in control.
Execution at scale is the only way to unlock real value
Agentic AI works, but only when you move past pilot projects. You don’t get lasting business impact from one agent automating invoice handling or filing a report. You need systems of agents, deployed across core processes, interacting in real time with your data, rules, and teams. That’s execution at scale.
Scaling means building an architecture that can handle modular complexity. You need standardized methods for deploying agents, monitoring their performance, and upgrading functionality as capabilities evolve. Governance is critical. Define audit trails, automated decision checkpoints, and accountability frameworks. This isn’t just operational hygiene, it’s how you build trust in the outcomes.
Some companies are already doing this. They’ve created internal operating models with integrated AI-agent teams. They’re using prepackaged agent libraries, low-code deployment studios, and orchestration frameworks that allow dynamic scalability. They’ve shifted from trial-and-error to repeatable design patterns. Once that maturity is in place, innovation moves faster. AI capabilities are absorbed continually, not in bursts.
This won’t happen if you’re waiting for tools to be perfect. It happens when you frame agentic AI as an enterprise capability, not a technology experiment. That requires alignment between tech, business, and leadership. It also means being willing to move while things are still evolving.
What separates the winners isn’t access to tools. It’s execution. The companies that operationalize broadly and refine continuously are already shaping industry benchmarks. If you want to lead, start now, scale fast, and stay adaptive. The longer you hesitate, the harder it gets to catch up.
Final thoughts
Agentic AI is already embedded in the leading platforms, and early adopters are pulling ahead. The tools exist. The value is proven. What matters now is strategic action.
For decision-makers, the next steps are straightforward: prioritize high-impact use cases, rethink operating models, and treat the orchestration layer as strategic infrastructure, not an add-on. Build capability while tools are still maturing. Use partnerships where useful, but keep control where it matters.
As ERP systems evolve from passive infrastructure to autonomous execution platforms, executives need to lead that evolution with clarity. Delay brings cost, complexity, and missed opportunity. Execution brings leverage.
The companies that act now, intentionally, decisively, won’t just keep up. They’ll take the lead.
