By Richard Sides
CEO, Adroit North America LLC
Food and Beverage manufacturing and distribution present a unique list of complex requirements that traditionally caused ERP implementation timelines in the industry to extend well beyond a year. These complexities, combined with heavy hand-coded customization required by generic ERPs and long cycles of rework, made extended timelines feel inevitable. Change cycles that stretch beyond nine months often begin to lose organizational support as competing priorities emerge and fatigue sets in.
At Adroit, we challenged that assumption. We have consistently delivered implementations of Aptean Food & Beverage ERP in the 9 to 12 month timeframe. This has been achieved by combining deep industry expertise, the food-centric functionality of Aptean F&B, and the integration of AI into our delivery model.
I continually push our teams to accelerate “time to value,” and they are delivering. This shift has not required clients to work harder but has required us to work smarter. By leveraging next-generation tools, we are able to generate a far more detailed and accurate set of requirements much earlier in the process. We take advantage of the platform-centric architecture of Microsoft Dynamics Business Central with Aptean Food and Beverage ERP and incorporate AI through Business Central MCP connections.
As we continue to integrate AI into our delivery model, we are further compressing the time it takes for organizations to realize value.
Rethinking Where ERP Projects Slow Down
Traditional ERP implementations tend to stall in familiar places. Discovery drags on as teams attempt to document every nuance of the current state. Requirements are captured inconsistently and often lack traceability to actual system behavior. Testing becomes reactive rather than structured. And by the time the system is ready, the business is still catching up.
The result is a long, expensive process that delivers value later than expected.
What we have found is that the key to compressing timelines is to generate superior detail from the very beginning of the project.
Building a Knowledge Model from Day One
One of the most impactful changes we’ve made is leveraging the sales cycle itself as the starting point for implementation. With permission, we generate transcripts of every discovery session and demo. From the outset, we begin developing a knowledge model that we use to securely train AI.
We build a detailed discovery book and begin mapping key enterprise structures, supply chain scenarios, system context diagrams, production steps, item hierarchies, and more. By the time the sales cycle is complete, our delivery teams are able to hit the ground running.
Our team then conducts a rigorous review of each key workstream. We focus not only on transactional requirements, but also on how decisions are made and how the business measures success. We seek to understand how an organization plans, produces, moves, and assures its product. Every conversation is captured, transcribed, and analyzed.
From these transcripts, we continuously refine and expand the knowledge model of the business.
We have effectively created a system that builds a self-learning model—one that becomes the foundation for everything that follows. Instead of restarting discovery after the deal is signed, we begin with a deep, structured understanding of the client’s operations, constraints, and priorities. This continuity eliminates redundancy and significantly accelerates the transition from sales to delivery.
From Knowledge to Requirements—With Precision
Once the knowledge model is established, we use it to generate detailed system requirements and test scripts.
AI plays a critical role at this stage. By analyzing a structured understanding of the business, we can rapidly produce requirements that are aligned to configuration, define end-to-end processes, and create test scenarios that are directly traceable to business outcomes.
This approach removes one of the most common sources of delay in ERP projects: ambiguity. Requirements are clearly defined, mapped to system behavior, and validated through structured testing from the outset.
Instead of spending months documenting and re-documenting requirements, teams can move quickly into configuration and validation with confidence that they are building the right solution.
Freeing the Team to Focus on What Matters
By compressing and strengthening the early phases of the project, we create space for something often neglected in traditional ERP implementations: true value creation.
Rather than consuming the majority of the timeline with documentation and rework, teams are able to focus on the capabilities that actually transform the business. This includes embedding food safety and compliance directly into operational workflows so that traceability, quality, and regulatory requirements are not afterthoughts, but core components of execution.
It also enables organizations to move beyond static planning models into more dynamic, responsive approaches that better reflect real-world variability in demand and supply. With better data and more intelligent planning frameworks, businesses can make faster and more confident decisions.
At the same time, advances in decision support—particularly through Copilot and natural language BI—allow users across the organization to interact with data in a more intuitive way. This shortens the distance between insight and action.
Finally, inventory control and operational execution can be optimized through better alignment between system design and the realities of the shop floor and warehouse. When paired with the right scanning, labeling, and device integrations, this creates a level of operational precision that is difficult to achieve in traditional implementations.
These are the elements that separate an ERP system that simply “goes live” from one that drives measurable business impact.
Why 9–12 Months Is the New Standard
The 9 to 12 month timeframe represents a balance between speed and completeness. It allows organizations to move with urgency while ensuring that the system is properly aligned, adopted, and capable of supporting the business from day one.
This timeline is achievable because the solution is purpose-built for the food and beverage industry, the implementation approach is structured and repeatable, and AI is used to accelerate the most time-consuming aspects of the project—documentation, requirements definition, and testing.
Equally important, the team remains focused on value rather than rework. The result is not just a faster implementation, but a more effective one.
Compressing Time to Value—Without Compromise
As AI continues to evolve, implementation timelines will continue to compress. But speed alone is not the objective. The real goal is to shorten the distance between investment and impact.
By building a knowledge model early, generating precise requirements, and focusing effort on high-value activities, ERP implementations can shift from being long, disruptive undertakings to focused, strategic initiatives.
For food and beverage manufacturers navigating increasing complexity across planning, execution, and compliance, this shift is meaningful. It enables faster access to better data, stronger operational control, and more agile decision-making.
ERP no longer needs to be a multi-year journey. With the right approach, it becomes a catalyst for meaningful change that is delivered in months, not years.