Digital Transformation Isn’t Just Digitization — What You Need to Prepare Before Introducing AI

Summary

Drawing from our real-world experience driving AI transformation in the manufacturing sector, we’ve found that true success doesn’t come from stacking advanced technologies. It begins with aligning the vision, assessing organizational capabilities, designing process rhythms, building replicable data products and implementation mechanisms—and ultimately, making AI a trusted member of the shop floor.

AI is no longer just a reporting tool; it has become a co-creation partner in every decision-making process. Only when usage becomes a habit, when systems and policies support adoption, and when every department knows how to ask the right questions and how to use AI effectively—can AI truly stay embedded in operations. That’s when transformation moves beyond surface-level digitization.

If you’re a business leader who’s “implementing AI but feeling stuck,” consider this insight from McKinsey’s The Economic Potential of Generative AI:
“AI itself doesn’t create value—value is created by how well an organization can use AI.”
Read on, as we walk you through why implementing the technology is just the beginning, and how institutional design and organizational readiness are the two critical elements to get right.

1. Transformation Is a Long-Term Battle

From our experience guiding digital transformation in the manufacturing industry, the most common obstacle isn’t technical limitations—it’s the lack of alignment from the start.
Implementing systems, adopting AI, upgrading equipment—each of these actions may seem progressive on its own. But without strategic coherence and cross-functional collaboration, they often turn into fragmented initiatives rather than forming a cohesive transformation framework.

We’ve seen companies proclaim, “We’re building a smart factory,” but when department heads are asked, “How do your goals align with this transformation?”—the response is often silence.
Vision ends up as a slogan on the wall. Digital transformation becomes a project owned by one department, not a shared path of industrial evolution across the organization.

But knowing “where the transformation is going” can’t just be a catchphrase.
Successful transformation begins with vision alignment and clearly defining three key elements:

• What are the core business problems we aim to solve?
  
  
• What does the ideal future operating model look like?
  
  
• How will departments contribute in sync and create supportive actions?
  

In our on-the-ground work with manufacturers, we’ve found that the most effective starting point is to create a shared transformation roadmap—not just a technical blueprint, but an integrated design that spans decision-making, workflows, talent, and data.
This allows each department to understand: what they’re responsible for, why it matters, and when it must be done. It also serves as a strategic guide for AI implementation and adoption.

This roadmap typically includes six key components:

1. Transformation pathway
   
   
2. Talent and organizational development
   
   
3. Operational model
   
   
4. Technology architecture
   
   
5. Data application strategy
   
   
6. User experience
   
   

It also drives forward transformation tasks through organizational restructuring and team coordination.

Importantly, the transformation roadmap isn’t a slide deck for show—it’s the anchor of your transformation management rhythm. A truly effective roadmap must have these three qualities:

• Continuously updated – adaptable to market shifts and internal realities, never a one-time draft
  
  
• Co-created across departments – involving both users and decision-makers in the design
  
  
• Aligned with execution rhythm – with quarterly milestones and measurable checkpoints
  
  

Our approach involves designing a four-quadrant model, breaking down goals into short-term pain-point solutions and long-term capability building.
Each department takes ownership of specific tasks and challenges, making transformation more than a top-down announcement—it becomes a concrete, actionable AI strategy framework, progressively moving the organization toward strategic alignment and collaborative execution.

2. Determined but No One to Deliver?

“We want to do it—but we don’t have anyone who can.”
This is a phrase we hear from almost every manufacturing company in the early stages of transformation.
Top management is eager, mid-level managers nod in support, but those responsible on the ground are left confused: What is AI? Where should it be used? What part is our department responsible for?

The issue isn’t just about a talent shortage—it’s more fundamentally about the lack of clear organizational design around who should do the work and how it should be done.
Transformation can’t rely on a few heroic IT personnel. It requires reviewing existing talent, redistributing roles, and building a defined implementation rhythm that enables effective cross-functional collaboration. Our suggestion: start with operational pain points and work backwards to identify capability gaps.

Based on our experience, the three most common disconnects are:

• Aging workforce and technical gaps – Senior employees know the process but resist new systems; younger staff understand digital tools but lack frontline context
  
  
• No clear ownership – New tools get launched without dedicated champions to drive adoption and improvement
  
  
• Insufficient AI/data capabilities – No one knows how to feed data, ask the right questions, or refine suggestions
  
  

These are all signs of a shallow talent bench.
Instead of looking outward for experts, companies should audit internal resources and identify the key roles needed—such as data engineers, product owners, AI users, and even cross-functional facilitators—to ensure knowledge continuity and sustainable momentum.

Step 1: Build an Implementation Squad—The Operational Nerve Center

We recommend selecting 3 to 5 people from existing teams to form a cross-departmental implementation squad, rather than creating a large, standalone digital unit.
Typical squad roles might include:

• A process owner familiar with frontline operations
  
  
• A data translator or analyst
  
  
• A systems integration coordinator
  
  
• A project facilitator with horizontal communication skills
  
  

This team serves as your initial AI Champion group—playing a key role in driving progress, gathering feedback, and connecting systems to real-world needs.

Their job isn’t to write code. It’s to bridge data products and decision-making workflows—turning field requirements, issue lists, data sources, and system features into a closed feedback loop.
After just two or three iteration cycles, the organization can start to accumulate reusable scenarios and practical templates—paving the way for broader adoption and scaling.

Step 2: Capability Mapping Workshop—A Pre-Transformation Check-Up

Before implementation, we often run a capability mapping workshop that includes:

• Pain point mapping – delivery delays, yield issues, cost overruns, excessive overtime
  
  
• Matching key capabilities – data collection, analytical tools, process optimization, decision authority
  
  
• Identifying current talent – who can participate, who needs support, and where the bottlenecks are
  
  
• Building a coverage plan – through training, external collaboration, or workflow redesign
  
  

This isn’t about performance evaluation. It’s a strategic reallocation of resources.
Transformation isn’t about finding the perfect people—it’s about identifying gaps through capability mapping and creating a team that can grow together.

Once the implementation squad starts running iterations, generating results, and delivering feedback to the frontline, the organization’s “digital muscles” start getting stronger.
That’s what fuels the next waves of system upgrades and AI optimization—and it’s also the first step in shifting your organizational culture: from simply adopting tools to building sustainable, scalable internal momentum.

3. Still Using a Decade-Old Org Structure for AI Transformation?

In many manufacturing companies, the real reason AI implementation stalls isn’t because the technology isn’t advanced enough—it’s because the organizational operating model hasn’t kept up with the rhythm of digital tools.
A common pattern: the company buys new tools, schedules training sessions, assigns the IT department to lead… but fails to rethink the way the organization itself operates.

If the Org Structure Doesn’t Change, AI Implementation Goes in Circles

Traditional vertical hierarchies, departmental silos, and multi-layered approval processes are major bottlenecks for AI, which thrives on rapid feedback and continuous iteration.
For example, in one case we encountered, an AI model correctly flagged equipment anomalies early on. But by the time the alert was verified by IT, approved by a manager, and handed to the engineering team—it was too late. Yield had already dropped, and customer complaints were on the rise.

This isn’t a technology failure—it’s a structural mismatch.
The organization lacked both a workflow for real-time response and the AI Champions or implementation squads with authority to act on the ground.

Technology Can Be Bought—Operating Models Must Be Rebuilt

AI transformation is not a one-off implementation. It’s a dynamic, feedback-driven process:

• Models must improve through continuous feedback.
  
  
• Data must be constantly updated to stay relevant.
  
  
• And organizations must be able to adjust as they go.
  
  

Trying to run today’s AI initiatives using org charts built for ERP projects from ten years ago is a recipe for delay—either stuck in communication loops or dragged down by outdated processes.

From our field experience, we’ve found that linear workflows and top-down decision relays are no longer sustainable for AI-driven scenarios.
The pace of technology, the scale of data, and the complexity of use cases demand a new level of organizational agility and responsiveness.

As McKinsey has also emphasized:

Transformation isn’t just about adopting new tools—it’s about changing how decisions are made and how teams are structured.
This is why the transformation roadmap must go beyond IT upgrades and include operating logic and team design.

Agile Thinking Is a Prerequisite for AI Projects

We strongly advocate applying agile principles to AI and digital initiatives:
Break implementations down into 2–4 week cycles of short, testable sprints—run quick trials, validate outcomes in real time, and make iterative improvements.
This isn’t just for tech companies—it’s exactly the kind of rhythm that the manufacturing floor needs.

This approach also works well for scenario simulation and experiential learning.
It allows departments to see firsthand how AI responds in real decision-making situations—building both understanding and trust.
And that trust is critical for long-term adoption.

Real Example: Agile Implementation of an APS System in a Metal Processing Plant

One metal processing factory did not wait for full system implementation to be completed before launching their APS system. Instead, they initiated three agile task streams in parallel:

• Scheduling Logic Validation:
  A small task force manually processed sample data to cross-check the AI’s scheduling suggestions.
  
  
• Data Format Standardization:
  The team inventoried all on-site data formats and helped clean and convert the information into usable structure.
  
  
• Query Simulation:
  Field users tested real-time, natural-language queries to validate whether AI responses were relevant and practical.
  
  

These agile efforts produced early results within a short timeframe. They not only improved the system’s usefulness, but also fostered buy-in from the team on the ground.
Employees started to say, “This system is something we helped build.”

The Same Approach Works Across Daily Operations

This agile, co-creative rhythm can be applied to other daily workflows, such as reporting, maintenance, and quality control. For example:

• How can a maintenance report be automatically fed into the AI analytics workflow?
  
  
• How can part replacement data be captured and used to optimize predictive models?
  
  

Each process must be designed to enable feedback, traceability, and iterative optimization—that’s the real key to integrating AI into frontline operations.

Rebuild, Don’t Just Tweak: A New Organizational Logic for AI

To meet the demands of AI transformation, organizations must go beyond isolated adjustments and restructure their operational model.
The traditional department-centered pyramid structure must evolve into a task-driven, cross-functional network.
This shift requires digitally fluent leaders who promote collaboration and foresight, along with adaptive task-based teams that can respond quickly to change.

We recommend forming implementation squads not through expansion, but by reallocating suitable members from existing departments:

• Engineering and Manufacturing Personnel – with firsthand knowledge of shop floor conditions and pain points
  
  
• IT and Data Specialists – to handle system integration and data management
  
  
• Production Planning / Quality Decision Makers – to ensure solutions match business requirements
  
  

These squads should participate in regular “Transformation Channel Meetings” to align progress, flag issues, and adjust the overall rhythm—preventing siloed execution.

This team-based, flexible working model is essential for managing the evolving challenges and strategic pivots that come with AI adoption.

This is more than a new way of running projects—it’s a new way of running the organization.
When a company can adapt its structure alongside its technology and reshape itself around real needs, that’s when AI has the fertile ground it needs to create real value.

4. Is Your Digital Transformation Stuck at “Digitization”?

Many companies, after launching new systems and automating reports, come to a frustrating realization:
Scheduling is still done manually, and decisions still rely entirely on experience.

Yes, digitization has taken place—but real transformation has not.
This reveals a common gap: the organization has adopted technology, but has not built value-driven data usage logic, nor designed data application workflows that target actual business pain points.

The Core Problem: Data Is Not Driving Decisions

The challenge often isn’t with the system or technology itself, but with the fact that:

• Data hasn’t been transformed into decision-driving products
  
  
• Processes haven’t been redesigned to enable AI usage
  
  
• Operational flows haven’t become more flexible or faster
  
  

We often hear frontline frustrations like:
“We’ve already implemented the system—why are we still scheduling manually?”
“Reports are automated, but managers still analyze everything in Excel.”

These symptoms reflect a deeper issue: the company may have completed “digitization,” but has not progressed into a data-driven transformation phase.
They lack meaningful data products, haven’t translated insights into actionable knowledge, and haven’t embedded AI into institutional processes.

You’ve Integrated Data—But What Problems Does It Actually Solve?

Many manufacturers invest heavily in “data integration” projects, only to end up with a massive data warehouse where:

• Departments query data independently
  
  
• Interpretations vary
  
  
• There’s no shared logic or standardized definitions
  
  

From our implementation experience, valuable data isn’t just integrated—it’s designed.
It must be shaped into data products that frontline teams can use to solve real problems.
And a data product isn’t just a database or dashboard—it’s a reusable, decision-support tool.

Only through strategically aligned data product design can each role—from shop floor to middle management—make better decisions.

Moving Beyond the Cloud and the Data Lake

Many businesses mistakenly believe that moving data to the cloud or building a data lake equals transformation.
But data that supports AI and decision-making must be more than accessible—it must be:

• Designed
  
  
• Standardized
  
  
• Organized for intentional use
  
  

That’s the core of the data product mindset.

So, What Makes a Well-Designed Data Product?

A mature and effective data product typically has the following characteristics:

What Makes a Well-Designed Data Product?

A mature and effective data product typically meets the following criteria:

• Clearly defined target users
  Who is it designed for—decision-makers, engineers, or operations teams?
  
  
• Reliable data sources with version control
  No more pulling raw reports manually each time—data follows a consistent structure and updates automatically.
  
  
• Documentation and feedback channels
  Users understand what the data means, where it comes from, and how to report or correct issues.
  
  
• Reusable across business logic
  Not just for visualization—usable for answering questions and driving process decisions.
  
  

We emphasize the concept of data products because the real challenge for AI in manufacturing is not technical capability, but whether the data design allows AI to respond to real production scenarios.
Digitization ≠ transformation—and the biggest gap often lies in whether the system can answer frontline questions through modular, actionable, and query-ready design.

The Most Common Pitfall: Data Without Answers

One real-world example:
After system implementation and data integration, the production planner asks:
“When is the latest possible delivery date for this batch of orders?”
The system returns: equipment utilization rate, average processing time, and a trend graph.

While technically accurate, none of this directly answers the business question at hand.

The Key Is Translating Data Into Business Language

For AI to participate in decision-making, data must be translated into a format that aligns with business logic.
Common frontline questions include:

• What’s the projected delivery date?
  
  
• Which jobs are at risk of delay?
  
  
• What’s the best rescheduling option given today’s capacity?
  
  

System outputs must go beyond raw data—they should incorporate scheduling algorithms, order priorities, and bottleneck capacities to provide actionable insights and risk assessments.
It’s not that the AI algorithms are weak—it’s that the data wasn’t designed in sync with how users make decisions.

This process is essentially about translating knowledge into decision-ready formats, and it’s the foundation for building trust in AI within the organization.

Case Study: Redesigning Data Products to Match Daily Decision-Making

At a metal processing plant, the AI module was technically capable of generating scheduling suggestions.
But the output format and logic didn’t align with production planning workflows, leading to long-term underuse of the system.

We worked with the client to redesign their data products and defined three key reports:

• Scheduling Recommendation Report
  Order-level dispatch suggestions, calculated based on due dates, priority, and resource conflicts.
  
  
• Bottleneck Alert Report
  Highlights orders likely to cause delays at key capacity points, along with estimated impact.
  
  
• Manual Override Log
  Tracks every instance where a planner overrode the AI recommendation, with reasons and impact assessments.
  
  

These reports were reviewed weekly and discussed in monthly feedback sessions.
Over six months, usage statistics told a clear story:

• Report access grew from under 5 times/week to over 20 times/week
  
  
• Adoption of AI-generated schedules rose to 80%
  
  
• The reports became part of the standard operating procedure for production planning
  
  

This was the turning point—when AI moved from producing data to enabling decisions.
From “only data-savvy people can use it,” to “every user knows what to ask and how to use it.”

Only with this level of alignment can AI evolve from being just a module in the system to becoming a knowledge partner—one that supports the rhythm of decisions and drives cultural evolution across the team.

5. Embracing AI Across the Organization

As a system solution provider, we used to focus heavily on implementation and technology.
It wasn’t until we read “The Economic Potential of Generative AI” by McKinsey that we fully grasped the real challenge after AI starts showing results:
The hard part begins not when AI can do something, but when the organization is asked to use it.

Inspired by this framework, we’ve seen firsthand that the real impact of AI adoption in manufacturing doesn’t come from algorithmic precision alone—it comes from whether users trust and adopt the tool.
If no one uses it, no one believes in it, or it’s constantly overridden, then even the best model with the cleanest data has no value.

1｜Driving User Adoption: Reforming Core Business Habits

Every department has its own operational habits and risk sensitivities.
When AI suggestions don’t align with existing workflows, the typical reaction is:
“This doesn’t fit our department”—instead of
“How can we adjust together to make it work?”

This is one of the biggest challenges in transformation:
AI’s ROI often depends less on the algorithm—and more on user adoption.

That’s why promoting usage shouldn’t rely solely on training or performance pressure.
We need to design an adoption path that makes AI easy to try, useful to use, and rewarding to return to.
This often involves systemic support from AI Champions and promotion squads, ensuring every user encounters AI within the comfort of familiar workflows.

Here are some effective tactics we’ve seen:

• Internal promoters and usage coaches embedded within departments
  
  
• Embedding AI tools into existing meetings, such as using AI scheduling reports in daily stand-ups
  
  
• KPI-based reverse incentives, like making AI adoption rates, report usage frequency, and override ratios transparent and visible
  
  

One example comes from a Tier 1 automotive components manufacturer.
We implemented an AI-powered scheduling system with an 85% accuracy rate right from the start.
Yet frontline supervisors refused to use it. Why?
“If delivery is delayed, I’ll be held accountable. I’d rather override it myself.”

To address this, we worked with leadership to make AI recommendations the default standard, and shift accountability to the system.
Errors would be reviewed collectively in recurring team meetings—not pinned on individuals.

In addition, we integrated AI reports into daily operations, requiring supervisors to use them in morning meetings.
Over time, usage frequency rose steadily, and those same supervisors began suggesting system improvements—evolving from reluctant users to co-creators of the solution.

This taught us a powerful lesson:
Adopting AI is not just a training challenge—it’s a system and culture design challenge.
It requires:

• Cultural shifts toward shared accountability
  
  
• Built-in routines that normalize usage
  
  
• And institutional safeguards that reduce fear and build trust
  
  

Only when these pieces are in place can AI move from “optional tool” to organizational capability—and ultimately, become a trusted partner in day-to-day decision-making.

2｜Designing Replicable and Reusable AI Solutions

As AI expands into multiple departments, a common question arises:
“Can this approach be replicated elsewhere?”

Many of our clients evaluate solutions by studying real-world cases and asking, “Can we apply this directly to our factory?”
But this often overlooks a key point: different scenarios require different simulations and problem lists. That’s why we emphasize modular flexibility and tailored scripts.

For example, we helped one metal component manufacturer successfully implement an AI scheduling system. But a similar client, working with custom orders, struggled to adopt it.
Their reasoning: “Our jobs are different—this won’t work for us.”

To address this, we designed interchangeable modules based on factory types (e.g., rush-order priority, batch splitting, bottleneck avoidance), and built three implementation scripts for clients to choose from:

• “Small-Batch Urgency” model
  
  
• “Equipment-Constrained” model
  
  
• “Skilled Labor-Driven” model
  
  

Each module came with preset fields, reporting formats, and error-handling logic, so local implementation teams could get started easily.

This is the essence of assetization:
A good AI solution isn’t one perfect one-off—it’s a reusable, adaptable framework that can be tuned by scenario.

To build scalable solutions, three elements are essential:

• Process standardization
  Define a consistent path: data preparation → problem definition → model execution → result validation → user feedback
  
  
• Technical modularity
  Make algorithms, data transformation logic, and outputs plug-and-play
  
  
• Support structure institutionalization
  Every implementation includes a “deployment coach” and a “transformation liaison”—not just the IT department doing it all alone
  
  

Because DigiHua has embedded these elements into its systems, AI solutions implemented in Factory A can be rapidly rolled out to Factories B, C, and D—each customized to local needs.

3｜Tracking Transformation Milestones Through Outcome-Driven Governance

Transformation should not rely on one-time successes.
It must be supported by systems that allow for ongoing operations, validation, and optimization.
That’s where three institutional mechanisms come in:

• KPI Alignment
  Include AI adoption rate, usage frequency, and prediction accuracy in performance tracking
  
  
• Stage-Gate Process
  At each milestone (e.g., system launch, feedback collection, cross-functional coordination), evaluate progress before moving forward
  
  
• Transformation Office
  A neutral unit that supports horizontally—coordinating tasks, troubleshooting, and ensuring alignment across departments
  
  

This governance framework helps close the gap between frontline feedback and management oversight—gradually embedding transformation into daily operations.

The ultimate goal?
To internalize AI usage across the company until the Transformation Office is no longer needed—because the transformation has evolved from a “project” into a shared organizational culture.

4｜Managing Risk and Building Trust

For users to fully embrace AI, trust is essential.
And trust is built when people can see the risks, understand the assumptions, and know what’s within their control.

In one case at a plastic injection factory, AI was “banned” after an early failure.
The issue? The prediction engine omitted mold changeover times. Operators said, “We can’t produce like this—the system doesn’t get it.”

This was a classic information gap.
We redesigned the model’s output to show:

• Justification fields
  
  
• Assumptions
  
  
• Risk summaries
  
  

We also added a manual override log, allowing supervisors to select override reasons (e.g., mold conflict, insufficient labor, special customer request).

Six months later, these feedback logs were used to retrain the model, improving its alignment with real-world needs.
Users began to say:
“My edits weren’t ignored—AI actually learned from them.”

This was a turning point—from confrontation to collaboration, and a key moment where the AI Champion team and frontline users deepened their partnership.

We recommend building the following risk-handling mechanisms into your AI adoption process:

• Categorize risk sources
  Separate causes like data errors, model mismatches, user misuse, and delayed feedback
  
  
• Prioritize risk response
  Based on business impact and frequency, determine what to fix first
  
  
• Establish feedback channels
  Allow users to report errors or suggestions anonymously, periodically, or in real time—so the model keeps improving rather than stagnating
  
  

When employees know “I have a voice when AI gets it wrong,” and “My input influences how the system evolves,”
that’s when trust takes root—and AI becomes a true part of the frontline decision-making environment.

From Usage to Habit, From Project to Culture

When AI is actively used by every department, regularly referenced in weekly meetings, and continuously improved through real-time feedback, it stops being just a feature of the IT system—it becomes part of the organization’s behavior.

Only then is AI truly accepted as a member of the company, rather than just an outsourced “report generator.”
It is only by building a trust-based learning culture that AI can evolve from a tool into an extension of organizational memory and collaboration.

We understand that the hardest part of AI isn’t whether it can be implemented—but whether it can stay.
This insight comes from more than 30 years of experience at DigiHua, where every team member has learned through hands-on projects, ongoing education, and the shared pursuit of improvement.
It is this spirit that enables us to create better products and smarter solutions—solutions that solve real problems for our clients.

6. AI Transformation Isn’t Just About Tools—It’s About Building a Usable, Trusted, and Sustainable Organizational Capability

Looking back on the past few years of helping companies implement AI, one truth stands out:

AI can generate value—but it won’t be used automatically.
You can deploy the technology—but that doesn’t mean transformation is complete.

Many companies still view transformation as the sum of a few technical projects.
But what truly turns AI into a competitive advantage isn’t the model’s precision—it’s the organizational design that enables AI to work.

Here’s what that design requires:

• Turn data into solutions
  It’s not just about integration—it’s about designing data products that answer real business questions.
  
  
• Adapt processes to fit the technology
  Don’t make AI wait for humans—redesign workflows so AI can participate.
  
  
• Build feedback-ready systems
  Don’t expect perfection on the first try—create rhythms for continuous feedback and refinement.
  
  
• Institutionalize adoption support
  Don’t rely on one department—build cross-functional, repeatable mechanisms.
  
  

These elements all contribute to a resilient, learning-driven, and strategically aligned organization.
From the initial AI Champion task force to the multi-department transformation architecture, every layer must be intentionally designed.

This typically maps to a comprehensive transformation roadmap—spanning data governance, process redesign, role training, and feedback mechanisms across seven core modules.
Each module isn’t just about technical logic—it’s a blueprint for cultural integration and institutional change.

Only then can AI shift from “tool” to “partner,” from proposal to execution, from isolated projects to everyday decision-making across the organization.

As shown in Chapter 5, the real challenge of AI isn’t technical—it’s about adoption barriers and system design.
When companies can:

• Design data products from the user’s perspective
  
  
• Develop replicable solutions despite department differences
  
  
• Align implementation with incentives and trust-building mechanisms
  
  

Then AI stops being just another IT buzzword—and starts becoming the new normal in transformation.

The future of manufacturing won’t be won by whoever implements the most systems.
It will be won by those who can make AI a trusted member of the shop floor, participating in every micro-decision, every scheduling meeting, every production judgment.

And that depends on whether your organizational culture can shift from tool-driven to co-creation-driven, where decision quality becomes a core business asset.

This is why we keep emphasizing:

The key to transformation is not technology—it’s holistic design.

Ready to Make AI Your Factory’s Decision Partner? Just Two Steps Away:

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or if you’ve had a few local wins and want to scale them plant-wide, here’s how to get started:

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