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Organizations don’t flip a switch from traditional planning to artifacts overnight. The transition requires deliberate planning, careful sequencing, and realistic expectations about what works during the migration period.
Most organizations face the same question: how do you move from quarterly budgets and annual forecasts to continuously evolving planning intelligence without disrupting operations or losing institutional knowledge built into existing models?
The Starting Point: Assessing What You Have
Before migrating to artifacts, inventory your current planning landscape. Most organizations discover they have more planning processes than they realized—formal budgets and forecasts, but also departmental capacity models, workforce allocation spreadsheets, and procurement schedules that function as plans even if no one calls them that.
Identify which planning processes consume the most time, cause the most frustration, or become outdated fastest. These are migration candidates. A planning process that requires complete rebuilds every quarter because assumptions change frequently is a better starting point than strategic planning that genuinely operates on longer horizons.
Document the business logic embedded in existing models. Finance teams often have driver-based formulas, allocation rules, and calculation methods refined over years. Operations has capacity constraints and utilization assumptions. HR has attrition models and hiring timeline patterns. This institutional knowledge shouldn’t be discarded—it becomes the foundation for training your first artifacts.
Choosing Your First Artifact: Which Process to Convert
Start with operational or tactical planning rather than strategic planning. Operational processes have shorter feedback loops, making it easier to validate that artifacts are learning correctly. You’ll see whether the artifact adapts appropriately within weeks rather than waiting quarters or years.
Strong first candidates share characteristics: they require frequent updates (monthly or more often), they have clean data feeds from source systems, and they’re contained within one or two departments. Avoid starting with planning processes that require extensive cross-functional coordination or executive approval for every adjustment.
A regional operations capacity plan works better as a first artifact than the annual corporate budget. A departmental workforce allocation model works better than enterprise-wide strategic workforce planning. Success with constrained-scope artifacts builds organizational confidence before tackling more complex planning domains.
The Technology Foundation: What You Need to Build Artifacts
Before configuring your first artifact, ensure you have the necessary technology infrastructure. Artifacts don’t require entirely new technology stacks—most organizations already have foundational components. The key is understanding what you have, what you need, and where gaps exist.
Data infrastructure requirements
Artifacts need structured, accessible data from the planning domains they’ll serve. This means automated data feeds from source systems rather than manually compiled spreadsheets. For a capacity planning artifact, you need production data, machine utilization metrics, and demand signals flowing systematically. For workforce artifacts, you need headcount data, attrition patterns, and hiring timelines from HR systems.
The data doesn’t need to be perfect, but it needs to be consistent. Artifacts struggle with data that arrives sporadically, uses inconsistent definitions across departments, or requires extensive manual cleanup before use. If your current planning process relies on emailed Excel files that someone manually consolidates, address this before attempting artifact implementation.
Platform and integration capabilities
Artifacts require platforms that support three core capabilities: AI and machine learning for pattern recognition and assumption adjustment, write-back functionality for two-way data flow so teams can interact with plans directly, and connected architecture that integrates planning across functions without creating new data silos.
Organizations working within Microsoft ecosystems comprising of Power BI, Excel, Azure, and Fabric, already have much of this foundation. The challenge is extending these tools beyond reporting into active, AI-powered planning.
Acterys Planning Intelligence addresses this by adding enterprise-grade write-back capabilities to Power BI and Excel, enabling AI-powered adaptation, and allowing custom application development within tools teams already use. This approach avoids adding another disconnected planning system to your technology stack.
Integration with existing systems
Your artifact needs connections to ERP, accounting, CRM, HR, and operational systems that contain the data driving planning. These integrations must be automated as artifacts can’t wait for manual data exports and imports.
What you likely already have:
Most organizations already have reporting and analytics platforms (Power BI, Tableau, or similar), data warehouses or databases where planning data could be consolidated, and connections from source systems to reporting tools. The foundation exists—artifacts extend these capabilities rather than replacing them.
What you likely need to add:
Two-way data flow and writeback capabilities that transform read-only dashboards into interactive planning environments, AI and machine learning integration for pattern recognition and assumption adjustment, and planning-specific data models that structure information for continuous learning rather than just reporting.
Cloud vs. on-premises considerations
Artifacts benefit from cloud deployment because AI capabilities, automatic scaling, and continuous updates work more seamlessly in cloud environments. However, organizations with strict data residency requirements or regulatory constraints can implement artifacts in hybrid or on-premises configurations.
Acterys supports multiple deployment options, such as full cloud, hybrid, and on-premises, ensuring artifacts work within your organization’s security and compliance requirements.
Seeding Artifacts with Existing Intelligence
Artifacts learn from patterns in your data, but they don’t need to start from zero. Your existing planning models contain business logic that took years to develop. This could include driver relationships, seasonal patterns, capacity constraints, planning rules, among others.
Configure your first artifact with these existing assumptions as starting parameters. If your capacity planning model uses specific utilization rates, machine efficiency factors, or demand multipliers, input those as initial artifact parameters. The artifact then refines these assumptions based on actual performance patterns rather than learning everything from scratch.
This approach serves two purposes. First, it accelerates time-to-value, meaning your artifact starts with reasonable assumptions rather than spending months discovering what your planning team already knows. Second, it makes validation easier. When the artifact begins adjusting assumptions, you can evaluate whether those adjustments make sense given your business context.
Acterys Modeller enables this seeding process by allowing you to configure planning logic, business rules, and driver relationships that artifacts then refine over time based on actual data patterns.
The Parallel Systems Period: Managing the Overlap
Expect to run parallel systems during migration. Your existing planning process continues while the artifact learns and proves itself. This overlap typically lasts one to three planning cycles depending on the process frequency.
During this period, both systems produce plans. Compare them. When they diverge, investigate why. Sometimes the artifact identifies patterns your traditional model missed. Sometimes the artifact needs recalibration because it’s responding to noise rather than signal. This comparison phase builds team understanding of how artifacts work and where they add value.
Set explicit criteria for when to trust the artifact enough to sunset the traditional process. These might include forecast accuracy within defined thresholds for X consecutive cycles, planning team confidence in understanding artifact recommendations, and successful handling of at least one scenario where business conditions changed significantly.
Don’t eliminate the traditional process prematurely. Organizations that succeed with artifacts typically run parallel systems longer than they initially planned, building confidence gradually rather than forcing adoption before teams are ready.
The Validation Challenge: Ensuring Artifacts Learn Correctly
How do you know an artifact is learning the right lessons from your data? Establish validation processes before putting artifacts into production.
Monitor what patterns the artifact identifies and how it adjusts assumptions. If your workforce artifact suddenly recommends drastically different attrition assumptions, you need to understand why. Is it responding to genuine pattern changes in your business, or is it over-weighting recent anomalies?
Create review checkpoints where planning teams evaluate artifact recommendations before they propagate through connected systems. Early artifacts benefit from human oversight until they prove consistent accuracy. As confidence builds, you can reduce review frequency and automate more decisions.
Track artifact performance against traditional planning approaches. If the artifact’s forecasts are less accurate than your existing process, something needs adjustment—either the artifact’s configuration, the data it’s accessing, or the assumptions it started with.
Addressing Organizational Resistance
Some team members will resist artifacts because continuous adaptation feels like losing control. Traditional planning has clear cycles. You know when planning happens, when it’s finalized, and when it’s stable. Artifacts never fully stabilize.
Address this by clarifying what changes and what doesn’t. Planning governance doesn’t disappear with artifacts as approval thresholds, budget constraints, and strategic guardrails remain. What changes is that assumptions adapt continuously within those guardrails rather than staying fixed between planning cycles.
Involve planning teams in artifact configuration and validation. When people understand how artifacts work and see their business knowledge incorporated into artifact logic, resistance decreases. This isn’t about AI replacing planners—it’s about AI handling assumption recalibration while planners focus on strategic decisions.
When to Sunset Traditional Processes
The transition from parallel systems to artifact-only planning requires judgment. Organizations typically sunset traditional planning when three conditions are met:
- The artifact consistently matches or exceeds traditional planning accuracy over multiple cycles.
- Planning teams understand artifact logic well enough to evaluate recommendations critically.
- Leadership trusts the artifact enough to make decisions based on its outputs.
This doesn’t mean artifacts are perfect. It means they’re reliable enough that continuing traditional planning alongside them creates more overhead than value.
When sunsetting traditional processes, preserve the institutional knowledge they contained. Document the business logic, driver relationships, and planning rules that were embedded in old models. Some of this knowledge now lives in artifact configuration, but explicit documentation ensures it isn’t lost.
The Second Artifact: Building on Initial Success
After successfully implementing your first artifact, resist the urge to immediately deploy artifacts everywhere. Use your second artifact implementation to address gaps learned from the first.
Common second-artifact choices either expand the same planning domain to adjacent departments or move to a connected planning function. If your first artifact handled operations capacity planning, the second might extend to procurement planning (which connects to capacity) or expand capacity planning to additional facilities.
This sequenced approach builds an artifact ecosystem gradually. Each implementation teaches lessons that improve the next one. Teams develop artifact literacy progressively rather than facing organization-wide transformation simultaneously.
Practical Implementation Timeline
Realistic timelines for artifact migration:
Months 1-2: Assessment and first artifact selection. Inventory planning processes, validate technology readiness, identify first candidate, document existing business logic.
Months 2-4: First artifact configuration and seeding. Establish data connections, configure initial assumptions, begin parallel operation with traditional planning.
Months 4-7: Validation and refinement. Monitor artifact performance, adjust configuration based on learnings, build team confidence through comparison with traditional planning.
Months 7-9: Transition to artifact-primary planning. Reduce traditional planning frequency, increase reliance on artifact outputs, maintain traditional planning as backup.
Months 9-12: Traditional process sunset and second artifact initiation. Eliminate parallel systems for first artifact, begin second artifact implementation using lessons learned.
Organizations attempting faster timelines typically struggle with adoption. The technical implementation moves quickly—data connections and artifact configuration take weeks. The organizational adoption takes months.
What Success Looks Like
Successful artifact migration doesn’t mean perfect forecasts or zero planning effort. It means planning teams spend less time on data consolidation and model rebuilding, more time on strategic analysis and scenario evaluation.
Planning cycle times compress not because teams work faster, but because artifacts handle assumption updates continuously rather than requiring manual intervention. The time between recognizing business changes and adjusting plans decreases from weeks to days.
Most importantly, planning intelligence compounds rather than resets. Each cycle builds on learnings from previous cycles. The fifth planning period with an artifact performs better than the first because the artifact has accumulated business-specific knowledge about your patterns, drivers, and relationships.
Organizations that succeed with artifacts view migration as organizational transformation, not just technology implementation. The technology enables new planning approaches, but success depends on people, processes, and patience during the transition.
How long should we run parallel systems before trusting an artifact?
Most organizations run parallel systems for 2-4 planning cycles of the process being migrated. For monthly operational planning, this means 2-4 months. For quarterly financial forecasting, this means 6-12 months. Don’t set arbitrary timelines—use performance criteria (forecast accuracy, team confidence, stakeholder trust) to determine when to transition.
What happens to our planning team's roles during migration?
Planning roles shift from data gathering and model maintenance toward analysis and strategic recommendation. Early in migration, teams spend significant time validating artifact outputs and refining configurations. As artifacts mature, teams focus on interpreting artifact insights, evaluating trade-offs, and making strategic planning decisions that require business judgment.
Should we migrate all planning processes eventually, or keep some traditional?
Strategic planning with multi-year horizons and infrequent updates may not benefit from artifacts. The value comes from continuous adaptation to changing patterns—processes that genuinely operate on stable, long-term assumptions don’t need continuous recalibration. Focus artifacts on planning domains where assumptions change faster than your traditional planning cycle can accommodate.
What's the biggest reason artifact migrations fail?
Attempting organization-wide transformation too quickly. Organizations that succeed start small, build confidence through successful limited implementations, and expand gradually. Those that fail typically try to replace all planning processes simultaneously, overwhelming teams and creating resistance when early implementations encounter inevitable problems.
