Introducing the next generation of Asset Investment Planning, designed to help organizations responsible for critical infrastructure makes faster, more transparent, and more defensible investment decisions.
Capital plans are now built under conditions the last generation of planning tools was never designed for: contested cost recovery, compressed decision windows, and value models that take a developer weeks to change.
IFS Copperleaf Next was built for those conditions.
Built on the proven Copperleaf Value Framework, IFS Copperleaf Next helps organizations managing critical infrastructure decide faster and defend those decisions with evidence. Every recommendation carries a full audit trail: the logic is visible, the calculation is traceable, and the decision stays yours — with far less manual work to get there.
What’s New in IFS Copperleaf Next
Capabilities that were not possible on the previous architecture.
Four capabilities generally available in November 2026, with a fifth on the roadmap. Each one closes a gap that planning teams have been working around for years.
Detects regulatory developments, maps each one to the investments it affects, and recommends a response. The loop closes inside the plan, not in a separate briefing document.
Create and evolve your Value Framework through a guided, AI-assisted editor. Changes that once meant a C# file, a YAML config and a deployment cycle now take hours — and your team makes them.
Turns engineering reports and planning documents into review-ready business cases. The work that took an engineer a month starts from a draft instead of a blank page.
A rebuilt experience that gives every user a guided entry point matched to their job — reviewer, approver, field engineer, finance contact. When the platform works for the whole team, the plan reflects the whole organization.
Compare spend per asset, asset health trajectory and investment timing against aggregated, anonymized industry data — so your plan holds up against external expectations, not just internal precedent. On our roadmap.
Why it Matters
IFS Copperleaf Next makes capital planning faster with AI-native enhancements. But our core optimization engine remains deterministic — not a black box. It is not AI at all: it is mathematics, and it returns the same plan every time it is given the same inputs. The AI runs in parallel, drafting, checking and explaining the work around that decision. Together, that is what we mean by Glass Box AI — every decision traceable back to the data and value logic that produced it, auditable by anyone who asks, and repeatable because the engine, not the model, produces the plan.
Glass Box AI — Traceable. Auditable. Repeatable.
Most AI in capital planning asks you to trust the output. Glass Box AI lets you interrogate it — and the plan underneath was never guesswork to begin with.
Evaluate More Options, Faster
Test more scenarios in the time you have, understand the trade-offs between them, and know why the plan landed where it did.
Walk Into the Hearing Room Ready
Evidence drawn from the same value logic that produced the plan. Evidence that reflects how a decision was actually made is considerably harder to unravel than evidence reconstructed after the fact.
Stop Waiting on the Development Queue
Value framework changes, business case drafting and investment entry move to the people who own the work. Configuration that once took weeks and a developer now takes hours and takes none.
Give Every Team the Same View
Planners, engineers, finance and executives working from one plan, one value framework and one audit trail — so the conversation is about the decision rather than about whose numbers are right.
Want to understand how these work together?
READ THE BROCHURE
Capabilities of IFS Copperleaf Next
Regulatory Intelligence
From reactive to ready — before the hearing room.
In a single year of U.S. utility commission proceedings, more than $100 billion in capital recovery was contested — not on the merits of the underlying investments, but on the quality of the evidence used to defend them.
What it does:
- AI-drafted responses to information requests, cited against the filed record, with attorney sign-off required before anything is submitted
- Precedent-backed recommendations with win-probability sizing
- Portfolio re-optimization and risk-adjusted NPV repricing when regulatory conditions change
- Built in partnership with HData, the market-leading regulatory data provider
- Monitoring across 50+ state commissions and FERC, mapped to the investments each signal affects
Planning Without Dependencies
Own your value framework — no developer, no wait.
What it does:
- Foresight Compose: describe the model you need in plain language and publish it the same day, validated and versioned.
- Business Case Assistant: turn planning and engineering documentation into review-ready business cases.
- Guided Planning Experience: a role-aware starting point for every user, so contributing to the plan does not require training first.
- Framework Governance: version history, approval workflow and rollback, so a framework change is never a one-way door.
- Next-Generation Self-Service Analytics: Richer reporting and planning insights across the organization.
- Multi-Division Views: asset and portfolio views that match how large organizations are actually structured — separate operating companies, separate jurisdictions, one optimized enterprise plan.
Glass Box AI, Explained
Two things run side by side. The optimization engine is deterministic mathematics — it weighs your constraints and returns the plan, and it returns the same plan whenever the inputs are the same. The AI works around it: drafting business cases, checking quality, answering questions, explaining what changed. It is grounded in your documentation and your value framework, constrained by the permissions you grant it, and logged on every action. People approve every change. The AI never touches how the plan is calculated.
Why Organizations Choose IFS Copperleaf
Proven across over 200 organisations
years of regulatory track record
customer retention
Trusted by the Advisors
IFS Copperleaf Insights
Blog
IFS Copperleaf Next: The Next Generation of Asset Investment Planning
Brochure
Regulatory Intelligence
Brochure
IFS Copperleaf Next: Understanding Next Generation AIP
IFS Copperleaf Next FAQs
1. Is IFS Copperleaf Next a new version of Copperleaf?
No. It is a new product on a new architecture. It carries forward the Copperleaf Value Framework and two decades of capital planning practice, and it makes capabilities possible that the previous architecture could not support.
2. How long does implementation take?
Typical implementation is 4-8 months for full deployment. We offer phased approaches to start with key capabilities first. Many customers see value within 3-4 months.
3. How does IFS Copperleaf Next help with regulatory defense?
Every recommendation carries a complete audit trail, so a regulator can see exactly how a decision was reached and on what basis. That is what we mean by Glass Box AI. Regulatory Intelligence goes further: it connects commission activity directly to the investments it affects, so your plan reflects regulatory reality rather than lagging behind it.
4. Does IFS Copperleaf Next require specialized developer resources?
No. Foresight Compose replaces C# files, YAML configuration and deployment cycles with a guided editor. Planning and configuration teams make changes directly, with version history, rollback and approval workflow built in.
5. What about data migration from my current platform?
Data migration is carefully planned as part of implementation. We provide phased migration options to minimize disruption to your planning cycles.
6. Is IFS Copperleaf Next cloud-only or can it be deployed on-premise?
IFS Copperleaf Next is cloud-native and deployed on Azure (SOC2/ISO 27001 certified). We do not offer on-premise deployment for Next, as the cloud architecture is fundamental.
7. How do we defend an AI-influenced decision to a regulator?
The optimization engine is deterministic — the same inputs produce the same plan every time. AI accelerates the analysis and drafting around the decision, and every recommendation traces back to the value framework and source data, so the reasoning can be examined line by line.
8. Is our data used to train AI models?
No. The underlying models are commercially available foundation models and no customer data is fed back into their training. Your context is supplied at the moment of the request so the system can reference it — it never becomes part of the model.