Enterprise AI has moved from being in the experiment stage to production at scale. Inside most Salesforce orgs, it’s already running support queues, scoring leads, and shaping decisions that impact revenue directly. But many businesses weren’t ready for it, which is why the ROI formula they’ve used for decades is starting to prove insufficient to decode what AI actually does.
Earlier return on investment models followed the simple logic: spend the money, get the return, close the file. AI value builds gradually through efficiency, expanded capacity, and stronger data foundations. That’s why ROAI is becoming the most accurate measure for Salesforce teams.
But businesses must understand what ROAI is to fully replace traditional return on investment in Salesforce projects. In this blog, we’ll explore 5 Salesforce Agentforce impact tracking metrics that show how to track impact more effectively. We’ll also talk about how to build a strategy to help you transit from traditional AI investment metrics enterprise to return on AI investment Salesforce.
Why Enterprises are Rethinking AI Investment Impact in Salesforce
Businesses assume stability once they have invested in the AI project. But it doesn’t work well with AI because it keeps evolving. When teams apply legacy formulas, they face issues like:
⚠
Fixed-input thinking breaks down fast — AI systems keep improving without any extra spending behind them.
⚠
Watching single transactions in isolation misses how one gain tends to spread quietly into other workflows.
⚠
Capacity gains from handling more work without new headcount are rarely included in ROI.
⚠
Data quality improvements from AI rollouts don’t appear in financial reporting, even though they create measurable returns.
What is ROAI?
ROAI offers a wider lens for evaluating what Salesforce AI projects actually produce. It calculates token consumption and model usage costs alongside efficiency, scalability, and data quality improvements. Thus, providing a more precise view than traditional ROI for Agentforce and enterprise AI investments.
ROAI Formula
ROAI = Economic Return / (Cost of Human Intelligence + Cost of Tokens)
ROAI vs ROI AI Projects: What is the Difference?
If traditional ROI focuses on knowing whether a project paid for itself, then ROAI asks something more useful — what can the business do now that it couldn’t manage before? That question only gets more important as agentic tools take on a bigger role. Salesforce Agentforce ROI metrics, for instance, need to reflect the independent judgment calls an agent makes on its own, not just how many tickets got closed by end of day.
Factor
Traditional ROI
ROAI
Core Metric Basis
Net financial gain versus investment cost
Business value from AI adoption across efficiency, revenue, and data quality
Formula Basis
ROI = (Gain – Cost) / Cost
No fixed formula — mixes cost savings, productivity, new revenue, and risk reduction
Focus Area
Purely financial outcomes: profits, margins, payback
Broader enterprise outcomes: automation, decision speed, customer experience
Time Horizon
Short-to-medium term, tied to a project’s lifecycle
Medium-to-long term, tracking AI maturity and scaling impact
How it Measures
Straightforward, drawn from accounting data
More complex, requiring tracking of intangible benefits like agility and competitive edge
Common Salesforce Agentforce ROI Metrics
Case Resolution Speed
Track the reduction in average time taken to close customer cases.
Agent Productivity
Calculates the boost in cases handled per agent without adding staff.
Customer Satisfaction
Focuses on any improvement in CSAT or NPS scores tied to faster resolutions.
Cost Efficiency
Measures savings from reduced escalations and lower support expenses.
Scalability
Assesses the ability to manage higher case volumes during peak demand without disruption.
5 Steps to Begin Transitioning to an ROAI Framework
Step 01
Audit What You’re Already Tracking
Review every metric tied to current technology projects and separate those that only measure speed or efficiency. This exposes gaps in reporting and sets the stage for a broader framework that captures value beyond operational quick wins.
Step 02
Map Capability Gains Directly
Document where teams manage greater demands or more complex tasks without adding staff. These gains often go unnoticed, but they show expanded organizational capacity. Capturing them provides a clearer view of how investments reshape what the business can realistically handle.
Step 03
Score Your Data Health Gains
Check and improve the quality of data, including the introduction of a more restrictive approach to data quality improvement — through better records and fields and proper use of data and information governance. By applying these changes you can enhance reporting accuracy, ensure compliance, and boost system performance. Unlike tool-specific benefits, these upgrades remain valuable long after individual solutions are replaced.
Step 04
Build a Three-Tier Dashboard
You need a reporting dashboard that integrates velocity, scalability, and data foundation metrics into one structured view. With this layered approach, you can see the value of the project across different systems. Leadership gets a clearer insight into a system’s performance and can spot the gaps, if any.
Step 05
Review Performance Quarterly
Annual checkpoints miss rapid shifts in technology. Conduct structured evaluation every three months to identify compounding benefits and detect early warning signs. This method lets you make timely iterations before escalation and stay updated to ever-evolving advancement in artificial technology and its subsets.
Key Considerations Before Measuring ROAI
01
Scaling Benefits Take Time
Not every AI initiative shows scaling benefits right away, and that’s expected. Some projects exist mainly to build the data foundation that other tools will depend on later.
02
Leadership Commitment Required
Capability gains surface slower than efficiency wins. Therefore, leadership must sustain commitment, as early metrics may underreport long-term enterprise transformation.
03
Cross-Team Impact Counts
Don’t ignore cross-departmental inputs. Data architecture improvements frequently benefit teams outside the one that requested the AI tool — narrow reporting may understate the real impact.
04
Baseline Data Essential
Solid baseline data matters most of all. Without accurate pre-AI benchmarks, proving full enterprise impact becomes difficult to highlight later.
Wrapping It Up: What is ROAI
The real question behind ROAI vs ROI isn’t which formula is more precise — it’s which one to focus on initially. Because ROI tells you whether a project broke even, and ROAI tells you what the organization can now do, and how much sturdier its foundation has become.
So, to capture return on AI investment Salesforce fully, organizations need expert guidance. Partner with a Salesforce AI consulting provider — the certified Salesforce experts will help you accelerate adoption, strengthen data foundations, and enable you to realize enterprise-wide transformation.
ROI tells you whether a project broke even. ROAI tells you what the organization can now do — and how much sturdier its foundation has become. That’s the metric shift enterprise AI demands.
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A moment arrives when a workflow stops being just a checklist and starts becoming more like a living system: it reacts to customer actions, business priorities, regulatory requirements, and many other factors that might not be necessarily known beforehand. If it is still working yet differently than before. This is because, as a business grows, the processes become increasingly unpredictable. What was a straightforward process on paper gradually stacks up multiple layers internally: approvals dependent on a deal value, escalations based on customer behavior, compliance checks that only surface under certain conditions, and cross-team dependencies.
That is where static automation starts showing its limits. It can execute the logic it was given, but it cannot adjust when live business conditions change. This is where Agentforce triggered agents are becoming relevant, bringing autonomous adaptability into Salesforce workflows so processes can react, reassess, and continue execution as conditions change.
What Are Agentforce Triggered Agents?
The Agentforce triggered agents are event-based AI agents within Salesforce which trigger actions based on certain business events and act depending on the environment of the event. Instead of limiting to a set of predetermined rules, they can analyze the data, reason with it, and determine the course of action that needs to be followed.
This changes the focus from automation to decision-making. Unlike static workflows, an Agentforce event-triggered agent does not stop at the trigger itself. It can assess live CRM records, pull data from connected systems, and execute multiple actions based on what the business situation looks like at that moment. That makes it a stronger fit for workflows where outcomes depend on live context rather than fixed paths.
How Are They Different from Traditional Automation?
Traditional Salesforce automation is built around predefined logic. The path is designed in advance, and each action follows the next based on conditions already configured. This works well when the workflow stays predictable. The difference becomes clearer in the discussion around Salesforce Flow vs Agentforce agent. A flow executes the logic it was assigned. An agent can evaluate the context before deciding what should happen next.
Salesforce Flow
Executes the logic it was assigned
VS
Agentforce Agent
Evaluates the context before deciding what should happen next
This is where Salesforce agentic process automation changes the model. The automation layer is no longer limited to following instructions. It becomes capable of operational reasoning. At the center of this is the Atlas Reasoning Engine, which allows triggered agents to analyze live data, connected records, and workflow signals before taking action. That could mean escalating a case, rerouting approvals, updating dependent records, or launching a sequence of actions without manual intervention. This is what makes Agentforce triggered agents more adaptable in workflows where conditions change while the process is still active.
How Agentforce Triggered Agents Automate Workflows Without Human Intervention
Agentforce triggered agents operate less like isolated automations and more like a layered workflow system. Each layer handles a specific function, allowing the process to move from detection to execution without waiting for manual intervention. This is what makes autonomous Agentforce workflow automation fundamentally different from static rule execution.
LAYER 1
The Sensing Layer: Detecting Business Events
Every workflow begins with an event. That could be a record update, a Platform Event, a Change Data Capture (CDC) signal, or an external system input entering Salesforce. This layer acts as the entry point.
The system detects the raw event as it happens. Until this point, no decisions have been made. The workflow has only recognized that something important has changed and may require action. This is the first step of Agentforce 2DX proactive workflows, where automation starts reacting the moment operational conditions shift.
LAYER 2
The Orchestration Layer: Building Operational Context
A trigger by itself does not contain enough information for making a decision. The orchestration layer is responsible for that part. Through integration with Flow, Apex, or Data Cloud (Data 360), Salesforce collects additional context information such as related records, customer history, dependencies, accounts, and possible conditions associated with the trigger.
That information is then structured into a usable payload. At this stage, the workflow shifts from raw event detection into contextual decision-making. This layer acts as the control point between system activity and agent reasoning.
LAYER 3
The Reasoning Layer: Deciding, Acting, and Verifying
This is where the Atlas Reasoning Engine Salesforce becomes central. The agent gets the contextual payload, decomposes the workflow into smaller tasks, and determines the sequential order of actions. It can choose between Flows, Apex classes, APIs, or third-party systems depending on the specific requirement.
Once execution happens, the agent verifies the outcome. If the workflow completes, the loop closes. But if some business rules prevent proceeding further, it can redirect, escalate, or even pass the task to a human queue.
Real-World Business Use Cases
Flexibility of Agentforce makes automation of operational workflows possible when time, collaboration, and context matter directly to the results.
Intelligent Customer Service
In case a customer faces a serious problem, triggered agents can analyze the situation’s severity, look into account history, find any similarities with previous cases, set the priority of the case, and notify relevant parties. This shortens response cycles and allows service teams to focus more on resolution than internal coordination.
Sales Pipeline Management
Sales processes often fail because of the high dependency on manual follow-ups. Triggered agents can identify stalled sales deals, notify account managers, suggest further action, and create reports after customer interactions. This keeps the pipeline active and reduces delays in decision-making.
Employee Onboarding
Employee Onboarding involves multiple teams working in sequence. Triggered agents can create employee records, assign training, notify IT for access setup, schedule orientation sessions, and track completion across departments. This creates a more connected onboarding process and reduces repetitive administrative effort.
Conclusion
Business workflows are becoming harder to predict. As operations grow more connected and conditions change faster, adaptability within the workflow may start carrying more value than speed alone. They will need to interpret, adjust, and keep moving.
That shift could redefine how businesses think about operational control.
Curious what triggered agents could automate in your own Salesforce org? Talk to Girikon’s Agentforce consulting team about building event-driven workflows tailored to your business.
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AI has come up as a powerful catalyst for businesses looking to engage with their customers, automate workflows, and boost employee productivity. Salesforce Agentforce has come up as a powerful platform that enables businesses to build self-functioning AI agents capable of managing customer queries, supporting employees, and executing business processes.
While several organizations are ready to embrace Agentforce — securing administrative approval for full-scale implementation might be challenging. Stakeholders often want proof that the technology will deliver tangible business value before promising significant budgets and resources.
Salesforce Agentforce proof of concept becomes crucial here.
A well-outlined proof of concept (POC) allows organizations to validate business outcomes, assess technical possibilities, identify implementation risks, and establish a roadmap for large scale adoption. Rather, it helps create the evidence needed to gain enterprise-scale funding, as well as approval.
This practical playbook explains how to execute, design and present an Agentforce POC that showcases value and speeds-up decision-making.
What is the Need for an Agentforce POC?
Many AI initiatives fail because companies attempt large-scale deployments without authenticating assumptions. Executives are becoming growingly careful about investing in AI technologies without any clear outcomes.
An Agentforce POC helps businesses:
✓
Authenticate real-world use cases
✓
Measure functional enhancements
✓
Exhibit AI precision and effectiveness
✓
Assess user adoption
✓
Identify integration requirements
✓
Estimate the cost of implementation
✓
Build stakeholder confidence
Rather than debating about the apparent benefits, a POC provides tangible results that leadership teams can evaluate. The goal isn’t just to build a complete solution. Rather, the objective is to prove that Agentforce can tackle specific business issues while generating measurable value.
1
Select the Right Business Use Case
For any POC to become successful, choosing the right use case is extremely useful. Several organizations commit the mistake of choosing complex workflows that need extensive integration, as well as customization. This causes unnecessary delays, as well as risk. Use cases that have a clear business value, can be measured independently, has moderate-level of complexity and can be completed within a manageable time frame — can be chosen.
2
Define Clear Metrics for Success
Way before development starts, key participants require a clear comprehension of what is required for success. Without a well-defined criterion, a proof of concept that is technically sound might not get executive approval. The metrics of success should be tied to the goals of a business rather than technical accomplishments. While technical innovation is critical, executives are focused on tangible results.
3
Creating a Credible Use Case
For gaining leadership support, it’s crucial to create a clear business case is crucial. Start by documenting current challenges, such as slow response times, support costs, redundant manual task and more. It’s crucial to explain how this platform can be utilized to fix these issues. Benefits such as cost efficiency, revenue growth, and optimal customer satisfaction, along with projected costs, risks, response plans etc., must be included. A well-orchestrated business case highlights why the investment is crucial.
4
Plan a Focused Agentforce Pilot
Organizations still pondering how to pilot Agentforce should start with a well-outlined yet low-risk approach. Begin with a certain user group but focus on a single workflow rather than several processes. Use controlled datasets and authentic knowledge resources to ensure dependable outcomes. Make sure to track performance metrics and carry out regular reviews. The most effective pilots are focused enough on delivering quick wins while generating intelligent insights.
5
Understand Agentforce Implementation Phases
The successful deployment of Agentforce follows an orchestrated approach. Begin with discovery to outline business goals, stakeholders, use cases and performance benchmarks. Next, design workflows, interactive paths, knowledge sources and integrations. During the build phase, automation, agent configuration, and security controls. Thorough testing validates compliance, performance, as well as user experience. After testing is completed, the solution must be tested on a limited user group and gather feedback for optimization. Once measurable success is demonstrated, scale Agentforce across teams and business functions. This ensures broader adoption, greater operational efficiency, and minimal implementation risk.
6
Agentforce ROI Calculation
Showcasing financial impact is crucial for attracting executive buy-in. A well-defined Agentforce ROI calculation helps decision-makers assess the business value of the initiative. Leadership teams look for clear answers to questions like What is the overall investment required? What kind of benefits will the solution deliver? And time required to achieve a positive return? capturing business benefits fortifies the business case and builds trust in the investment. Organizations should assess operational gains, faster response times, higher retention, and new opportunities for revenue growth. Together, these benefits create a exciting ROI case for Agentforce.
7
Capture Stakeholder Input
Securing enterprise approval isn’t possible with just technical performance. Feedback from end users, managers, IT teams, and stakeholders must be sought. Additionally, factors such as response accuracy, usability and revenue gains, improvements in customer experience etc., must also be considered. Strong support and feedback provide validation, fortifies the business case, and builds trust among people considering broader Agentforce adoption.
8
Present Results in Executive Language
Emphasizing too much on technical capabilities rather than outcomes is a common mistake. While technical teams might show interest in prompt engineering, AI models, and integration architecture, executives tend to be focused on tangible values. Show results in terms of time reduced operational costs, savings and optimal productivity and customer satisfaction, and revenue growth. Translating results in business terms makes it simpler for executive team to assess the impact and build a case for broader adoption.
What Drives Agentforce POC Enterprise Approval?
Agentforce POC enterprise approval relies on showcasing tangible business value, low implementation risk, strong user adoption, and a clear path to enterprise-scale growth. To augment success rates, avoid some mistakes such as handling several use cases, ignoring ROI measurement, requiring sponsorship from management, over customizing the solution, and failing to define tangible success metrics.
Final Words
A Salesforce Agentforce POC should consider business value rather than just technical capabilities.
Success relies on the right use case, shaping clear objectives, evaluating ROI, and following an orchestrated Agentforce implementation phase. Organizations that emphasize tangible outcomes and stakeholder alignment are likely to attain sustainable AI-powered transformation.
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Something has quietly shifted in how Salesforce describes its own platform. Sales Cloud is now Agentforce Sales. Service Cloud is now Agentforce Service. If you read that as mere branding, you are probably reading the Salesforce Summer ’26 release notes and skimming too fast. The renaming is not cosmetic. It is a structural declaration—one that extends beyond sales and service into the broader Salesforce ecosystem, including Salesforce Marketing Cloud Consulting, where organizations are rethinking how AI-driven customer engagement fits into their digital strategy. Enterprise buyers evaluating renewal or expansion in the back half of 2026 should probably sit with that for a moment before they approve the next contract line item.
The Agentforce updates Summer 2026 span 822 pages of official release documentation across 23 product pillars. That is not a point release. That is a platform repositioning wearing the clothes of a quarterly update.
Anyway, let us back up a little.
The critique most enterprise teams quietly held against Agentforce through 2025 was fair: the agents were experimental, the deployment stories were messy, and the vendor case studies tended to present the clean rollouts. The harder ones, the partial implementations and integrations that required three consultants to maintain, were less prominently featured. This is precisely why many organizations began relying on an experienced Agentforce consulting Partner to navigate complex deployments and build solutions that could scale beyond pilot projects. Summer ’26 does not erase that critique entirely, but it moves the goalposts in ways that matter.
Salesforce Summer ’26 Release — What Is Actually Shipping and What It Does to the Buying Equation
Four capabilities have attracted the most practical attention from implementation partners reviewing this cycle: the Customer Engagement Agent (referenced in pre-release material under the Agentforce SDR Summer ’26 positioning), Triggered Agents, Multi-Agent Orchestration, and Agentforce Self-Service. For any salesforce implementation partner, these capabilities represent some of the most impactful advancements in the platform. They are not unrelated. Salesforce has clearly engineered them to work as compounding layers rather than standalone SKUs.
Here is what each one actually does in plain enterprise terms:
Capability
What It Replaces
Primary Buyer Benefit
Customer Engagement Agent
Human SDR for inbound lead response
24/7 lead qualification without headcount scaling
Triggered Agents
Manually configured Flow automations
Event-driven AI responses that bypass human routing delay
Multi-Agent Orchestration
Single-agent architectures
Complex workflows distributed across specialized subagents
Agentforce Self-Service
Legacy chatbots and help portals
Conversational resolution in under 6-click setup
Worth noting: these are not all generally available at the same time. Multi-Agent Orchestration ships in beta. Enterprise buyers who have been burned before by committing to a beta roadmap and then waiting eighteen months for stability will want to confirm GA timelines before building procurement decisions around it.
The Triggered Agent Question — Because It Is More Complicated Than the Demos Suggest
The Agentforce triggered agents release is probably the most architecturally significant update in this cycle, and it tends to get less conference-floor attention than the SDR headline feature. Triggered agents respond to defined events — a deal stage change, a support ticket escalation, or a customer signal from Data Cloud — and activate AI-driven actions without a human initiating the process. The practical implication is that your AI agents can now behave less like tools you pick up and more like colleagues who notice things while you are still in meetings. For organizations planning a salesforce classic to lightning migration, these event-driven AI capabilities highlight the advantages of the modern Lightning platform, making the transition an opportunity to adopt more intelligent, automated workflows rather than simply updating the user interface.
The problem — and it has never been fully solved across any vendor’s event-driven AI stack — is that triggered automation only works cleanly when your underlying data is clean and your event logic is well-designed. Teams with fragmented CRM data, inconsistent pipeline stages, or customizations layered over customizations layered over a decade of org debt will find that triggered agents amplify those inconsistencies at speed.
The failure mode is not that the agent does nothing, but it does the wrong thing confidently. By the time someone notices, the customer interaction has already happened. By which point the trust in the system has usually already taken a hit.
That is not a reason to avoid the feature. It is a reason to treat data quality as a prerequisite rather than a parallel workstream.
Three Practical Signals for Enterprise Buyers Evaluating Renewal
If you are in procurement or IT leadership trying to translate this release into a real decision, here is a framework that is a little more honest than what you will typically encounter in a Salesforce pitch deck.
1
Agentforce is now the architecture, not a feature set
Sales Cloud and Service Cloud rebranding as Agentforce Sales and Agentforce Service signals that future development flows through the agentic layer first. Organizations delaying adoption are not just missing tools — they are creating a growing gap between their platform version and where the roadmap is heading. That gap compounds over release cycles.
2
Multi-Agent Orchestration is still in beta, and that is worth acknowledging honestly
Specialized subagents working under one orchestrator is what unlocks genuinely complex enterprise workflows — no bloated single agent trying to do everything, just coordinated layers doing what they’re each built for. It is genuinely different from what existed twelve months ago — though “genuinely different” and “production-stable” are not always the same sentence.
3
Security changes in this release are mandatory, not advisory
The SAML migration requirement, Apex user-mode defaults, and legacy channel retirements are not features you can defer without consequence. Organizations that have not already audited their authentication setup and custom code risk SSO failures, broken integrations, and routing errors that surface at the worst possible moments.
What the Collaboration Layer Actually Changed
One update that does not lead any Salesforce press release but carries real workflow implications: Slack channels now replace Chatter as the default in new orgs. For organizations that have spent years routing approvals, alerts, and internal conversations through Chatter, this is not a flip-of-a-switch change — it is a dependency audit that tends to reveal more embedded usage than anyone initially expects. The transition is manageable, but it lands on IT teams who are already managing a longer preparation checklist than most anticipated entering this cycle. As organizations modernize these collaboration workflows, many also evaluate salesforce integration services to ensure Slack, existing business applications, and automated processes continue to work together without disrupting daily operations.
💡
Tip for Enterprise Teams
The Agentforce Self-Service setup claiming six clicks or fewer is closer to accurate than it sounds — for net-new deployments on clean orgs. The complexity comes in Knowledge Base integration and customization, not initial configuration. If your pilot scope is deliberately narrow, the six-click promise holds.
The Headcount Conversation — More Nuanced Than the Vendor Pitch
What the agent genuinely delivers, in organizations with reasonable data hygiene and a clear qualification framework, is speed-to-engagement and consistency. Consider what that actually changes in practice:
A human SDR joining at 9am cannot respond to the demo request that arrived at 2am from a different time zone. The agent does, every time, with the same qualification logic applied regardless of hour or volume.
When ten reps apply ten slightly different readings of what qualifies a prospect, the inconsistency does not show up as a single error — it shows up as noise across the entire pipeline, gradually, until someone pulls a cohort report and wonders why conversion rates look different from what the top-of-funnel numbers suggested.
Response time reduction tends to show measurable impact on conversion rates at the top of funnel, where speed-to-engagement has an outsized effect compared to later pipeline stages.
The agent handles simultaneous inbound volume without the capacity ceiling that affects human teams during campaign launches or event-driven traffic spikes.
It is not really a headcount story. It is a lead data integrity story that compounds into pipeline quality over multiple quarters — and that framing tends to survive post-deployment scrutiny far better than the headcount version does.
Deployment Scenario
Realistic Agent Benefit
Risk to Watch
Clean CRM, defined ICP
High consistency, measurable lead velocity
Occasional edge-case handling failures
Mid-market org, some data gaps
Moderate benefit, escalation volume rises
Agent confidence without sufficient human oversight
Complex enterprise, heavy customization
Benefit visible after prerequisite prep work
Event logic errors amplified at scale
Reading the what is new in Salesforce 2026 coverage across implementation partner blogs, one pattern stands out: the organizations getting early traction are not the ones with the largest budgets. They are the ones who ran a narrow, well-defined pilot with clean data before expanding scope.
The Buying Decision Nobody Is Saying Out Loud
Enterprise buyers in a renewal cycle right now are essentially being asked to accept that the platform they licensed eighteen months ago has been structurally rebuilt around AI agents — and that opting out of the agentic layer increasingly means opting out of where the product roadmap is going. Salesforce knows it. Most implementation partners know it too.
The sharper question isn’t whether to adopt Agentforce. It’s how deep into agentic architecture the organization is realistically willing to go over the next year — and whether the data hygiene and governance guardrails are actually in place before any agent gets near a live customer record. The organizations that frame it that way tend to spend less time untangling things six months in. Teams that start with the vendor’s ROI calculator tend to have more interesting conversations six months later, after the deployment.
The Salesforce Summer ’26 release is real progress — the triggered agents architecture, the orchestration primitives, the security enforcement that finally stops being advisory and starts being required. But the gap between what the platform can do and what a given enterprise is actually ready to execute on remains larger than the release notes suggest.
That gap is the real work, and it tends to fall on teams who were already at capacity before the upgrade notification arrived.
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