Decision Pack
Sample Decision Pack for Application Rationalization
How an application portfolio becomes a sign-off-ready rationalization decision: assess applications, spot redundancies, prioritize top actions, and trace assumptions back to the data source.
Real Arqlee output based on a synthetic example portfolio (Meyer Brandt Components). No customer data, no customer reference, no live scan.
Example scope
IT cost reduction at Meyer Brandt Components
Meyer Brandt Components is a synthetic DACH midmarket company — SAP-shaped core, growing SaaS estate, decentralized tool buying, constrained IT team. The CFO demands a tangible IT cost reduction.
Employees — manufacturing, multiple sites, SAP-shaped core
IT cost reduction demanded by the CFO
Applications detected from 5 existing exports (IT, finance, licenses, SSO) — no tool rollout, no integration
The guiding question: where can costs be cut without putting SAP, production, security, or customer processes at risk?
Overview
The result at a glance
One run turns 435 data rows into a defensible decision basis. The example run identifies €925,260 in expected annual savings — around 10.6% of the €8.75M annual cost basis.
Business Applications Assessed
0 / 123
100% Coverage
Expected Savings
-
identified potential €2.09M
Quick Wins
0
< 3 months feasible
Critical Risks
0
HIGH-Risk business applications
Run-rate impact
Current cost base
€8.75M
current base p.a.
Expected savings
−€925k
carried by 55 actions
New run-rate
€7.83M
after all actions implemented
Where the savings potential comes from
55 actions
Impact beyond savingsreduce risk, clarify ownership, strengthen data quality
€913k (99%) of the potential sits in low-to-medium implementation effort — 54 of 55 actions.
Actions
What to do: 72 prioritized actions
The actions view brings together the measures that emerge from diagnosis, cost and usage signals, risk, and data trust. This rebuild is interactive — real numbers from the example run across 123 assessed applications, filterable like in the platform.
Actions
0
of 72 in the register
Savings
Source-/confidence-adjusted and de-duplicated across the portfolio — the same figure as on Outcomes and in the Decision Pack. Identified gross potential: €2.09M.—
expected / year · portfolio
Quick Wins
0
low effort
Open decisions
0
not yet decided
By action type
Interactive preview with the 12 most important of 72 actions from the example run — expand rows for the decision question and next step. The full register is part of the Decision Pack.
Findings
What the portfolio shows as a whole
Findings condense what individual actions cannot show: patterns, gaps, and opportunities across the portfolio — each insight with evidence, reach, and linked actions. All nine findings from the example run, filterable by lens and type.
Findings
0
of 9 total
Critical
0
high severity
Linked measures
0
derived from these
By lens
Through which business or architecture lens do we read the portfolio?
All 9 findings from the example run — expand rows for the full detail.
Platform view: the portfolio in plain terms
Shown live in the call — here as an interactive rebuild with all 123 applications from the example run: diagnosis distribution, attention, risk, and savings potential per application.
By diagnosis
How does the portfolio break down by condition? Click to filter the list.
123 of 123 applications from the example run — filter, sort, and scroll like in the platform.
Google Workspace Business Plus
GoToMeeting Legacy
Nextcloud Internal
SharePoint 2013 Team Sites
SAP BusinessObjects
Talend Open Studio
Microsoft Dynamics NAV Legacy
Lexware Lohn Legacy
Moodle Internal
SAP Solution Manager
AnyDesk
Matrix42 Service Management
Adobe Marketo
Hootsuite
SAP ECC MM
Bitbucket Server Legacy
Mantis Bug Tracker
PTC Windchill Legacy
Redmine Legacy
Trello Business Class
Pipedrive
SAP CRM 7.0
Sophos Central
SQL Server Reporting Services
SAP ECC FI/CO
Greenhouse
Zoho CRM
Dropbox Business
SAP ECC PP
SAP ECC SD
Basware Invoice Workflow
SAP HCM Payroll
Partner Portal Legacy
SurveyMonkey
SAP PI/PO
Freshservice Pilot
Canva Pro
Jenkins CI
Adobe Sign
Excel VBA Reporting Hub
Microsoft 365 E5
Slack Enterprise Grid
ChatGPT Team
Informatica PowerCenter
Looker Studio
Power BI Premium
Brevo
Cisco Webex Meetings
Mailchimp
Coupa Procurement
Celonis
Kenjo Time
Microsoft Dynamics CRM
Workday HCM
TeamViewer Tensor
Mural
Intercom
Alteryx Designer
TravelPerk
ServiceNow ITSM
Airtable
Salesforce Sales Cloud
Customer Portal Custom
Zendesk Support
Personio HR
HubSpot Marketing Hub
Smartsheet
Okta Workforce Identity
SAP BW on HANA
Udemy Business
Box Business
DocuSign
Typeform
SolidWorks PDM
LinkedIn Recruiter
proAlpha APS
SAP GTS
AutoCAD
Siemens Teamcenter PLM
Miro
d.velop documents
Asana
Notion Team Wiki
Sprinklr Social
Tableau Cloud
monday.com
Databricks
SAP SuccessFactors Recruiting
Azure DevOps
Zoom
Microsoft Teams Phone
SharePoint Online
Salesforce Service Cloud
Snowflake
Jira Software
CrowdStrike Falcon
Microsoft Entra ID
Infor WMS
SAP Concur
Qlik Sense
Lucidchart
Azure Synapse
Google Analytics 4
DocuWare
GitLab
Confluence
SAP EWM
GitHub Enterprise
IBM Maximo Maintenance
Splunk Enterprise
Veeam Backup & Replication
SAP Signavio
Cornerstone LMS
EPLAN Electric P8
SonarQube
QMS CAQ.Net
MES HYDRA
CargoSoft TMS
JFrog Artifactory
Leapsome
Microsoft Planner
DATEV Unternehmen online
Exchange Online
Data Trust
How reliable the data basis is
Every recommendation discloses what it is based on: coverage, reconciliation against the input data, confidence per assessment, and the origin of every field — input, derived, AI-estimated, or missing.
See data handlingTrust Verdict
Decision-ready — broadly backed by validated data.
123 of 123 business applications assessed. 72 % of the Data Trust fields are backed by input data, 10 % is filled in by Arqlee Intelligence through derivation or estimation — 18 % remain open (confidence mostly HIGH).
100 %
72 %
HIGH
0
Data Coverage
assessed + no judgement + not captured = total
123 business applications total
123
100%
Business applications with a reliable recommendation from Arqlee Intelligence
0
0%
Seen by Arqlee Intelligence but not assessable — too little reliable information.
0
0%
Did not come back from Arqlee Intelligence — not analysed.
Input reconciliation
All 123 input business applications were reconciled.
Compares the imported input applications with the AI output after matching, before Data Trust separates assessed apps from apps without judgement.
100%
reconciled
Data Confidence
98 % of assessed business applications have HIGH confidence.
Confidence reflects the evidence quality of the recommendation — not the completeness of the input data. Arqlee Intelligence estimates missing fields from experience (~ prefix in the dashboard); that lowers confidence to MEDIUM or LOW.
Data Origin — input, derivation and estimate
72% Input
8% Derived
2% Estimate
18% Missing
aggregated across 8 Data Trust fields
Business Owner
No owner assigned — lowers confidence, blocks validation
Validated
Business Criticality
Criticality missing — business-fit score less reliable
Validated
Usage
User or usage data — missing values weaken low-usage and business-fit conclusions
Validated
Cost basis
No validated costs — savings are flagged as ~estimate
Validated
Technology stack
Technology evidence — missing values make technical risk less precise
Validated
Lifecycle phase
Lifecycle evidence missing — Arqlee Intelligence estimates from experience
Validated
Lifecycle dates
EOL or renewal-adjacent dates — missing values limit timing conclusions
Validated
Savings estimate
Quantified savings — missing when no defensible savings derivation is available
Validated
Two views of the same decision
What you just tried out above is the platform view — live in the customer call. The same result is available as a Decision Pack to take away.
Platform view
Interactive in the customer call: filter the portfolio, open findings, trace assumptions back to the source.
Decision Pack (export)
As PPTX for the board and XLSX for the detail. To pass on, without platform access.
Decision Pack: the deliverable
The same result as a sign-off-ready management presentation. An excerpt from the example run, slide by slide.
From the presentation
Click a slide to enlarge it
We walk through the full Decision Pack in a conversation, built on your own portfolio.
See it on your portfolioWhat a Decision Pack contains
Six building blocks, the same structure on every run.
See the methodology in detailProblem and scope
What was analyzed, with which data and what boundaries.
Methodology
Scoring dimensions plus the TIME model, extended by 6R for non-SaaS.
Portfolio view
Every application with diagnosis, recommendation and priority.
Measures and savings
Prioritized actions with effort, sequence and defensible savings potential.
Assumptions and limits
What was assumed and where validity ends. Deliberately disclosed.
Data Trust
Confidence per recommendation and traceability to the data source.
Assumptions & limits
What the Decision Pack deliberately discloses
A Decision Pack should accelerate decisions, not hide uncertainty. Assumptions, data gaps, and scope boundaries are therefore shown directly next to the recommendations.
This is not fine print: these limits prevent overclaiming and show which points should be clarified commercially, technically, or functionally before final sign-off.
More on security and privacyLicense cost and savings
Assumption
Finance, vendor, and license records are condensed into annual savings potential where the available data basis supports it.
Limit
This does not replace final contract review. Discounts, minimum commitments, termination windows, and special clauses need to be checked before implementation.
Usage signals
Assumption
SSO, login, and license usage indicate whether an application is likely active, underused, or a cleanup candidate.
Limit
Usage signals are not full process mining. Business necessity, offline usage, or technical service accounts can change the interpretation.
Migration and exit effort
Assumption
Actions separate savings potential, priority, and implementation effort so quick cleanups are not mixed with complex modernization work.
Limit
Migration cost, exit fees, technical detail architecture, and change effort are not automatically priced in unless they are available as input data.
Data quality and source conflicts
Assumption
Arqlee shows whether statements come from imported data, were derived, or need review because sources contradict each other.
Limit
Validity depends on export quality, matching, owner coverage, and the freshness of the provided data. Conflicts are disclosed, not silently smoothed away.
Critical systems
Assumption
High cost is interpreted in the context of business fit, technical fit, risk, and criticality, not treated as an automatic savings candidate.
Limit
Systems close to production, finance, security, or customer processes require customer-side validation before a hard measure is approved.
Final decision
Assumption
The Decision Pack provides the traceable decision basis: diagnosis, priority, evidence, assumptions, and next steps.
Limit
Final sign-off stays with the customer. Arqlee prioritizes and explains, but does not replace governance, procurement, an architecture board, or legal review.
See a Decision Pack on your portfolio
In a short conversation we show Arqlee on a slice of your data and clarify the next step.