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Performance Management with Fabric: The Next Frontier of Organizational Intelligence

#fabcon2026 #session #learning #microsoft #fabcon2026-session

Description

This session will explore how the principles behind "Performance Management IQ"—an analogy inspired by Microsoft’s new "Work IQ," "Fabric IQ," and "Foundry IQ" products—can be realized using Fabric as the central data platform for AI-ready insights. Finance, operations, and HR data become harmonized in Fabric to enable powerful performance management insights for analytics, data science, and AI.

Key Takeaways

My Notes

Action Items

Slides

Rich Lamorena
Principal, Data & AI Practice Leader
BDO USA
Noah Mattern
Data & AI Solution Director
BDO USA
Performance
Management
THE NEXT FRONTIER OF
ORGANIZATIONAL INTELLIGENCE
Unlocking insights with Microsoft
Fabric, Copilot, and Foundry.
Imagine a Future…
Humans and AI agents seamlessly
driving company results with
consistent, measurable criteria
SECTION 1
Introduction to
Frontier Firm Vision
“The Year the Frontier Firm Is Born”
MICROSOFT 2025 WORK TREND INDEX







Reveals the emergence of “Frontier Firms” — organizations that
successfully integrate AI agents into daily workflows
Based on a global study of 31,000 professionals across 31
countries
Frontier Firms are outperforming peers: 71% report thriving,
compared to 37% of other companies
Highlights a four-stage AI adoption journey
Emphasizes the new role of “agentic” leaders who manage AI
agents to drive results
82% of business leaders say 2025 is a make-or-break year to adopt
AI strategically
Offers insights for companies looking to scale, stay agile, and
redefine productivity in the AI era
BDO Recommendation: A defined KPI blueprint needs to blend the
performance management of both humans and AI against established
business targets.
Source: 2025: The Year the Frontier Firm Is Born
SECTION 2
Business Case For
Performance
Management
WHY NOW?
Real and Growing Impact of AI
MARGIN PROTECTION
PRODUCTIVITY & EFFICIENCY
 Stay ahead of competitors
 Reduce process waste
 Be resilient to evolving business
environment
 Reduce costs of doing business
 Drive new revenue opportunities
EMPLOYEE EXPERIENCE
 Reduce risks of tenured staff leaving
 Attract new generation of workers
 Improve experience and elevate talent
 Boost efficiency and cost savings
 Better integrations with existing systems
 Enhance your human capital
BUSINESS
VALUE
QUALITY & INSIGHTS
 Improve accuracy and quality of
information
 Get timely insights and 360 views
 Improve ability to quickly plan and reforecast
AI Adoption is Rising, Boosting
Productivity and ROI
$3.7x
for every $1 a company invests in a successful
generative AI deployment, the ROI is $3.7x.3
70%
of transformations fail to achieve their desired
results due to change management challenges.2
75%
of knowledge workers
around the world use
generative AI at work.1

  1. AI at Work Is Here. Now Comes the Hard Part
  2. Boston Consulting – How to Create a Transformation that lasts.
  3. IDC’s 2024 AI opportunity study: Top five AI trends to watch - The Official Microsoft Blog
    Data Readiness is Critical to AI Evolution
    DATA ALIGNS TRUST BETWEEN HUMANS AND AI
    Through 2025, 30% of generative AI (GenAI) projects will be abandoned after
    proof of concept due to poor data quality, inadequate risk controls, escalating
    costs or unclear business value.
    AI-ready data means that
    your data must be
    representative of the use
    case, key patterns, errors,
    outliers and unexpected
    emergence that is needed to
    train or run the AI/ML models.
    Data readiness for AI is not
    something that is build once
    and for all – it is a process,
    and a practice based on
    availability of metadata to
    align, qualify, and govern the
    data, supported by right
    integrations and modern data
    platform setup.
    Evolving State of AI’s Impact on Organizations
    TRANSFORMING TEAMS AND ORG STRUCTURES THROUGH THE DEPLOYMENT OF AI
    Increasing Value and Impact
    AI transformation begins by empowering individuals and advances through team-based process improvements being
    driven by organizational outcomes. The result is a more agile and dynamic workforce and ultimately creating scale.
    401 – AI OPERATING MODEL
    Orchestrating human and digital capital through dynamic structures enables seamless
    processes that drive business outcomes
    301 - TEAM AUGMENTATION
    Deploys AI capabilities alongside human team members to deliver outcomes
    201 – INDIVIDUAL ASSISTANCE
    AI works alongside the individual to support simple processes
    101 - INDIVIDUAL EMPOWERMENT
    Accelerates individual productivity with AI-powered search, summarization, analysis, and content creation
    Maturity Timeline
    KPI Blueprint: Attribution Process
    MEASURING COMPANY PERFORMANCE IMPACTS ATTRIBUTED TO PEOPLE AND TECHNOLOGY
    With a proper data and AI platform configured, you can measure critical KPIs like
    sales, revenue, and profitability. Take it one step further by attributing those KPIs
    to people, organizational initiatives, and technology investments where ROI can
    be directly reviewed.
    Prerequisite: Business alignment to track, govern, and measure KPIs
    Define KPIS
    Define the specific hard
    and soft KPIs based on
    company goals and
    availability of data to
    measure.
    Initiation
    Set Goals and Targets
    Set targeted goals (by
    people, teams,
    departments, job
    functions, and AI
    innovations).
    Periodically
    Review and Manage
    Measure and drive
    performance
    continuously utilizing
    actual KPI results.
    Continuously
    ATTRIBUTION
    With the right level of invested
    configuration in your data & AI
    platform, measure and
    associate/attribute company
    KPIs like invoiced revenue and
    profitability to the following
    corporate investments:

    Individual team members

    Teams (by organizational
    structure like departments
    or “virtual” teams of
    people)

    Sales strategy initiatives

    Marketing initiatives

    Digital and AI tool
    investments
    KPI BLUEPRINT
    Recommended KPIs for Professional Services
    Below is intended to be a starting point and will need to be tailored to your firm:
    Top Line Measures
     Pipeline
     Sales Revenue
     Billed Revenue
     Invoiced Revenue
     YoY Growth
     AR Aging
     Marketing
    Attribution
    Bottom Line
    Measures
     Gross Contribution
     EBITDA
     Expense Alignment
     Forecast Accuracy
    Rates and Pricing
     Average Rates
     Pricing and
    Realization
     Service Line
    Profitability
    Utilization
     Billable Hours/
    Available Hours
     Capacity and
    Headcount
     Seasonal Forecasts
    vs. Actuals
     Talent Acquisition
    Time to Fill
    KPI BLUEPRINT
    Governed Measurement
    of KPI Goals
    This dashboard shows comprehensive performance details
    for an individual team member, business initiative like
    marketing campaign, team performance, or AI initiative.
    The right summarizes hard KPI performance expectations
    vs. attainment to date.
    A good attribution model will ensure that KPI measurement
    (like revenue) follow the firm’s established rules. It shows
    the role performed during the KPI goal attainment (for
    example an individual who leads a sales deal closure vs. an
    AI agent that is utilized to support the sale).
    This section shows soft KPI attainment (in this example
    positive survey feedback), to provide additional context on
    the performance.
    KPI BLUEPRINT
    Detailed Context of KPI
    Attainment
    Analyze KPI actuals for an individual, team, marketing
    campaign, or AI initiative. Color coded indicators show
    areas of performance enhancement or attention required.
    Analyze details like the individual leader accountable or
    other supporting context to identify where to follow up.
    Toggle between different views of hard KPIs vs. soft KPIs,
    so you get specific lenses on performance context.
    Filter or drill into details like employee, team, service line,
    client, geographic region, industry, or AI initiative for
    further business context and planning.
    Guiding Advice
    FABRIC: AI READY DATA
    AI DEVELOPMENT MINDSET
     It’s okay to move quickly and build one-off
     Low Code – for rapid delivery and early end
     But to build long term intelligence for humans
     Pro Code – to fully control what you build and
    operational reports
    and AI, your enterprise data fabric matters
    • Descriptive analytics and semantic models
    • Data science and advanced analytics
    • Leveraging evolving Microsoft Fabric IQ,
    Foundry IQ, and Work IQ
    user feedback
    customize
    SECTION 3
    Microsoft Fabric Central Data
    Intelligence
    Fabric Data
    Platform
    Native support for
    semantic models
    Unified, lake-centric
    architecture
    Platform
    Capability
    TOOLS &
    TECHNOLOGY
    Seamless access across domains
    (finance, ops, people)
    Medallion Architecture
    Silver
    Bronze


    Raw, uncurated source
    data from ERP, SPA, HRIS,
    ATS, LMS, etc.
    Maintains traceability
    and auditability for
    finance and HR data


    Enriched, conformed
    data
    Standardize formats,
    filter data, fix source
    errors, remove
    duplicates, add lineage
    attributes, join data
    from multiple systems
    for MDM
    Gold



    Dimensional modeling
    and aggregation
    Data aligned with
    business entities with
    human-readable
    table/column names
    Business rules applied
    and views created for
    semantic model/
    reporting
    Semantic Model


    Logical description of
    analytics domain with
    metrics and business
    friendly terminology
    Unify sources to compute
    project margin,
    contribution margin,
    employee utilization,
    bench time analytics etc.
    Fabric Architecture
    Compute
    Scheduling and
    Orchestration
    Sources
    Ingestion
    Storage
    Presentation
    Finance
    Lakehouse
    Lakehouse
    Lakehouse
    Silver
    Gold
    Operations
    Warehouse
    Bronze
    People
    Operations
    Source Control
    Deployment
    Governance
    Monitoring
    Secrets
    Fabric Architecture Demo
    SECTION 4
    Advanced Analytics
    and Data Science
    RISK ANALYSIS
    Data Clustering
    Data Extraction
    Aggregate metrics by
    PM and Consultant
     Hours
     Revenue
     Client volume
     Time since hire
    Data Preparation
    Normalize features
    with StandardScaler
    function in Python for
    clustering
    Clustering Analysis
    K-Means elbow
    analysis and
    segmentation into
    clusters for PMs and
    Consultants
    Visualization
    t-SNE 2D plot of data
    to reveal distinct
    clusters, trends, and
    outliers
    Business Labeling
    Assign labels to each
    PM and Consultant
    cluster, and load data
    into Fabric lakehouse
    for creating
    PM/Consultant risk
    matrix
    RISK ANALYSIS
    Deployment
    Checklist

    Dev, test, and production workspaces are setup in Fabric to support multiple environments

    Entra ID security groups are created by environment to control access to the workspaces and
    resources within

    The risk analysis clustering model uses data from the gold layer of the medallion architecture
    that lives in a Fabric lakehouse

    CI/CD pipelines in DevOps are used for deployment of resources within the solution between
    environments:
     The lakehouse that has the gold layer data and any shortcuts
     A series of notebooks that performs clustering of PMs and consultants and then creates a
    matrix of those clusters for visualization
     The notebooks are setup to process and apply the clustering model to data from the
    current year by default to identify revenue leakage risks. They can easily be updated to
    analyze data for prior years through a notebook parameter.
     The CI/CD pipeline replaces GUIDs for lakehouses (and their workspace) that the
    notebooks are attached to based on the environment
     The lakehouse that stores the clustered data and matrix for visualization



    Semantic model built on top of the processed data combined with other gold layer tables
    Risk analysis dashboard in Power BI
    A data pipeline that orchestrates the execution of notebooks and refreshes the semantic
    model and the schedule it executes on
    RISK ANALYSIS
    Demo
    RISK ANALYSIS
    Demo
    RISK ANALYSIS
    Demo
    RISK ANALYSIS
    Demo
    SECTION 5
    Data Agents
    DEMO
    Fabric Data Agents
    Generative AI Trends
    93%
    61%
    Organizations are experimenting
    with multiple models1
    People are wary about trusting AI systems3
    50%
    30%
    Enterprises using generative AI will
    launch agentic AI pilots by 20272
    Or fewer generative AI experiments
    moved to production4
  4. 16 Changes to the Way Enterprises Are Building and Buying Generative AI | Andreessen Horowitz
  5. Autonomous generative AI agents | Deloitte Insights
  6. Trust in artificial intelligence – 2023 Global study on the shifting public perceptions of AI, KPMG
  7. GenAI and the future enterprise | Deloitte Insights
    Fabric Data Agent Demo
    DEMO
    Copilot Studio
    COMPLETE TECH STACK TO BUILD YOUR GENERATIVE AI SOLUTIONS
    Copilot and AI Stack
    Copilot Studio

Azure AI Foundry
Data
Visual Studio
+
Azure
Infrastructure
Cloud to Edge
GitHub
Trustworthy AI
Copilot Studio Demo
DEMO
Foundry
Azure AI Foundry
Model Catalog
Copilot Studio
Visual Studio
Azure AI
Foundry SDK
Foundational models
Azure
OpenAI Service
Evaluations
Open-source models
Azure
AI Search
Task models
Azure AI
Content
Safety
Azure AI
Agent Service
Customization
Governance
Industry models
Azure Machine
Learning
Monitoring
Observability
Foundry Demo
The Next AI
Platform Is Here
TO
FROM
Simple POCs
Platform Shift
Single model
Multi-model
Automated Workflows
Continuous improvement
Point-in-time ROI
Innovate with Azure AI Platform
ESSENTIALS FOR GETTING AI READY

Adopt financial best practices

Pricing

Skilling

Migrate and modernize workloads and data collocation

Platform landing zone

Trustworthy AI: security, privacy and transparency

Architectural guidance

Generative AIOps

Data governance

Monitoring and security

Management and optimization

Architectural assessments and remediations
Manage
and
Optimize
Readiness
and
Foundation
Design
and Govern
SECTION 6
Emerging AI Features
In Fabric and Foundry
Emerging Features - Foundry
Move from prototype
to production in
hours, not weeks:
The new Microsoft
Agent Framework and
Hosted Agents let you
build, test, and
deploy multi-agent AI
systems with
enterprise-grade
security—no
Kubernetes or
container headaches.
Orchestrate any
model, anywhere:
Model Router and BYO
Model Gateway let
you mix and match
thousands of models
(including Claude,
GPT, and your own)
with unified
governance and
compliance—no code
changes required.
Ship agents to Teams
and M365 with
one click:
New low-code/nocode tools, templates,
and deployment
channels make it easy
to launch and scale AI
agents for your users.
Build smarter, more
reliable workflows:
Multi-agent
orchestration,
persistent memory,
and deep Microsoft
365 integration enable
robust, context-aware
solutions for complex
enterprise scenarios.
Access the best
models in one place:
Azure is now the only
cloud with both
Anthropic’s Claude
and OpenAI’s GPT
models—choose the
right tool for
every job.
Emerging Features - Fabric
Fabric IQ
Ontology
Anomaly Detection
New semantic foundation
within Microsoft Fabric.
Fabric IQ is not a
replacement for your data
estate; it's a force
multiplier for every
investment you’ve
already made.
Lets you define entity
types, relationships,
properties, and other
constraints to organize
data according to your
business vocabulary.
With a no-code interface,
automatic model
selection, and flexible
alerts, tracking changes
and unexpected events
is easy.
Create Embeddings in
Eventhouse with built-in
Small Language Models
Built-in Small Language Models
(SLMs) in Eventhouse can
generate text embeddings
locally using ai_embeddings
plugin (preview), enabling
semantic search, RAG pipelines,
and high-volume embedding
generation without external
endpoints, callout policies, or
per-request costs.
SECTION 7
Conclusion – Realizing
the Frontier Firm
Evolving State of AI’s Impact on Organizations
TRANSFORMING TEAMS AND ORG STRUCTURES THROUGH THE DEPLOYMENT OF AI
Increasing Value and Impact
AI transformation begins by empowering individuals and advances through team-based process improvements being
driven by organizational outcomes. The result is a more agile and dynamic workforce and ultimately creating scale.
401 – AI OPERATING MODEL
Orchestrating human and digital capital through dynamic structures enables seamless
processes that drive business outcomes
301 - TEAM AUGMENTATION
Deploys AI capabilities alongside human team members to deliver outcomes
201 – INDIVIDUAL ASSISTANCE
AI works alongside the individual to support simple processes
101 - INDIVIDUAL EMPOWERMENT
Accelerates individual productivity with AI-powered search, summarization, analysis, and content creation
Maturity Timeline
Guiding Advice
FABRIC: AI READY DATA
AI DEVELOPMENT MINDSET
 It’s okay to move quickly and build one-off
 Low Code – for rapid delivery and early end
 But to build long term intelligence for humans
 Pro Code – to fully control what you build and
operational reports
and AI, your enterprise data fabric matters
• Descriptive analytics and semantic models
• Data science and advanced analytics
• Leveraging evolving Microsoft Fabric IQ,
Foundry IQ, and Work IQ
user feedback
customize
Sound off.
The mic is all yours.
Influence the product roadmap.
Join the Fabric User Panel
Join the SQL User Panel
Share your feedback directly with our
Fabric product group and researchers.
Influence our SQL roadmap and ensure
it meets your real-life needs
https://aka.ms/JoinFabricUserPanel
https://aka.ms/JoinSQLUserPanel
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