Tag: technology

Analytics, BI Administration, Business Intelligence, Business Intelligence (BI) Development, Data Integration, Data Integration (ETL), Data Modeling, Data Strategy, Data Warehousing, DP-600, Microsoft Certification, Microsoft Fabric, Power BI, Power Query

Implement Incremental Refresh for Semantic Models

 
This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Implement and manage semantic models (25-30%) 
    --> Optimize enterprise-scale semantic models 
        --> Implement Incremental Refresh for Semantic Models

Overview

Incremental refresh is a key optimization technique for enterprise-scale semantic models in Microsoft Fabric and Power BI. Instead of fully refreshing all data during each refresh cycle, incremental refresh allows you to refresh only new or changed data, significantly improving refresh performance, reducing resource consumption, and enabling scalability for large datasets.

In the DP-600 exam, this topic appears under Optimize enterprise-scale semantic models and focuses on when, why, and how to configure incremental refresh correctly.

What Is Incremental Refresh?

Incremental refresh is a feature for Import mode and Hybrid (Import + DirectQuery) semantic models that:

  • Partitions data based on date/time columns
  • Refreshes only a recent portion of data
  • Retains historical data without reprocessing it
  • Optionally supports real-time data using DirectQuery

Incremental refresh is not applicable to:

  • Direct Lake–only semantic models
  • Pure DirectQuery models

Key Benefits

Incremental refresh provides several enterprise-level advantages:

  • Faster refresh times for large datasets
  • Reduced memory and CPU usage
  • Improved reliability of scheduled refreshes
  • Better scalability for growing fact tables
  • Enables near-real-time analytics when combined with DirectQuery

Core Configuration Components

1. Date/Time Column Requirement

Incremental refresh requires a column that:

  • Is of type Date, DateTime, or DateTimeZone
  • Represents a monotonically increasing timeline (for example, OrderDate or TransactionDate)

This column is used to define data partitions.

2. RangeStart and RangeEnd Parameters

Incremental refresh relies on two Power Query parameters:

  • RangeStart – Beginning of the refresh window
  • RangeEnd – End of the refresh window

These parameters:

  • Must be of type Date/Time
  • Are used in a filter step in Power Query
  • Are evaluated dynamically during refresh

Exam tip: These parameters are required, not optional.

3. Refresh and Storage Policies

When configuring incremental refresh, you define two key time windows:

PolicyPurpose
Store rows from the pastDefines how much historical data is retained
Refresh rows from the pastDefines how much recent data is refreshed

Example:

  • Store data for 5 years
  • Refresh data from the last 7 days

Only the refresh window is reprocessed during each refresh.

4. Optional: Detect Data Changes

Incremental refresh can optionally use a change detection column (for example, LastModifiedDate):

  • Only refreshes partitions where data has changed
  • Reduces unnecessary refresh operations
  • Column must be reliably updated when records change

This is especially useful for slowly changing dimensions.

Incremental Refresh with Real-Time Data (Hybrid Tables)

Incremental refresh can be combined with DirectQuery to support real-time data:

  • Historical data → Import mode
  • Recent data → DirectQuery

This configuration:

  • Uses the “Get the latest data in real time” option
  • Is commonly referred to as a Hybrid table
  • Balances performance with freshness

Deployment and Execution Behavior

  • Incremental refresh is defined in Power BI Desktop
  • Partitions are created only after publishing
  • Refresh execution happens in the Fabric service
  • Desktop refresh does not create partitions

Exam tip: Many questions test the difference between design-time configuration and service-side execution.

Limitations and Considerations

  • Requires Import or Hybrid mode
  • Date column must exist in the fact table
  • Cannot be configured directly in Fabric service
  • Schema changes may require full refresh
  • Partition count should be managed to avoid excessive overhead

Common DP-600 Exam Scenarios

You may be asked to:

  • Choose incremental refresh to solve long refresh times
  • Identify missing requirements (RangeStart/RangeEnd)
  • Decide between full refresh vs incremental refresh
  • Configure refresh windows for historical vs recent data
  • Combine incremental refresh with real-time analytics

When to Use Incremental Refresh (Exam Heuristic)

Choose incremental refresh when:

  • Fact tables are large and growing
  • Only recent data changes
  • Full refresh times are too long
  • Import mode is required for performance

Avoid it when:

  • Data volume is small
  • Real-time access is required for all data
  • Using Direct Lake–only models

Exam Tips

For DP-600, remember:

  • RangeStart / RangeEnd are mandatory
  • Incremental refresh = Import or Hybrid
  • Partitions are service-side
  • Refresh window ≠ storage window
  • Hybrid tables enable real-time + performance

Summary

Incremental refresh is a foundational optimization technique for large semantic models in Microsoft Fabric. For the DP-600 exam, focus on:

  • Required parameters (RangeStart, RangeEnd)
  • Refresh vs storage windows
  • Import and Hybrid model compatibility
  • Real-time and change detection scenarios
  • Service-side execution behavior

Practice Questions:

Here are 10 questions to test and help solidify your learning and knowledge. As you review these and other questions in your preparation, make sure to …

  • Identifying and understand why an option is correct (or incorrect) — not just which one
  • Look for and understand the usage scenario of keywords in exam questions to guide you
  • Expect scenario-based questions rather than direct definitions

Question 1

You have a large fact table with 5 years of historical data. Only the most recent data changes daily. Which feature should you implement to reduce refresh time?

A. DirectQuery mode
B. Incremental refresh
C. Calculated tables
D. Composite models

✅ Correct Answer: B

Explanation:
Incremental refresh is designed to refresh only recent data while retaining historical partitions, significantly improving refresh performance for large datasets.

Question 2

Which two Power Query parameters are required to configure incremental refresh?

A. StartDate and EndDate
B. MinDate and MaxDate
C. RangeStart and RangeEnd
D. RefreshStart and RefreshEnd

✅ Correct Answer: C

Explanation:
Incremental refresh requires RangeStart and RangeEnd parameters of type Date/Time to define partition boundaries.

Question 3

Where are incremental refresh partitions actually created?

A. Power BI Desktop during data load
B. Fabric Data Factory
C. Microsoft Fabric service after publishing
D. SQL endpoint

✅ Correct Answer: C

Explanation:
Partitions are created and managed only in the Fabric service after the model is published. Desktop refresh does not create partitions.

Question 4

Which storage mode is required to use incremental refresh?

A. DirectQuery only
B. Direct Lake only
C. Import or Hybrid
D. Dual only

✅ Correct Answer: C

Explanation:
Incremental refresh works with Import mode and Hybrid tables. It is not supported for DirectQuery-only or Direct Lake–only models.

Question 5

You configure incremental refresh to store 5 years of data and refresh the last 7 days. What happens during a scheduled refresh?

A. All data is fully refreshed
B. Only the last 7 days are refreshed
C. Only the last year is refreshed
D. Only new rows are loaded

✅ Correct Answer: B

Explanation:
The refresh window defines how much data is reprocessed. Historical partitions outside that window are retained without refresh.

Question 6

Which column type is required for incremental refresh filtering?

A. Text
B. Integer
C. Boolean
D. Date/DateTime

✅ Correct Answer: D

Explanation:
Incremental refresh requires a Date, DateTime, or DateTimeZone column to define time-based partitions.

Question 7

What is the purpose of the Detect data changes option?

A. To refresh all partitions automatically
B. To detect schema changes
C. To refresh only partitions where data has changed
D. To enable real-time DirectQuery

✅ Correct Answer: C

Explanation:
Detect data changes uses a change-tracking column (e.g., LastModifiedDate) to avoid refreshing partitions when no data has changed.

Question 8

Which scenario best fits a Hybrid incremental refresh configuration?

A. All data must be queried in real time
B. Small dataset refreshed once per day
C. Historical data rarely changes, but recent data must be real time
D. Streaming data only

✅ Correct Answer: C

Explanation:
Hybrid tables combine Import for historical data and DirectQuery for recent data, providing real-time access where needed.

Question 9

What happens if the date column used for incremental refresh contains null values?

A. Incremental refresh is automatically disabled
B. Only historical partitions fail
C. Refresh may fail or produce incorrect partitions
D. Null values are ignored safely

✅ Correct Answer: C

Explanation:
The date column must be reliable. Null or invalid values can break partition logic and cause refresh failures.

Question 10

When should you avoid using incremental refresh?

A. When the dataset is large
B. When only recent data changes
C. When using Direct Lake–only semantic models
D. When refresh duration is long

✅ Correct Answer: C

Explanation:
Incremental refresh is not supported for Direct Lake–only models, as Direct Lake handles freshness differently through OneLake access.

BI Administration, Business Intelligence Platform, Data Security, DP-600, Microsoft Certification, Microsoft Fabric

Implement workspace-level access controls in Microsoft Fabric

 
This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Maintain a data analytics solution 
    --> Implement security and governance 
        --> Implement workspace-level access controls

To Do:
Complete the related module for this topic in the Microsoft Learn course: Secure data access in Microsoft Fabric

Workspace-level access control is the first and most fundamental security boundary in Microsoft Fabric. It determines who can access a workspace, what actions they can perform, and how they can interact with Fabric items such as Lakehouses, Warehouses, semantic models, reports, notebooks, and pipelines.

For the DP-600 exam, you should clearly understand workspace roles, their permissions, and how workspace security integrates with broader governance practices.

What Are Workspace-Level Access Controls?

Workspace-level access controls define permissions at the workspace scope, applying to all items within that workspace unless further restricted by item-level or data-level security.

These controls are managed through workspace roles, which are assigned to:

  • Individual users
  • Microsoft Entra ID (Azure AD) security groups
  • Distribution lists (limited scenarios)

Workspace Roles in Microsoft Fabric

Microsoft Fabric workspaces use role-based access control (RBAC). There are 4 roles that users can be assigned to for workspace access and each role grants a predefined set of permissions.

1. Admin

Highest level of access

Admins can:

  • Manage workspace settings
  • Add or remove users and assign roles
  • Delete the workspace
  • Control capacity assignment
  • Access and manage all items

Typical use cases

  • Platform administrators
  • Lead analytics engineers

Exam note
Admins automatically have all permissions of lower roles.

2. Member

Full content creation and collaboration role

Members can:

  • Create, edit, and delete Fabric items
  • Publish and update semantic models and reports
  • Share content
  • Run pipelines and notebooks

Members cannot:

  • Delete the workspace
  • Manage capacity settings

Typical use cases

  • Analytics engineers
  • Senior analysts

3. Contributor

Content creation with limited governance control

Contributors can:

  • Create and modify items they have access to
  • Run notebooks, pipelines, and queries
  • Publish reports and datasets

Contributors cannot:

  • Manage workspace users
  • Modify workspace settings

Typical use cases

  • Data analysts
  • Developers contributing content

4. Viewer

Read-only access

Viewers can:

  • View reports and dashboards
  • Read data from semantic models
  • Execute queries if explicitly allowed

Viewers cannot:

  • Create or edit items
  • Publish or share content

Typical use cases

  • Business users
  • Report consumers

Summary table:

RoleDescriptionCan / CannotTypical use cases
Admin– Highest level of access.
– Full workspace administration access including ability to delete.		Admins Can:
– Manage workspace settings
– Add or remove users and assign roles
– Delete the workspace
– Control capacity assignment
– Access and manage all items		– Platform administrators
– Lead analytics engineers
Member– Full content creation and collaboration role.
– Can manage members with same or lower permissions.		Members can:
– Create, edit, and delete Fabric items
– Publish and update semantic models and reports
– Share content
– Run pipelines and notebooks

Members cannot:
– Delete the workspace
– Manage capacity settings		– Analytics engineers
– Senior analysts
Contributor– Content creation with limited governance control
– Can create and manage workspace content		Contributors can:
– Create and modify items they have access to
– Run notebooks, pipelines, and queries
– Publish reports and datasets

Contributors cannot:
– Manage workspace users
– Modify workspace settings		– Data analysts
– Developers contributing content
Viewer– Read-only access to the workspaceViewers can:
– View reports and dashboards
– Read data from semantic models
– Execute queries if explicitly allowed

Viewers cannot:
– Create or edit items
– Publish or share content		– Business users
– Report consumers

How Workspace-Level Security Is Enforced

Workspace-level access controls:

  • Are evaluated before item-level or data-level security
  • Determine whether a user can even see workspace content
  • Apply consistently across all Fabric workloads (Power BI, Lakehouse, Warehouse, Data Factory, Real-Time Analytics)

This makes workspace roles the entry point for all other security mechanisms.

Best Practices for Workspace-Level Access Control

Use Security Groups Instead of Individuals

  • Assign Microsoft Entra ID security groups to workspace roles
  • Simplifies access management
  • Supports scalable governance

Separate Workspaces by Purpose

Common patterns include:

  • Development vs Test vs Production
  • Department-specific workspaces
  • Consumer-only (Viewer) workspaces

Apply Least Privilege

  • Grant users the lowest role necessary
  • Avoid overusing Admin and Member roles

Relationship to Other Security Layers

Workspace-level access controls work alongside:

  • Item-level permissions (e.g., sharing a report)
  • Row-level, column-level, and object-level security in semantic models
  • File-level security in OneLake
  • Capacity-level governance

For exam scenarios, always identify which security layer is being tested.

Common Exam Scenarios to Watch For

You may be asked to:

  • Choose the correct workspace role for a given user persona
  • Identify why a user cannot see or edit workspace content
  • Decide when to use Viewer vs Contributor
  • Understand how workspace roles interact with RLS or file access

Key Exam Takeaways

  • Workspace roles control who can access a workspace and what actions they can perform
  • Admin, Member, Contributor, and Viewer each have distinct permission boundaries
  • Workspace security is broader than item-level sharing
  • Always think workspace first, data second when designing security

Exam Tips

If the question is about who can create, edit, share, or manage content, the answer almost always involves workspace-level access controls.

Expect scenario-based questions that test:

  • Choosing the least-privileged role
  • Understanding the difference between Member vs Contributor
  • Knowing when workspace security is not enough and must be combined with RLS or item-level access

Practice Questions

Question 1 (Single choice)

Which workspace role in Microsoft Fabric allows a user to publish content, manage permissions, and delete the workspace?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: D

Explanation:

  • Admin is the highest workspace role and includes full control, including managing access, deleting the workspace, and assigning roles.
  • Contributors and Members cannot manage workspace-level permissions.
  • Viewers have read-only access.

Question 2 (Scenario-based)

You want analysts to create and edit items (lakehouses, notebooks, reports) but prevent them from managing access or deleting the workspace. Which role should you assign?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: C

Explanation:

  • Members can create, edit, and publish content but cannot manage workspace access or delete the workspace.
  • Contributors have more limited permissions.
  • Admins have excessive privileges for this scenario.

Question 3 (Multi-select)

Which actions are possible for a user assigned the Contributor role? (Select all that apply.)

A. Create new items
B. Edit existing items
C. Manage workspace permissions
D. Publish reports to the workspace

Correct Answers: A, B

Explanation:

  • Contributors can create and edit items.
  • They cannot manage permissions or perform full publishing/administrative actions.
  • Publishing to app audiences or managing access requires Member or Admin.

Question 4 (Scenario-based)

A workspace contains sensitive data. You want executives to view reports only, without seeing datasets, lakehouses, or notebooks. What is the BEST approach?

A. Assign Viewer role
B. Assign Contributor role
C. Assign Member role
D. Assign Admin role

Correct Answer: A

Explanation:

  • Viewer role provides read-only access and prevents exposure to underlying assets beyond consumption.
  • Other roles expose authoring and object-level visibility.

Question 5 (Single choice)

Workspace-level access controls in Fabric are applied to:

A. Individual tables only
B. Semantic models only
C. All items within the workspace
D. Reports published to apps only

Correct Answer: C

Explanation:

  • Workspace-level roles apply across all items in the workspace unless further restricted using item-level or semantic-model security.
  • Finer-grained security must be implemented separately.

Question 6 (Scenario-based)

You need to ensure that workspace access is centrally governed and users cannot self-assign roles. What is the BEST practice?

A. Allow Members to manage access
B. Restrict access management to Admins only
C. Use Viewer roles exclusively
D. Disable workspace sharing

Correct Answer: B

Explanation:

  • Only Admins should manage workspace access for governance and compliance.
  • Members should not be allowed to assign roles in controlled environments.

Question 7 (Multi-select)

Which of the following are valid workspace roles in Microsoft Fabric? (Select all that apply.)

A. Viewer
B. Contributor
C. Member
D. Owner

Correct Answers: A, B, C

Explanation:

  • Valid Fabric workspace roles are Viewer, Contributor, Member, and Admin.
  • “Owner” is not a Fabric workspace role.

Question 8 (Scenario-based)

A user can view reports but receives an error when attempting to open a semantic model directly. What is the MOST likely reason?

A. They are a Contributor
B. They are a Viewer
C. The dataset is in Import mode
D. XMLA endpoint is disabled

Correct Answer: B

Explanation:

  • Viewers can consume reports but may not have permissions to explore or access underlying semantic models directly.
  • This behavior aligns with workspace-level access restrictions.

Question 9 (Single choice)

Which statement about workspace-level access vs. item-level security is TRUE?

A. Workspace access overrides all other security
B. Workspace access is more granular than item-level security
C. Item-level security can further restrict access granted by workspace roles
D. Workspace access only applies to reports

Correct Answer: C

Explanation:

  • Workspace roles grant baseline access, which can then be restricted using item-level security, RLS, or object-level permissions.
  • Workspace access does not override more restrictive controls.

Question 10 (Scenario-based)

You want to minimize administrative overhead while allowing self-service analytics. Which workspace role strategy is MOST appropriate?

A. Assign Admin to all users
B. Assign Member to authors and Viewer to consumers
C. Assign Contributor to executives
D. Assign Viewer to data engineers

Correct Answer: B

Explanation:

  • This is a recommended best practice:
    • Members for authors/builders
    • Viewers for consumers
  • It balances governance and agility while minimizing risk.
AI, Artificial Intelligence (AI), Data Analysis, Data Strategy, Machine Learning (ML), Natural Language Processing (NLP)

AI in Gaming: How Artificial Intelligence is Powering Game Production and Player Experience

The gaming industry isn’t just about fun and entertainment – it’s one of the largest and fastest-growing industries in the world. Valued at over $250 billion in 2024, it’s expected to surge past $300 billion by 2030. And at the center of this explosive growth? Artificial Intelligence (AI). From streamlining game development to building creative assets faster to shaping immersive and personalized player experiences, AI is transforming how games are built and how they are played. Let’s explore how.

1. AI in Gaming Today

AI is showing up both behind the scenes (in development studios and in technology devices) and inside the games themselves.

  • AI Agents & Workflow Tools: A recent survey found that 87% of game developers already incorporate AI agents into development workflows, using them for tasks such as playtesting, balancing, localization, and code generation PC GamerReuters. For bug detection, Ubisoft developed Commit Assistant, an AI tool that analyzes millions of lines of past code and bug fixes to predict where new errors are likely to appear. This has cut down debugging time and improved code quality, helping teams focus more on creative development rather than repetitive QA.
  • Content & Narrative: Over one-third of developers utilize AI for creative tasks like dynamic level design, animation, dialogue writing, and experimenting with gameplay or story concepts PC Gamer. Games like Minecraft and No Man’s Sky use AI to dynamically create worlds, keeping the player experience fresh.
  • Rapid Concept Ideation: Concept artists use AI to generate dozens of initial style options—then pick a few to polish with humans. Way faster than hand-sketching everything Reddit.
  • AI-Powered Game Creation: Roblox recently announced generative AI tools that let creators use natural language prompts to generate code and 3D assets for their games. This lowers the barrier for new developers and speeds up content creation for the platform’s massive creator community.
  • Generative AI in Games: On Steam, roughly 20% of games released in 2025 use generative AI—up 681% year-on-year—and 7% of the entire library now discloses usage of GenAI assets like art, audio, and text Tom’s Hardware.
  • Immersive NPCs: Studios like Jam & Tea, Ubisoft, and Nvidia are deploying AI for more dynamic, responsive NPCs that adapt in real time—creating more immersive interactions AP News. These smarter, more adaptive NPCs react more realistically to player actions.
  • AI-Driven Tools from Tech Giants: Microsoft’s Muse model generates gameplay based on player interaction; Activision sim titles in Call of Duty reportedly use AI-generated content The Verge.
  • Playtesting Reinvented: Brands like Razer now embed AI into playtesting: gamers can test pre-alpha builds, and AI tools analyze gameplay to help QA teams—claiming up to 80% reduction in playtesting cost Tom’s Guide. EA has been investing heavily in AI-driven automated game testing, where bots simulate thousands of gameplay scenarios. This reduces reliance on human testers for repetitive tasks and helps identify balance issues and bugs much faster.
  • Personalized Player Engagement: Platforms like Tencent, the largest gaming company in the world, and Zynga leverage AI to predict player behavior and keep them engaged with tailored quests, events, offers, and challenges. This increases retention while also driving monetization.
  • AI Upscaling and Realism
    While not a game producer, NVIDIA’s DLSS (Deep Learning Super Sampling) has transformed how games are rendered. By using AI to upscale graphics in real time, it delivers high-quality visuals at faster frame rates—giving players a smoother, more immersive experience.
  • Responsible AI for Fair Play and Safety: Microsoft is using AI to detect toxic behavior and cheating across Xbox Live. Its AI models can flag harassment or unfair play patterns, keeping the gaming ecosystem healthier for both casual and competitive gamers.

2. Tools, Technologies, and Platforms

Let’s take a look at things from the technology type standpoint. As you may expect, the gaming industry uses several AI technologies:

  • AI Algorithms: AI algorithms dynamically produce game content—levels, dialogue, music—based on developer input, on the fly. This boosts replayability and reduces production time Wikipedia. And tools like DeepMotion’s animation generator and IBM Watson integrations are already helping studios prototype faster and more creatively Market.us
  • Asset Generation Tools: Indie studios like Krafton are exploring AI to convert 2D images into 3D models, powering character and world creation with minimal manual sculptingReddit.
  • AI Agents: AI agents run thousands of tests, spot glitches, analyze frame drops, and flag issues—helping devs ship cleaner builds fasterReelmindVerified Market Reports. This type of AI-powered testing reduces bug detection time by up to 50%, accelerates quality assurance, and simulates gameplay scenarios on a massive scale Gitnux+1.
  • Machine Learning Models: AI tools, typically ML models, analyze player behavior to optimize monetization, reduce churn, tailor offers, balance economies, anticipate player engagement and even adjust difficulty dynamically – figures range from 56% of studios using analytics, to 77% for player engagement, and 63% using AI for economy and balance modeling Gitnux+1.
  • Natural Language Processing (NLP): NLPs are used to power conversational NPCs or AI-driven storytelling. Platforms like Roblox’s Cube 3D and Ubisoft’s experimenting with AI to generate dialogue and 3D assets—making NPCs more believable and story elements more dynamic Wikipedia.
  • Generative AI: Platforms like Roblox are enabling creators to generate code and 3D assets from text prompts, lowering barriers to entry. AI tools now support voice synthesis, environmental effects, and music generation—boosting realism and reducing production costs GitnuxZipDoWifiTalents
  • Computer Vision: Used in quality assurance and automated gameplay testing, especially at studios like Electronic Arts (EA).
  • AI-Enhanced Graphics: NVIDIA’s DLSS uses AI upscaling to deliver realistic graphics without slowing down performance.
  • GitHub Copilot for Code: Devs increasingly rely on tools like Copilot to speed coding. AI helps write repetitive code, refactor, or even spark new logic ideas Reddit.
  • Project Scoping Tools: AI tools can forecast delays and resource bottlenecks. Platforms like Tara AI use machine learning to forecast engineering tasks, timelines, and resources—helping game teams plan smarter Wikipedia. Also, by analyzing code commits and communication patterns, AI can flag when teams are drifting off track. This “AI project manager” approach is still in its early days, but it’s showing promise.

3. Benefits and Advantages

Companies adopting AI are seeing significant advantages:

  • Efficiency Gains & Cost Savings: AI reduces development time significantly—some estimates include 30–50% faster content creation or bug testing WifiTalents+1Gitnux. Ubisoft’s Commit Assistant reduces debugging time by predicting where code errors may occur.
  • Rapid Concept Ideation: Concept artists use AI to generate dozens of initial style options—then pick a few to polish with humans. Way faster than hand-sketching everything Reddit.
  • Creative Enhancement: Developers can shift time from repetitive tasks to innovation—allowing deeper storytelling and workflows PC GamerReddit.
  • Faster Testing Cycles: Automated QA, asset generation, and playtesting can slash both time and costs (some developers report half the animation workload gone) PatentPCVerified Market Reports. For example, EA’s automated bots simulate thousands of gameplay scenarios, accelerating testing.
  • Increased Player Engagement & Retention: AI keeps things fresh and fun with AI-driven adaptive difficulty, procedural content, and responsive NPCs boost immersion and retention—users report enhanced realism and engagement by 35–45% Gitnux+2Gitnux+2. Zynga uses AI to identify at-risk players and intervene with tailored offers to reduce churn.
  • Immersive Experiences: DLSS and AI-driven NPC behavior make games look better and feel more alive.
  • Revenue & Monetization: AI analytics enhance monetization strategies, increase ad effectiveness, and optimize in-game economies—improvements around 15–25% are reported Gitnux+1.
  • Global Reach & Accessibility: Faster localization and AI chat support reduce response times and broaden global player reach ZipDoGitnux+1.

For studios, these benefits and advantages translate to lower costs, faster release cycles, and stronger player engagement metrics, resulting in less expenses and more revenues.

4. Pitfalls and Challenges

Of course, it’s not all smooth sailing. Some issues include:

  • Bias in AI Systems: Poorly trained AI can unintentionally discriminate—for example, failing to fairly moderate online communities.
  • Failed Investments: AI tools can be expensive to build and maintain, and some studios have abandoned experiments when returns weren’t immediate.
  • Creativity vs. Automation: Overreliance on AI-generated content risks creating bland, formulaic games. There’s worry about AI replacing human creators or flooding the market with generic, AI-crafted content Financial Times.
  • Legal Risks, Ethics & Originality: Issues around data ownership, creative rights, and transparency are raising developer anxiety ReutersFinancial Times. Is AI stealing from artists? Activision’s Black Ops 6 faced backlash over generative assets, and Fortnite’s Vader stirred labor concerns WikipediaBusiness Insider.
  • Technical Limitations: Not all AI tools hit the mark technically. Early versions of NVIDIA’s G-Assist (now patched) had performance problems – it froze and tanked frame rates – but is a reminder that AI isn’t magic yet and comes with risks, especially for early integrators of new tools/solutions. Windows Central.
  • Speed vs. Quality: Rushing AI-generated code without proper QA can result in outages or bugs—human oversight still matters TechRadar.
  • Cost & Content Quality Concerns: While 94% of developers expect long-term cost reductions, upfront costs and measuring ROI remain challenges—especially given concerns over originality in AI-generated content ReutersPC Gamer.

In general, balancing innovation with human creativity remains a challenge.

5. The Future of AI in Gaming

Looking ahead, we can expect:

  • More Personalized Gameplay: Games that adapt in real-time to individual player styles.
  • Generative Storytelling: Entire narratives that shift based on player choices, powered by large language models.
  • AI Co-Creators: Game development may become a hybrid of human creativity and AI-assisted asset generation.
  • Smarter Communities: AI will help moderate toxic behavior at scale, creating safer online environments.
  • Games Created from Prompts: Imagine generating a mini-game just by describing it. That future is teased in surveys, though IP and ethics may slow adoption PC Gamer.
  • Fully Dynamic Games: AI-generated experiences based on user prompts may become a reality, enabling personalized game creation—but IP concerns may limit certain uses PC Gamer.
  • NPCs That Remember and Grow: AI characters that adapt, remember player choices, and evolve—like living game companions WIREDFinancial Times.
  • Cloud & AR/VR Boost Growth: AI will optimize streaming, drive immersive data-driven VR/AR experiences, and power e-sports analytics Verified Market ReportsGrand View Research.
  • Advanced NPCs & Narrative Systems: Expect smarter, emotionally adaptive NPCs and branching narratives shaped by AI AP NewsGitnux.
  • Industry Expansion: The AI in gaming market is projected to swell—from ~$1.2 billion in 2022 to anywhere between $5–8 billion by 2028, and up to $25 billion by 2030 GitnuxWifiTalents+1ZipDo.
  • Innovation Across Studios: Smaller indie developers continue experimenting freely with AI, while larger studios take a cautious, more curated approach Financial TimesThe Verge.
  • Streaming, VR/AR & E-sports Integration: AI-driven features—matching, avatar behavior, and live content moderation—will grow more sophisticated in live and virtual formats Gitnux+2Gitnux+2Windows Central.

With over 80% of gaming companies already investing in AI in some form, it’s clear that AI adoption is accelerating and will continue to grow. Survival without it will become impossible.

6. How Companies Can Stay Ahead

To thrive in this fast-changing environment, gaming companies should:

  • Invest in R&D: Experiment with generative AI, NPC intelligence, and new personalization engines. Become proficient in the key tools and technologies.
  • Focus on Ethics: Build AI responsibly, with safeguards against bias and toxicity.
  • Upskill Teams: Developers and project managers need to understand and use AI tools, not just traditional game engines.
  • Adopt Incrementally: Start with AI in QA and testing (low-risk, high-reward) before moving into core gameplay mechanics.
  • Start with High-ROI Use Cases: Begin with AI applications like testing, balancing, localization, and analytics—where benefits are most evident.
  • Blend AI with Human Creativity: Use AI to augment—not replace—human designers and writers. Leverage it to iterate faster, then fine-tune for quality.
  • Ensure IP and Ethical Compliance: Clearly disclose AI use, respect IP boundaries, and integrate transparency and ethics into development pipelines.
  • Monitor Tools & Stay Agile: AI tools evolve fast—stay informed, and be ready to pivot as platforms and capabilities shift.
  • Train Dev Teams: Encourage developers to explore AI assistants, generative tools, and optimization models so they can use them responsibly and creatively.
  • Focus on Player Trust: Transparently communicating AI usage helps mitigate player concerns around authenticity and originality.
  • Scale Intelligently: Use AI-powered analytics to understand player behavior—then refine content, economy, and retention strategies based on real data.

There will be some trial and error as companies move into the new landscape and try/adopt new technologies, but companies must adopt AI and become good at using it to stay competitive.

Final Word

AI isn’t replacing creativity in gaming—it’s amplifying it. From Ubisoft’s AI bug detection to Roblox’s generative tools and NVIDIA’s AI-enhanced graphics, the industry is already seeing massive gains. As studios continue blending human ingenuity with machine intelligence, the games of the future will be more immersive, personalized, and dynamic than anything we’ve seen before. But it’s clear, AI will not be an option for game development, it is a must. Companies will need to become proficient with the AI tools they choose and how they integrate them into the overall production cycle. They will also need to carefully choose partners that help them with AI implementations that are not done with in-house personnel.

This article is a part of an “AI in …” series that shares information about AI in various industries and business functions. Be on the lookout for future (and past) articles in the series.

Thanks for reading and good luck on your data (AI) journey!

Other “AI in …” articles in the series:

AI in Hospitality

AI, Artificial Intelligence (AI), Data News, HR analytics, Workday

Workday’s Game-Changing Move: Acquiring Paradox (an AI Recruitment software player)

Workday announced, alongside its Q2 2026 financial results, a definitive agreement to acquire Paradox on August 21, 2025. The acquisition is a strategic move to integrate Paradox’s conversational AI for high-volume candidate experience into Workday’s enterprise platform. As someone that works in the data space, works with Workday, and supports the HR function, this is of interest to me.

Who They Are

Workday
Founded in 2005, Workday is a leading cloud-based enterprise software provider specializing in human capital management (HCM) and financial services. Trusted by over 11,000 organizations—including more than 65% of the Fortune 500—it helps companies manage payroll, recruiting, and more through its AI-first platform. Wikipedia. Workday has been going all in on AI, which is why they were interested in Paradox.

Paradox
Launched in 2016, Paradox is an innovative player in conversational AI for recruitment. Known for its digital assistant Olivia, Paradox streamlines high-volume hiring processes—handling things like screening, scheduling, and candidate Q&A via chat, SMS, or mobile interfaces. It serves clients like McDonald’s, Unilever, and Chipotle and has powered over 189 million candidate interactions, achieving conversion rates above 70% and cutting time-to-hire to as fast as 3.5 days. PR Newswire+1. That is an impressive time-to-hire statistic!

Why This Acquisition Matters

  1. Extends Workday’s Talent Acquisition Suite
    Integrating Paradox lets Workday offer a full-spectrum hiring solution—from AI-based candidate matching via HiredScore to Paradox’s conversational interface and back-office onboarding through Workday Recruiting—all within one seamless platform. IT ProPR NewswireAInvest
  2. Gains Ground in Frontline Hiring
    Frontline roles (think retail, hospitality, logistics) make up a huge swath of global jobs—roughly 70%. Paradox delivers exactly what that market needs: fast, high-volume, scalable hiring. Josh Bersin even called this “a highly strategic move” that could reshape Workday’s growth trajectory. JOSH BERSINHR Tech Feed
  3. Built-In AI Innovation
    Adding Paradox brings not just the tech, but the talent behind its AI tools. This deepens Workday’s AI capabilities and supports its long-term vision of building an AI agent-centric architecture. JOSH BERSINAInvest
  4. Proven ROI on Day One
    Paradox has delivered significant outcomes—Chipotle saw a 75% reduction in time-to-hire and doubled candidate flow; other clients report streamlined scheduling and improved candidate experience. PR Newswire+1HR Executive

What’s in It for Workday—and how it changes the HR Tech Landscape

For Workday:

  • Expands its offering to include a strong solution for frontline and contingent workers, not just white-collar roles.
  • Enhances AI-driven hiring tools—turning a fragmented process into a unified, intelligent workflow.
  • Likely to drive stronger customer loyalty and cross-sell opportunities.
  • Will immediately add to Workday’s bottomline.

For the broader HR applications market:

  • Sets a higher bar for talent acquisition platforms—more emphasis on candidate experience, AI-driven efficiency, and conversational interfaces.
  • Adds pressure on competitors like Oracle, SAP, and ADP to step up their AI and frontline hiring solutions. ReutersInvesting.com

Final Thoughts

Workday’s acquisition of Paradox isn’t just about buying another tool—it’s a strategic leap into a broader, more intelligent, and more conversational hiring experience for a wider swath of the workforce. With the deal expected to close in Q3 of its fiscal year 2026 (ending October 31, 2025), Workday is positioning itself as a go-to AI-powered talent platform—built for the volume and complexity of today’s global labor markets.

Thanks for reading!

Analytics, Data Governance, Data Integration, Data Warehousing, Microsoft Fabric, Microsoft OneLake, Power BI

Understanding Microsoft Fabric Shortcuts

Microsoft Fabric is a central platform for data and analytics, and one of its powerful features that supports it being an all-in-one platform is Shortcuts. Shortcuts provide a simple way to unify data across multiple locations without duplicating or moving it. This is a big deal because it saves a LOT of time and effort that is usually involved in moving data around.

What Are Shortcuts?

Shortcuts are references (or “pointers”) to data that resides in another storage location. Instead of copying the data into Fabric, a shortcut lets you access and query it as if it were stored locally.

This is especially valuable in today’s data landscape, where data often spans OneLake, Azure Data Lake Storage (ADLS), Amazon S3, or other environments.

Types of Shortcuts

There are 2 types of shortcuts: table shortcuts and file shortcuts

  1. Table Shortcuts
    • Point to existing tables in other Fabric workspaces or external sources.
    • Allow you to query and analyze the table without physically moving it.
  2. File Shortcuts
    • Point to files (e.g., Parquet, CSV, Delta Lake) stored in OneLake or other supported storage systems.
    • Useful for scenarios where files are your system of record, but you want to use them in Fabric experiences like Power BI, Data Engineering, or Data Science.

Benefits of Shortcuts

Shortcuts is a really useful feature, and here are some of its benefits:

  • No Data Duplication: Saves storage costs and avoids data sprawl.
  • Single Source of Truth: Data stays in its original location while being usable across Fabric.
  • Speed and Efficiency: Query and analyze external data in place, without lengthy ETL processes.
  • Flexibility: Works across different storage platforms and Fabric workspaces.

How and Where Shortcuts Can Be Created

  • In OneLake: You can create shortcuts directly in OneLake to link to data from ADLS Gen2, Amazon S3, or other OneLake workspaces.
  • In Fabric Experiences: Whether working in Data Engineering, Data Science, Real-Time Analytics, or Power BI, shortcuts can be created in lakehouses or KQL (Kusto Query Language) databases, and you can use them directly as data in OneLake. Any Fabric service will be able to use them without copying data from the data source.
  • In Workspaces: Shortcuts make it possible to connect across lakehouses stored in different workspaces, breaking down silos within an organization. The shortcuts can be generated from a lakehouse, warehouse, or KQL database.
  • Note that warehouses do not support the creation of shortcuts. However, you can query data stored within other warehouses and lakehouses.

How Shortcuts Can Be Used

  • Cross-Workspace Data Access: Analysts can query data in another team’s workspace without requesting a copy.
  • Data Virtualization: Data scientists can work with files stored in ADLS without having to move them into Fabric.
  • BI and Reporting: Power BI models can use shortcuts to reference external files or tables, enabling consistent reporting without duplication.
  • ETL Simplification: Instead of moving raw files into Fabric, engineers can create shortcuts and build transformations directly on the source.

Common Scenarios

  • A finance team wants to build Power BI reports on data stored by the operations team without moving the data.
  • A data scientist needs access to parquet files in Amazon S3 but prefers to analyze them within Fabric.
  • A company with multiple Fabric workspaces wants to centralize access to shared reference data (like customer or product master data) without replication.

In summary: Microsoft Fabric Shortcuts simplify data access across locations and workspaces. Whether table-based or file-based, they allow organizations to unify data without duplication, streamline analytics, and improve collaboration.

Here is a link to the Microsoft Learn OneLake documentation about Shortcuts. From there you will be able to explore all the Shortcut topics shown in the image below:

Thanks for reading! I hope you found this information useful.