#Copilot for SAP

[autogeneity]

it is pretty sweet when I can feel reasonably confident that if my tests pass then my code will also work correctly when run

[neeraj82]

https://saxon.ai/blogs/when-sap-joule-meets-microsoft-copilot-whats-in-it-for-you/

[luxurychristmaspudding]

FUTUREPROOF | Masterlist

joel miller x f!reader

summary: joel miller can't remember the last time he was starstruck. so it takes him by surprise to be lost for words in front of you. with his daughters begging him to at least take a picture with the hottest rockstar in town, joel gives you your first real glimmer of warmth since your move to la. what follows is an unlikely friendship, the talk of the tabloids. but what if there's truth behind the rumours? how long can a secret stay secret, and how long until you're forced to leave each other alone?

pairing: rockstar!reader x country star!joel

ratings/warnings: 18+, MDNI. joel gets both his daughters bc i'm a sap :) unspecified age gap (i guess joel is in his 40s and reader late 20s? but have at it!). check individual chapters for warnings, but there will be: drinking, swearing, pining, idiots in love, bad friends, LOTS of smut, joel miller's whore mouth, angst, drug talk, and plenty of glitz and glam.

reader has hair and is generally able bodied, but is otherwise undescribed.

an: for my real life rockstars, who i am eternally grateful to call my friends and have hold my hands. i love you.

prologue - mdb

part i - diamonds and pearls

part ii - comfortable

part iii - censor

part iv - satisfied

part v - tempt you

part vi - parachute

part vii - tourniquet

part viii - foreign language

part ix - thinkin' bout you

part x - i forgot to be your lover

extras

MEET COPILOT

-> ADIE GILMAN -> CAM ROBERTSON -> JACK ETZLER -> VIC WALKER

moodboard

*chapters may change*

follow @pudding-notifs for updates!

[catchyhuh]

WHO WOULD BE BEST TO GO ON A 6 HOUR CAR TRIP WITH!!

THE ANSWER? IT’S COMPLICATED! car trips are such a delicate science that when i did this ranking in my head it fell apart almost immediately so instead i will present the information and YOU, yes YOU get to decide who you’d be able to put up with and who you’d murder before you even reached the highway

suggested reading for you: unbearable habits. keep this in mind as you go and venture safely 

lupin:

well. it wouldn’t be boring at least. he can make conversation out of almost anything. you pass a weird tree and ten minutes later he’s going on about how one time he had to learn about some tree info to get this rare sap thing (fujiko said she heard it tasted yummy-- it didn’t btw it sucked but he just had to see y’know) and he found out that there’s like thirty types of maple trees, one of which is actually invasive in north america, which is weird because it feels like north america is the area that’s the BIGGEST on tree tapping so why would it be a problem if they had so many norway maples-- hey are you listening to m

actually not that bothered if he’s not the one driving. might poke at you about things like “obeying the speed limit” and such but he’s perfectly content to kick back. uh oh, did that lull you into a false sense of security? don’t sigh in relief yet, you have mr. passenger princess as your copilot. fucks with the radio, the ac, constantly adjusting his seat, messing with windows, all to HIS comfort level. after all, you’re only the person driving.

insists on stopping at a convenience store. if you try some fast food drivethru bullshit he’s gonna be like “what schedule are we on! why are you in such a rush? fucking-- live a little, man. there’s a sheetz here”

jumping off that last bit the payment method AT the store will change entirely depending on what type of comment you make to him beforehand. if you say “in what world is it worth it to steal a 5 dollar slushie” he’s stealing. if you say “in what world is a slushy worth 5 fucking dollars” he will be paying in full with his own money. guarantee

jigen:

reclines his seat WAY far back. like crushing the person behind him’s legs, far back. like, would probably be a safety hazard even if he was in the car by HIMSELF far back. the reason airplanes have a locking mechanism to stop you from turning the seat into a twin bed far back.

not a horrible conversationalist but it will entirely rely on how much he “likes” (read: is kinda okay with) you and his mood. he won’t push you to talk, but if you want to talk and he DOESN’T, you are getting the driest answers. however if you are anything like me and only need minimal engagement to take as a sign to keep talking endlessly, he will whittle down to the point where he starts TALK talking to you a bit more.

can easily keep himself occupied regardless of mood. just grabs a crossword or some shit. miraculously doesn’t get a headache, but if you even make one remark about the fact that reading in the car gives people headaches, he’ll INSTANTLY remind you nothing could make his head hurt more than his current company. even if he doesn’t mind your company! it’s a reflex.

if you don’t let him drive he’s going to be a bitch. i promise. if you don’t let him drive he will grumble about every little thing so you know what. just make peace with it and hand over the wheel. pop it off the little stick thing and hand it right over to ol’ smoky. at the very least he’ll shave off a half hour from the ETA, somehow. it’s jarring because he doesn’t actually seem to be going faster but surprise! we’ve reached our destination.

goemon:

well. if you were stressing over lupin never shutting up i have good news for you. it doesn’t matter who his company is, he’s just consistently a man of very few words, unless you get him off on some specific thing he’s passionate about (which is very, very niche, and will be harder to trigger than you’d anticipate), but hey, he’s okay with that. unfortunately if silence is torture for you i have equally bad news,

honestly cannot understate how likely it is you’ll forget he’s in the back if you’re both silent for more than a mile stretch of road. he doesn’t shift around a lot. when you first get in he might take about 5 or so minutes to really get comfy but he prefers the back seat. every time goemon has had a choice, he goes right for the back. more legroom. so, yeah, very easy to forget he’s present

going to act like he can keep himself entertained just tuning out his surroundings and meditating but that’s just. not true. he’s going to last an impressive amount of time, maybe three and a half hours? but he is ultimately human and when you have to make that first gas station stop the gross ass smell of gasoline is going to knock him RIGHT out of it. 

really the only way you’re pulling any significant interaction out of this is if someone ELSE is manning the car and you can either turn completely around to interact with him or if you’re both sitting side by side. mostly the latter, as he’ll be less tempted to kind of emotionally shut you out if you’re right beside each other. just don’t expect him to move to give you much more space to yourself lmao

fujiko:

unique problem where you might EXPECT her to be somewhat talkative (like a reasonable amount) but no. she’s not talking to you. she’s not even ignoring you with headphones or anything she’s just content in her own world. unless of course you made one comment that just barely slightly annoyed her, in which case she pulls out the biggest, shiniest, most obnoxious headphones and tunes you out entirely. tread carefully

if you get hungry you’re eating at a SIT DOWN DINE-IN MEAL. NO fast food NO convenience store and ESPECIALLY no 3 dollar mini dorito bags, not on miss mine’s watch. don’t even fucking pretend it’s an option. but of course, this adds like an hour to your drive time, so… half and half. you Will be dining and dashing

probably has some kind of car trip kit. firstly, the fact she’s actually taking a CAR trip must mean you need to be afraid of something, because that’s gotta be her last resort. she could fly, take a train, fucking fly a helicopter herself, fly ANYTHING herself she’s UNSTOPPABLE and she wants to kick her feet on the dash for a fourth of a very valuable day?? but beyond that. has a nice pillow (NOT a neck pillow. hurts her neck. just a real full pillow. she doesn’t nap anyway idk why she’s got that) some kinda heating thingy to keep her back from getting sore in that uncomfy seat, large cup with a delicious bev of choice, just anything you could imagine being convenient. oh my god remember the tiddy bear? google tiddy bear. she’s got one of those

very creative when it comes to filling up time without getting VERY silly. now, make no mistake, if you’re both exhausted enough multiple hours in, she MIGHT be ok playing some car color counting game (especially if the winner gets 20 bucks) but usually she’s gonna just come up with some shit like “everytime you complain about x i’m going to cut this blank check into confetti and when the ride is over i’m dumping it on you.” isn’t she such a catch!

zenigata:

well. it definitely won’t be the same moment to moment. either you’re about to be miserable for multiple hours or somehow accidentally unlock the most bizarre yet interesting information about him. no inbetween. maybe even both!

probably the only one who has even a tiny chance of falling asleep, and even then that’s gotta be a hiiiighly specific setup. most possible if you just shut up for long enough and then he’ll kinda doze, but don’t bring UP the fact that you’re trying to get him to chill the fuck out and nap for a bit, don’t even joke about it, because then if you try to employ the long period of silence he’ll just go “... wait a minute I KNOW WHAT YOU’RE DOING” and it’ll be a whole thing

the most adamant about getting the damn thing over with. he’s not going to be a BITCH about it per se, but he IS going to be like “no no not that gas station look at that line. if we just wait till the next one it’ll save us like 15 minutes” and you look at the gas mileage and go “uh” and he goes “no we can make it. trust me.” and cut to 30 minutes later you’re both trying to push the truck to the closest pump which is STILL a good 700 feet away. save time my ass. because of this insistence he will be the one that takes the LONGEST to get from point a to point b, just because he WANTS to be the fastest and god is cruel

goes through like fifty highs and lows throughout unrelated to anything. traffic, the weather, fuck man the ac could be busted, and he’ll be fine, but then 20 minutes later he’s snippy about EVERYTHING. you are microdosing having him as a roommate. stay sane to the best of your ability because god knows he won’t

they ALL get bitchy about music, god help you if you try to fuck with the music

[aymannn]

The Role of iContent Foundry in AI Governance and Compliance

Artificial Intelligence is reshaping business operations, but with its widespread adoption comes a critical responsibility: ensuring AI systems remain fair, traceable, and compliant. Organizations across industries—from Oil & Gas to Utilities, Mining, and Manufacturing—must align innovation with regulation. That’s where AI and compliance with iContent Foundry intersect. 

At PiLog Group, our proprietary iContent Foundry is tackling this challenge head-on. As an AI-powered, ISO-compliant content repository, iCF streams standardized, validated data into enterprise systems—fueling AI deployments that are ethical, auditable, and regulation-ready. 

Why AI Needs Compliance: The Data Factor 

AI’s foundation is data. Compliance isn’t optional—it’s essential: 

Data quality and traceability. Without ISO-grade master data, AI-driven decisions can be erratic. iContent Foundry ensures data meets standards like ISO 8000 and UNSPSC before being used in AI or ERP systems. 

Explainability over black boxes. Regulators demand transparency. PiLog’s governance tools enforce metadata tracking, audit trails, and classification—making decisions traceable. 

Fairness and bias checks. Standardized attribute structures help flag anomalies and reduce bias early in the data pipeline. 

Global data compliance. ISO-aligned taxonomies and governance workflows (e.g., role-based approvals) help businesses meet GDPR, SOX, and industry-specific rules. 

Core Compliance Challenges in AI 

Even with the best tools, AI compliance comes with unique hurdles: 

Source data validation – Raw data must be cleansed, deduplicated, and enriched before AI ingestion. 

Ongoing monitoring – AI models should be audited routinely to catch drift or emerging compliance risks. 

Cross-team coordination – Legal, compliance, and technical teams must collaborate from day one. 

Regulatory evolution – Laws like the EU’s AI Act place heavier scrutiny on high-risk AI systems. 

How PiLog Supports Compliance-Ready AI 

PiLog’s integrated approach empowers organizations to meet these challenges head-on: 

🔹 iContent Foundry (iCF) 

A central repository of 20 million+ golden records and 25 000+ templates and hierarchies standardized to ISO taxonomies. Data entering AI or SAP systems—whether for procurement, asset management, or analytics—is clean, compliant, and fit for purpose. 

🔹 Data Quality & Governance Suite 

This SAP-endorsed suite blends AI tooling with industry best practices. With features like AI‑Lens conversational agents, approval workflows, audit logs, and taxonomy rules aligned with ISO standards, the suite delivers proactive governance  

Models receive structured input, compliance is baked into creation flows, and governance spans across SAP S/4HANA and ERP ecosystems. 

🔹 Continuous Monitoring & AI Lens 

AI Lens acts as a conversational copilot—helping users validate data, identify anomalies, and follow governance workflows in real time. It supports predictive alerts, classification recommendations, and audit history visibility. 

Real-World Compliance in Action: SAP Integrations 

Considering enterprises running SAP—whether it’s S/4HANA or any module—clean, structured, and compliant data isn’t optional. It’s the backbone of reliable operations, accurate reporting, and successful digital transformation. 

But here’s the challenge: most organizations struggle with fragmented, outdated, or inconsistent master data. This doesn’t just slow down workflows—it creates serious compliance risks, especially in regulated industries like energy, manufacturing, and public utilities. 

That’s where PiLog steps in. 

One example involves a large industrial organization preparing for a global SAP S/4HANA migration. While their technical infrastructure was ready, their master data told a different story. Product descriptions were inconsistent, duplicate vendors cluttered the system, and maintenance asset hierarchies lacked standard classification. 

This puts their compliance at risk—not just with internal audit teams, but also with external regulators who require traceability, proper categorization, and usage of logs for critical systems. 

By implementing PiLog’s iContent Foundry and Data Governance Suite, the organization was able to: 

Cleanse and enrich existing master data using ISO-based taxonomies 

Remove duplicates and standardize formats across business units 

Establish traceability and approval workflows aligned with regulatory requirements 

Ensure audit-ready data across procurement, finance, maintenance, and supply chain systems 

What was once a compliance bottleneck turned into a strategic advantage. Their AI-driven analytics became more accurate, SAP performance improved, and audits—both internal and external—were passed without friction. 

In short, when compliance is embedded at the data level, everything downstream works better: automation, insights, decisions, and trust. 

 Best Practices: Embedding AI Compliance from Day One 

Start with standardization. Use repositories like iContent Foundry to ensure master data is harmonized, traceable, and regulation aligned. 

Design governance first. Embed ISO standards into data flows and approval systems before deployment. 

Add transparency. Use logs, version control, and conversational AI to make processes explainable. 

Monitor continuously. Track data and model behavior to spot drift and bias early. 

Collaborate across teams. Legal, compliance, IT, and business teams must be part of the conversation from the beginning. 

In the future, the viability of AI programs is not only going to be underpinned by the innovative process but also certain ethical compliance of lawful standards. Companies who are investing in well-regulated, compliant data in the present will be companies who drive the future. 

Let’s Drive Responsible AI Together 

Conformity is not a passing exam, but rather a winning streak. By having your AI machines fine-grained, your systems become easy to understand, starting with regulators and across your organization as a whole.  

At PiLog Group we take enterprises through the journey of moving AI compliance out of compliance requirements then into strategic assets all on the clean, governed, ISO data frameworks.  

Would you like to know more about how AI and compliance overlap in your context? Let’s connect. We cannot wait to demonstrate iContent Foundry, conduct compliance analysis or preview our governance tools. 

[marketresearchreportss]

Artificial Intelligence In Construction Market: Impact on Project Management

The global artificial intelligence in construction market was valued at approximately USD 2.93 billion in 2023 and is anticipated to reach USD 16.96 billion by 2030, growing at a CAGR of 26.9% from 2024 to 2030. This growth is fueled by the various advantages AI offers in the construction sector, including the prevention of cost overruns by forecasting budgets based on historical data and aiding in the development of predictive models aligned with project timelines. This efficiency renders mega-construction projects more cost-effective.

Artificial intelligence (AI) also improves 3D model-based processes, lightening the workload for engineers, construction professionals, and architects. Additionally, it enhances project planning through robots that conduct 3D scans of construction sites, providing data that helps management tackle on-site issues.

Another significant driver of this market is the enhanced risk control on job sites, which assures contractors and subcontractors can concentrate on productive tasks. Furthermore, advanced AI applications, such as autonomous construction machinery, improve on-site efficiency by automating labor-intensive activities like bricklaying and concrete pouring. Concurrently, AI-driven workforce optimization mitigates labor shortages by intelligently distributing workloads, analyzing performance, and allocating resources. The diverse benefits of AI in construction significantly boost the demand for related products and services, propelling market growth.

Key Market Trends & Insights

• North America Artificial Intelligence in construction market dominated the market with 40.4% share in 2023. The factors responsible for this growth are attributed to the huge investments in technology and heavy usage of AI in project management, risk management, supply chain management, and many more fields which is increasing the demand for the product and growing the market in the region significantly.

• The U.S. dominated the North American AI in the construction market with a share of 75.3% in 2023 due to real-time collaborations, stringent regulatory compliances, and technological advancements which led to a surge in the demand for the product and the growth of the market significantly in the country.

• In terms of offering segment, solution offerings dominated the market with a revenue share of 81.7% in 2023. This growth is primarily driven by significant advancements in AI technologies, including Natural Language Processing (NLP), deep learning, and machine learning.

• Based on application, the project management applications segment accounted for the 36.2% market revenue share in 2023. This growth is primarily driven by the integration of machine learning algorithms.

Order a free sample PDF of the Artificial Intelligence In Construction Market Intelligence Study, published by Grand View Research.

Market Size & Forecast

• 2023 Market Size: USD 2.93 Billion • 2030 Projected Market Size: USD 16.96 Billion • CAGR (2024-2030): 26.9% • North America: Largest market in 2024

Key Companies & Market Share Insights

Some prominent companies in the artificial intelligence (AI) sector of the construction market include Microsoft, Oracle, SAP SE, and others. Organizations are striving to expand their customer base to secure a competitive advantage in the industry. As a result, major players are implementing various strategic initiatives, including mergers and acquisitions, along with partnerships with other leading firms.

• Microsoft has entered this market by offering AI solutions like Copilot, Dynamics 365, and Microsoft Azure AI, which enhance daily operations such as client management, marketing, regulatory compliance, and more.

• Oracle is partnering with companies like Rosedin Electric to establish innovative labs in this field and promote market innovation. They provide data-driven decision-making software, including Oracle Construction Intelligence Cloud Services.

Key Players

• Autodesk Inc. • International Business Machines Corporation • Microsoft • Oracle • SAP SE • Trimble Inc. • ALICE Technologies Inc. • BuildingConnected • The Access Group • Doxel.

Explore Horizon Databook – The world's most expansive market intelligence platform developed by Grand View Research.

Conclusion

The global artificial intelligence in construction market growth is driven by AI's ability to prevent cost overruns, enhance 3D modeling processes, and improve project planning through data-driven insights. Additionally, AI facilitates better risk control on job sites, allowing contractors to focus on productivity. Advanced applications like autonomous machinery automate labor-intensive tasks, while workforce optimization strategies address labor shortages. Overall, AI's multifaceted benefits are significantly boosting demand for related products and services, fueling market expansion.

[insurgentepress]

SecurityBridge se asocia con Microsoft para mejorar la seguridad de SAP con Microsoft Sentinel

SecurityBridge, el Centro de Mando de Ciberseguridad para SAP, se complace en anunciar su colaboración con Microsoft para integrar los datos de SAP en Microsoft Sentinel. Gracias a esta integración, SecurityBridge puede compartir sin problemas los eventos de seguridad de SAP con la información de seguridad y gestión de eventos (SIEM, por sus siglas en inglés) nativa en la nube de Microsoft Sentinel, lo que mejora la visibilidad para detectar y responder a las amenazas en todos los entornos SAP cuando la situación lo requiere.

«Esta estrecha colaboración representa un avance significativo en la seguridad de SAP. La combinación de nuestra plataforma de ciberseguridad nativa de SAP con las capacidades de Microsoft Sentinel hace que los complejos eventos de seguridad de SAP sean accesibles y procesables para todos los equipos de seguridad, proporcionando en última instancia a los clientes los conocimientos necesarios para proteger mejor los sistemas SAP frente a las amenazas», explicó Ivan Mans, director de tecnología y cofundador de SecurityBridge.

Los sistemas SAP forman parte fundamental de muchas organizaciones, ya que gestionan procesos empresariales críticos y datos confidenciales. Sin embargo, proteger estos sistemas contra las ciberamenazas ha sido desde siempre un reto debido a su complejidad y a la naturaleza especializada de las aplicaciones SAP. La combinación de SecurityBridge y Microsoft Sentinel ofrece las siguientes ventajas a los clientes de SAP:

Inteligencia de seguridad potenciada por IA: combine las capacidades de aprendizaje automático de Microsoft Sentinel y Microsoft Security Copilot con los conocimientos de seguridad SAP de SecurityBridge para identificar patrones de ataque sofisticados en todo su entorno.

Cobertura integral de la seguridad de SAP: mejore el monitoreo nativo de SAP de Microsoft Sentinel con las funciones especializadas de gestión de vulnerabilidades y análisis de código ABAP de SecurityBridge, que ofrecen una mayor protección para su entorno SAP.

Detección unificada de amenazas: al reenviar los eventos de seguridad a Microsoft Sentinel, las organizaciones pueden consolidar los datos de seguridad de SAP con otra información de seguridad. De este modo, se logra una visión unificada del panorama de amenazas en la plataforma Unified SecOps de Microsoft. Este enfoque holístico garantiza que los eventos de seguridad críticos de SAP se monitoreen y gestionen de forma centralizada.

Mayor eficacia de los SOC: la integración dota a los Centros de Operaciones de Seguridad (SOC, por sus siglas en inglés) de información procesable sobre las aplicaciones SAP, con lo que se cierra la brecha entre la TI y la seguridad SAP. Los analistas de seguridad ahora pueden acceder a eventos de seguridad SAP fácilmente comprensibles y que permiten tomar decisiones, y así se reduce la complejidad y se mejoran los tiempos de respuesta.

Despliegue a escala y flexible: la solución admite entornos SAP complejos y a gran escala, ya sea en las instalaciones, en la nube, SAP RISE o entornos híbridos. Esta flexibilidad garantiza que las organizaciones puedan adaptar la integración a sus necesidades específicas, dando soporte tanto a los sistemas SAP locales como a los basados en la nube.

«Microsoft adopta un enfoque holístico de la seguridad de SAP, que va más allá de las conversaciones aisladas. Al integrar la inteligencia sobre amenazas y los copilotos de seguridad en una plataforma unificada, demostramos que la seguridad no se limita a las aplicaciones o los datos de SAP, sino que afecta a todo el ecosistema. SecurityBridge complementa ese esfuerzo con sus funciones destacadas previas a las intrusiones, como la gestión de vulnerabilidades SAP o el análisis de código ABAP», señaló Martin Pankraz, director de producto de Seguridad SAP de Microsoft.

[aiandblockchainchronicles]

The Rise of Enterprise-Grade AI Copilot Solutions: A Game Changer for Modern Workflows

In the fast-evolving landscape of enterprise technology, businesses are constantly seeking innovative ways to optimize operations, enhance productivity, and streamline decision-making. One of the most significant technological advancements revolutionizing modern workflows is the emergence of enterprise-grade AI Copilot solutions. These intelligent assistants are not just productivity boosters—they’re strategic tools that enable organizations to operate more efficiently, scale faster, and stay competitive in an AI-first world.

Understanding AI Copilot Solutions: What Are They?

AI Copilot solutions are intelligent software systems designed to assist users by automating tasks, generating insights, managing workflows, and enhancing decision-making through natural language interaction. Unlike traditional automation tools, AI Copilots work alongside human teams in a collaborative manner—similar to a virtual colleague who understands context, anticipates needs, and delivers results in real time.

Enterprise-grade AI Copilots take this a step further. They are built with robust architecture, designed for scale, integrated with enterprise systems (like CRMs, ERPs, and data warehouses), and adhere to strict compliance and security standards. These AI-driven assistants are capable of handling high-value, high-volume tasks across departments including marketing, finance, HR, customer service, and IT.

Why AI Copilots Matter for Modern Enterprises

The modern enterprise faces a relentless volume of data, communication overload, and the need for faster decision cycles. AI Copilots alleviate these pressures by acting as digital team members capable of processing information quickly, understanding intent through natural language processing (NLP), and executing tasks autonomously or with minimal supervision.

By integrating with enterprise tools and processes, AI Copilots become the connective tissue across siloed systems—bringing together data, automation, and intelligence in one conversational layer. This unlocks a range of business benefits:

Improved efficiency through task automation

Faster decision-making via real-time insights and analysis

Enhanced collaboration through contextual assistance

Reduced operational costs with fewer manual interventions

Higher employee satisfaction by eliminating mundane tasks

Key Features of Enterprise-Grade AI Copilot Solutions

Unlike consumer-grade AI assistants, enterprise-grade Copilots are built for scale, security, and integration. Here are the core features that define them:

1. Deep Enterprise Integration

Enterprise-grade Copilots connect seamlessly with internal systems like Salesforce, SAP, Microsoft 365, Jira, ServiceNow, and custom-built platforms. This allows users to retrieve data, automate workflows, and interact with core systems through a single AI interface.

2. Contextual Understanding

These Copilots leverage advanced NLP models and fine-tuned language processing to understand the enterprise context—user roles, department-specific terminology, historical interactions, and ongoing tasks—ensuring more accurate and relevant responses.

3. Multi-Modal Capabilities

Enterprise AI Copilots support text, voice, and in some cases, visual inputs. Whether through a chatbot interface, a voice assistant in a meeting room, or a dashboard embedded into internal tools, they adapt to how teams work best.

4. Security and Compliance

Built with enterprise-grade encryption, identity and access management, and compliance with standards like SOC 2, HIPAA, or GDPR, these solutions ensure data security and privacy are never compromised.

5. Personalization and Learning

These systems continuously learn from interactions and user feedback. Over time, they adapt to individual preferences, team behaviors, and business rules—delivering a personalized, context-aware experience for each user.

Top Use Cases of AI Copilots in the Enterprise

Enterprise-grade AI Copilot solutions are not confined to a single use case—they are being deployed across departments and functions to drive measurable impact. Here are some real-world applications:

1. Sales and Marketing Automation

AI Copilots can draft personalized emails, summarize customer interactions, generate sales reports, and recommend marketing strategies. Integrated with CRMs and campaign platforms, they help sales teams close deals faster and marketers execute more targeted campaigns.

2. Customer Support and Service

Conversational AI Copilots enable support teams to resolve issues quickly by surfacing knowledge base content, generating ticket responses, and analyzing customer sentiment. They also power chatbots that provide 24/7 support across digital channels.

3. Finance and Accounting

From automating invoice processing to generating financial summaries and forecasting cash flows, AI Copilots reduce the burden of manual financial tasks and help teams make faster, more accurate decisions.

4. Human Resources

AI Copilots assist HR teams in recruiting, onboarding, performance reviews, and employee engagement. They can screen resumes, schedule interviews, send personalized onboarding messages, and answer policy-related questions.

5. IT and DevOps

AI Copilots streamline incident management, automate code documentation, monitor system performance, and assist developers by generating code snippets or resolving bugs—all through conversational interfaces.

Benefits of Implementing AI Copilot Solutions

The adoption of enterprise-grade AI Copilots is not just about automation—it’s about unlocking strategic advantages. Let’s explore the benefits in more depth:

Increased Productivity

By taking over repetitive, time-consuming tasks, AI Copilots free up employees to focus on strategic and creative work. This results in a significant productivity boost across all departments.

Real-Time Decision Support

AI Copilots analyze data from multiple sources and present it in an actionable format. Executives and team leads can make informed decisions quickly, without having to dig through dashboards or spreadsheets.

Enhanced Employee Experience

With AI Copilots handling administrative and low-value tasks, employees experience reduced burnout and higher engagement. These assistants also serve as always-available support for day-to-day operations.

Scalability Across Functions

Once integrated into a company’s tech stack, Copilots can be customized for various departments and scaled across teams—making them a flexible, enterprise-wide solution.

Faster Time-to-Value

Modern AI Copilot platforms offer pre-trained models, integration libraries, and low-code customization tools, allowing businesses to deploy solutions quickly and start seeing ROI in weeks—not months.

Real-World Example: AI Copilots in Action

Consider a global consulting firm with thousands of employees working across time zones. Before AI Copilots, project managers spent hours compiling reports, employees struggled to find relevant documents, and HR was swamped with routine queries.

After implementing an enterprise-grade AI Copilot:

Project managers simply ask the Copilot to generate a status report or pull data from JIRA and Confluence.

Employees find internal documentation using natural language queries instead of searching through folders.

HR teams automate FAQs and onboarding tasks, saving hours each week.

The result? Improved productivity, faster workflows, and better resource utilization—without adding headcount.

Choosing the Right AI Copilot Development Partner

The success of an enterprise AI Copilot solution hinges on selecting the right development partner. Look for a provider that:

Offers custom development aligned with your enterprise workflows

Understands domain-specific language models relevant to your industry

Provides integration support with your existing tech stack

Ensures data privacy and regulatory compliance

Has a track record of enterprise deployments and client success

The Future of Work Is AI-Augmented

As AI continues to reshape the way we work, enterprise-grade Copilots are poised to become standard fixtures in the digital workplace. They are not just automating tasks—they’re transforming how organizations operate, compete, and scale in an increasingly complex world.

Whether it’s enabling smarter collaboration, reducing operational friction, or enhancing employee satisfaction, AI Copilots represent a massive leap forward in enterprise intelligence.

Final Thoughts

The rise of enterprise-grade AI Copilot solutions marks a new era for business productivity and efficiency. Far from being a passing trend, these intelligent assistants are becoming essential infrastructure for companies aiming to thrive in the AI age. With the right strategy and development partner, enterprises can harness the full power of Copilot technology to supercharge workflows and gain a decisive edge in their industries.

[jcmarchi]

The Sequence Radar #516: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

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The Sequence Radar #516: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

The announcements at GTC showcased covered both AI chips and models.

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Next Week in The Sequence:

We do a summary of our series about RAG. The opinion edition discusses whether NVIDIA is the best VC in AI. The engineering installement explores a new AI framework. The research edition explores the amazing Search-R1 model.

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📝 Editorial: NVIDIA’s AI Hardware and Software Synergies are Getting Scary Good

NVIDIA’s GTC never disappoints. This year’s announcements covered everything from powerhouse GPUs to sleek open-source software, forming a two-pronged strategy that’s all about speed, scale, and smarter AI. With hardware like Blackwell Ultra and Rubin, and tools like Llama Nemotron and Dynamo, NVIDIA is rewriting what’s possible for AI development.

Let’s start with the hardware. The Blackwell Ultra AI Factory Platform is NVIDIA’s latest rack-scale beast, packing 72 Blackwell Ultra GPUs and 36 Grace CPUs. It’s 1.5x faster than the previous gen and tailor-made for agentic AI workloads—think AI agents doing real reasoning, not just autocomplete.

Then there’s the long game. Jensen Huang introduced the upcoming Rubin Ultra NVL576 platform, coming in late 2027, which will link up 576 Rubin GPUs using HBM4 memory and the next-gen NVLink interconnect. Before that, in late 2026, we’ll see the Vera Rubin NVL144 platform, with 144 Rubin GPUs and Vera CPUs hitting 3.6 exaflops of FP4 inference—over 3x faster than Blackwell Ultra. NVIDIA’s clearly gearing up for the huge compute demands of next-gen reasoning models like DeepSeek-R1.

On the software side, NVIDIA launched the Llama Nemotron family—open-source reasoning models designed to be way more accurate (20% better) and way faster (5x speed boost) than standard Llama models. Whether you’re building math solvers, code generators, or AI copilots, Nemotron comes in Nano, Super, and Ultra versions to fit different needs. Big names are already onboard. Microsoft’s integrating these models into Azure AI Foundry, and SAP’s adding them to its Joule copilot. These aren’t just nice-to-have tools—they’re key to building a workforce of AI agents that can actually solve problems on their own.

Enter Dynamo, NVIDIA’s new open-source inference framework. It’s all about squeezing maximum performance from your GPUs. With smart scheduling and separate prefill/decode stages, Dynamo helps Blackwell hardware handle up to 30x more requests, all while cutting latency and costs.

This is especially important for today’s large-scale reasoning models, which chew through tons of tokens per query. Dynamo makes sure all that GPU horsepower isn’t going to waste. While Blackwell is today’s star, the Rubin architecture is next in line. Launching late 2026, the Vera Rubin GPU and its 88-core Vera CPU are set to deliver 50 petaflops of inference—2.5x Blackwell’s output. Rubin Ultra scales that to 576 GPUs per rack.

Looking even further ahead, NVIDIA teased the Feynman architecture (arriving in 2028), which will take things up another notch with photonics-enhanced designs. With a new GPU family dropping every two years, NVIDIA’s not just moving fast—it’s setting the pace.

The real story here is synergy. Blackwell and Rubin bring the power. Nemotron and Dynamo help you use it smartly. This combo is exactly what enterprises need as they move toward AI factories—data centers built from the ground up for AI-driven workflows. GTC 2025 wasn’t just a product showcase—it was a blueprint for the next decade of AI. With open models like Nemotron, deployment tools like Dynamo, and next-gen platforms like Rubin and Feynman, NVIDIA’s making it easier than ever to build smart, scalable AI. The future of computing isn’t just fast—it’s intelligent. And NVIDIA’s making sure everyone—from startups to hyperscalers—has the tools to keep up.

🔎 AI Research

Synthetic Data and Differential Privacy

In the paper“Private prediction for large-scale synthetic text generation“ researchers from Google present an approach for generating differentially private synthetic text using large language models via private prediction. Their method achieves the generation of thousands of high-quality synthetic data points, a significant increase compared to previous work in this paradigm, through improvements in privacy analysis, private selection mechanisms, and a novel use of public predictions.

KBLAM

In the paper “KBLAM: KNOWLEDGE BASE AUGMENTED LANGUAGE MODEL” Microsoft Research propose KBLAM, a new method for augmenting large language models with external knowledge from a knowledge base. KBLAM transforms knowledge triples into continuous key-value vector pairs and integrates them into LLMs using a specialized rectangular attention mechanism, differing from RAG by not requiring a separate retrieval module and offering efficient scaling with the knowledge base size.

Search-R1

In the paper “Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning” researchers from the University of Illinois at Urbana-Champaign introduce SEARCH-R1, a novel reinforcement learning framework that enables large language models to interleave self-reasoning with real-time search engine interactions. This framework optimizes LLM rollouts with multi-turn search, utilizing retrieved token masking for stable RL training and a simple outcome-based reward function, demonstrating significant performance improvements on various question-answering datasets.

Cosmos-Reason1

In the paper“Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning” researchers from NVIDIA present Cosmos-Reason1, a family of multimodal large language models specialized in understanding and reasoning about the physical world. The development involved defining ontologies for physical common sense and embodied reasoning, creating corresponding benchmarks, and training models through vision pre-training, supervised fine-tuning, and reinforcement learning to enhance their capabilities in intuitive physics and embodied tasks.

Expert Race

This paper,“Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts”, presents additional results on the ImageNet 256×256 dataset by researchers who trained a Mixture of Experts (MoE) model called Expert Race, building upon the DiT architecture. The results show that their MoE model achieves better performance and faster convergence compared to a vanilla DiT model with a similar number of activated parameters, using a larger batch size and a specific training protocol.

RL in Small LLMs

In the paper “Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn’t” AI researchers investigate the use of reinforcement learning to improve reasoning in a small (1.5 billion parameter) language model under strict computational constraints. By adapting the GRPO algorithm and using a curated mathematical reasoning dataset, they demonstrated significant reasoning gains on benchmarks with minimal data and cost, highlighting the potential of RL for enhancing small LLMs in resource-limited environments.

📶AI Eval of the Weeek

(Courtesy of LayerLens )

Mistral Small 3.1 came out this week with some impressive results. The model seems very strong in programming benchmarks like Human Eval.

Mistral Small 3.1 also outperforms similar size models like Gemma 3.

🤖 AI Tech Releases

Claude Search

Anthropic added search capabilities to Claude.

Mistral Small 3.1

Mistral launched Small 3.1, a multimodal small model with impressive performance.

Model Optimization

Pruna AI open sourced its famout AI optimization framework.

📡AI Radar

NVIDIA acquired synthetic data platform Gretel AI.

Perplexity is raising a new round at $18 billion valuation.

SoftBank announced the acquisition of semiconductor platform Ampere Computing.

Data analytic company Dataminr raised $85 million in new funding.

AI security platform Orion Security emerged from stealth mode with $6 million in funding.

Roblox launched Roblox Cube, a new gen AI system for 3D and 4D assets.

Halliday blockchain-agentic platform raised $20 million in new funding.

ClearGrid raised $10 million to automated debt collection with AI.

Tera AI raised $7.8 million for its robotics navigation platform.

AI presentation platform Present raised $20 million in new funding.

[ikyamsolutions]

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[ericvanderburg]

SAP Enhances Copilot Joule with Collaborative AI Capabilities ?

http://securitytc.com/TFf8bt

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IFI Techsolutions Cloud Solutions Provider

IFI Techsolutions is a Microsoft Cloud Solutions Provider Company. We offer solutions like Azure Infrastructure, Data and AI, Datacenter Transformation, Big Data & Analytics, Microsoft Copilot, Microsoft Fabric, Modern Workplace, Modern Applications, Security & Compliance, SAP on Azure, DevOps and, Microsoft Dynamics 365 CRM.