What Is Gen AI and Why Care

By – Sai Srinija Potnuru
 
What Is Generative AI and Why Should Organizations Care?

Let’s face it—we’re living in a world where AI isn’t just a sci-fi buzzword anymore. It’s real, it’s here, and it’s already changing how businesses operate—fast.

And at the heart of this shift? Generative AI (or GenAI, as we like to call it).

But what *is* it really? And more importantly—why should your organization care?

 
So… What Is GenAI?

GenAI is a type of artificial intelligence that doesn’t just analyze data—it *creates*. We’re talking text, images, code, video, audio, even 3D models. It’s the engine behind tools like ChatGPT, DALL·E, GitHub Copilot, and more.

Instead of just answering questions, GenAI collaborates. It learns from massive datasets and responds in human-like ways—drafting emails, summarizing reports, writing code, and generating ideas that honestly feel… creative.

In short? GenAI is a productivity booster that helps people get more done—faster, smarter, and with more impact.

 
Why Should Your Org Care?

Because GenAI isn’t just another tech trend. It’s a fundamental business shift. And if your competitors aren’t already testing it out… they will be soon.

Here’s what GenAI is bringing to the table:

1. Things Get Done—Way Faster GenAI cuts hours off repetitive tasks. Instead of manually formatting that 20-page report? Let AI do the heavy lifting, so your team can focus on the good stuff—like strategy and innovation.

2. Creativity Gets a Major Upgrade Need a dozen ad copy variations? Personalized emails for different customer segments? A fresh product description that actually stands out?

Done, done, and done. GenAI acts like a creative partner—not a replacement—but a powerful one.

3. Personalization at Scale This is where GenAI really shines. It helps you deliver customized experiences to thousands of people—without losing the human touch. Smarter bots, adaptive learning, AI-curated recommendations… even at enterprise scale, your users will feel like you’re speaking directly to them.

 
Real-World Use Cases (That Aren’t Just Hype)

Still feels a bit abstract? Let’s talk about what this looks like in the real world:

  • Retail: Personalized promotions, 24/7 human-sounding support, auto-generated product descriptions
  • Finance: AI-driven risk summaries, plain-language policy explanations, market trend recaps
  • Manufacturing: Auto-generated manuals, predictive maintenance alerts, real-time scenario planning

This isn’t “someday” stuff. It’s happening *right now*—and it’s working.

 
So… What’s the Catch?

With great power comes—yep, you guessed it—responsibility.

GenAI is powerful, but it needs strong governance. Ethics, bias monitoring, data privacy, transparency… these aren’t optional. They’re essential for building trust and staying compliant.

That’s why leading orgs are investing in AI Centers of Excellence, building responsible AI frameworks, and upskilling their teams to lead the charge.

 
Our Take? You Don’t Have to Be First—But You Can’t Be Last

GenAI isn’t a passing trend. It’s a tectonic shift in how work gets done.

Organizations that start exploring now will shape the future. The ones that wait? They’ll be catching up in a world that’s already moved on.

Whether you’re just curious or ready to scale, now’s the time to start asking the big questions about how GenAI can support your business.

Because the real question isn’t *if* you’ll use GenAI… it’s *how soon* you’ll make it work for you.

How to Make Gen AI Work for You

By -Srinija Potnuru

Hey!

Srinija here—I’m part of the team at Tek Analytics. Lately, I’ve been diving deep into Generative AI—reading up, watching demos, and, honestly, the coolest part? Seeing it come to life in the work we’re doing with our clients. Between helping businesses get started and watching my team work their magic, I’ve learned a lot.

It’s everywhere right now, right? Feels like everyone’s talking about how AI is going to change business forever. And from what I’ve seen—it’s not just talk.

And honestly? It’s true. AI is changing the game. I’ve seen it firsthand.
But… (and this is a big but), getting AI to work in your business? That part’s not always so simple.

I’ve had many conversations where people tell me:
“Srinija, we know we need AI, but we don’t know where to start.”
Or, “We tried a small AI project, and it kind of worked… but now what?”
And, of course, “How do we make sure we don’t screw up the security or break some compliance rule?”

If any of this sounds familiar—you are not alone. I see it all the time.

 

What I’ve Noticed – The Real Struggles with Generative AI

From everything I’ve read, watched, and experienced through my work, I’ve noticed that most businesses run into the same roadblocks when it comes to AI.

The first is just figuring out what to do with it.
AI is this big, exciting thing, and everyone’s talking about it. But when it’s time to apply it to your business, you can get stuck. It’s like, “Okay, cool… but what problem is AI actually going to solve for us?” I’ve seen companies go all-in on AI tools without a plan, and they end up frustrated because it didn’t really make a difference.

Then, there’s the part where you try AI in one area, and it works… kind of.
Maybe you automate a task here or streamline a process there. It’s great—but it’s just that one thing. I’ve seen businesses get stuck in this “pilot phase” where AI is only helping in one corner of the company. They want to scale it up, but connecting AI to all their systems—especially the older ones—feels like trying to mix oil and water.

And honestly? Data and security worries are huge.
This one comes up in every single meeting. “How do we protect our data? What if we accidentally share something sensitive? Are we even allowed to do this under our industry rules?” I’ve seen businesses hesitate to move forward with AI just because they’re nervous about getting it wrong—and I don’t blame them.

 

How We Make AI Actually Work – What We Do at Tek Analytics

This is exactly why, at Tek Analytics, we’ve built a way to help businesses get AI working—without the confusion or guesswork.

We don’t just throw some AI tools at you and say, “Good luck!” We collaborate with you, side by side.

The first thing we always do is sit down and talk—really talk. We call it our AI Innovation Clinic, but honestly, it’s more like a brainstorming session. We dig into what’s working in your business, what’s not, and where AI can help. I’ve had so many of those “Oh, we didn’t even think of that!” moments in these chats.

Once we know what’s right for you, we help you get started—properly.
I’ve seen businesses rush into AI, skip the basics, and hit roadblocks later. That’s why we created AI Jump Start—to set up secure, compliant AI solutions that work with your team and systems, from day one.

And when you’re ready to scale? We’ve got you.
With AI Elevate, we help you move from a few small wins to AI powering your business. We will automate more, fine-tune your models, and make sure AI is driving real, long-term value—without the headaches.

Oh, and this is the part we’re really excited about—we’ve just launched our Rapid Deployment Solutions!
Sometimes you need AI up and running fast. We get that. These solutions are designed to get you started quickly, so you can see results in weeks—not months—without cutting corners on security or quality. We have already seen how much of a game-changer this can be, and I can’t wait to see what it does for more businesses like yours.

 

Why I Think We’re Different

One thing I’ve noticed that really sets us apart at Tek Analytics is that we don’t do this cookie-cutter, one-size-fits-all AI stuff.
Every business is different, and we get that. We make sure whatever AI solution we’re helping you with fits into your business, with your people, and your systems.

Plus—this is a big one—we take governance and security seriously. We’re not just excited about AI; we’re also the people making sure you’re staying compliant and not putting your data at risk.
I’ve seen businesses underestimate this part, and it can cause major headaches later. We make sure you don’t have to worry about that.

 

Let’s Chat?

So yeah, AI can be overwhelming—I get it. But it doesn’t have to be.
With the right approach (and the right partner), it can be pretty smooth—and, honestly, kind of exciting.

If you’re curious, or even if you’re just feeling stuck and want to bounce around some ideas, let’s talk. I’m always happy to share what I’ve seen work.

And if you want to check out more about how we help businesses with Generative AI, here’s the link:
Tek Analytics Generative AI Services

Solving Legacy BI Headaches for a Global Distributor

By – Merri Beckfield

From my vantage points, I have the privilege of observing organizations at varying points in their journey to data insights.   And for SAP customers who have been waiting for the right moment for SAP Datasphere, there is now a focus on starting the journey.

Today, I’d like to share a bit about the journey of a supply chain management and logistics solutions provider who went all in on modernizing their SAP BI solutions.

 

THE PROBLEM

When we first started together, they were an SAP ECC shop with an on-premises, legacy BW data warehouse solution with over 500 BEX queries.  Users were sourcing data from a variety of systems for reporting and decision making.  On the front end, SAP Business Objects and Tableau were in the mix.    This is certainly a very common scenario.

With their legacy BI solution, the organization was facing some common challenges. They were struggling to achieve the necessary speed and cost-effective storage needed for their operations. Additionally, the lack of integration resulted in the loss of SAP context for data stored in third-party databases, further complicating their data management efforts.

 

THE SOLUTION

To overcome the challenges, together we came up with a strategy and plan to seize-the-moment with an upcoming S/4 implementation.  Rather than rework the legacy BI solutions to fit S/4 into the picture, the team took the opportunity to replace the existing BI solution with an all-in move to SAP Datasphere.  To mitigate risk, a “joint success plan” was put into place which laid out a phased migration (with a pilot) approach.  This allowed the team to work in an agile fashion, apply learnings along the way, and reap some business benefits along the way.

This all involved fully implementing a 3-tier Datasphere landscape, along with BW bridge.  To be honest, we learned a few things about BW bridge (and its limitations) along the way! The data and data flows were fully migrated from BW to Datasphere in a manner that allowed us to take advantage of the work previously done in BW (why reinvent the wheel?).  Security was fully integrated into the new solution.  The queries for the front end were updated to the new solution. And a data lake solution was put into place for cost-effective data storage.

 

THE RESULTS

At the end of the day, the outcome was a solution that provided:

  • Enhanced agility and scalability vs. their prior solution.
  • Streamlined data access by integrating SAP and non-SAP data sources
  • Real-time integration implemented to S/4 Hana for faster time to insight.

Ready to move your data strategy forward and unlock the potential of SAP Datasphere? This client success story is just one example of how we help businesses modernize their data architectures, streamline operations, and drive faster decision-making.

Let us help you achieve the same success! Contact us today to discuss how we can support your migration to SAP Datasphere.   Contact TEK – Tek Digital Transformations

Tek AI

Tek AI

The AI/ML industry has witnessed significant advancements since its inception, primarily driven by the growth in computational power and the availability of vast datasets. Historically, the field has evolved from rule-based systems to complex algorithms capable of learning and making predictions.

The global AI market, currently valued at over $196 billion, is expanding rapidly with a projected CAGR of 38.1% between now and 2030, a potential significant increase in value over the next several years. (Grand View Research)

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Embedded Data Lake

Mastering SAP HANA Cloud Development with HDI Containers and SAP Datasphere

Introduction

It has become apparent that organizations need to store and analyze both their transactional data and their “big data” (unstructured text, video, and so on) together. However, historically, this has been a challenge as there were different types of repositories required depending on which type of data was being processed. Fortunately, solutions to this historic challenge are starting to become a reality. Thus, the integration of enterprise data with big data has become a pivotal strategy for organizations seeking to derive actionable insights. SAP introduced an embedded data lake to SAP Datasphere specifically to address this challenge. This blog delves into the potential of the Embedded Data Lake within SAP Datasphere, addressing common data integration challenges and unlocking the potential for added business value.

The Challenge

Across industries, enterprises grapple with the complexities of integrating SAP transactional data with other types of data. This challenge is rooted in the historical evolution of data repositories. Until relatively recently, there have been different types of repositories required depending on which type of data was being processed. Data Warehouses do a great job as a repository for transactional data. Data Lakes do a good job as a repository for raw, unstructured and semi-structured data. But they stand as separate silos, the implications of this include the following:

  • Complexity of Data Analysis: It is a challenge to manage, integrate, and analyze data across multiple repositories. The data is not in one unified environment which can be challenging for business users to navigate creating extra overhead and inefficiencies.
  • Cost Implications: With multiple repositories, organizations face additional expenditures on software, hardware, licensing, and appropriately skilled resources.
  • Operational Overheads: Solutions for items such as data tiering and archiving need to be designed for each repository, creating additional operational overhead.


Meeting the Challenge: Embedded Data Lake in SAP Datasphere

In a strategic move to address these challenges head-on, SAP unveiled SAP Datasphere, the evolutionary successor to SAP Data Warehouse Cloud, on March 8, 2023. A cornerstone of this innovative offering is the integration of an Embedded Data Lake, providing a seamless and unified data management experience within the SAP ecosystem.

Understanding the Embedded Data Lake

What is a Data Lake?

Before exploring the specifics of the Embedded Data Lake, it’s essential to understand the concept of a data lake. A data lake is a centralized repository that allows organizations to store all their structured and unstructured data at any scale. Unlike traditional data storage systems, data lakes can retain data in its raw format, enabling advanced analytics and deriving valuable insights from diverse data sources.

Embedded Data Lake in SAP Datasphere

An embedded data lake in SAP Datasphere integrates the powerful data lake functionality directly within the SAP environment. This integration provides users with a unified platform where they can store, manage, and analyze their data, leveraging SAP’s advanced analytics tools and applications. By embedding a data lake within SAP Datasphere, organizations can streamline their data management processes and unlock new possibilities for data-driven decision-making.
Benefits of Embedded Data Lake in SAP Datasphere

Unified Data Management

The Embedded Data Lake facilitates seamless integration of data within a single platform, streamlining data management processes and reducing operational complexity. The centralized nature of the data lake ensures that all relevant data is readily available, empowering users to make informed choices based on the most up-to-date information.

Scalability and Cost Efficiency

By leveraging the cost-effective data storage options within SAP Datasphere, and eliminating the costs of multiple repository solutions, organizations can optimize their data management costs. By eliminating the need for separate data integration solutions and infrastructure, the Embedded Data Lake drives cost efficiencies and maximizes ROI for businesses.

Data Tiering Scenarios: Cold-to-Hot and Hot-to-Cold

Effective data management often requires balancing performance and cost, which is where data tiering comes into play. The Embedded Data Lake in SAP Datasphere supports two data tiering scenarios to optimize your data storage strategy.

  • Cold-to-Hot: In a Cold-to-Hot tiering scenario, data that is initially stored in a cold tier (less frequently accessed and lower cost) is moved to a hot tier (frequently accessed and higher cost) as it becomes more relevant for real-time analysis. This ensures that critical data is readily available when needed, without incurring high storage costs for less frequently accessed data.
  • Hot-to-Cold: Conversely, in a Hot-to-Cold tiering scenario, data that starts in a hot tier (frequently accessed) is moved to a cold tier (less frequently accessed) as its relevance decreases over time. This helps manage storage costs by keeping only the most relevant data in the more expensive, high-performance storage tier.


Real-Time Analytics

With SAP Datasphere’s real-time processing capabilities, organizations can derive actionable insights from data in real-time, enabling agile decision-making.

In Conclusion – A Point of View

The Embedded Data Lake in SAP Datasphere represents a paradigm shift. By leveraging the full power SAP Datasphere, it paves the way for a future where data-driven decision-making is not just a possibility but a reality. As we look towards the future, the Embedded Data Lake stands poised to revolutionize the way we harness the power of data, ushering in a new era of innovation and growth. Feel free to reach out to us with questions or to schedule a free live demonstration of the SAP Datasphere embedded data lake.

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HDI Containers

Mastering SAP HANA Cloud Development with HDI Containers and SAP Datasphere

What Are HDI Containers?

Before we get into the nitty-gritty, let’s demystify HDI containers. HDI stands for SAP HANA Deployment Infrastructure, a key service that helps you deploy database development artifacts into containers. Think of them as specialized storage units for your database artifacts. These artifacts include:

  • Tables
  • Views
  • Procedures
  • Advanced Artifacts: Calculation views, flowgraphs, replication tasks

The beauty of HDI is that it maintains a consistent set of design-time artifacts that describe the target state of SAP HANA database features, streamlining both development and deployment processes.

Integrating HDI Containers with SAP Datasphere

SAP Datasphere allows the assignment of built HDI containers to its spaces, providing immediate bi-directional access between HDI containers and Datasphere spaces without requiring data movement. This integration enhances flexibility and efficiency in data management and modeling processes.

  • Deploy HDI Containers: Use SAP Business Application Studio (BAS) to create and deploy HDI containers in the underlying SAP HANA Cloud database.
  • Assign Containers to Spaces: In SAP Datasphere, enable HDI Container access and assign the deployed HDI containers to specific spaces to access their objects and content immediately.
  • Refine Models in SAP Datasphere: Use the Data Builder in SAP Datasphere to create and refine models within your HDI containers. You can combine these models with others in Datasphere, ensuring seamless integration.
  • Refine Models in HDI Containers: Allow models and datasets from SAP Datasphere’s space schema to be utilized within your HDI containers, enabling a two-way interaction.

Business Use Cases for HDI Containers within SAP Datasphere

HDI container-based developments support a wide range of scenarios, including:

  • Migration from HANA Enterprise Data Mart to SAP Datasphere: Organizations can leverage multi-model analytics capabilities while migrating from HANA Enterprise Data Mart to SAP Datasphere. This transition allows for advanced data analytics and modeling within a modern, integrated environment.
  • Migration from SAP BW to SAP Datasphere: By utilizing native HANA developments, companies migrating from SAP BW to SAP Datasphere can maintain their existing data processes and enhance their data warehousing capabilities with the advanced features of SAP HANA Cloud.
  • External OData Consumption or Web API Exposure: SAP Datasphere enables the publication of space objects as external OData services or Web APIs. This capability facilitates seamless data sharing and integration with external applications and services.
  • Complex On-Prem Use Cases: Handle complex on-prem scenarios with limitations in adopting Datasphere.
  • Complex DB Procedures for Actionable Functionality: Develop and manage complex database procedures to implement actionable functionalities.
  • HANA Sidecar Phased Retirement: Gradually retire HANA sidecar systems by integrating with SAP Datasphere.
  • Migrate PAL and APL Use Cases: Migrate Predictive Analysis Library (PAL) and Automated Predictive Library (APL) use cases from on-premises to HANA Cloud.
  • Leverage Machine Learning Capabilities: Utilize embedded machine learning and advanced analytics within SAP Datasphere without data extraction.
  • Data Science Enrichment: Use existing Python or R environments to trigger calculations in SAP Datasphere, train ML models, and store prediction results in HDI container tables.
  Benefits of HDI Containers in SAP Datasphere

The integration of HDI containers within SAP Datasphere offers several significant advantages:

  • Immediate Access: Objects and content of HDI containers are instantly accessible within SAP Datasphere spaces without the need for data movement.
  • Seamless Workflow: Users can harness SAP HANA Cloud’s advanced features while enjoying a user-friendly environment in SAP Datasphere.
  • Advanced Data Modelling: HDI containers support complex developments and provide advanced functionalities that complement the user-oriented features of SAP Datasphere.
  • Git Versioning: HDI introduces the usage of versioning tools like Git, which helps in conflict resolution and allows many developers to develop in parallel without interference. This supports modern development styles and accelerates development cycles on the database.
  • Life Cycle Management: Supports automated CI/CD pipelines for efficient life cycle management.
  • Higher Parallelism: HDI supports higher parallelism with no singleton deployment, allowing for more efficient and faster deployment processes.
  • Debugging and Performance Optimization: HDI provides robust debugging and performance optimization capabilities, leveraging SAP HANA optimization techniques such as pruning and parallelization to ensure high performance.
  Conclusion

Combining the development strengths of HDI containers with the user-friendly features of SAP Datasphere offers the best of both worlds. This hybrid approach supports advanced and complex data developments while ensuring ease of use and maintainability. For large projects with multiple developers, the choice between HANA and Datasphere will depend on specific requirements, such as the need for version control and Git integration.

By leveraging HDI containers in SAP Datasphere, organizations can achieve seamless data management and complex data modeling capabilities, ultimately enhancing their data warehousing solutions.

For more detailed guidance on implementing HDI container-based developments in SAP Datasphere, refer to the comprehensive resources available on the SAP Community.

Feel free to contact us with questions or to schedule a demonstration of this capability.

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Data Fabric – A Game Changer

Data Fabric - A Game Changer 

Today, businesses have access to more data than ever before. It comes in all kinds of sizes, types, and locations. With all this data comes a challenge – how to manage it effectively to drive meaningful insights and results. That’s where data fabric
comes in. Data fabric is an emerging technology that enables businesses to manage their data in a more efficient and streamlined way. It’s essentially a unified data management platform that brings together data from multiple sources and makes it accessible in one place.

This game-changing technology is set to transform the way businesses operate, allowing them to make more informed decisions and ultimately, drive growth. In this article, we’ll explore what data fabric is, how it works, and its benefits for businesses of all sizes. Read on to discover how data fabric can help you achieve
your goals

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A Look at Artificial Intelligence in SAP’s Business Technology Platform

A Look at Artificial Intelligence (AI) in SAP's Business Technology Platform (BTP) 

Looking to elevate your company’s performance? Aiming to enhance the speed and effectiveness of your operational processes? Or maybe seeking a superior method for forecasting? fI so, it’s time to explore the Artificial Intelligence solutions offered by SAP within the SAP Business Technology Platform. By harnessing the power of Al, your organization can significantly enhance performance, improve decision making, advance innovation, and boost customer satisfaction. As demonstrated by the quick rise of Al tools like ChatGPT, Al has now reached a level of maturity where it can offer significant benefits to businesses that utilize this technology. In this article, we will explore the role of Al in modern businesses, the key components of SAP BTP’s Al offerings, and real-world use cases that demonstrate how SAP’s Al solutions are transforming business.

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Plan, Visualize, Manage Promotion and Incentive Program Budget

Plan, Visualize, Manage Promotion and Incentive Program Budgets

In the modern digital world customers with information at finger tips are making more informed decisions in buying or leasing products.

Manufacturers can now connect with customers directly, transform the traditional selling practices by offering customized incentive programs. Using advanced analytics manufacturers can get deeper insights into customer sentiments and influencing factors for closing a deal with the customer.

With dynamically changing ownership models and competitive incentives landscapes, manufactures and retailers need more tools to manage incentive programs and budgets to swiftly modify programs by gaining insights into customer’s preferences to win deals.

TekAnalytics offers a flexible and easy to customize solution for Trade Promotion and Incentive Programs Management. The end-to-end Incentive Planning and Trade Promotion management solutions covers process steps for a series of activities aimed at successful product sales. Sales promotions and program activities, retailer incentives, promotional campaigns and many other tasks can be accurately planned, budgeted, executed and audited using TEK-TPIM®.  Get your complimentary whitepaper copy today!! 👉👉

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Optimized inventory with Improved Product Flow and Turn

About the Client: Major US Manufacturer and Distributor

The Client is a manufacturer and distributor of food packaging and foodservice products, supplying packers, processors, supermarkets, restaurants, institutions, and foodservice outlets across North America.

 

Business Challenge:

  • The current setup has no Integrated supply chain consolidated reports
  • For manufacturers, not only is it difficult to predict the maintenance needs of equipment but also to determine the necessary inventory of spare parts for potential repairs.
  • Increase availability of spare parts for servicing and repairing of machinery and manufacturing assets and reduce the cost of maintaining inventory

 

The Solution:

  • Gather data from supply chain history for drilling, rigging, pipeline materials, other inputs, part failure, and part usage history, lead time history (for reorders), history of disruptions to supply chain, planned and the actual history of ramping of capacity.
  • Establish an optimal freight cost model that can ensure on-time delivery.
  • Characterize statistical properties (distributions and correlations) in parts demand and lead times.
  • Understand and account for historical disruptions by leveraging data from SAP BW and flat files to leverage SAP Analytics Cloud (Analytics, Planning, and Predictive ).
  • Link data sources with an analytics engine.
  • Using the current state of inventory and chosen economic scenario, automatically set optimal inventory levels for all parts.   

 

Results and Benefits:

  • Develop a flexible framework for using inputs of historical data, economic and manufacturing capacity scenarios, and predictive models to set optimal inventory levels by using SAC predictive tools
  • Optimized inventory level-setting with fully quantified operating characteristics of expected cost and probability of stocking out

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