Author: Kartik Patel

Low-Code No-Code Software Development Options

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September 24, 2025

Consider Low Code and/or No Code Development

If you are considering the development of a software solution or app for business use or consumer release, you know that there are numerous crucial considerations involved in the process of software development. One of the most important factors in solution development is choosing the platform upon which you will build your mobile application, your software product or your business application. Today’s industry publications and technology journals are full of articles about low-code and no-code development technologies and techniques.

There is growing popularity among development teams and technology companies to suggest that the Low-Code and No-Code development (LCNC) approach has taken hold and that it has enabled businesses to move quickly to address market and business needs.

Gartner Defines Enterprise Low-Code Application Platforms (LCAPs) as platforms for accelerated development and maintenance of applications, using model-driven tools for the entire application’s technology stack, generative AI and prebuilt component catalogs.  According to Gartner, ‘low-code platforms will be used in 65% of application development by the end of this year.’

Low code/no code application development is a game-changing approach for software development and challenges the assumptions about the cost and time it takes to create a software solution or app. Low-Code, No-Code development approaches facilitate seamless and efficient development and allow businesses to expeditiously address individual client requirements with ease and precision.

The capabilities and advantages of the low-code, no-code or LCNC approach include:

Streamlined Development – Leveraging reusable components and a visual interface streamlines the development process exponentially, speeding up development and ensuring swift application delivery.

Promotes Inclusivity – The drag-and-drop features empower non-technical individuals with little to no coding knowledge to participate in development, thus promoting inclusivity.

Cost-Effectiveness – Low code/no code development demands no extensive coding or programming expertise, which lowers development costs, making it a cost-effective solution.

Rapid Prototyping – The visual approach of no code/low code development not only simplifies development but also fosters innovation by enabling rapid prototyping and easy project adaptability.

Low-Code and No-Code Development Options Like Creatio Ensure Speed and Reliability

Let’s take a moment to consider the differences between the Low-Code and No-Code development approach:

Low-Code Development

This approach supports the development team by decreasing the amount of ‘from scratch’ coding required, and creating a foundation of reusable code with components that become building blocks for future development. This development approach allows programmers to leverage low-code user interface components roughly 80% of development tasks, thereby limiting the manual coding efforts to 20% of developer time. Because low-code application development requires a knowledge of other tools and development skills, it is typically used by professional programmers with coding skills and knowledge. A Low Code development platform allows developers to extend component libraries and web frameworks to address specific use cases. Developers can work faster and more precisely with proven tools and combine coding and programming experience with tools designed for rapid software development.

No-Code Development

The No Code approach utilizes a visual workflow in an Integrated Development Environment (IDE), eliminating the need for manual coding. This technique creates a kind of ‘snap in’ system of components that developers can use and reuse to solve problems and create features and functionality. This closed system of tools restricts the developer by limiting use to pre-existing capabilities and user interface tools. The No-Code approach can provide a start-up with quick results and foundational features and allow them to enter the market and build visibility. Now that we have a better understanding of low-code/no-code app development, let us consider how technology research firms and publications see these development techniques.

If you are looking for a popular No-Code platform, you may wish to consider Creatio. Creatio is ideal for businesses that are focused on Customer Relationship Management (CRM), marketing, sales and service, and for the banking, insurance, credit unions, retail, transportation, business services, and telecommunications industries. Its no-code, open platform environment enables custom business applications for quick results.

The Creatio platform is designed with composable architecture, and does not require coding. It provides a simple, secured environment with frequent upgrades, and good performance and reliability. The Creatio framework enables fast and easy integration, integrating easily with other services and apps within your ecosystem and it supports rapid deployment and implementation and  configuration and ensures data security and appropriate data governance, so it is a good option for a project with tight timelines and/or budgetary controls.

Whether you are considering LCNC as one component of your software or app development project, or as a foundational development approach, it is important to understand the opportunities and challenges it may present and to engage an IT partner who can review your requirements and make the best recommendations to satisfy those requirements.

Contact Us to discuss your needs and to find out more about Low-Code No-Code Capabilities and the Creatio Framework.  Explore our free White Paper: ‘Understanding The Concept And Value Of Low-Code And No-Code Development’, and our blog article, ‘When Not To Use LCNC And Creatio.’

Original Post: Low-Code No-Code Software Development Options!

Is One BI Tool Enough or Do I Need More?

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August 27, 2025

Should I Have One, Two, Three or More BI Tools?

Whether your business wishes to implement its first business intelligence solution, or you wish to upgrade your solution to satisfy additional requirements, or your business divisions have diverse business needs, the RIGHT business intelligence and augmented analytics solution is crucial. To protect your Return on Investment (ROI) and reduce your Total Cost of Ownership (TCO), you must select a solution that will suit all your team members, and be flexible enough to grow with your organization and provide support now and in the future.

Making the Case for One vs. Multiple Business Intelligence Solutions

Gartner names 12 mandatory and common features of a comprehensive BI tool, which includes:

  1. Data visualization
  2. Governance
  3. Reporting
  4. Analytics Catalog
  5. Data preparation
  6. Data science integration
  7. Automated insights.
  8. Metrics layer
  9. Data storytelling
  10. Natural language query (NLQ)
  11. Collaboration
  12. Composability

For many businesses today, the decision can lead them down a winding path to the question: ‘Can I truly find one solution to suit all my needs and, if I can’t, is it possible to successfully combine and integrate more than one BI tool?’

Let’s take a closer look at these questions in an effort to help you understand the tradeoffs and the decision-making process.

Choosing ONE BI Tool

Benefits

  • Data Centralization – Team members can seamlessly access a single data solution
  • Provides a centralized, simplified platform for user management and access rights
  • Maintenance and Administration – The IT team has only one system to manage
  • Reporting – Dashboards and reports leverage one data model, and reporting formats and scenarios are uniform and interconnected
  • Ensures one focused, experienced IT and technical team

Challenges

  • Scalability – As your organization grows, you may have issues supporting an expanded user base and data volume
  • Performance Issues – For organizations with a large data volume, users may experience a lag in response time
  • Data Security – If your business has multiple business units, the administration of a single solution may be a challenge, as the model will require multi-layered, granular security and permissions
  • Constrains the business to one solution, with one roadmap for the future provided by one vendor

Choosing Multiple Solutions

Benefits

  • Scalability – Smaller, more modular solutions allow the organization to execute development independently without affecting other systems
  • Performance – User experience (Ux) for dashboards and reporting is likely to be better, with suitable speed and responsive dashboards and reporting
  • Support – Each independent module or solution can be supported independently without affecting other solutions

Challenges

  • Duplicate Data or Analytics – If proper architecture is not in place, the systems may duplicate data or analysis or a system may use the wrong model for reporting
  • Integration – Combining and integrating multiple systems requires a sophisticated roadmap to accommodate cross-functional reporting and analytics and data access
  • Continuity – Data mapping, distribution and consistency can be a challenge
  • User Management and Access Rights – It can be challenging to manage these processes across disparate platforms
  • Technical and IT – Multiple tools require expertise across all frameworks and platforms

These are just a few of the examples of benefits and challenges of the one vs. many approach of business intelligence solutions. While the temptation to consolidate and choose one tool can seem practical, the enterprise should consider the benefits and the challenges of each approach and compare them to their needs. Each tool may offer specific benefits that cannot be achieved by a one solution approach, and may better address user expectations and needs and satisfy the goals of the organization.

For a long time, the analytics industry has touted the idea of ‘best of breed,’ and there is a case to be made for choosing the ‘best’ for each scenario. Gartner’s most recent Magic Quadrant places 6 of 20 vendors in the niche quadrant and many of these niche vendors may offer solutions that meet a specific need within your own enterprise. So, don’t be too quick to eliminate the possibility of multiple solutions.

Many CIOs think that at standardization on one platform should always be considered at the enterprise level. While that approach has its advantages, it has lot of disadvantages too.

Advantages include:

  • One vendor, one relationship
  • Concentrated skill set within the team
  • A simple, uncluttered application landscape

Disadvantages include:

  • Commitment to one vendor (who may or may not be dependable)
  • Enterprise is confined to one vendor roadmap, limitations, upgrades and future development
  • One solution platform limits enterprise platform choices and flexibility
  • One tool may not satisfy niche or specific use cases across all divisions, departments and teams

While a one solution choice may work for a smaller organization or one without complex needs, you may wish to consider a balanced approach. When you strike a balance using a practical, limited best of breed approach, you can address the needs of specific business units or users. Be judicious about your choices, so as not to clutter the application landscape with too many applications and complicate your architecture.

You may wish to choose one solution for enterprise IT reporting, or one for smart data visualization for business users, or one that supports Citizen Data Scientists, or a tool that is focused on big data processing. Your assessments should be based on your use cases, user needs, license needs, budget, etc. While some organizations use as many as five (5) BI tools, the enterprise should limit their expansion to 2-3 tools in order to avoid chaos in integration, maintenance, the need for IT skills, costs etc.

While the assessment of needs and requirements for ANY software solution can be time consuming, it does pay off in the end and, especially if you are considering multiple BI tools, it is worth taking the time to thoroughly assess your needs so your business can create the ideal analytical landscape for IT, data scientists, business analysts and business users.

Contact Us to discuss your analytical needs and to find out more about Modern And Traditional BI Tools, Augmented Analytics solutions, Citizen Data Scientist training and the process of choosing the right Analytics Solution for your business. Explore our free White Papers: ‘Enabling Business Optimization And Expense Reduction Through The Use Of Augmented Analytics,’ and ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI Tools.’

Original Post : Is One BI Tool Enough or Do I Need More?

What is MCP and Why is it Important to My Business?

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July 30, 2025

How Does MCP Help AI Application Development?

Technology is great! But it can be hard to keep up. Even if you have made a career in technology, the pace of change today is so rapid that, if you miss one issue of your favorite tech publication, you may risk falling behind.

Perhaps nothing has increased the pace of change in technology more than Artificial Intelligence (AI) and, because AI potential seems to be unconstrained, the need for expanded capabilities and foundations is constant.

One such development is Model Context Protocol (MCP). MCP is an open protocol that was created by Anthropic to simplify the process of interacting with Large Language Models (LLMs) and to standardize the way in which applications provide context to LLMs and help them interpret data.

It may help to think of MCP as a translator or a way to make connections. Much like a USB adapter can connect an external hard drive to your laptop, MCP can connect various tools and data sources to enable interaction, integration and context for use in AI.

While early stage AI struggled to connect disparate data sources, tools and Application Programming Interfaces (APIs), the advent of Model Context Protocol (MCP) provides a bridge to external data and services to connect AI models using a standardized communication framework to allow for AI reasoning and processing. So, AI models like Azure OpenAI, GPT, Atlassian and others can fetch data, connect and interact with APIs and perform tasks, going well beyond the knowledge contained in the model to produce new, expanded outputs.

What is Model Context Protocol (MCP) and WHY Should I Care?

In the good old days of AI (just last year), your users might ask a complex question or a question that exceeded the information contained within an LLM training data set. That question could elicit an answer that made no sense or the system might simply frustrate the user by saying, ‘I don’t know.’ In order to solve that problem, you would have to provide data refinement to ensure that the LLM had context or you would have to add another tool or secondary source. That can be complex, time consuming and expensive.

In short, in order to succeed with your LLM, you were constrained by the amount of training data, and how well you could anticipate what your clients or users would ask or need. Sure, the information exists out there somewhere, but your LLM doesn’t include that data! You could use APIs but that process of application integration is complex and can be difficult to implement in a meaningful way, and you have to hard-code each connection! Using this technique to provide information to an LLM requires you to review documentation and data, identify the end point of the search, verify authentication, structure requests and then make sure it all works seamlessly so your users are not frustrated.

MCP allows you to create a bridge between apps and tools and establish automated workflows, using the power of LLMs to perform tasks and provide clear, concise information across all technology frameworks and platforms. MCP allows developers and content managers to establish what the LLM should know and provide that in a standard format that the LLM can understand. In essence, MCP acts as the go-between or the middleman, simplifying the relationship and connection between the LLM and APIs, tools and data repositories. Rather than your app reaching out to the API, it communicates with an MCP server. The MCP server will then translate that information and decide how to communicate with the API to satisfy the user request. It’s a translator!

Model Context Protocols (MCP) provide support for application developers using AI so they can more easily build apps and integrate information, ensuring that the app is flexible enough to support future integration of tools and data. Its open-source accessibility allows software developers and software vendors to leverage these tools to create business and consumer apps.

The team can create apps that are extensible at runtime and connect tools and APIs to an MCP server, to use the app immediately without extensive coding and deployment. The process is simple.

  • When a user enters a query, the Large Language Model (LLM) sends a request to the MCP server
  • The MCP server translates the request and decides where it should go (API, tool, etc.), and then sends it to the appropriate source
  • The response to the query is returned through the MCP server
  • The MCP server sends that response to the LLM
  • The user receives the response

It’s just that simple.

If you, your IT staff, your management team or your customers are asking about the potential of AI and LLMs, it is time to consider MCP and how it can support your needs. The incorporation of this approach can save development time and expense and alleviate rework and developer and user frustration.

If your business wishes to improve productivity, timelines, budgets and dependability of in-house applications, you will want to find a vendor and service provider who appropriately employs AI and LLMs to support its development model. If you are planning to engage an IT expert to augment your own software product or solution, it is wise to look for this capability when you interview prospective partners. Contact Us to find out how to integrate AI And LLM capabilities into your software project, website, analytics initiative or other project. Explore our free White Papers: ‘What Is AI And How Can It Help My Business,’ and ‘The Practical Use Of GenAI In BI And Analytics Tools.’

Original Post: What is MCP and Why is it Important to My Business?

Look For a Software Development Partner That Uses AI and LLMs

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June 25, 2025

AI and LLMs Support Developer and DevOps Productivity

A recent Copilot study revealed an interesting fact about the use of AI and Large Language Models (LLM) in the software development process. The study included developers from Microsoft, Accenture, and a Fortune 100 electronics firm and reported a 26% boost in productivity, increasing output from the usual eight hour workday to what amounts to ten hours of traditional output. This improved output increased even more for less experienced developers.

By leveraging Artificial Intelligence (AI) and Large Language Models (LLM), the DevOPS organization can greatly improve output, code quality, developer productivity and consistency. As businesses embrace the collaborative and team-oriented concepts of DevOps, the use of AI and LLMs can be utilized across the organization, and forward-thinking organizations are looking at the set of practices in DevOps (software development IT operations) to automate processes and accelerate the software development lifecycle.

Where software vendors employ these techniques, clients, customers and end-users can benefit from this approach. The development team can work more quickly and efficiently to satisfy requirements, design, develop and test and deploy, so business projects can be completed more rapidly and dependably.

If a business is considering a vendor or a software product for implementation within the walls of the enterprise, it is worth asking the prospective vendor and service provider how they are currently using cutting-edge technology to improve their development process and lifecycle.

Elements and Aspects of AI in Software Development

The Use of AI and Large Language Models (LLM) Improves the Development Process

Prompt Engineering uses natural language interfaces to study interactions with and the programing of LLM computation systems to enable complex problem solving, looking for patterns and focusing on reusable solutions. Infrastructure-as-Code (IaC), Code-as-Data and CodeQL LLMs support developers by exploring the code, studying requirements and documentation and analyzing infrastructure to find issues and inconsistencies.

Automated Code Generation allows the development team to optimize testing and deployment. Developers can use AI code review tools like Codiga and testing tools like DiffBlue Cover to review and analyze code and find issues, and AI-based code generators like GitHub and Copilot.

Generative AI (GenAI) leverages LLMs to streamline the steps in the development process, including analysis of requirements, coding and testing.

Natural Language Processing (NLP) enables code generation with machine learning and produces suggestions to develop or complete code, thereby reducing the occurrence of human error and allowing developers to focus on other, more complex aspects of code and development.

Testing and Debugging can be automated to detect and address bugs, inefficiencies and vulnerabilities in the code. These tools can be used to generate unit tests, create test cases and increase the effectiveness of the testing phase to improve overall quality.

Translation Tools enable translation of other programming languages for projects where the team must migrate code to other programming languages. The process uses large language models to complete the translation, leaving developers free to focus on architecture.

Documentation Support includes development of documents for code comments, regulatory requirements etc. Prompt Engineering generates summaries and answers questions and provides examples so developers who review the code for later upgrades have appropriate documentation to support the software evolution.

Project Management for all of DevOps is supported by automated routines and integration of information and documentation throughout the process, monitoring system performance, analyzing test results and optimizing implementation. The ongoing analysis of test planning, data migration, compliance documentation and architecture supports the entire DevOps team.

If your business wishes to improve productivity, timelines, budgets and dependability of in-house applications, you will want to find a vendor and service provider who appropriately employs AI and LLMs to support its development model. If you are planning to engage an IT expert to augment your own software product or solution, it is wise to look for this capability when you interview prospective partners. Contact Us to find out how to integrate AI and LLM capabilities into your software project, website, analytics initiative or other project. Explore our free White Papers: ‘What Is AI And How Can It Help My Business,’ and ‘The Practical Use Of GenAI In BI And Analytics Tools.’

Original Post: Look For a Software Development Partner That Uses AI and LLMs!

Use Data Quality and Data Insight Tools to Avoid ‘Bad Data’

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May 27, 2025

Use Data Quality and Data Insight Tools to Avoid ‘Bad Data’

When a business sets out to initiate data democratization and improve data literacy, it must choose the right approach to business intelligence and select an augmented analytics product that is self-serve, intuitive, easy to implement and easy for business users to embrace. Transitioning business users into the role of a Citizen Data Scientist can be challenging.

By some estimates, bad data costs global organizations more than five trillion USD annually, and at the enterprise level, the quality of data can be a burden on IT, analysts and business users and acceptance of bad data can be inherent in business processes.  Improving the overall quality of data increases confidence in decisions, reporting, strategies and the adoption of dependable analytical models across the organization.

Data Analytics Tools with Data Quality and Data Insight Features Assures Confident Decisions

When a business implements Data Quality, Data insight and Data Quality Management tools and techniques it can establish a comprehensive process with a solid set of tools to identify errors, enhance data quality, and boost productivity. Business users can leverage intuitive tools to uncover hidden insights and improve the overall quality of data with actionable recommendations to take prompt action.

Benefits:

  • Ease-of-Use and intuitive tools for business users and team members – no technical skills required
  • Improved accuracy and dependability of data for confident decision-making
  • Data Quality supported by statistics and machine learning to assure credibility
  • Improved data insight without delays or re-work
  • Assured agility and decentralization of analytics
  • Consistency of data quality and availability
  • Improved User Adoption

Data insight takes data to the next level by providing comprehensive data analysis and quality assurance features that empower business analysts and users to quickly and easily identify errors, enhance data quality, and boost productivity. The business can harness the power of statistics and machine learning to uncover those crucial nuggets of information that drive effective decision, and to improve the overall quality of data. This approach allows users to let the system do the work for them and make confident decisions.

A foundational augmented analytics solution with machine learning, natural language processing and automation within an advanced analytics solution suite can improve results and support its team with augmented analytics designed as self-serve solutions for business users. Users can gather and analyze information with assurance of sustained data quality and produce results that are clear and concise.

Advanced data management features ensure data quality and provide crucial data insights with tools like Column Analysis, Feature Importance, Missing Value Analysis and Observations. Tools that support data insight include numerous data quality management techniques. These tools allow users to see and work with datasets in a way that is targeted and provides clear, actionable information for decisions and strategies.

If your business wishes to improve the easy of analytics and Quality Of Its Data and achieve data insight in a timely, dependable manner, find out more by watching this free Smarten Webinar: ‘Improving Data Quality With Data Insights,’ and read our free blog article, ‘Balance Data Quality With Data Agility.’ Explore our Smarten Augmented Analytics Products And BI Tools.

Original Post : Use Data Quality and Data Insight Tools to Avoid ‘Bad Data’!

AI-Enabled Analytics and Business Intelligence Has Its Benefits

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April 16, 2025

Why Choose BI Tools and Analytics with AI?

Today, the use of Artificial Intelligence (AI) has a wealth of potential and prospective application in the field of analytics and its integration within analytical products provides numerous benefits to the business. There are many ways in which artificial intelligence (AI)  can augment the capabilities of existing analytics solutions, and provide additional insight, support and results.

 

World-renowned technology research firm, Gartner, predicts that ‘40% of application development teams will be using automated data science and machine learning services to build models and add AI capabilities to applications.’

 

True to this prediction, many business intelligence and analytics solution vendors have added AI capabilities to self-serve analytics to create an environment that encourages productivity, fact-based decisions and efficient business processes, approval processes, automated alerts, etc.

 

There are a number of ways that artificial intelligence can enhance and improve the features and functionality within an enterprise using the augmented analytics environment:

Business Intelligence (BI) – Artificial Intelligence can be used to analyze large datasets and to sort and present data to achieve actionable insight, recommendations and suggestions, spotting trends, providing forecasts and optimizing results.

Generative AI (GenAI) Applications – Using Natural Language Processing (NLP) and Machine Learning (ML), AI tools can create content including images, text, video and other components to enhance presentation, interact with customers and suppliers in a targeted way and personalize messages.

Analytics Tools and Techniques – Team members and end-users can leverage self-serve analytics with AI to identify patterns and trends, gain insight, present data in a way that is meaningful to a particular target audience, predict outcomes, analyze customer buying behavior and analyze performance of products, services and other operational components.

Marketing and Advertising – The organization can analyze data from disparate data sources to identify market trends, changes in targeted customer preferences, requirements for customer relationship management, and other factors that relate to competitive advantage and customer retention.

Analytics Features and Development – Vendors and solution providers can use AI to quickly and easily upgrade analytics solutions, add features and functionality and reduce development time to keep up with client and market demands.

 

Current Artificial Intelligence technologies like ChatGPT, GenAI, and Agentic AI all provide specific capabilities to satisfy business requirements and inform and improve analytics with data gathered from within the organization that can be repurposed, targeted and used to solve problems, identify opportunities, present data to management, partners and customers, and communicate with all stakeholders using relevant data and information garnered from within and outside the enterprise.

Choose Business Intelligence and Augmented Analytics with Artificial Intelligence to Improve Outcomes
  • Improve Data Visualization – Create interactive dashboards, graphs and charts to help users present and share data in a way that is meaningful to a particular audience, and to clearly present data for confident decision-making. It can recommend and suggest visualization techniques to improve and refine how data is presented.
  • Improve Analytics with Task Automation – Automate activities and tasks, using customized automation scripts, and baseline filters and rules to extract and present data that meets user parameters. It can schedule and produce repetitive reports, and scripts can be altered change parameters, thereby freeing users to perform other operational or more strategic activities.
  • Predictive Analytics – Create predictive models using self-guiding UI wizard and auto-recommendations for swift, effortless forecasting and predictive analytics using data from numerous data sources.
  • Natural Language Processing (NLP) – Expand the capabilities of text generation and human language processing. It can enhance low resolution images, recognize and synthesize images and generate images for creative presentation of data and information.
  • Auto Insights and Machine Learning – Automates the process of interpreting and presenting results using rich visualization techniques, and includes all salient details, so users can review, share or edit content as they please.
  • Automated Alerts – Analyze results and trigger and generate alerts to protect against security violations, fraud and other risks, by analyzing normal behavior and results and comparing it to current and real-world results to identify anomalies.
  • Reporting – Using visualization, graphs, images and combining those with summaries and details can provide reports and presentations that are clear and suitable for all audiences, including management and executives, as well as teams and staff members.
  • Interpretation and Summarization – Quickly interpret and summarize data without spending a lot of time creating content, editing and preparing.
  • Data Preparation – Improve data transformation and cleansing and help prepare data and improve the quality of that data using phonetics for clustering, identifying data types, and hierarchies, suggesting alternate values etc.
  • Support for Citizen Data Scientists – Use AI cutting-edge tools to support team members with sophisticated, intuitive tools that leverage artificial intelligence (AI) and analytical techniques to produce concise results without requiring the skills of Data Scientists.

 

The analytical solutions market is moving quickly to adopt Artificial Intelligence and if your business wishes to succeed, it too must move to find and improve products and services as quickly as possible to meet customer expectations and to satisfy the ever-changing landscape of business competition.

 

Select and implement an Augmented Analytics Solution With Artificial Intelligence (AI) components to ensure affordable, flexible solutions that every user can leverage, no matter their skillset or technical capabilities. Read White Papers, ‘Generative AI (GenAI): The Benefits And Applications Of AI In Analytics,’ and ‘The Practical Use Of GenAI In Business Intelligence And Analytics Tools’ and explore the benefits of AI in analytics and the full spectrum of benefits and advantages of current artificial intelligence (AI) technologies.

 

Original Post : AI-Enabled Analytics and Business Intelligence Has Its Benefits!

Understanding GenAI and Agentic AI: What’s the Difference?

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March 19, 2025

Choose or Combine GenAI and/or Agentic AI for Apps

The only way to avoid news of Artificial Intelligence (AI) is to move to the top of a mountain and leave all your devices behind. Talk of AI is everywhere. So, it is no surprise that most businesses are considering how to incorporate artificial intelligence (AI) into their consumer apps, business applications, websites and mobile applications.

Gartner predicts that within the next few months, ‘…40% of enterprise applications will have embedded conversational AI.’

As you discuss AI opportunities with your team and your IT consultant, be sure you understand the terminology. There is a distinct difference among AI technology, products and solutions and the industry often uses the terms interchangeably.

In this article, we will discuss the difference between two types of Artificial Intelligence (AI) development your business may be considering, namely, Generative AI (GenAI) vs. Agentic AI.

Generative AI (GenAI)

This technology is form of AI designed to understand and respond to prompts and to generate text, images (including video) and other media. To function, GenAI models must be trained, using large datasets. By analyzing these datasets, the system can learn to spot repetitive results, trends and patterns. Generative AI utilizes neural networks to recognize and identify these patterns in ‘training’ data, and use that data to generate content.

Here are some of the models in use today:

Multimodal Models

These models can process and integrate information in the form of text, audio, images and video, gestures and facial expressions, etc. Tools like DALL-E, Stable Diffusion, and ChatGPT are based on multimodal models.

Large Language Models (LLM)

LLM is used to understand and generate language. It uses a large volume of data and parameters to train the model.

Variational Autoencoder (VAE)

This model provides probabilistic graphical models and variational methods.

Generative Adversarial Network (GAN)

This machine learning framework consists of two neural networks competing for a ‘win’ or for the best result.

Use Case Examples

Marketing – A business might use Generative AI (GenAI) to create customized, targeted marketing content and social media posts to attract a certain demographic or customer without the need for professional knowledge or human intervention, so the team can focus on critical operations and strategic goals. Using training data, the GenAI model will produce contextual content specifically designed to target customers in a particular market niche.

Reporting and Visualization – When an analytical solution incorporates GenAI within its software or app, it can improve the clarity and precision of the data presented. Using visualization, graphs, images and combining those with summaries and details can provide reports and presentations that are clear and suitable for all audiences, including management and executives, as well as teams and staff members.

Technology – Combine GenAI with search optimization, rules-based systems for natural language generation and chatbots, with simulation, with non-generative ML to classify and segment data, or with graphs. Combining techniques can reduce costs, while delivering appropriate performance, efficiency and accuracy.

For more information about Generative AI (GenAI) benefits and uses, see our free white paper, ‘Generative AI (GenAI): The Benefits And Application Of AI In Analytics.’

Click Here to download the whitepaper.

Agentic AI

This artificial intelligence (AI) approach goes well beyond the ubiquitous platforms such as ChatGPT and other popular AI tools with sophisticated reasoning and iterative planning features to autonomously solve complex, multi-step problems.
  • Flexibility and precision
  • Extended reach and scalability
  • Autonomous capabilities
  • Intuitive capacity
Agentic AI independently and autonomously performs tasks and augments other systems to complete workflow and tasks using tools and processes within a solution or system. It is capable of solving complex problems and taking action and can perform interactive tasks, operating outside the typical machine learning (ML) environment of a classic AI trained model to achieve true process automation.

Use Case Examples

Marketing – Your business might use Agentic AI to automate tasks and schedules, track performance and monitor spending. These AI agents can be categorized to handle specific tasks like creating copy and content, choosing a target audience and monitoring and reporting on marketing campaigns.

Research – Use multi-agent AI models to scan and analyze research, articles and databases and suggest improvements, identify new solutions or products using existing technologies, materials, etc.

Manufacturing – Agentic AI uses sensors attached to machines, components, and other physical assets to predict wear-and-tear and production outages, and sending alerts or initiating processes to mitigate probable issues, avoiding unscheduled downtime and associated costs to manufacturers.

Gartner has predicted that ‘Agentic AI will introduce a goal-driven digital workforce that autonomously makes plans and takes actions — an extension of the workforce that doesn’t need vacations or other benefits.’

Is GenAI OR Agentic AI Right for My Business or Consumer App, or Should I Choose Both?

When GenAI and Agentic AI are combined, the business can build a technology that creates contextual content and is capable of taking autonomous action and making routine decisions, so the enterprise can optimize human and technology resources to scale operations and provide targeted, personalized customer service to enhance customer satisfaction and ensure efficiency and productivity within the organization.

By employing cutting-edge Artificial Intelligence (AI) Technology and expert predictive and data science services, the enterprise can gather, produce and analyze information from disparate data sources, and use that data to create products, enhance services, improve productivity and improve market position, all with the support of a team that is skilled in AI, Data Science, Data Engineering and Predictive Analytics. Contact Us to find out how Generative AI (GenAI), Agentic AI and other AI technologies and services can support your software applications, mobile application, or software product ideas, and advance Digital Transformation (Dx).

Original Post : Understanding GenAI and Agentic AI: What’s the Difference?

DeepSeek Unseats U.S. AI, and Reveals India Potential

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February 6, 2025

DeepSeek Reveals the Potential for India’s AI Market

The shock waves from the DeepSeek announcement in the artificial intelligence (AI) space are still being felt. As AI technology businesses, software developers and consumers digest the new reality, there is no doubt the market will shift, adapt and change in a drastic way.

At the heart of this change is the Chinese company, DeepSeek, a new venture that has achieved a significant breakthrough in inference speed, leapfrogging over previous, world-renowned AI models like OpenAI, and causing a massive derailment of chip-maker Nvidia’s stock by proving that lower-end chips can be used to achieve high-performance results in the AI space. This open-source market entry has radically changed the U.S. and global market and, in so doing, it has also changed the mindset of AI developers and investors in other countries, including India.

Prior to the DeepSeek announcement, the prevailing AI strategy was a scenario in which a business required a huge capital investment to build Large Language Models (LLMs) for training and to fund massive processing capabilities and a huge talent pool for development. That meant government support, and an industry and environment to achieve these multi-billion dollar investments and goals.

DeepSeek built an AI product for less than ten million USD, with only 200 engineers.

That reality has shifted the India AI landscape by proving that the India software industry can move beyond an AI app focus to create base models and fully functional concepts. In fact, India has done it before! In defiance of all common expectations, India’s ISRO space program reached the moon at a fractional cost of the U.S. NASA program.

So, why not now? India (and other countries) have often watched from a distance as their own technology talent developed the best and brightest applications in the U.S. Today, the India government has the opportunity expand the capabilities of AI within the country, going beyond chatbots and AI contextual applications to leverage the technology talent within its borders and Create Solutions For Government, Agriculture And Other Industries.

The media and industry publications like to use the word ‘gamechanger,’ but in this case, the revelation of DeepSeek is a gamechanger in the true sense of the word, especially for India. The government is now supporting the new concept of AI evolution and development with the acquisition of 10k GPUs to support development and building the infrastructure to roll out AI models in India within 4-6 months. Yes, it’s possible!

Is the DeepSeek Moment an Opportunity for India and Global AI Potential?

Union IT Minister Ashwini Vaishnaw recently announced that, ‘the planned compute facility (cloud-based servers that are geared to run and handle AI inference), Vaishnaw stated that it has exceeded initial expectations with the country securing more than 18,600 GPUs. Among these are 12,896 Nvidia H100 GPUs, 1,480 Nvidia H200 GPUs, and 742 AMD MI325 and MI325X GPUs. Originally, the target was to procure 10,000 high-end AI chipsets. “DeepSeek AI was trained on 2,000 GPUs, ChatGPT was trained on 25,000 GPUs, and we now have 18,000 high-end GPUs available,” the IT Minister said.

As the technology market evolves, we can expect to see more changes, and those changes will come faster and more frequently. DeepSeek is just one example of the need for India to establish a nimble, adaptable approach that will respond quickly to new developments and identify the opportunities for growth and application of these developments in the global and local market.

The game is changing, and India no longer needs to defer to the U.S. or other countries in the AI market or in the use of its own development talent and investment infrastructure. It can build an ecosystem that will stand on its own without deference to other market leaders.

As IT Minister Vaishnaw said, ‘India will offer the cheapest compute in the world at less than $1 per hour for high-end chips that power generative artificial intelligence (GenAI) as the government’s ₹10,000 crore IndiaAI Mission comes into play….’

There is much to research, investigate and understand about the most recent DeepSeek development. But it does prove one thing. We do not need complacency or acceptance in technology. We need innovation, and India is up to the challenge!

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Original Post : DeepSeek Unseats U.S. AI, and Reveals India Potential!

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