Author: Kartik Patel

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!

Select an AI, partner for Artificial Intelligence Application Development, or a partner for  predictive analytics and technology partner and a solution 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 our 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 : DeepSeek Unseats U.S. AI, and Reveals India Potential!

What is Agentic AI and Why Should I Consider it for Apps

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January 8, 2025

Use Agentic AI for Autonomous Workflow and Task Completion

Artificial Intelligence (AI) is advancing, evolving and changing at lightning speed. It is nearly impossible to keep up with the changes, and to understand what, if anything, each of the new products and developments can offer to a business.

Continue reading “What is Agentic AI and Why Should I Consider it for Apps”

Consider Your Business Needs Before Choosing GenAI

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December 4, 2024

Is GenAI Right For Your Business?

You don’t have to be in the technology business to know about Generative AI (GenAI). The buzz about this technology advancement is everywhere! The media is talking about its impact, governments are discussing regulation, and technology companies are looking for ways to integrate GenAI into existing products and to create new products that will excite consumers, and improve productivity, results and revenue.

Continue reading “Consider Your Business Needs Before Choosing GenAI”

Minimum Viable Products and Their Value

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November 6, 2024

Minimum Viable Products Provide Metrics for Success

If you aren’t familiar with the term ‘Minimum Viable Product,’ here is a brief definition: A Minimum Viable Product or MVP is a version of a product that provides minimal features – just enough for customers to use and provide feedback on the product. That feedback is then incorporated into the final plan for the product, thereby allowing the creative team or software vendor to ensure user adoption and anticipate features and functionality the customers want now or may want in the future.

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Choose Augmented Analytics Designed for Business Users!

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September 11, 2024

Avoid Complex Analytics Solutions (Your Users Will Hate)

When a business is considering a business intelligence or analytics solution, it is important to recognize that today’s solutions are very different than the solutions of the past. Not only do they include more analytical techniques and features, but they have come a long way in providing access to sophisticated analytics for the average enterprise team member.

Harvard Business Review Analytics Service reports that

a) businesses can substantially improve business performance by giving frontline workers modern self-service analytics tools to enable fast intelligent action and,

b) not all self-service analytics provide this effective approach.

Choose Augmented Analytics Designed for Business Users and Get the Most From Your Solution

The Harvard Business Review Analytics Service surveyed nearly 500 executives and found that they reported significant performance improvement when they empowered frontline workers with augmented analytics. More than one-third of those surveyed noted improvement in customer and employee engagement and in product and service quality.

While some businesses may still be using business intelligence and analytics that are designed for data scientists and IT professionals, most of those are actively working to upgrade and/or migrate to augmented analytics and solutions that are designed for self-serve business user access.

Here’s why:

  • Search-based, self-serve analytics provides swift access to data and familiar natural language processing (NLP) search capability so business users can ask a question, get an answer and drill down to discover the root cause of issues. There is no need for the user to wait for IT or a data scientist to produce a report. They can continue to work on a task or a problem with full insight into results, challenges and possibilities.
  • The enterprise can enable data democratization and data literacy across the business landscape, thereby ensuring that there is a rapid response to market and competitive changes and to changing customer buying behavior.
  • Business users can leverage their industry knowledge and functional skillset and combine data insight with experience to produce the best results.
  • Intuitive, easy-to-use solutions help to combat user resistance and ensure user adoption. While there are always cultural issues surrounding this type of adoption and the perceived changes in responsibilities, when business users see the value of having crucial information at their fingertips, the enterprise can ease the transition and ensure user adoption.
  • No matter the role of the user, the team can enjoy the benefits of augmented analytics and make the transition to Citizen Data Scientists to improve collaboration, data sharing and fact-based decision-making.
  • The business can understand quality and maintenance issues, refine customer targeting and marketing optimization, and make appropriate financial investments, and they can analyze trends and patterns and make forecasts and predictions.
  • When the enterprise adopts these tools and techniques, they allow Citizen Data Scientists to perform analytics on a day-to-day basis and, where appropriate to effectively interact with and collaborate with the IT team and data scientists to refine data and prepare it for more strategic initiatives, so there is a seamless handoff from the business user to the analytical community, when and as necessary.

When the business is ready to acquire augmented analytics or to upgrade from existing, more restrictive solutions designed for professional analytical resources, it is important to choose the right solution – one with sophisticated tools that are presented in an intuitive user interface with auto-suggestions and recommendations to assist business users, and ample personalization of dashboards and reports.

With the right IT consulting partner, you can select and implement an Augmented Analytics Solution with business intelligence (BI) and advanced capabilities, and ensure that every user can leverage these tools, no matter their skillset or technical capabilities. Explore our free white paper, ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI Tools.’

Original Post: Choose Augmented Analytics Designed for Business Users!

Improve Apps and Software with Artificial Intelligence (AI)

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August 1, 2024

Can Artificial Intelligence (AI) Improve My Business and Consumer Apps?

It is impossible to listen to the news, read a business publication or browse social media without finding an article or story about Artificial Intelligence (AI).

Continue reading “Improve Apps and Software with Artificial Intelligence (AI)”

Should My Business Invest in Generative AI?

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June 26, 2024

Can Generative AI (GenAI) Help My Business?

Whether you are in the business of technology, or an average citizen, there is no doubt you know about Artificial Intelligence (AI) and you have probably heard or read about the advances made in Generative AI (GenAI).

According to Gartner business surveys, GenAI has become one of the most adopted and deployed technologies, and it is either in use in many industries and businesses, or it is in the works.

As a senior manager in a software business, or a corporate CIO, a software engineer or a consumer, you are probably thinking about how you can use GenAI to make your product better, or your life simpler. If you ARE in the business of technology, you may already have started a GenAI project.

But, it may be wise to exercise some caution. You know the old adage, ‘just because you can, doesn’t mean you should?’ That certainly applies to GenAI.

Yes, this technology has advanced enough to offer some value in some instances, but it should not be widely adopted without understanding its current limitations. Don’t let the promise of GenAI blind you to the fact that it is not yet a mature technology and that it is not suitable for all applications.

Can Generative AI (GenAI) Help My Business?

Before you start your project, you should consider the following factors:

  • GenAI alone is not the magic potion many think it is. In fact, the best use of GenAI is as a component of a holistic landscape of technologies, which may or may not include other types of artificial intelligence (AI) techniques.
  • Business use cases are a wise addition to your strategic discussion. How and where will your business use GenAI and does the addition of this technology add value or provide competitive, productivity or collaborative improvements? Implementing GenAI just because it is cutting edge, does not add value to your organization.
  • The cost of implementing these new technologies must be considered. That consideration should include the estimated useful life of the investment and its return to the organization.
  • For some tasks like content summarization for presentations or reports, or routine content creation, standard product descriptions, etc., GenAI may be a welcome addition, and will provide productivity improvement for your team.
  • When considering the use of GenAI, it is important to understand how and when your team will use the capabilities and set reasonable expectations for its use. If and when you decide to implement GenAI, it is equally important to train and inform your team and help them understand what they can achieve with this technology. You must also ensure that the team understands what GenAI will NOT do for them. There is a lot of hype out there, and team members may think that GenAI is going to either a) replace their position, or b) give them back 50% of their time for other tasks. It is likely that neither of those scenarios will be true, so be sure your team understands the transition, what they can expect, and what you will expect of them.
  • Involving experienced team members in the AI process is imperative. You can’t ‘set and forget’ your tasks without risking incorrect output or issues that will affect customer satisfaction or put your business at risk. Consider how and when you will monitor and manage output and what human intervention is required for the use you have imagined in your use cases.
  • While we are on the subject of expectations, let’s remember that, while the future of GenAI is promising, the current state technology is not a boiler plate. It is not a one-size-fits-all solution that can be hurriedly put into place, nor will it solve all your problems.

There has been much marketing hype about GenAI, but at the end of the day, if your business is going to invest time and money in this technology, it must establish a reasonable strategic initiative with measurable metrics and risk assessment and management reviews.

Before your business tackles a GenAI investment project, and all the technology, cultural and management changes that it requires, ask yourself a) what problems does my business have and what are our most important priorities, b) Can GenAI realistically help our business solve or reduce these problems and c) how can I measure and manage this new approach to prove my theories?

As we have outlined in this article, it is important to recognize the limitations of the current Generative AI (GenAI) solutions, and develop a thorough and complete understanding of your prospective business use before making a decision to invest in and implement this type of solution in your organization. Contact Us to find out how we can help you plan and achieve your goals. Artificial Intelligence (AI) in Analytics, Artificial Intelligence Development, White Paper: What is AI and How Can It Help My Business? Explore our articles on AI: Generative AI, the Benefits and Applications, Ideas to Get You Started with Generative AI, Understand AI, OpenAI and Chat GPT.

Original Post : Generative AI (GenAI) Is NOT a One-Size-Fits-All Solution!