Reality Check: You’re Spending Too Much Time Searching for Information

The great Benjamin Franklin once said, “Time is money”. Even though he uttered these words in the 1700s, the sentence still rings true today. Timing is everything in business – how quickly a product can be manufactured, shipped and bought, all play vital roles in the direct success of a company. When that product is put out to the market and sold is crucial. 


The same principle applies to analytics – timing is integral to the efficient absorption and utilization of data. For example, if you are in a meeting and you are presented with interesting data/information but it is not relevant to the current topic or project at hand, then that data is simply a distraction in the moment; no matter the quality of that information. If it does not play an active role in pushing the needle forward, then it is inefficient analytics. 


What are personalized data stories? 


Personalized data storytelling is when data is transformed into an easily digestible message that anyone can understand, using advanced machine learning technology and augmented analytics. This message is usually presented in a customized manner using images, audio or any other creative AI means.


Personalized data stories are an up-and-coming technological advancement that has proven to help mitigate this inefficiency and according to many experts, will be the most used analytics method in future. With the speed of digital transformation, now more than ever, every employee should be empowered with some sort of personalized data to make confident data-driven decisions. 


Why are personalized data stories important?


Too often we are stuck sifting through data and other insufficient analytics platforms looking for what we need at any given time. According to recentresearch, “19.8% of business time – the equivalent of one day per working week – is wasted by employees searching for the relevant information they need to do their jobs”. 


Tools must be aware of users’ contexts – who they are, what they are asking, why they’re asking, and when they are engaging. Contextual understanding is crucial for delivering the most important information so leaders can decide and act at a particular moment. It also mitigates the possibility of integral information slipping through the crack.  


Advanced data analytics platforms and AI assistants, or data translators, have the ability to understand your goals, user roles, and specific challenges you are tackling at the moment in order to drastically improve your efficiency as an employee as well as the efficiency of the organization as a whole. 


To learn more about how your organization can employ personalized data storytelling to improve business outcomes, contact info@conversight.aior request a demo.

Augmented Analytics and Creative AI are Closing The Data Knowledge Gap

What is Augmented Analytics? 


Augmented analytics is an approach to data analytics that employs the use of enabling technologies such as machine learning, artificial intelligence and natural language processing to automate analysis processes such as data preparation, data processing, insight generation and insight explanation; all of which are tasks normally completed by an analyst or data scientist. Simply put, it is used to increase the depth in which people explore and analyze data in business intelligence and analytics platforms through automating data storytelling. Unfortunately, for many companies, especially small businesses, there is a shortage in data scientists that has created a growing skills gap required to employ data stories and use them in their decision making processes. Read on to learn how augmented analytics can bridge this knowledge gap and finally allow anyone within an organization to gain value from data insights. 


What is Creative AI? 


Recent Improvements in machine learning techniques, such as deep learning and neural networks, have enabled AI to generate imagery, video, text and audio content. Creative AI is when artificial intelligence uses past data to learn from an experience, allowing it to fine tune itself and generate new solutions and recommendations through audio and visual techniques. It ultimately helps in personalizing the output of data insights in a medium that makes the most sense for the end user, overall creating more value in the mass amounts of data businesses collect. 


How do they work together? 


Companies of all sizes and industries can use augmented analytics, combined with the power of Creative AI, to transform their organization at all roles to gain a deeper understanding of its data and analytics. According to recent research by Gartner, augmented analytics is a disruptive trend that will be a key component in the future of data, and that data and analytics leaders should plan for new Machine-Learning/Artificial-Intelligence based data storytelling capabilities that will automate tasks and drastically transform how analytics as a whole is done. In fact, according to another article, by 2025, 75% of data stories will be generated using augmented analytics and creative AI techniques.  


To learn more about how your organization can employ augmented analytics to automate tasks and improve decision-making process, contact info@conversight.aior request a demo.

Why Your Supply Chain Needs to Know The Difference Between Demand Planning and Demand Forecasting

Imagine you are a supply chain manager who has done everything in your power to develop and sell new products that have now taken off in the market. However, the demand planning process you enacted for the product was forecasted inaccurately and now you’re running low on the inventory – which means you are missing out on a major revenue opportunity. This happened recently to Sony when the PlayStation5 was released late last year, costing the company millions of dollars in missed revenue. The problem with this situation and a problem that many companies face today, is that managers put a lot of emphasis on demand forecasting, but fail to successfully implement demand planning. 


Demand forecasting is the process of predicting demand based on historical data and patterns, while demand planning begins with forecasting but then goes a step further and takes into consideration many other aspects that are important in order to get an accurate prediction – like distribution, seasonality, where the inventory will be housed, where it will be sold, external factors like a global pandemic, etc. 


So What Exactly is Demand Planning? 


Demand planning is a supply chain management process of predicting or estimating how much inventory your customers will buy from you to help businesses meet customer demand for products while minimizing excess inventory. It involves creating a demand plan based on a statistical forecast that takes into consideration many factors that can influence demand – such as inventory levels, marketing strategies, buying trends, etc – and then details where to distribute the products in order to meet the anticipated demand.    


Why is it Important? 


Demand Planning is important because it can lead to an increase in profitability, customer satisfaction, efficiency and so much more. Projecting sales and planning for peaks in customer demand is crucial to keeping customers happy and maintaining a successful business. Poor planning can have far-reaching negative consequences on a business’s growth, operations and brand – and can even lead to a loss of customers. 


According to research by EnsembleIQ, 34% of supply chain professionalsreport a lack of demand forecasting and planning accuracy. This challenge is consistent across professionals in every sector. In fact, 6 in 10 retailers report that they are taking steps to improve their inventory management through the use of demand planning software. 


How Can AI Improve Demand planning? 


The main goal of demand planning is to maintain the right amount of inventory to meet customer demand without incurring shortages or wasting capital on producing and storing surplus inventory. A key element to ensure this is data collection. Advanced machine learning makes access to real-time data increasingly available, improving forecast accuracy and thus demand planning immensely. It also provides collaboration tools that enhance the ability of planners to share information amongst one another and react more quickly to changes that occur in supply and demand. 


Demand planning solutions are increasingly being used across a large variety of industries – healthcare, e-commerce, food & beverage, automotive, retail and many more. According to recent research, increasing demand planning accuracy can increase revenue by up to 3%. Additionally, according to research by the Aberdeen Group, companies that are able to produce accurate demand plans are 7.3% more likely to hit their quotas and are 10% more likely to make improvements to their bottom line. While implementing demand planning solutions such as AI-driven software may be an adjustment and investment for a business, owners can expect to see a return on investment in less than a year.


To discover how your supply chain business can improve profitability and efficiency through the use of AI-driven demand planning, contact info@conversight.aior request a demo

Augmented Experiences are Shaping the New Supply Chain

As supply chains become more complex, they create the need for more streamlined approaches for reporting and analyzing. As businesses react and evolve to the long-term effects of the COVID-19 pandemic, efficiencies in operations and analytics are brought into focus revealing an overwhelming lack of timeliness, cost-efficiency, and organization across supply chains. Gartner predicts that by 2023, “overall analytics adoption will increase from 35% to 50%, driven by vertical- and domain-specific augmented analytics solutions.” Read on to learn how augmentation, a performance of artificial intelligence, is a front running technology in aiding supply chains in their digital transformation.


Today’s traditional reporting software is unable to keep up with the demands of fast-paced operations. Most reporting tools require a developer to configure reports, which leads to added costs and unnecessary time in waiting for clear and succinct results. With the introduction of augmentation in these processes, supply chains are able to automate the supply chain to develop reports, automate time consuming, repetitive tasks and eliminate countless hours and costly resources.


Supply chain planners typically analyze business’ inventory, as well as supply and demand lead times, to make more informed, educated decisions. Artificial intelligence agents, like’s Athena, augments this analysis to provide recommendations on purchase orders, inventory levels and more.


Once reports are developed, decision makers are able to easily and quickly to gather insights and summarized information on their supply chain to answer user questions such as:

  • “Did my price increase?”
  • “How many orders did my customers purchase?”
  • “What are our daily sales?”


Additionally, the augmentation of business user’s analytics allows for proactive monitoring of specific inquiries and data. Issues or anomalies are caught early on before turning into costly, long-tail errors.


Human reporting is becoming a tool of the past, as the automation and augmentation of the supply chain through artificial intelligence creates more efficient, smoother transitions for the process of reporting, analyzing, and recommending. Additional research by Gartner, states “organizations that seize the opportunities presented by the newly catalyzed market will be able to dramatically hasten their analytics-related maturation, which could potentially enable them to make competitive breakthroughs, in comparison with slower-maturing rivals.” How does your supply chain shape up to the defined status quo?


To learn more about ways your business can improve reporting times and optimize operations, visit

Conversational Intelligence | What You Need To Know

Last week, Microsoft took on a $16 billion dollar acquisition of Nuance, a speech recognition company. Conversational AI has the ability to transform the way people interact with technology, as the adoption of this speech-recognition software encourages the streamlining of data management.


Strategic acquisitions such as these are especially significant in the world of speech and language recognition software and its impact on the businesses across verticals and industries. As larger-scale companies, such as Microsoft, begin to implement speech recognition software into their own companies, it points to greater growth and development within the industry. Not only will the use of AI continue to increase in big-name businesses, but attention will continue to be drawn to the exponential power of conversational AI and its applicability to the everyday user across businesses of all sizes.


Conversational AI interfaces understand the context and intent of business questions by leveraging machine learning, data science, knowledge graphs and cognitive techniques. Enterprises are then able to create advanced dialogue systems that utilize memory, personal preferences and contextual understanding to deliver a realistic and engaging conversation with data sets.


Success in deploying conversational AI in the business needs key skills, not just technical, but mostly business domain & subject matter knowledge. Conversational AI bots need to understand the domain and speak the domain, not just English. That’s where the domain-focused business platforms like, which is empowering Supply Chain leaders, are emerging.’s powered intelligent business companion, Athena, understands user behavior, context and intent of the conversation to deliver personalized insights and action. There are two primary technology components to the Athena platform 1) the data ingestion and knowledge creation and 2) the process by which end-user queries are answered.’s characteristics includes:

  • Connect to multiple data sources, both structured and unstructured
  • Integrate with online applications through web services and APIs
  • Access to insights through narratives and questions (e.g., supply chain metrics)
  • Uncover data anomalies through data science
  • Provide personalized proactive-insights
  • Retrieve information (e.g., product searches)


To discover how your supply chain business can improve productivity and efficiency through the use of speech and language recognition, contact or request a demo. Receives New Patent for Contextual and Intent Based Natural Language Processing System and Method

As businesses gather vast amounts of data, they suffer with the problem of being able to extract value from the available information. Artificial intelligence-driven solutions have the ability to understand, organize, and comprehend this data that ultimately increases usability for the customer. has developed a contextual and intent based natural language processing system and method, patented this month, to enable businesses to optimize their supply chain through conversational AI.


While most businesses have successfully connected to their data, the challenge still lies in accessing it and truly leveraging it for decision making. This patent is a huge step in for creating an accessible, user-friendly interface with data, allowing businesses to glean insights like never before using the words and channels they are most comfortable within.


ConverSight’s patent accomplishes three main tasks in understanding and processing natural language questions:


  1. Ability to learn and understand in a zero shot or few shot learning setup.
  2. Ability to use layered knowledge to derive new knowledge while interacting with users to learn users behaviour pattern and provide personalization
  3. Ability to understanding the user’s intent and context from previous interaction to  provide insights through contextual learning centered around specified metrics


So what does all of this mean?’s natural language processing system connects to businesses’ data sources, then transfers the data to’s built in AI system where it can be understood by simply loading it in. The AI system picks up on the vocabulary used throughout the data, making note of it for future use.


Following the connection to the data source,’s AI technology extracts the critical parts of the data, creating charts and graphs depicting the information. These knowledge graphs allow users to have access to easy-to-understand data, without the struggle of mining the data and correlating insights themselves.


After extraction and usage of data,’s natural language processing system then uses this information to make future predictions and recommendations to the user (i.e. proactive recommendations, suggested search queries, proposed next actions). The technology is able to understand common metrics that the customer desires, and uses contextual learning to propose crucial data for future use – a capability that has not previously been successfully developed in conjunction with natural language processing.


While most businesses have successfully connected to their data, the challenge still lies in accessing it and truly leveraging it for decision making. This patent is a huge step in creating an accessible, user-friendly interface with data, allowing businesses to glean insights like never before using the words and channels they are most comfortable with.


To learn more about the patent or, contact or request a demo.