Research & Resources

Some opportunities to leverage AI in your organization
May 31, 2020

Improving Organization Readiness using AI-led Automation process

Arvind Handuu & Zoran Gacovski

These days you cannot help but see nearly all industries talking about revolution in the offing  as a result of Artificial Intelligence, Analytics, Robotics, Robotic Process Automation and whatever other ‘nom de guerre’ your industry uses.

Here is an attempt to catalog a few opportunities for a business executive responsible for helping the companies take advantage of the opportunities ahead.


Digital transformation becomes highly-integrated into the business processes and AI is leading the business technology upsurge. Applying AI in the business process automation is bringing a lot of benefits that come with it.

The automation of processes is a strategic approach that can help organizations to improve the workflow efficiency and responsiveness in their environments. Automation will lead to speeding-up the processes, ensuring flawless outcomes, while reducing the overall costs.

AI-led Automation Use Cases

AI-led automation process is based on different business intelligence and analytics solutions, applied in different scenarios – from decision-making to customers care. This is typically classified in the following categories:

– Decision Support and Strategic
– Operations efficiency
– Engagement Experience

Here we present a few opportunities for you to explore:

1.Decision-making at managerial level:

AI-led automation models can assist the managers in making decisions within a business process. The advancements in AI introduced multiple machine learning algorithms (decision trees, neural networks etc.). These algorithms can help the managers and business-owners to solve problems by applying previous datasets for getting the expected output. For example, the models can help to decide whether a customer should be sent a product recommendation, or needs a follow-up call.

2.Repetitive tasks automation:

Automation of the recurrent tasks is one of the most common applications of AI. AI-based process automation as a tool that applies machine learning and AI methods – can help to handle a large volume of repetitive processes that otherwise need a committed human workforce.

3.Recognition and prediction:

The AI can be used to learn, observe, and analyze data sets that are collected from various channels and identify patterns that humans cannot. Businesses can apply AI methods to determine their course of action. For example, analysis of the data patterns for fraudulent activities related to online transactions, or segmenting clients based on their buying behavior for marketing purposes.

4.Customer Experience :

Let’s consider an example of calls handling at a telecom provider, a chatbot, which, based on Artificial Intelligence, presents the customer with best offers, and subsequently starts automated process for recording the order in an order management system. The benefits are higher turnover, faster and more effective handling time and lower costs.


the management of business processes is an intensive process that requires significant financial resources, as well as time. The automation, on the other hand, is cost-effective and can use the available resources. Thus, process management steps can once be implemented within an automation technique – and applied many times later.

6.Estimation of Claims:

: At insurance companies, as an example, the AI-based tool can assess the level and accuracy of the claim by analyzing photographs of the damage. Then the tool will ensure that the damage data is recorded in the claims handling system.

7.Handling of paper-based information or records::

AI tools can be trained to scan a large number of records and extract the information to database and made available for future processing. Companies can leverage AI-based automation tools to manage typically paper-based processes, e.g. legacy land records, invoice to payment cycles, oil well leases and exploration rights management, Title insurance, data exchange between otherwise disconnected applications.

8.Handling of remote processes:

in energy providers – data about meter readings are gathered from incoming unstructured emails using AI technology. These meter readings are then recorded in the backend system and processed as necessary.


AI-facilitated process automation solutions will help businesses to overcome performance barriers and improve their work efficiency and responsiveness. AI is no more a technology that is frightening and hard to adopt.

Small, as well as large organizations can deliberately understand the power of AI-based automation tools for optimizing their business processes. In the upcoming years, the adoption of AI for business process automation will become ubiquitous and more accessible for all organizations.

Digital Transformation : Conagra Case Study
July 23, 2019

CIO Helps Conagra Turn Food Trends Into Products

A case for how  Conagra’s CIO, Mindy Simon, leveraged the AI platform to use technology to help drive growth, including the launches of new products under the company’s existing brand names. The platform sources data from the likes of Facebook, Google, Instagram, Pinterest as well as from various Market Research companies, with frequent updates. Pulling Data is automated, as is prepping it for analytics applications.

The tool lets the business identify “pockets of growth” that might have otherwise gone unnoticed. This is largely because the platform can tie data together in one place. Click on the link below to read about this project.

Conagra AI application ( 2 pages / 850KB)CIO


Copyright (c) 2019 Dow Jones and Company, Inc. CIO Journal 07/22/2019

Business Intelligence in the age of Analytics and Artificial Intelligence
July 8, 2019


Jonathan Bach & Arvind Handuu






These days, one cannot pick any trade publication and not notice a convergence on dia del sabor, namely Analytics and its ‘on-steroids version’ Artificial Intelligence (AI). The collective wisdom points to the efficacy of data utilization to transform the business. Many of our clients have gone through this stage of data envy. Some continue to allocate disproportionate scarce resource of time and treasure to “monetize their data”.

Like all gold rush stories, this one is fraught with peril. The primary reason appears to be the significance attached to the technology as opposed to the business objectives. We’ve seen this before with corporations jumping from one tool to another in hopes that the next tool will be the one to save the initiative, company or the project. The problem is in training, or lack thereof, of the training in the organizations to ask the right question. In this, the question becomes the answer.

The goal, it appears, is to replace or at least augment an organizations intuition and experience-based decision making with an element of data centricity to avoid missteps and to shine a spotlight on certain blind spots. The data centricity assumes a level of Data availability and education or without this any further progress is impossible. We propose a following step approach to help clients adequately optimize the operations and leverage data assets where possible and necessary.

Automate and Optimize – most businesses have implemented some level of automation, some more than others, the starting point is that. Before one begins any large-scale transformation, challenge what is already implemented. Even if it is nothing more than a general ledger at this time. This is a foundational step to transforming a company. The key watchword in this stage is flexible and adaptable. Know that what we build here will need to be changed and rebuilt. The automation platform should be flexible and adaptable.

Digital from the outset – This is an appropriate analytical step in the organization’s life cycle, as the model should be to think innovatively about key aspects of the business. The approach here is to develop transformative competencies that have a potential to be disruptive, think Uber to Taxis, Airbnb to hotels and also operations transparency. In this stage the company starts to build data competencies.

Value creation – with the core of data collection in place, the organization may launch the build stage of analytics framework. The organization decides to make key data available to executives for informing tactical decisions and strategic choices. The key success factors in this process are:

Analytics Evangelist – an organization needs to allocate a role of developing data insights and educating line managers on the possibilities of data analysis

Speed to deliver – Time kills energy, this is true in physics as it is in companies. The organization should have the necessary strategy in place to make the results of analytics available to the managers within on a few days of demand

Keep it simple – an organization must simplify the learning and deployment of analytics by reducing rework. Its often helpful to choose technology that offer a consolidated BI and analytics approach.

Adopt and adapt – Reduce human variability – The weakness of the data driven decision-making is often in ignoring the human factors in adoption:

Education- Implement programs to drive overall data competency in an organization

Build Momentum through ease of access, make it easier for executives to get analytical answers

Implement and measure the use of analytical elements in the corporate choice selection

Reward the utilization of analytical tools

New applications – Encourage the use of analysis, by promoting new applications and new analysis. Share and promote.

Critique – Set periodic review points to analyze the decisions made and supported by hard measured data as well as decisions made without the adequate data. Compare outcomes.

Educate and Evolve – Business Intelligence and Analytics is a high ROI and can be implemented very cost effectively. The organization needs to ensure a steady process of training the users about new data and analysis being made available and effective strategies for utilization

In the end, Data can be a very effective tool available to the decisionmakers in an organization, the power of data becomes available to the organization as more investments are made in development of human capital to ask the right questions of the business. Features in modern BI and Analytical Tools can help users overcome delays and difficulties by automating aspects of data exploration and analytics development and delivering information answers and recommendations to users in the context in which they need it.

Jonathan Bach is a Client Solutions Partner at Visvero, Inc. He works closely with various F2000 clients in optimizing the use of professional services for “ADVANCE”; ing along the BI/ Analytics path. Jon is based out of the Visvero, Pittsburgh.


Arvind Handuu is a Practice Manager for Business Intelligence & Analytics at Visvero. Arvind is an analytics value purist. He believes that a BI & Analytics platform should be a self-contained and sovereign solution. “Its value drops to zero the instant you are using a different data source to inform your decision.”; Arvind is based out of Visvero, Pittsburgh.