Company News – Proven

Proven Consult Announces Partnership with ConverSight.ai

ConverSight.ai, a conversational insights platform that enables users with real-time insights to accomplish tasks through a voice and conversational bot called Athena, has announced a partnership with Proven Consult, a technology consulting firm who advises organizations to solve complex business challenges through digital transformation and is based out of Saudi Arabia, with offices in the United Arab Emirates, India and the United Kingdom.

The partnership combines local expertise and a unique set of solution areas to guide organizations through the digital transformation process as they take steps to become more agile, increase overall business efficiency and optimize processes. Through this collaboration, Proven Consult and ConverSight.ai will empower business leaders to make informed decisions through accurate and proactive insights derived from the mass amounts of data that modern day enterprises collect on a daily basis.

“Proven Consult is pleased to announce our partnership with ConvertSight.ai as we move forward together to support our clients in continued growth.” said Zaid Al Mashari CEO and Co Founder of Proven Consult. “The success of AI implementation is dictated by the knowledge of the team as well as the quality of the solution and we are very happy to be partnering with a company that delivers on both of those. By merging their expertise with our own we are able to deliver projects that continue to drive business results.”

“The ConverSight.ai team is honored to join Proven Consult in a shared mission to support customers’ transition to become efficient and data driven in order to advance in the new digital era. Conversational AI is a critical innovation that takes businesses to the next level and Proven Consult lends the expertise to equip organizations with the competitive advantage required to advance and excel in their markets.” said Ganesh Gandhieswaran, CEO and co-founder of ConverSight.ai. “This partnership underpins the quest we share to improve the decision-making process with real time information in the enterprise to drive better and faster results.”

As businesses look to become global entities in an ever-increasing competitive market, they are looking internally to maximize all departments and become a more strategic business units. Proven Consult advises and implements bespoke solutions to enable digital transformation and combined with AI solutions like those from ConverSight.ai they are able to amplify results and deliver better success stories from data.

“Companies are bombarded with large amounts of data from multiple sources. Without a clear data management strategy, organizations risk gaining true value from the data being gathered.” said Gandhieswaran. “As businesses develop their digital transformation strategy for the new decade, it will be critical for leaders to incorporate solutions that provide the intelligence and proactive capabilities that will set them apart from their competitors.”

How automation improves operations in the automotive retail industry

The automotive industry is evolving and expanding, consumers utilize cars in increasingly different ways, including buying, leasing and car-sharing.Now, the operational aspect of retail in the automotive industry requires the order process for cars, repairs, parts and fleet inventory which needs to be managed with the customer needs, business needs, and time frames kept in mind. Providing an effective and reliable service is crucial for automotive companies, especially as the automotive industry struggles to remain competitive with the increase of the used-car and car sharing industry.

Automotive companies are looking for new ways to adapt and stay competitive and one way they can do this whilst responding to consumer demands, and improving their service quality is through focusing on automating operations.

Fleet inventory management is an essential part of automotive retailers, and this is a manual task with the amount of data entry involved. The data comes from multiple levels of the organisation and from customers. Utilizing automation for fleet management can simplify the data collection process, reduce inefficiencies and prevent inaccuracy.

Fleet management also has high compliance requirements as the fleet needs to be kept at specific standards for sale or use. Through automation software, fleet management tasks such as mileage, part replacements, and repairs can be completed, on time with minimal manual labour. This can also include customer involvement if they choose to opt in to reminders for their car repairs and updates they’ll require, leading to increased customer loyalty.

Automation software can also help improve the data collected from customers which helps automotive retail companies operate more efficiently. With the right implementation, errors, delays and inefficiencies can be significantly reduced resulting in cost savings and more strategic work produced from employees.

Information Extraction with AI

Over the past decade, data has become paramount in driving growth for modern-day businesses. In a world where industries rely on the internet to a great extent, data has become more abundant than ever. This has inspired enterprises to find new ways of extracting information from a plethora of consumer data. As data mining is one of the key techniques for mining structured information, companies are using information extraction technology to automate such processes.

Information Extraction (IE) helps organizations in digitizing and automating the processes that are dependent on paper. Invoicing and consumer surveys are one of the great examples of this, where texts in a specific format can be extracted and are used for driving insight. These texts can be the name of the product, customer or demographic data in a specific layout.

Using Optical Character Reader (OCR), written text from an invoice can be recognized and digitized. The information derived from the text can be further used for automating trend recognition. Although OCR is one of the common tools for IE, it is only limited to recognize text in a structured format. Therefore, organizations use different IE approaches to tackle different problems.

Traditionally, companies used Natural Language Processing (NLP) which analyses the sentence structure based on grammatical rules to identify entities in a paragraph. These entities can be the name of the brand, company or a customer can be extracted to create a structured form of data. However, as businesses aim to scale their data mining activities, the process of identifying entities from a large amount of text must be automated. Developers automate this process by building Machine Learning (ML) models where programs are tested with a plethora of data to capture information. However, one of the limitations is that it requires a large number of texts with labels that describe what that text is about. When there is only a limited amount of labeled data, developers use a few samples and train ML models to identify patterns and classify data into a structured format.

However, these approaches limit enterprises to extract only entities such as product or customer name. Companies use AI to extract more nuanced information such as consumer opinion. AI is a combination of technologies that integrate human intelligence into machines, hence the information is more reliable and accurate.

As the consumer opinion can be in the video, image or text format, extracting insight from such unstructured data is challenging. Be it – a mobile phone or a property, consumers are expressing their opinion on every purchase on the World Wide Web. With rising social media and vlogging culture among young consumers, the gap between structured data and unstructured data such as consumer opinion will continue to expand.

These trends are inspiring businesses to use information extraction tools with AI to collect consumer opinions rather than the traditional survey method. The AI model analyzes the structure of the sentences and based on the words used, it determines whether the opinion is positive or negative. Although traditional IE was used to digitize paper-based processes, today AI and ML has changed the way companies look at IE.

Retail CFO challenges that RPA can solve

The retail sector is a complex and dynamic sector experiencing rapid advancement due to disruptive technology and the finance and accounting departments experience every shift in the company. Chief financial officers (CFO) have many responsibilities and on top of budget meetings, financial planning, evaluating business risk, managing talent and strategic partnerships, they must overcome the challenges presented daily. CFOs are expected to be able to react quickly to the changes in the market and be a catalyst for change within their organisation.

A key driver in adaptability in retail organisations is technology, it’s the most efficient and effective way for companies to gain a competitive edge. Namely, robotics process automation (RPA) has been a leading solution as it offers numerous benefits such as cost savings, enhanced accuracy, scalability and increased compliance. Here are 3 ways RPA can benefit retail companies and improve your quality of product.

Budget approval

A common scenario for a CFO is the delayed time required for a budget approval. Often, proposals are full of unclear benefits or foreseeable ROI. However, with RPA, budget proposals can be created that demonstrate the significant value to the entire organisation through end-user application guidance and task automation. This provides a greater ROI on the automation investment and delivers business process improvement.

Deflated revenue due to human error

Traditional finance and accounting processes are completed manually, such as procure-to-pay, order-to-cash, record-to-report, and have ample space for human error which can snowball into larger issues. This is leading to an increasing use of RPA to manage the growing transaction volumes by automating these processes. Once RPA has been implemented, bots work consistently with accuracy, eliminating the margin of human error, increasing processing efficiency and cutting down on operational costs.

Compliance requirements

Constant changes in government regulations impact how finance and accounting processes are fulfilled. Finance and accounting departments are required to constantly adjust to ensure that they are compliant which can lead to further error when learning to adhere to new processes. Implementing RPA helps minimize the error involved in compliance, which is a vital part of the company and leads to faster execution of processes and greater efficiency within departments.

Facilitating retail finance and accounting processes with RPA not only benefits the department but also has ripple benefits company wide. A well-functioning finance and accounting department with little to no error positively impacts employees, projects, management and stakeholders. It also reduces unnecessary workloads for the CFO and cuts out menial tasks for employees, streamlining workloads and increasing productivity.

Strategizing Data as a Service

Importance of Big Data is evident in every industry and in every scale of business. The need for greater storage capacity, faster data services, and agile solutions has increased substantially over the past couple of decades. These are the needs of the new-age enterprises to sustain and survive in a competitive market. These needs have inspired enterprises to go beyond an internal IT team and look for a holistic data solution, even if it required outsourcing. Data as a Service (DaaS) is a data management strategy where companies outsource data service. Be it—data storage, processing, analytics, or monetization, these services are offered via a cloud network where data is stored in servers and transmitted through the internet.

Traditionally cloud technology was only meant for hosting an app or basic data storage. Although, the recent innovation in computing power and internet speed has enabled cloud computing services to go beyond its legacy approach. This has enabled enterprises to strategize how they can use integrate data at all the points of sale and touchpoints to know and serve their customers better. Along with this, cloud computing is responsible for analytics which requires higher computing power, therefore, greater cost.

As managing cost is one of the biggest challenges in Data as a Service, it is clearer that monetizing data is even more essential. Therefore, businesses partner with data service enterprises that offer a holistic solution and flexible data services as per their business’s needs.

However, some DaaS firms limit the number of tools that a business can use, to offer services at a low cost. Users can work with only those tools that are hosted on or compatible with their DaaS platform, as opposed to using tools of their choice. Hence, business leaders must prioritize agility and flexibility while building their DaaS strategy.

Although, depending on the location and the region you are operating your business in, this strategy can change. As countries have different data and privacy regulations, it can affect your DaaS strategy. For instance, few countries may have privacy laws that require Data of a consumer in a specific country, be stored in local servers. As the data and privacy laws differ from one country to another dealing with different government compliances is one of the roadblocks in DaaS. This also may lead to an addition in latency and cost of accessing data globally.

In the times where companies focus on rapid and disruptive growth, an increased latency time is challenging for companies’ growth. Accessing data of a consumer in Sweden from the US can be difficult because of the transmitting time between servers in two different countries. As a result, companies store these data in multiple locations for faster delivery of data.

With growing consciousness about data privacy and security, the DaaS service must comply with all the regulations and be transparent with consumers. In the times where consumers are more aware of the usage of their data, focusing on building security features and trust is crucial. Encrypting data, building a firewall system, and a clear privacy policy helps build a trustworthy relationship between a brand and a consumer.

Be it flexibility, agility, security or latency, these challenges require a holistic solution that guarantees quality standards.