Tag: Data Analysis

Chris, could you start by introducing yourself and your role at Zennify and sharing a little about your background in the finance and technology sectors?

I joined Zennify in April 2023 as Chief Executive Officer. I’m a customer success and IT services veteran with over 15 years of experience in the Salesforce ecosystem and 30 years in technology.

Most recently, I was the Senior Vice President of Customer Success at Salesforce. I led the North American Success team responsible for ensuring the retention and growth of the $15B customer base. Before that, I was the COO of Model Metrics (acquired by Salesforce in 2011) and was a board advisor to Silverline and 7Summits, services firms within the Salesforce ecosystem. I was privileged to advise them on scaling and company growth.

We have a fantastic opportunity at Zennify to push boundaries and change the way consulting is done, using AI and tools to accelerate implementations and customer time to value. We strive to be the top boutique Salesforce and nCino consultancy for financial services firms. I’m proud to be here at Zennify and to continue upholding our reputation as one of the go-to partners for financial institutions that want to see accelerated outcomes.

Why financial institutions should ban AI at their own risk:

Chris, you’ve raised the idea that financial institutions should not ban AI at their own risk. Could you elaborate on why you believe AI is crucial for the financial sector’s future and what potential risks they face by not embracing it?

AI has and will continue to impact the breadth, depth, and quality of products and services offered by financial institutions. There are multiple use cases for AI – and a lot of them focus on increased efficiencies. For example, teams can use AI to better predict and assess loan risks, improve fraud detection, provide better and faster customer support through smarter personalization, and analyze data in unstructured ways – all while reducing costs. These are use cases that would have typically taken more time and have more room for errors. Understanding and implementing AI thoughtfully leads to sustainable business growth and staying ahead of your competitors.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-chris-conant-ceo-at-zennify/

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Information Security and the C-suite

We are living in an era of change, where industries are changing their traditional way of managing and streamlining organizational goals. SMEs and SMBs are gradually gaining market share and developing well-known brands, eliminating the term monopoly, as any business with an appropriate data strategy can create its own space in this competitive landscape.

To stay competitive, businesses are attracted to two potential technologies: artificial intelligence (AI) and business intelligence (BI). Combined, they offer a powerful tool that transforms raw data into implementable insight by making data accessible to BI managers. This collaboration between AI and BI enables companies to steer large-scale data efficiently and make quick business decisions.

This article provides an overview of the current landscape of AI and BI, highlighting the evolution of BI systems after integrating artificial intelligence. 

The Synergy Between BI and AI

The partnership between artificial intelligence and business intelligence has become the backbone of the modern business world.

In this competitive market, businesses across all industries strive to drive innovation and automation as an integrated strategy that reshapes organizations from a mindset of data and data-driven decision-making.

When BI managers integrate AI into BI systems in businesses, it harnesses big data’s power, providing previously inaccessible insights.

Traditionally, BI systems were focused on historical data analysis, which was collected and analyzed manually with the help of a data team, which tends to be a tedious job, and businesses often face data bias.

However, AI-powered BI systems have become a dynamic tool that uses predictive analysis and real-time decision-making skills to identify market patterns and predict future trends, providing a more holistic view of business operations and allowing your organization to make informed decisions.

The current landscape of AI-driven BI is a combination of big data analytics, machine learning (ML) algorithms, and AI in traditional BI systems, leading to a more sophisticated tool that provides spontaneous and automated analytical results.

As the AI field diversifies, the BI system will mature continuously, posing an integral role in shaping the future of business strategies across various industries.

Artificial intelligence is transforming business intelligence in numerous ways by making it a powerful tool for BI managers and their teams to work efficiently and effectively and have access to a wider range of customers. Even small businesses and enterprises are trying their hands at AI-powered BI software, intending to automate the maximum work of data analytics to make quick decisions.

In the coming years, we can expect more potential use cases of AI-powered business intelligence software and tools, helping businesses solve the greatest challenges and reach new heights.

To Know More, Read Full Article @ https://ai-techpark.com/transforming-business-intelligence-through-ai/

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Edge Computing TrendsImportance of AI Ethics

In the current business landscape, artificial intelligence (AI) is revolutionizing the way companies conduct experiments across the organization. This transformative approach is not just about automating processes through robotics, but redefining the very essence of experimentation. AI’s capabilities in designing experiments, learning from outcomes, and moving beyond traditional A/B testing are opening new frontiers for businesses as it allows them to identify previously unavailable opportunities and drive innovation.

Expanding Beyond Traditional A/B Testing

The evolution of experiments with AI extends beyond the limits of conventional A/B testing, where singular outcomes are manually analyzed. AI enables the exploration of a myriad of micro-changes, each potentially leading to significant insights.

Unlike traditional methods where experiments are often binary, AI can test a multitude of variations simultaneously. This capability allows businesses to explore a vast array of options quickly. In the context of website optimization, instead of just testing two versions of a webpage, AI can simultaneously test hundreds of variations, analyzing how minute changes in design, content, or layout affect user engagement.

AI’s ability to test numerous variations also comes with the capacity to analyze and extract meaningful insights from these tests. This is crucial in environments where small changes can have significant impacts. For instance, in financial services, AI can test numerous investment strategies over vast data sets, quickly identifying approaches that yield the best returns under different market conditions.

Another critical aspect of AI-driven experimentation is its capability for real-time analysis and adaptation. Traditional experiments are often static with analysis occurring post-experiment. AI, however, can analyze data in real-time, adapting the experiment as it progresses. This is especially beneficial in fast-changing environments like social media, where consumer preferences can shift rapidly.

The integration of AI into experimental processes marks a paradigm shift in how businesses approach innovation and problem-solving. By assisting in designing experiments, learning from outcomes, and moving beyond traditional A/B testing, AI is enabling companies to explore a broader spectrum of possibilities.

To Know More, Read Full Article @ https://ai-techpark.com/ai-driven-experimentation-revolution/

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Diversity and Inclusivity in AIImportance of AI Ethics

Generative AI (GenAI) has the potential to automate a broad range of tasks, which boosts productivity, offers new opportunities, and reduces costs as it does not require technical skills to use generative AI tools or software and is widely available. Tech visionaries believe that GenAI will be accessible to workers worldwide to access information and skills across broader roles and business functions. This makes generative AI one of the most disruptive trends of this decade. According to Gartner, by 2026, more than 80% of companies will have employed generative AI APIs and models and implemented GenAI-enabled apps in production environments, compared to less than 5% in 2023.

In this article, we will explore what democratized generative AI is, how it works, and its current applications.

What is Democratized Generative AI?

Traditionally, artificial intelligence (AI) technologies were limited to technical experts; however, the growing availability of democratized generative AI marks the beginning of a paradigm shift in the technology landscape. The democratized GenAI aims to make AI technology more accessible to a wider range of audiences and focuses on providing user-friendly tools and platforms that allow users to create and interact with AI-powered models.

Democratization enables users from various fields, such as journalism, marketing, the arts, and others, to leverage AI algorithms and models to enhance their tasks and gain valuable insights from the given data.

Why Is Generative AI Democratization so Transformational?

At its core, generative AI democratization revolves around numerous data sources and insights from which numerous businesses and institutes can benefit. From decision-making in business to better public services in government sectors, generative AI reduces business costs by, for example, cutting expenditures and supporting development in working on important tasks.

Here are some specific areas where democratized generative AI can be transformational:

Reducing Entry Barriers

AI democratization reduces the entry barriers to using AI and machine learning (ML) algorithms for businesses and individuals so that they can use open-source datasets to train their AI models across any corner of the world without any financial investment.

To Know More, Read Full Article @ https://ai-techpark.com/paving-the-path-to-democratized-generative-ai/

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