Don't be left behind in the AI data analytics revolution

AI is transforming business operations, and 73% of U.S. companies already use AI, with 75% planning to adopt AI analytics by 2026. Here's what you need to know to stay ahead:

• Why it matters: AI analytics processes data faster, improves accuracy, and boosts decision-making, helping businesses gain insights in minutes instead of weeks.

• Key tools: Machine learning, natural language processing (NLP), AutoML, and real-time processing make data analysis accessible and actionable.

• Business benefits: Faster decisions, better forecasting, and up to 40% productivity improvement. Companies like J.P. Morgan and Shell already see results.

• Industries impacted: Retail, supply chain, finance, and more. For example, AI improves supply chain costs by up to 30% and enhances financial risk analysis.

• How to start: Choose AI tools with natural language interfaces, dynamic dashboards, and team collaboration features. Platforms like Querio AI save time and money.

AI is reshaping the future of business. Start now to leverage its potential and keep your organization competitive.

The Impact of Generative AI on Business Intelligence

AI Data Analytics Basics

Modern AI systems transform raw data into insights businesses can act on. According to Gartner, machine learning is set to power 80% of data analytics solutions by 2025, a jump from 50% today [2].

Core AI Analytics Tools

AI analytics platforms rely on several technologies to process and interpret data:

The global NLP market, worth $29.1 billion in 2023, is expected to grow to $92.7 billion by 2028, showcasing the increasing role of AI in text analysis [3].

Luca Fossati, Global Head Coach of Data & Analytics at FDM Group, highlights this trend:

"AI has always been intimately connected with data & analytics, and the inter-dependencies between them continue to expand... A more recent trend has been to incorporate AI in the pipeline itself, when processing the data." [4]

Main Business Advantages

AI-powered analytics offers a range of benefits across business operations:

• Faster Decision-Making: Unlike traditional analytics, which can take days or weeks, AI processes millions of data points instantly. This enables real-time decisions and boosts productivity by 40% [5].

• Better Accuracy and Insights: AI algorithms analyze vast datasets simultaneously, uncovering patterns that might escape human analysts. David Harvey, FDM Senior Delivery Consultant, notes:

  "In 2025 we will see a further advancement and adoption of self-service Analytics Tools & Reports... These Reports are now simply created by an end-user writing their requirements into a dialogue box for the application to then create a series of Dashboards and Insights which it calculates are aligned to the user's requirement." [4]

• Automated Intelligence: AI continues to learn from structured and unstructured data, including text, images, and real-time streams. Adoption rates are already high, with 85% in aerospace and 83% in IT [5].

Nancy Young from millermedia7 adds:

"The organizations that will thrive embrace AI not just as a technology but as a core element of their business model while proceeding carefully!" [6]

These benefits highlight the importance of choosing the right AI analytics software to meet specific business needs.

Selecting AI Analytics Software

Picking the right AI analytics platform is a key move for businesses navigating today’s data-focused world. Once you’ve identified your needs, the next step is finding a platform that aligns with them.

Key Features to Look For

When assessing AI analytics tools, focus on these critical features:

Now, let's see how Querio AI stands out by meeting these requirements.

Querio AI: Platform Overview

Querio AI is part of a new wave of analytics platforms designed to be both user-friendly and powerful. Its AI agents connect directly to your databases while keeping your business context in mind, delivering insights that are more accurate and relevant.

Jennifer Leidich, Co-Founder & CEO of Querio, highlights the platform’s impact:

"Querio has revolutionized how we handle data. What used to be a weeks-long process now takes minutes, and our teams feel empowered to make data-driven decisions on their own. The impact on our efficiency and accuracy is unparalleled." [7]

Querio has helped businesses save $45,000 annually and cut data processing times from weeks to just minutes.

Setup Tips

To get the most out of an AI analytics platform, follow these steps during setup:

• Connect your existing databases and ensure data quality standards are in place.

• Set up role-based access controls in line with your security policies.

• Create dashboards that focus on your key metrics and frequently asked questions.

Look for a solution that blends advanced AI capabilities with a simple, intuitive design. The best platforms make it easy for both business and data teams to work together while keeping data accurate and actionable.

Business Use Cases for AI Analytics

Customer Analysis Tools

AI-driven customer analytics helps businesses offer tailored experiences that fuel growth. Here are some standout examples:

• engelhorn (using Einstein Product Recommendations):

  • Online conversion rate increased by 2.5%.

  • Average order value grew by 1.5%.

  • Revenue per visitor rose nearly 4% [8].

• Icebreaker (leveraging AI for personalization):

  • Product recommendation clicks jumped by 40%.

  • Revenue from recommended products grew by 28%.

  • Average order value increased by 11% [8].

"Through personalization, brands now have the power to turn every shopping interaction into a meaningful and rewarding experience." - Salesforce [8]

AI isn't just about understanding customers - it also plays a key role in streamlining supply chain operations.

Supply Chain Management

AI helps businesses optimize supply chains, cutting costs and improving efficiency. Here's a look at how it impacts key metrics:

Mazda Motor Logistics, for example, uses Oracle Transportation Management to enhance its European network, leading to better on-time deliveries [9].

"AI's ability to contextualize and interpret documents in real-time changes how we manage supply chain processes." - Ninaad Acharya, CEO & Founder, Fulfillment IQ [10]

AI's impact doesn't stop there - it also transforms financial risk analysis and internal auditing.

Financial Analysis and Risk

AI is reshaping how financial institutions handle risk and analysis. Take KPMG's collaboration with MindBridge, for instance:

"By combining KPMG's in-depth industry expertise with MindBridge's advanced technologies, we continue to digitally transform the audit, providing increased quality and value to clients and enhancing public trust." - Sebastian Stöckle, KPMG Global Head of Audit Innovation [11]

Polaris also demonstrates how AI enhances internal audits:

"Instead of doing a little bit of everything at one of our divisions or subdivisions, we can locate the areas of highest risk and focus our time on them." - Chris Swanson, Manager of Internal Audit Data Solutions at Polaris [11]

AI-powered anti-money laundering (AML) systems are another game-changer. These systems analyze transactions in real time, assign risk scores, minimize false positives, and continuously improve accuracy through machine learning [12].

Preparing for AI Analytics Growth

Creating Data-First Teams

Building strong data-focused teams requires a mix of the right culture and technology. Alejandro Martinez, CEO U.S. & Canada of SDG Group, sums it up well: "Creating effective, high-performance teams is as much about establishing the right culture as it is using the right technology" [13].

Here’s how you can shape a successful data analytics team:

"As a data leader, it is your job to set direction, define strategy, and provide the support your people need to succeed, and then just let them do their job." – Josh Johnston, A8 Consultant [14]

While forming strong teams is critical, keeping them trained and prepared for AI advancements is equally important.

Staff Training Plans

Nearly half - 49% - of U.S. employees struggle to master AI tools [17], yet effectively using AI can improve output quality by over 40% [18]. Stephen McClelland, Digital Strategist at ProfileTree, explains, "When learning is embedded into our daily routine, the uptake of new AI tools becomes a habit rather than a hurdle" [16].

To promote AI proficiency, organizations should:

• Evaluate Current Skills: Identify where employees excel and where gaps exist.

• Develop Tailored Programs: Create training paths specific to each role.

• Provide Hands-On Learning: Let teams practice with real-world scenarios.

• Track Progress: Use metrics to measure learning outcomes and improvements.

"On the job learning with guidance from a team lead or peer is the best method of training to quickly acquire the required knowledge and implement the required changes at the earliest. Formal training may provide the basic learning, however it might evaporate if one does not have a chance to use it on the job." – Fareej Mohamed Abdul Munaff, Solutions Architect at Standard Chartered Bank [15]

Skill-building is essential, but organizations must also ensure AI is used responsibly.

AI Ethics Guidelines

Clear ethical guidelines for AI are critical to maintaining trust and compliance. Companies must balance progress with responsible practices.

Here are some key ethical principles and strategies:

IBM's Watsonx Orchestrate exemplifies ethical AI by ensuring fairness in hiring through diverse candidate pools and unbiased evaluation methods [20].

"We need to be sure that in a world that's driven by algorithms, the algorithms are actually doing the right things. They're doing the legal things. And they're doing the ethical things." – Marco Iansiti, Harvard Business School Professor [19]

PathAI also sets an example by validating its AI-powered diagnostic tools through clinical testing and peer-reviewed studies, ensuring both accuracy and ethical compliance in healthcare [20].

Conclusion

AI is reshaping the way businesses operate. With the global big data and business analytics market projected to hit $924.39 billion by 2032 [23], the time to act is now.

Though 74% of companies face challenges in scaling and gaining value from AI [21], those leading in AI adoption are reaping clear rewards:

These numbers highlight the importance of streamlined data strategies and adaptable workflows.

"There are only two types of companies in this world, those who are great at AI and everybody else." - Mark Cuban [21]

To build on earlier discussions about AI tools and integration, businesses should focus on three critical areas:

1. Data Integration: Break down silos and unify your data. Currently, 55% of organizations struggle with this [22].

2. Workflow Transformation: Combine human expertise with AI to achieve up to 4.5× higher efficiency [22].

3. Strategic Implementation: Amazon’s AI Product Leader Ash Sharma emphasizes the importance of completing the full analytics cycle:

"The largest mistake is not understanding the full cycle of analytics: Data → Insights → Communication → Comprehension → Corrective Action. Most strategies end at the Insights stage and perhaps a throw-over-the-wall communication. The point of analysis is to make sure the target stakeholders understand it, benefit from it, and will be able to action it towards improvements." [1]

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