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In the ever-evolving world of artificial intelligence, Google has once again raised the bar with its latest update to the Gemini app. Announced on March 13, 2025, this update introduces groundbreaking personalization features that promise to make Gemini not just a tool, but a tailored extension of its users. With the ability to connect to...

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March 13, 2025 – The world of mobile technology is buzzing with excitement as Android rolls out a groundbreaking update: Auracast support. This cutting-edge feature promises to redefine how we interact with audio in public spaces, particularly for those with hearing aids. With Google and Samsung devices leading the charge, this free upgrade is set...

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A series of QUT research studies have shed light on the importance of involving individuals with intellectual disabilities in the development of assistive technologies.

Decision-making is complex, but getting the right insights shouldn’t be.

However, business leaders often face delays due to traditional analytics workflows and overwhelmed data teams. At the same time, AI leaders encounter lengthy deployment cycles and integration challenges.

In fact, 66% report lacking the right tools to deploy AI solutions that align with company goals. Integration challenges and long deployment cycles—often seven months or more—delay progress and make it harder to meet executive expectations.

Generative AI and agentic AI promise a way forward, but adoption remains difficult. 77% of business leaders fear they are already falling behind and are pushing their teams to accelerate implementation.

The answer isn’t more complex tooling—it’s pre-built, configurable agentic AI apps. 

These agentic AI apps empower AI leaders to scale AI faster while giving business leaders the instant, intuitive, and reliable AI solutions they seek.

Roadblocks to AI-driven answers

While AI holds the promise of transforming decision-making, several entrenched obstacles continue to hinder its effective implementation:

•  Overwhelmed data and AI teams:  The increasing demand for AI-powered insights is stretching teams thin. Time-sensitive requests pile up faster than they can be addressed, leading to bottlenecks and burnout. In addition, AI teams face challenges in scaling solutions efficiently, hindering timely adoption and impact.
  
  
• Slow AI deployment and orchestration: Even when AI solutions are available, moving them from concept to production is a significant challenge. Integrating with enterprise systems, ensuring data is AI-ready, and aligning with governance policies can take months — far too long for today’s fast-paced business environment.
  
  
• Limited self-service, complex queries: Traditional Business Intelligence (BI) dashboards provide visibility, but real-time ad-hoc analysis with AI recommendations and insights still requires SQL, custom queries, or advanced analytics — making business users reliant on technical teams. Instead of acting on insights, they find themselves waiting for data analysts to generate reports.
  
  
• Security and compliance hurdles: Strict data privacy regulations like GDPR and HIPAA, along with internal security controls, are essential for protecting sensitive information. However, each data request requires approvals, permissions, and secure handling, adding friction that slows down access to critical business insights.
  

These persistent challenges underscore the need for a transformative approach to getting businesses the insights they want as fast as they need — one that streamlines processes and empowers both business and AI teams to achieve faster, more reliable outcomes.

Move from data to decisions instantly with agentic AI

Business leaders need a faster, more intuitive way to get AI-powered insights without overburdening technical teams or waiting on complex reports. 

This is why the Talk to My Data agentic AI app was developed.

Unlike traditional BI dashboards that require constant human input, the Talk to My Data agentic AI app actively retrieves and synthesizes data, using a chain-of-thought prompting to deliver business-ready answers in real time.

For business leaders, this means:

• No more navigating BI dashboards, submitting insight requests, or relying on SQL queries.  
  
• The ability to ask questions in plain language and getting instant, contextual responses.
  

For AI leaders, this means: 

• Removing manual query bottlenecks.
  
• Accelerating AI adoption while maintaining governance and scalability.
  

With schema intelligence, enterprise data integration, and built-in compliance, Talk to My Data enables AI teams to deploy faster, reduce operational overhead, and align AI with business goals.

A GPS for business decisions

Think of the Talk to My Data agentic app as a GPS for your business decisions. Instead of mapping the route yourself, just ask where you need to go—and the right path appears instantly.

But just like a GPS doesn’t suggest random routes, Talk to My Data factors in business context, historical trends, and predictive insights to deliver the most relevant answers.

• Versatile applications: Whether you’re optimizing sales performance, tracking financial health, or identifying operational bottlenecks, the AI dynamically retrieves, interprets, and refines queries—ensuring both speed and accuracy.
  
  
• Comprehensive outputs: Talk to My Data provides visual summaries, tables, and even source code for deeper exploration, allowing AI teams to customize or extend analytics as needed.
  
  
• Empowered decision-making: By eliminating delays in data access, leaders at all levels can identify high-value opportunities, pivot quickly, and maximize ROI, all while reducing dependence on technical teams for routine analytics.

“With Talk to My Data agentic AI app, business leaders and their teams can confidently make informed decisions without waiting on technical support. AI leaders can drive faster AI adoption and ensure scalability, while empowering business users to ask questions, get trusted answers, and visualize insights instantly—all on their terms.”
– Justin Swansburg, VP Applied AI & Technical Field Leads

How Talk to My Data agentic AI app empowers AI and business teams drive impact

The Talk to My Data app offers several features designed to enhance efficiency and effectiveness:

• Built-in AI, security, and app logic
  Deploying AI solutions often requires extensive customization and integration. However, with built-in AI logic, security logic, and app logic, AI teams can quickly customize the app to their organization’s unique business needs. This approach enables business users to immediately leverage AI for reporting and insights, minimizing the need for extensive adjustments. 
  
  
• Seamless data integration
  Working with data from various systems—such as Databricks, Snowflake, Google BigQuery, and even local files—often presents challenges due to manual integration processes.
  
  The Talk to My Data app addresses this by incorporating a built-in schema layer that automates data alignment, reducing the need for manual reconciliation across diverse data tables and sources. This automation minimizes the time data and AI teams spend resolving data issues, enabling business leaders to access faster, more reliable insights.
  

• Cost-effective system optimization
  Selecting appropriate AI systems is crucial for balancing performance and cost. By offering a library of LLMs tailored to specific business needs, AI teams can choose underlying components that optimize expenses while maintaining effectiveness. This flexibility ensures that AI initiatives remain both efficient and economical.
  
  
• Natural language interaction
  Accessing data insights shouldn’t require technical expertise. By enabling natural language queries, users can explore data, uncover insights, and make decisions faster, without the need for SQL or waiting on analysts for routine queries.
  
  For technical teams, the availability of underlying Python or SQL code allows for review, modification, and reuse, offering deeper analytical capabilities when needed.
  
  
• Simplified advanced analytics
  Leveraging AI-powered insights and Python-based analytics tools without coding can democratize data analysis. Users can generate charts, tables, and source code to answer questions effortlessly, making advanced analytics accessible to a broader audience.
  
  
• Built-in security and compliance
  Ensuring compliance with standards such as GDPR and HIPAA is essential in today’s data-driven environment. Built-in security features ensure that data access is secure, allowing decision-making processes to proceed without compromising compliance.
  
  
• Industry-specific adaptability
  Different industries face unique challenges. By offering real-time visualizations and analyses tailored to specific industry needs, users can gain precise, context-aware insights.
  
  Customizable prompts and visualizations—including charts, graphs, and tailored recommendations—enable deeper analysis and informed decision-making, aligning with the specific priorities of each industry.
  

The right answers from your data right when you need them

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​Imagine your AI team delivering a powerful agentic AI experience that your business leaders rely on daily to obtain precise answers by simply querying your extensive data sources.

That’s what’s possible with the Talk to My Data agentic AI app.

Seamlessly integrate generative and agentic AI into your organization’s decision-making process, eliminating delays, complexities, and technical dependencies.

No more enduring lengthy AI development cycles, integration challenges, or concerns over security and governance. 

Discover how it works, and invite your team to experience it firsthand.

The post Talk to My Data: Instant, explainable answers with agentic AI appeared first on DataRobot.

For the first time ever, AGILOX will showcase four different AMR models operating simultaneously in a live tradeshow environment. Attendees will experience real-world automation in action just like in an actual manufacturing facility.

Locomotion, the ability to move from one place to another, is an essential survival strategy for virtually every organism. Adapting to the unpredictable terrain they run into, cells, fungi and microorganisms autonomously move and change shape to explore their environments, while animals run, crawl, slither, roll and jump.

In an unexpected move, Apple has rolled out iOS 18.3.2, a crucial security update for iPhone users worldwide. Released on March 12, 2025, this update addresses a significant vulnerability in the WebKit engine powering Safari, which could have allowed hackers to exploit malicious web content and gain remote access to your device. If you haven’t...

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March 12, 2025 – The mobile gaming world is buzzing with seismic news: Niantic, the innovative force behind Pokémon GO, has sold its gaming division to Scopely for a staggering $3.5 billion. This blockbuster deal, announced today, marks a pivotal shift for both companies and raises big questions about the future of augmented reality (AR)...

The post Pokémon GO’s New Chapter: Scopely Acquires Niantic’s Gaming Empire for $3.5 Billion appeared first on 1redDrop.

Introducing Gemini Robotics and Gemini Robotics-ER, AI models designed for robots to understand, act and react to the physical world.

Native image output is available in Gemini 2.0 Flash for developers to experiment with in Google AI Studio and the Gemini API.

Take a warehouse worker who spends a significant amount of time moving boxes from point A to point B as an example; automation can handle this aspect of the job instead, freeing that worker to focus on more complex tasks like inventory management and quality control.

The most capable model you can run on a single GPU or TPU.

Empa researchers are working on producing artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic yet powerful structures using 3D printing.

Researchers are working on artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic, yet powerful structures using 3D printing. One day, these could be used in medicine or robotics -- and anywhere else where things need to move at the touch of a button.

TL;DR:

Fragmented AI tools are draining  budgets, slowing adoption, and frustrating teams. To control costs and accelerate ROI, AI leaders need interoperable solutions that reduce tool sprawl and streamline workflows.

AI investment is under a microscope in 2025. Leaders aren’t just asked to prove AI’s value — they’re being asked why, after significant investments, their teams still struggle to deliver results.

1-in-4 teams report difficulty implementing AI tools, and nearly 30% cite integration and workflow inefficiencies as their top frustration, according to our Unmet AI Needs report.

The culprit? A disconnected AI ecosystem. When teams spend more time wrestling with disconnected tools than delivering outcomes, AI leaders risk ballooning costs, stalled ROI, and high talent turnover. 

The hidden costs of fragmented AI tools

AI practitioners spend more time maintaining tools than solving business problems. The biggest blockers? Manual pipelines, tool fragmentation, and connectivity roadblocks.

Imagine if cooking a single dish required using a different stove every single time. Now envision running a restaurant under those conditions. Scaling would be impossible. 

Similarly, AI practitioners are bogged down by the time-consuming, brittle pipelines, leaving less time to advance and deliver AI solutions.

AI integration must accommodate diverse working styles, whether code-first in notebooks, GUI-driven, or a hybrid approach. It must also bridge gaps between teams, such as data science and DevOps, where each group relies on different toolsets. When these workflows remain siloed, collaboration slows, and deployment bottlenecks emerge.

Scalable AI also demands deployment flexibility such as JAR files, scoring code, APIs or embedded applications. Without an infrastructure that streamlines these workflows, AI leaders risk stalled innovation, rising inefficiencies, and unrealized AI potential. 

How integration gaps drain AI budgets and resources 

Interoperability hurdles don’t just slow down teams – they create significant cost implications.

The top workflow restrictions AI practitioners face:

• Manual pipelines. Tedious setup and maintenance pull AI, engineering, DevOps, and IT teams away from innovation and new AI deployments.
  

• Tool and infrastructure fragmentation. Disconnected environments create bottlenecks and inference latency, forcing teams into endless troubleshooting instead of scaling AI.
  
  
• Orchestration complexities.  Manual provisioning of compute resources — configuring servers, DevOps settings, and adjusting as usage scales — is not only time-consuming but nearly impossible to optimize manually. This leads to performance limitations, wasted effort, and underutilized compute, ultimately preventing AI from scaling effectively.
  
  
• Difficult updates. Fragile pipelines and tool silos make integrating new technologies slow, complex, and unreliable. 
  

The long-term cost? Heavy infrastructure management overhead that eats into ROI. 

More budget goes toward the overhead costs of manual patchwork solutions instead of delivering results.

Over time, these process breakdowns lock organizations into outdated infrastructure, frustrate AI teams, and stall business impact.

Why code-first users and developers struggle with AI tools 

Code-first developers prefer customization, but technology misalignment makes it harder to work efficiently.

• 42% of developers say customization improves AI workflows.
  
  
• Only 1-in-3 say their AI tools are easy to use.
  

This disconnect forces teams to choose between flexibility and usability, leading to misalignments that slow AI development and complicate workflows. But these inefficiencies don’t stop with developers. AI integration issues have a much broader impact on the business.

The true cost of integration bottlenecks

Disjointed AI tools and systems don’t just impact budgets; they create ripple effects that impact team stability and operations. 

• The human cost. With an average tenure of just 11 months, data scientists often leave before organizations can fully benefit from their expertise. Frustrating workflows and disconnected tools contribute to high turnover.
  
  
• Lost collaboration opportunities. Only 26% of AI practitioners feel confident relying on their own expertise, making cross-functional collaboration essential for knowledge-sharing and retention.
  

Siloed infrastructure slows AI adoption. Leaders often turn to hyperscalers for cost savings, but these solutions don’t always integrate easily with tools, adding backend friction for AI teams. 

Generative AI and agentic are adding more complexity

With 90% of respondents expecting generative AI and predictive AI to converge, AI teams must balance user needs with technical feasibility.

As King’s Hawaiian CDAO Ray Fager explains:
“Using generative AI in tandem with predictive AI has really helped us build trust. Business users ‘get’ generative AI since they can easily interact with it. When they have a GenAI app that helps them interact with predictive AI, it’s much easier to build a shared understanding.”

With an increasing demand for generative and agentic AI, practitioners face mounting compute, scalability, and operational challenges. Many organizations are layering new generative AI tools on top of their existing technology stack without a clear integration and orchestration strategy. 

The addition of generative and agentic AI, without the foundation to efficiently allocate these complex workloads across all available compute resources, increases operational strain and makes AI even harder to scale.

Four steps to simplify AI infrastructure and cut costs  

Streamlining AI operations doesn’t have to be overwhelming. Here are actionable steps AI leaders can take to optimize operations and empower their teams:

Step 1: Assess tool flexibility and adaptability

Agentic AI requires modular, interoperable tools that support frictionless upgrades and integrations. As requirements evolve, AI workflows should remain flexible, not constrained by vendor lock-in or rigid tools and architectures.

Two important questions to ask are:

• Can AI teams easily connect, manage, and interchange tools such as LLMs, vector databases, or orchestration and security layers without downtime or major reengineering?
  
  
• Do our AI tools scale across various environments (on-prem, cloud, hybrid), or are they locked into specific vendors and rigid infrastructure?
  

Step 2: Leverage a hybrid interface

53% of practitioners prefer a hybrid AI interface that blends the flexibility of coding with the accessibility of GUI-based tools. As one data science lead explained, “GUI is critical for explainability, especially for building trust between technical and non-technical stakeholders.” 

Step 3: Streamline workflows with AI platforms

Consolidating tools into a unified platform reduces manual pipeline stitching, eliminates blockers, and improves scalability. A platform approach also optimizes AI workflow orchestration by leveraging the best available compute resources, minimizing infrastructure overhead while ensuring low-latency, high-performance AI solutions.

Step 4: Foster cross-functional collaboration

When IT, data science, and business teams align early, they can identify workflow barriers before they become implementation roadblocks. Using unified tools and shared systems reduces redundancy, automates processes, and accelerates AI adoption. 

Set the stage for future AI innovation

The Unmet AI Needs survey makes one thing clear: AI leaders must prioritize adaptable, interoperable tools — or risk falling behind. 

Rigid, siloed systems not only slows innovation and delays ROI, it also prevents organizations from responding to fast-moving advancements in AI and enterprise technology. 

With 77% of organizations already experimenting with generative and predictive AI, unresolved integration challenges will only become more costly over time. 

Leaders who address tool sprawl and infrastructure inefficiencies now will lower operational costs, optimize resources, and see stronger long-term AI returns

Get the full DataRobot Unmet AI Needs report to learn how top AI teams are overcoming implementation hurdles and optimizing their AI investments.

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