#cognitive automation

[jcmarchi]

How Does AI Use Impact Critical Thinking?

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How Does AI Use Impact Critical Thinking?

Artificial intelligence (AI) can process hundreds of documents in seconds, identify imperceptible patterns in vast datasets and provide in-depth answers to virtually any question. It has the potential to solve common problems, increase efficiency across multiple industries and even free up time for individuals to spend with their loved ones by delegating repetitive tasks to machines.    

However, critical thinking requires time and practice to develop properly. The more people rely on automated technology, the faster their metacognitive skills may decline. What are the consequences of relying on AI for critical thinking?

Study Finds AI Degrades Users’ Critical Thinking 

The concern that AI will degrade users’ metacognitive skills is no longer hypothetical. Several studies suggest it diminishes people’s capacity to think critically, impacting their ability to question information, make judgments, analyze data or form counterarguments. 

A 2025 Microsoft study surveyed 319 knowledge workers on 936 instances of AI use to determine how they perceive their critical thinking ability when using generative technology. Survey respondents reported decreased effort when using AI technology compared to relying on their own minds. Microsoft reported that in the majority of instances, the respondents felt that they used “much less effort” or “less effort” when using generative AI.  

Knowledge, comprehension, analysis, synthesis and evaluation were all adversely affected by AI use. Although a fraction of respondents reported using some or much more effort, an overwhelming majority reported that tasks became easier and required less work. 

If AI’s purpose is to streamline tasks, is there any harm in letting it do its job? It is a slippery slope. Many algorithms cannot think critically, reason or understand context. They are often prone to hallucinations and bias. Users who are unaware of the risks of relying on AI may contribute to skewed, inaccurate results. 

How AI Adversely Affects Critical Thinking Skills

Overreliance on AI can diminish an individual’s ability to independently solve problems and think critically. Say someone is taking a test when they run into a complex question. Instead of taking the time to consider it, they plug it into a generative model and insert the algorithm’s response into the answer field. 

In this scenario, the test-taker learned nothing. They didn’t improve their research skills or analytical abilities. If they pass the test, they advance to the next chapter. What if they were to do this for everything their teachers assign? They could graduate from high school or even college without refining fundamental cognitive abilities. 

This outcome is bleak. However, students might not feel any immediate adverse effects. If their use of language models is rewarded with better test scores, they may lose their motivation to think critically altogether. Why should they bother justifying their arguments or evaluating others’ claims when it is easier to rely on AI? 

The Impact of AI Use on Critical Thinking Skills 

An advanced algorithm can automatically aggregate and analyze large datasets, streamlining problem-solving and task execution. Since its speed and accuracy often outperform humans, users are usually inclined to believe it is better than them at these tasks. When it presents them with answers and insights, they take that output at face value. Unquestioning acceptance of a generative model’s output leads to difficulty distinguishing between facts and falsehoods. Algorithms are trained to predict the next word in a string of words. No matter how good they get at that task, they aren’t really reasoning. Even if a machine makes a mistake, it won’t be able to fix it without context and memory, both of which it lacks.

The more users accept an algorithm’s answer as fact, the more their evaluation and judgment skew. Algorithmic models often struggle with overfitting. When they fit too closely to the information in their training dataset, their accuracy can plummet when they are presented with new information for analysis. 

Populations Most Affected by Overreliance on AI 

Generally, overreliance on generative technology can negatively impact humans’ ability to think critically. However, low confidence in AI-generated output is related to increased critical thinking ability, so strategic users may be able to use AI without harming these skills. 

In 2023, around 27% of adults told the Pew Research Center they use AI technology multiple times a day. Some of the individuals in this population may retain their critical thinking skills if they have a healthy distrust of machine learning tools. The data must focus on populations with disproportionately high AI use and be more granular to determine the true impact of machine learning on critical thinking. 

Critical thinking often isn’t taught until high school or college. It can be cultivated during early childhood development, but it typically takes years to grasp. For this reason, deploying generative technology in schools is particularly risky — even though it is common. 

Today, most students use generative models. One study revealed that 90% have used ChatGPT to complete homework. This widespread use isn’t limited to high schools. About 75% of college students say they would continue using generative technology even if their professors disallowed it. Middle schoolers, teenagers and young adults are at an age where developing critical thinking is crucial. Missing this window could cause problems. 

The Implications of Decreased Critical Thinking

Already, 60% of educators use AI in the classroom. If this trend continues, it may become a standard part of education. What happens when students begin to trust these tools more than themselves? As their critical thinking capabilities diminish, they may become increasingly susceptible to misinformation and manipulation. The effectiveness of scams, phishing and social engineering attacks could increase.  

An AI-reliant generation may have to compete with automation technology in the workforce. Soft skills like problem-solving, judgment and communication are important for many careers. Lacking these skills or relying on generative tools to get good grades may make finding a job challenging. 

Innovation and adaptation go hand in hand with decision-making. Knowing how to objectively reason without the use of AI is critical when confronted with high-stakes or unexpected situations. Leaning into assumptions and inaccurate data could adversely affect an individual’s personal or professional life.

Critical thinking is part of processing and analyzing complex — and even conflicting — information. A community made up of critical thinkers can counter extreme or biased viewpoints by carefully considering different perspectives and values. 

AI Users Must Carefully Evaluate Algorithms’ Output 

Generative models are tools, so whether their impact is positive or negative depends on their users and developers. So many variables exist. Whether you are an AI developer or user, strategically designing and interacting with generative technologies is an important part of ensuring they pave the way for societal advancements rather than hindering critical cognition.

[voyant-du-vide]

This sort of AI integration could be really cool (if we ignore the many ethical concerns surrounding AI, which. We shouldn’t. But that’s not the point of this particular post). Spell check used to work by having a dictionary of acceptable words and checking your spelling against the dictionary.

The way this new spell check is working is more like auto-complete. You teach your phone’s auto-complete how you write every time you send a text. It learns to sound like you. That’s also basically how LLMs like ChatGPT work. It has its own database, but it also learns from you. It sounds so good to the people who use it a lot partially because it’s learned to imitate them really well.

This kind of spellcheck learns from everyone who is using it. So if a significant amount of people misspell something the same way, it’s going to use that spelling over the correct spelling.

That’s cool because we can use it to ask critical questions about our language, like does the standard spelling make sense? And if not, should we change the standard spelling? We can use it to look at how language is evolving right in front of us. It can also be helpful if you’re trying to make your writing voice sound more contemporary.

However. That’s not what people are using fuckin google docs for lmao. It’s trying to use the same dataset for people journaling and for people writing stories and for people writing business proposals and for people writing academic essays. Plus it’s google so it’s probably also taking from as much of the internet as it can, including comments sections and blogs and tons and tons of other informal writing.

It needs to separate those into different categories so the students working on essays aren’t getting spelling suggestions scraped from youtube trolls. It’s not doing that. And afaik, it doesn’t give you the option to go back to the old version that’s just a dictionary. This is why forced AI integration fucking sucks.

Do you think I meant to write "stoopid" google docs. Do you think I meant to describe a character as "stoopid." Do you think that is what I meant to do

[honestkindlereviews]

Gemini AI Time Hacks

Gemini AI Time Hacks: Automate Tasks, Prioritize Goals, and Reclaim 10+ Hours Weekly

Let's be honest. In today's hyper-connected, always-on world, time feels like our most precious and scarce resource. We juggle emails, meetings, projects, personal commitments, and the relentless stream of information, often feeling like we're drowning in a sea of tasks. The promise of productivity tools has been around for years, offering calendars, to-do lists, and project managers. And while they help, they often feel like bandaids on a deeper wound – the fundamental challenge of managing not just tasks, but our attention and energy in a way that aligns with our true goals.

The Intelligent Investor’s Mind: AI-Powered Psychology for Wealth, Wisdom, and Well-being: A Modern Approach to Financial Success Through Self-Awareness and AI: BUY EBOOK CLICK HARE

I’ve spent decades studying productivity, testing systems, and coaching individuals and teams on optimizing their workflows. I’ve seen the evolution from paper planners to complex software suites. But nothing, absolutely nothing, has felt as transformative as the advent of sophisticated AI models like Gemini. We're not just talking about another tool; we're talking about a potential paradigm shift in how we interact with our work and our lives. The idea of reclaiming 10, 15, even 20 hours a week might sound like hyperbole, but I'm seeing it become a reality for those who learn to truly partner with AI.

Think of your current workflow. How much time do you spend on repetitive tasks? Scheduling emails, drafting standard responses, summarizing documents, transcribing notes, organizing files, researching basic information, creating first drafts of content? These are the necessary gears of our professional lives, but they often consume hours that could be spent on higher-level thinking, creative problem-solving, strategic planning, or simply, well, living. These are the hours AI is poised to give back to you.

I remember a time, not so long ago, when preparing for a significant client meeting involved hours of manual work. I'd sift through past correspondence, pull up relevant reports, summarize key points, research the client's recent activities, and then try to synthesize it all into concise briefing notes. It was tedious, but essential. Now? I can feed Gemini access to relevant documents and email threads, ask it to summarize the client's history with us, highlight key discussion points for the upcoming meeting, and even draft a personalized opening based on recent news about their company – all in minutes. The difference isn't just speed; it's the ability to arrive at that meeting feeling truly prepared, having spent my valuable time on thinking about the strategy, not just compiling the background.

This is the core promise of AI-powered time hacks: offloading the cognitive burden of routine tasks to free up human capacity for what we do best.

Automate Tasks: Putting Your Workflow on Autopilot

The most immediate and tangible benefit of integrating Gemini into your workflow is automation. Not the complex, code-heavy automation of the past, but natural language-driven automation that feels less like programming and more like delegation.

Let's break down how this works across common areas:

Email Management: Taming the Inbox Beast

The inbox is a notorious time sink. We spend hours reading, sorting, responding, and searching. Gemini can become your email co-pilot.

The Intelligent Investor’s Mind: AI-Powered Psychology for Wealth, Wisdom, and Well-being: A Modern Approach to Financial Success Through Self-Awareness and AI: BUY EBOOK CLICK HARE

Drafting Responses: For routine inquiries, standard updates, or even initial outreach, Gemini can draft emails based on a few key points you provide. You can refine it, inject your personal tone, but the heavy lifting of structuring sentences and finding the right words is done instantly. Imagine needing to decline a meeting request politely, provide a project update, or send a follow-up email. Instead of staring at a blank screen, you give Gemini the context and the core message, and it provides a ready-to-send draft. This isn't just about speed; it reduces decision fatigue associated with crafting countless messages daily.

Summarizing Threads: Ever open a long email thread and groan? Feed it to Gemini and ask for a concise summary of the key decisions, action items, and participants. Instantly, you grasp the essence without wading through every single reply. This is invaluable for catching up after time off or quickly getting context on an ongoing discussion.

Scheduling and Coordination: While dedicated scheduling tools exist, Gemini can assist in the natural language back-and-forth of finding a time. You can ask it to suggest meeting times based on your calendar availability (with appropriate privacy controls, of course) or even draft emails proposing options to others.

Filtering and Prioritizing: While email clients have rules, AI can potentially understand the intent and urgency of emails more effectively. Imagine an AI that learns which senders, keywords, and types of requests are genuinely high priority for you, helping you focus on what matters most when you open your inbox.

This isn't about achieving "inbox zero" for the sake of it; it's about reducing the time spent in the inbox, freeing you to focus on tasks that require your unique human intelligence.

Document Handling: From Clutter to Clarity

We work with documents constantly – reports, articles, contracts, research papers. Managing, understanding, and extracting information from them is a significant time investment.

Summarization: The ability to instantly summarize lengthy documents is a game-changer. Need to get the gist of a 50-page report before a meeting? Feed it to Gemini. Want to quickly understand the key arguments of an article? Ask for a summary. This saves hours of reading time while ensuring you grasp the core information.

Information Extraction: Need to pull out specific data points, dates, names, or figures from a document? Instead of scanning page by page, ask Gemini to extract them for you. This is particularly useful for research, data compilation, or reviewing contracts.

Drafting and Outlining: Starting a new document from scratch can be daunting. Gemini can help generate outlines, draft initial sections, or even create different versions of content based on different tones or target audiences. This overcomes the inertia of starting and provides a solid foundation to build upon.

Translation and Simplification: Working with documents in different languages or needing to explain complex topics simply? Gemini can provide quick translations or simplify jargon-filled text, making information more accessible and saving time on manual interpretation or explanation.

By automating these document-related tasks, you transform your interaction with information from passive consumption and manual processing to active engagement with synthesized insights.

Data Management and Analysis: Turning Numbers into Narratives

While complex data analysis often requires specialized tools, Gemini can significantly expedite the initial stages and help in understanding the results.

Data Cleaning and Formatting: For simple datasets, Gemini can assist with formatting, identifying inconsistencies, or even generating basic code snippets (like Python) to perform cleaning tasks.

Generating Summaries and Insights: Provide Gemini with a dataset (within privacy and security limits, of course) and ask for a summary of key trends, outliers, or correlations. It can help you quickly identify interesting patterns that warrant further investigation.

Creating Visualizations (with support): While Gemini itself might not create charts, it can generate the code or instructions needed for charting libraries based on your data, saving you the time of looking up syntax or figuring out the right chart type.

Explaining Complex Data: If you're looking at a complex report or spreadsheet, you can ask Gemini to explain specific metrics, formulas, or the meaning of certain data points in plain language.

This level of assistance turns data interaction from a chore into a more intuitive exploration, allowing you to get to the insights faster.

Prioritize Goals: Focusing on What Truly Matters

Automation is powerful, but without clear prioritization, you just become more efficient at doing the wrong things. This is where AI's ability to understand context and goals becomes crucial.

The Intelligent Investor’s Mind: AI-Powered Psychology for Wealth, Wisdom, and Well-being: A Modern Approach to Financial Success Through Self-Awareness and AI: BUY EBOOK CLICK HARE

AI-Assisted Goal Alignment

Breaking Down Large Goals: Have a big, daunting goal? Share it with Gemini and ask for a breakdown into smaller, actionable steps. It can help you create a project plan, identify potential roadblocks, and suggest a logical sequence of tasks.

Identifying High-Leverage Activities: Based on your stated goals and the tasks on your plate, Gemini can help you identify which activities are most likely to move the needle. You can ask, "Given my goal to [achieve X], which of these tasks [list tasks] should I focus on first?" AI can analyze the potential impact and dependencies, offering a more objective perspective than your potentially overwhelmed brain.

Connecting Tasks to Objectives: We often have long to-do lists without a clear sense of why we're doing each item. You can use Gemini to help connect daily tasks back to larger projects or long-term goals, providing a sense of purpose and helping you prioritize based on strategic importance rather than just urgency. "Remind me how completing [Task A] contributes to [Project B] and my overall goal of [Goal C]."

Dynamic Task Management

Intelligent Task Scheduling: Beyond simple calendar blocking, AI can potentially learn your energy levels, your focus patterns, and the typical duration of certain tasks. It could then suggest optimal times to work on specific types of tasks, scheduling your deep work for your peak focus hours and routine tasks for when your energy is lower. "Based on my past performance, you seem to be most focused between 9 AM and 11 AM. Would you like to schedule [high-focus task] during that time?"

Adaptive Prioritization: Priorities change. New urgent requests come in, deadlines shift. Instead of manually reshuffling your entire task list, you can inform Gemini of the change, and it can help you dynamically re-prioritize your remaining tasks based on the new information and your overarching goals.

Identifying Bottlenecks: By analyzing your workflow and task dependencies, AI can help you identify potential bottlenecks before they become major problems. "I notice you've been stuck on [Task X] for several days, and it's blocking progress on [Task Y] and [Task Z]. Let's explore why and how to move forward."

This isn't about AI dictating your priorities, but about providing an intelligent framework and objective analysis to help you make better, more informed decisions about how you spend your time. It’s like having a strategic advisor constantly reviewing your workload against your objectives.

Reclaim 10+ Hours Weekly: The Cumulative Impact

So, how does all this automation and prioritization translate into reclaiming significant chunks of your week? It's the cumulative effect of saving minutes here and there across dozens of daily activities.

Think about the time spent:

Opening and processing non-essential emails.

Searching for information scattered across different documents or platforms.

Drafting and revising routine communications.

Getting started on a new task because you lack a clear outline or first draft.

Feeling overwhelmed by a long to-do list and not knowing where to start.

Switching between tasks inefficiently.

Attending meetings that lack clear objectives or summaries.

Each of these might only take a few minutes, but multiplied across a day, a week, a month, they add up to hours – hours that are often spent in low-leverage activities that drain your energy without moving you closer to your most important goals.

By using Gemini to:

Automate drafting and summarizing: You save time on writing and reading.

Extract key information: You save time on searching and synthesizing.

Break down and prioritize tasks: You save time on planning and decision-making inertia.

Get help with initial drafts: You save time on overcoming the blank page.

Identify high-leverage activities: You ensure the time you do spend is on what matters most.

The impact is exponential. Saving 15 minutes on email processing, 30 minutes on document review, 20 minutes on drafting a proposal outline, and 10 minutes on prioritizing your morning tasks might seem small individually. But repeated daily, across a range of activities, these small increments quickly accumulate.

The Intelligent Investor’s Mind: AI-Powered Psychology for Wealth, Wisdom, and Well-being: A Modern Approach to Financial Success Through Self-Awareness and AI: BUY EBOOK CLICK HARE

I've seen clients, initially skeptical, start by using Gemini for simple tasks like summarizing articles. Then they move to drafting emails. Then to breaking down project plans. As they get comfortable and see the time savings, they start looking for more opportunities to delegate routine cognitive work to the AI. The 10+ hour figure isn't pulled from thin air; it's a realistic outcome when you systematically apply AI to the repetitive, low-value tasks that currently consume your week.

Beyond Efficiency: The Impact on Well-being

Reclaiming time isn't just about being more productive; it's about creating space for well-being. Those reclaimed hours can be reinvested in ways that truly enrich your life:

Deep Work: Spending uninterrupted time on complex problems that require your full cognitive capacity.

Learning and Development: Acquiring new skills, reading, or exploring new ideas.

Creativity and Innovation: Engaging in activities that spark new ideas and solutions.

Strategic Thinking: Stepping back to see the big picture and plan for the future.

Relationships: Spending quality time with family, friends, and colleagues.

Rest and Recharge: Prioritizing sleep, exercise, and hobbies to prevent burnout.

When you're not constantly battling the clock and feeling overwhelmed by a never-ending task list, you have the mental and emotional capacity to focus on what truly brings you value and joy, both professionally and personally. This is the ultimate time hack – using AI to create a more sustainable, fulfilling way of working and living.

Getting Started with Gemini Time Hacks

Adopting AI into your workflow doesn't require a complete overhaul overnight. It's a process of experimentation and integration.

Identify Time Sinks: Start by tracking where your time actually goes for a few days. Be honest. Are there recurring tasks that feel tedious or time-consuming? These are prime candidates for AI assistance.

Experiment with One Task: Pick one specific task you'd like to automate or streamline using Gemini. Maybe it's drafting initial emails, summarizing meeting notes, or breaking down a small project.

Learn the Prompts: Get comfortable with how to phrase requests to Gemini to get the best results. Experiment with different wording and levels of detail. Think of it as learning to delegate effectively to a very capable, but literal, assistant.

Integrate Gradually: As you find success with one task, look for other opportunities. How else can Gemini help you with document handling, data analysis, or planning?

Establish Boundaries and Review: Remember that AI is a tool. You are in control. Review the output, refine it, and ensure it aligns with your standards and privacy requirements. Regularly assess how the AI is impacting your workflow and adjust your approach as needed.

This journey is less about finding a magic button and more about developing a new partnership. It's about understanding AI's strengths – its ability to process information rapidly, identify patterns, and generate text – and leveraging those strengths to complement your own.

The future of productivity isn't about working harder; it's about working smarter, and AI is the most powerful lever we've had in decades to achieve that. By embracing Gemini AI time hacks, you're not just optimizing your workflow; you're investing in your capacity for higher-level work, strategic thinking, and ultimately, a more balanced and fulfilling life. The hours are there, waiting to be reclaimed. The intelligent use of AI is your key.

The Intelligent Investor’s Mind: AI-Powered Psychology for Wealth, Wisdom, and Well-being: A Modern Approach to Financial Success Through Self-Awareness and AI: BUY EBOOK CLICK HARE

[cybersecurityict]

Cognitive Process Automation Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: Emerging Markets Poised for Explosive Growth

The Cognitive Process Automation Market was valued at USD 6.55 billion in 2023 and is expected to reach USD 53.48 billion by 2032, growing at a CAGR of 26.33% from 2024-2032.

The Cognitive Process Automation (CPA) Market is witnessing a dynamic transformation as organizations across industries leverage AI-powered automation to enhance operational efficiency, accuracy, and decision-making. By combining Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA), CPA transcends traditional automation, enabling systems to mimic human cognition, interpret data, and adapt processes autonomously. Enterprises are increasingly investing in CPA technologies to streamline complex workflows, reduce manual errors, and drive smarter business outcomes.

Cognitive Process Automation Market is no longer just a futuristic concept—it's a present-day necessity. As digital transformation becomes integral to business strategy, the CPA market is emerging as a cornerstone of innovation and competitive advantage. From banking and finance to healthcare, retail, and manufacturing, industries are adopting CPA to accelerate productivity and optimize customer engagement by automating tasks that require judgment, language understanding, and contextual reasoning.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/6051 

Market Keyplayers:

Automation Anywhere (Automation 360, Bot Insight)

Blue Prism (Blue Prism Cloud, Decipher IDP)

EdgeVerve Systems Ltd. (AssistEdge RPA, XtractEdge)

International Business Machines Corporation (IBM Robotic Process Automation, IBM Watson Assistant)

Microsoft Corporation (Power Automate, Azure Cognitive Services)

NICE (NICE Robotic Process Automation, NEVA)

NTT Advanced Technology Corp. (WinActor, WinDirector)

Pegasystems (Pega Robotic Process Automation, Pega Customer Decision Hub)

UiPath (UiPath Studio, UiPath Orchestrator)

WorkFusion, Inc. (Intelligent Automation Cloud, Smart Process Automation)

Celonis (Process Mining, Execution Management System)

Contextor (Contextor RPA, Contextor Studio)

Kofax (Kofax RPA, Kofax TotalAgility)

SAP (SAP Intelligent RPA, SAP Conversational AI)

Oracle (Oracle Intelligent Process Automation, Oracle Digital Assistant)

Google (Google Cloud AI, Google Dialogflow)

Appian (Appian RPA, Appian AI)

SAS Institute, Inc. (SAS Viya, SAS Intelligent Decisioning)

TIBCO Software Inc. (TIBCO Spotfire, TIBCO Data Science)

Teradata Corporation (Teradata Vantage, Teradata IntelliCloud)

Datameer, Inc. (Datameer Spectrum, Datameer X)

DataRobot, Inc. (DataRobot AI Cloud, DataRobot AutoML)

Market Analysis The CPA market is experiencing rapid acceleration driven by the convergence of AI and enterprise automation. Leading technology vendors are developing intelligent automation solutions that can handle unstructured data, analyze trends, and make data-driven decisions. The integration of CPA with existing systems such as CRMs and ERPs enhances their capability to perform complex tasks without human intervention. Regulatory compliance, demand for cost optimization, and the need for real-time insights are further catalyzing market growth.

Market Trends

Surge in AI and ML adoption in enterprise automation

Rising demand for intelligent chatbots and virtual assistants

Integration of CPA with cloud-based platforms and SaaS tools

Focus on hyperautomation strategies across sectors

Increased R&D investments in natural language processing (NLP)

Expansion of use cases in fraud detection, HR automation, and claims processing

Growing emphasis on scalable, cognitive-first architectures

Market Scope

Cross-Industry Adoption: CPA is applicable across finance, healthcare, retail, and supply chain sectors

Unstructured Data Handling: Capable of processing text, audio, and images intelligently

Enhanced Decision Support: Empowers decision-makers with contextual, data-driven insights

Seamless Integration: Easily integrates with existing IT infrastructure

Agility & Scalability: Scales with business needs without significant infrastructure overhaul

The market scope for CPA is vast, with its transformative power extending beyond automation to enable human-like intelligence in decision-making processes. As organizations aim to create self-optimizing systems, CPA offers a bridge between operational efficiency and cognitive intelligence.

Market Forecast The future of the CPA market is bright, driven by continuous AI innovation and increasing enterprise-level automation demand. Organizations are projected to accelerate CPA adoption as part of their broader digital transformation agendas. The technology’s potential to eliminate bottlenecks, personalize customer interactions, and improve compliance management positions CPA as a key component in next-generation intelligent business ecosystems. Its role in achieving operational resilience and agility ensures its sustained relevance across industries.

Access Complete Report: https://www.snsinsider.com/reports/cognitive-process-automation-market-6051 

Conclusion As the Cognitive Process Automation market evolves, it’s not just about automating processes—it’s about unlocking a new level of intelligence within organizations. CPA is redefining how businesses think, respond, and grow. It offers a strategic leap from reactive operations to proactive, intelligent execution. For enterprises ready to lead in the digital age, investing in CPA is more than a technological choice—it's a competitive imperative.

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[ezeetester]

Rethinking Manual Testing: Exploring Real Value

A human thinking effort that is lost in mundane, repetitive work is basically marginal value, creating toxic cycles during your so called manual testing effort. The word “manual” in manual testing is misleading. Every testing effort needs a tool. Ex: To document your tests you need an Excel sheet or a tool like Jira. If I would use the word manual, I would mean thinking or I would just call it…

[visionaryvogues03]

Can Artificial Intelligence in Health Solve America’s Physician Burnout Crisis?

[Source - LinkedIn]

Physician burnout has reached alarming levels in the United States, with over 63% of doctors reporting symptoms such as emotional exhaustion, depersonalization, and reduced personal accomplishment. As healthcare systems across the country wrestle with staff shortages, increasing administrative burdens, and growing patient demands, one question continues to surface: Can artificial intelligence in health be the remedy for America’s physician burnout crisis?

Understanding the Burnout Epidemic

Burnout is not simply about fatigue; it represents a deeper systemic problem. U.S. physicians today face a unique combination of electronic health record (EHR) overload, repetitive tasks, and mounting regulatory demands. Studies from the American Medical Association reveal that doctors now spend twice as much time on administrative work as they do with patients. This imbalance is not only unsustainable but also threatens care quality, patient satisfaction, and workforce retention.

Enter Artificial Intelligence in Health

Artificial intelligence in health is more than just an emerging technology; it is an enabler of transformation. AI tools can perform repetitive and time-consuming tasks, analyze large datasets in seconds, and provide clinical decision support, all of which can significantly reduce physician burden. By handling routine responsibilities, AI allows physicians to redirect their time and focus on direct patient care, restoring both the quality of healthcare delivery and the joy of practicing medicine.

EHR Automation and Documentation Relief

One of the primary contributors to physician burnout is excessive time spent on EHR documentation. Artificial intelligence in health is making significant inroads in this area. Natural Language Processing (NLP)-based tools, such as ambient listening devices, now transcribe conversations between doctors and patients into structured notes in real time. This eliminates the need for repetitive manual data entry, reducing documentation time by up to 60%.

Companies like Suki and Nuance have developed AI-powered digital assistants that learn from physicians' workflow preferences and adapt over time, further personalizing the process. In pilot studies, physicians using such tools reported improved work-life balance and greater job satisfaction.

Clinical Decision Support and Reduced Cognitive Load

Clinical decision-making can be mentally exhausting, especially in high-stakes or high-volume settings like emergency departments. Artificial intelligence in health provides advanced decision-support systems that analyze patient data, medical history, and current guidelines to offer real-time diagnostic suggestions.

For example, the University of Pittsburgh Medical Center implemented an AI-based system for sepsis detection that flagged patients hours before traditional methods would have, enabling earlier intervention and reducing the burden on clinical staff. Such tools act as cognitive partners, not replacements, empowering physicians to make faster and more confident decisions.

Reducing Alert Fatigue with Smarter Systems

Ironically, the tools meant to help physicians often become part of the problem. Traditional EHRs generate numerous alerts, most of which are irrelevant or low priority, leading to "alert fatigue."

Artificial intelligence in health is transforming these systems into intelligent, context-aware platforms. AI filters out noise and delivers only clinically relevant alerts based on patient risk profiles and physician behavior patterns. The result? Fewer distractions, less stress, and a more streamlined clinical experience.

Workflow Optimization and Time Reallocation

Another way artificial intelligence in health supports physicians is by optimizing scheduling and care coordination. AI-driven platforms can forecast patient demand, manage appointment workflows, and even predict no-shows, helping hospitals and clinics make better staffing decisions.

Massachusetts General Hospital, for instance, uses AI to predict daily patient volumes and staff accordingly. This reduces overbooking, avoids clinician burnout, and ensures patients receive timely care. When optimized effectively, such systems also allow physicians more control over their schedules—a key factor in reducing burnout.

AI and Emotional Well-being

Although often overlooked, emotional exhaustion plays a critical role in burnout. Artificial intelligence in health doesn’t just address workload; it can also play a part in supporting physician mental health.

AI-powered mental health apps designed specifically for healthcare professionals offer confidential support, stress-reduction strategies, and personalized interventions. Tools like Ginger or Headspace Health use machine learning algorithms to track mood and behavior patterns, recommending tailored mindfulness and coping techniques to help physicians manage stress in real time.

Addressing Skepticism and Building Trust

Despite its promise, artificial intelligence in health is met with skepticism by some physicians who worry about losing clinical autonomy or patient trust. The key to overcoming this is transparency. When AI solutions are co-developed with clinicians, explainable in their function, and integrated seamlessly into existing workflows, adoption rates soar.

At the Mayo Clinic, AI solutions are developed in partnership with frontline providers, ensuring that the technology complements rather than replaces human expertise. Physicians are more likely to embrace these tools when they feel like collaborators rather than observers.

The Road Ahead: Collaboration Over Replacement

Artificial intelligence in health should be seen as a collaborative partner, not a competitor. By reassigning tedious tasks, supporting clinical decisions, and enhancing the overall patient journey, AI empowers physicians rather than displaces them.

Policy makers, hospital administrators, and tech developers must come together to ensure these solutions are accessible, secure, and designed around physician needs. Reimbursement policies and training programs should also evolve to support AI adoption, ensuring that doctors are equipped to use the tools that are built to help them.

Conclusion

America’s physician burnout crisis isn’t going away overnight, but artificial intelligence in health offers a tangible, tech-driven path forward. From documentation relief to mental health support, AI is not just a tool—it’s a catalyst for systemic reform. If embraced thoughtfully and strategically, artificial intelligence in health can restore balance, reignite purpose, and preserve the most valuable asset in our healthcare system: our physicians.

Uncover the latest trends and insights with our articles on Visionary Vogues

[technologyequality]

AI-Powered Decision-Making: How to Execute with Precision and Confidence

AI-Powered Decision-Making How to Execute with Precision and Confidence Scaling a business is one thing, but making the right decisions at the right time? That’s the real challenge. We’ve already explored AI-powered leadership, customer experience, innovation, and strategic planning. Now, it’s time to connect the dots and focus on something that determines whether all of those efforts succeed…

[jcmarchi]

Robert Pierce, Co-Founder & Chief Science Officer at DecisionNext – Interview Series

New Post has been published on https://thedigitalinsider.com/robert-pierce-co-founder-chief-science-officer-at-decisionnext-interview-series/

Robert Pierce, Co-Founder & Chief Science Officer at DecisionNext – Interview Series

Bob Pierce, PhD is co-founder and Chief Science Officer at DecisionNext. His work has brought advanced mathematical analysis to entirely new markets and industries, improving the way companies engage in strategic decision making. Prior to DecisionNext, Bob was Chief Scientist at SignalDemand, where he guided the science behind its solutions for manufacturers. Bob has held senior research and development roles at Khimetrics (now SAP) and ConceptLabs, as well as academic posts with the National Academy of Sciences, Penn State University, and UC Berkeley. His work spans a range of industries including commodities and manufacturing and he’s made contributions to the fields of econometrics, oceanography, mathematics, and nonlinear dynamics. He holds numerous patents and is the author of several peer reviewed papers. Bob holds a PhD in theoretical physics from UC Berkeley.

DecisionNext is a data analytics and forecasting company founded in 2015, specializing in AI-driven price and supply forecasting. The company was created to address the limitations of traditional “black box” forecasting models, which often lacked transparency and actionable insights. By integrating AI and machine learning, DecisionNext provides businesses with greater visibility into the factors influencing their forecasts, helping them make informed decisions based on both market and business risk. Their platform is designed to improve forecasting accuracy across the supply chain, enabling customers to move beyond intuition-based decision-making.

What was the original idea or inspiration behind founding DecisionNext, and how did your background in theoretical physics and roles in various industries shape this vision?

My co-founder Mike Neal and I have amassed a lot of experience in our previous companies delivering optimization and forecasting solutions to retailers and commodity processors. Two primary learnings from that experience were:

Users need to believe that they understand where forecasts and solutions are coming from; and

Users have a very hard time separating what they think will happen from the likelihood that it will actually come to pass.

These two concepts have deep origins in human cognition as well as implications in how to create software to solve problems. It’s well known that a human mind is not good at calculating probabilities. As a Physicist, I learned to create conceptual frameworks to engage with uncertainty and build distributed computational platforms to explore it. This is the technical underpinning of our solutions to help our customers make better decisions in the face of uncertainty, meaning that they cannot know how markets will evolve but still have to decide what to do now in order to maximize profits in the future.

How has your transition to the role of Chief Science Officer influenced your day-to-day focus and long-term vision for DecisionNext?

The transition to CSO has involved a refocusing on how the product should deliver value to our customers. In the process, I have released some day to day engineering responsibilities that are better handled by others. We always have a long list of features and ideas to make the solution better, and this role gives me more time to research new and innovative approaches.

What unique challenges do commodities markets present that make them particularly suited—or resistant—to the adoption of AI and machine learning solutions?

Modeling commodity markets presents a fascinating mix of structural and stochastic properties. Combining this with the uncountable number of ways that people write contracts for physical and paper trading and utilize materials in production results in an incredibly rich and complicated field. Yet, the math is considerably less well developed than the arguably simpler world of stocks. AI and machine learning help us work through this complexity by finding more efficient ways to model as well as helping our users navigate complex decisions.

How does DecisionNext balance the use of machine learning models with the human expertise critical to commodities decision-making?

Machine learning as a field is constantly improving, but it still struggles with context and causality. Our experience is that there are aspects of modeling where human expertise and supervision are still critical to generate robust, parsimonious models. Our customers generally look at markets through the lens of supply and demand fundamentals. If the models do not reflect that belief (and unsupervised models often do not), then our customers will generally not develop trust. Crucially, users will not integrate untrusted models into their day to day decision processes. So even a demonstrably accurate machine learning model that defies intuition will become shelfware more likely than not.

Human expertise from the customer is also critical because it is a truism that observed data is never complete, so models represent a guide and should not be mistaken for reality. Users immersed in markets have important knowledge and insight that is not available as input to the models. DecisionNext AI allows the user to augment model inputs and create market scenarios. This builds flexibility into forecasts and decision recommendations and enhances user confidence and interaction with the system.

Are there specific breakthroughs in AI or data science that you believe will revolutionize commodity forecasting in the coming years, and how is DecisionNext positioning itself for those changes?

The advent of functional LLMs is a breakthrough that will take a long time to fully percolate into the fabric of business decisions. The pace of improvements in the models themselves is still breathtaking and difficult to keep up with. However, I think we are only at the beginning of the road to understanding the best ways to integrate AI into business processes. Most of the problems we encounter can be framed as optimization problems with complicated constraints. The constraints within business processes are often undocumented and contextually rather than rigorously enforced. I think this area is a huge untapped opportunity for AI to both discover implicit constraints in historical data, as well as build and solve the appropriate contextual optimization problems.

DecisionNext is a trusted platform to solve these problems and provide easy access to critical information and forecasts. DecisionNext is developing LLM based agents to make the system easier to use and perform complicated tasks within the system at the user’s direction. This will allow us to scale and add value in more business processes and industries.

Your work spans fields as diverse as oceanography, econometrics, and nonlinear dynamics. How do these interdisciplinary insights contribute to solving problems in commodities forecasting?

My diverse background informs my work in three ways. First, the breadth of my work has prohibited me from going too deep into one specific area of Math. Rather I’ve been exposed to many different disciplines and can draw on all of them. Second, high performance distributed computing has been a through line in all the work I’ve done. Many of the techniques I used to cobble together ad hoc compute clusters as a grad student in Physics are used in mainstream frameworks now, so it all feels familiar to me even when the pace of innovation is rapid. Last, working on all these different problems inspires a philosophical curiosity. As a grad student, I never contemplated working in Economics but here I am. I don’t know what I’ll be working on in 5 years, but I know I’ll find it intriguing.

DecisionNext emphasizes breaking out of the ‘black box’ model of forecasting. Why is this transparency so critical, and how do you think it impacts user trust and adoption?

A prototypical commodities trader (on or off an exchange) is someone who learned the basics of their industry in production but has a skill for betting in a volatile market. If they don’t have real world experience in the supply side of the business, they don’t earn the trust of executives and don’t get promoted as a trader. If they don’t have some affinity for gambling, they stress out too much in executing trades. Unlike Wall Street quants, commodity traders often don’t have a formal background in probability and statistics. In order to gain trust, we have to present a system that is intuitive, fast, and touches their cognitive bias that supply and demand are the primary drivers of large market movements. So, we take a “white box” approach where everything is transparent. Usually there’s a “dating” phase where they look deep under the hood and we guide them through the reasoning of the system. Once trust is established, users don’t often spend the time to go deep, but will return periodically to interrogate important or surprising forecasts.

How does DecisionNext’s approach to risk-aware forecasting help companies not just react to market conditions but proactively shape their strategies?

Commodities trading isn’t limited to exchanges. Most companies only have limited access to futures to hedge their risk. A processor might buy a listed commodity as a raw material (cattle, perhaps), but their output is also a volatile commodity (beef) that often has little price correlation with the inputs. Given the structural margin constraint that expensive facilities have to operate near capacity, processors are forced to have a strategic plan that looks out into the future. That is, they cannot safely operate entirely in the spot market, and they have to contract forward to buy materials and sell outputs. DecisionNext allows the processor to forecast the entire ecosystem of supply, demand, and price variables, and then to simulate how business decisions are affected by the full range of market outcomes. Paper trading may be a component of the strategy, but most important is to understand material and sales commitments and processing decisions to ensure capacity utilization. DecisionNext is tailor made for this.

As someone with a deep scientific background, what excites you most about the intersection of science and AI in transforming traditional industries like commodities?

Behavioral economics has transformed our understanding of how cognition affects business decisions. AI is transforming how we can use software tools to support human cognition and make better decisions. The efficiency gains that will be realized by AI enabled automation have been much discussed and will be economically important. Commodity companies operate with razor thin margins and high labor costs, so they presumably will benefit greatly from automation. Beyond that, I believe there is a hidden inefficiency in the way that most  business decisions are made by intuition and rules of thumb. Decisions are often based on limited and opaque information and simple spreadsheet tools. To me, the most exciting outcome is for platforms like DecisionNext to help transform the business process using AI and simulation to normalize context and risk aware decisions based on transparent data and open reasoning.

Thank you for the great interview, readers who wish to learn more should visit DecisionNext.

[zapperrr]

Harnessing the Power of Artificial Intelligence in Web Development

[industrydesignservices]

Surprising AI data that show how these technologies will affect many facets of our lives in the coming years are presented. To know more about browse: https://teksun.com/ Contact us ID: marketing@teksun.com

[talkthru]

AI Revolution: Transforming Mental Health Accessibility

In recent years, Artificial Intelligence (AI) has been transforming various industries, and mental health care is no exception. With the increasing prevalence of mental health issues and the growing demand for accessible and affordable services, AI has emerged as a powerful tool in revolutionizing the way we approach mental health support. Here's how AI is making mental health more convenient and affordable:

Personalized Virtual Assistants

Early Detection and Intervention

Online Therapy Platforms

AI-Enhanced Diagnosis and Treatment

Automated Cognitive Behavioral Therapy (CBT)

Remote Monitoring and Support

In conclusion, AI is revolutionizing mental health care, making it more convenient and affordable for millions of people worldwide. With personalized virtual assistants, early detection capabilities, online therapy platforms, automated CBT, AI-enhanced diagnosis, and remote monitoring, AI is empowering individuals to take charge of their mental well-being and promoting a more inclusive and accessible mental health support system.

[gghostwriter]

Language of Devotion

Spencer Reid x Fem! Reader

Summary: You caught Spencer learning a new skill—your native language

Trope: Fluff! just fluff

Warning: Language learning app inaccuracies, that’s it really. I wrote this in a frenzy and no proofreading was done

Main masterlist

At around 6:30pm, you arrived at your boyfriend’s apartment complex with takeout on hand. The whole day you’ve spent slumped on your office desk, slaving away on documents that needed your attention and wishing time would move faster. You were knackered and planned to spend the rest of the evening charging within your boyfriend’s arms. You knocked twice on his mahogany apartment door but there was no answer.

“Spence. Spence,” you called out. “You there?”

Silence.

Strange, even though it was a week night, he mentioned that no call came in for a case—strictly paperwork day. You juggled the takeout to your other hand as you reached into your bag for the spare key with slight difficulty.

As you let yourself in the apartment, a ping sound echoed in the confined space. The source of the noise coming in from the bedroom door that was slightly ajar. You quietly placed all your items on the dining table and crept towards the room at the further end of the apartment.

Heart beating loudly on your chest, you peeked inside the room and breathed a sigh of relief. It was Spencer, hunched over his desk, furiously scribbling on a notebook and his phone light reflecting on his glasses.

“Hey Spencer,” you lovingly greeted and although you’ve already announced your presence multiple times earlier on, the sound of your voice made him jump and if you didn’t know any better, a whimper of fright also escaped his lips—he’d deny this, of course.

“Hey, Y/N,” he raked his hand through his hair. “I didn’t hear you come in.”

You smiled coyly. “Y’know for an agent, you’re awfully jumpy.”

He laughed, the tone of his voice warming your heart. “I was just busy with something,” his hands closing the notebook and pushing it aside, as if he didn’t want you to see what had occupied the entire capacity of his brain.

That intrigued you. Spencer wasn’t really the type to keep things hidden from you unless it’s case related and in which, he doesn’t bring it back home for him to study. When your relationship started that was one of your laid out boundary and he had respected and agreed to it—the days and nights that he’s not on call were meant to enjoy each other’s company.

You tried to creep closer, curious as to what he was doing. Being adept with your body language, Spencer tried to divert your attention—keyword ‘tried’. “What’s for dinner? I’m starving,” he rubbed his stomach for emphasis.

“I got us some pasta from the Italian place around the block,” you answered, still distracted by the secret contents of his notebook.

He wrapped his arms around you, seemingly intent on manhandling you out to the dining, before his idle phone notified with a green owl flashing on its screen and an automated voice in your first language spoke through the speaker: Dr. Reid, are you still there? Your chapter and lesson progress will not be counted should you exit.

You turned your head to watch Spencer’s cheeks turning pink.

“Spence, are you—are you using Duolingo?” A giggle escaping your lips. “To learn my first language?”

He smiled with a hint of guilt. “Uh—well, research published in Psychological Science indicates that multilingual individuals exhibit better attention control, cognitive flexibility, and problem-solving skills than monolinguals.”

“Uh-huh, that doesn’t explain why you’re learning my first language specifically.”

He caressed your cheek and smiled. “It’s the first language you learned to speak and it’s part of who you are, Y/N. I mean, you entered the US for your job as a translator,” he explained, staring into your eyes as if you were the most important thing in the world—you were, he assured, you and his mom were. “Do you know you only speak in your language when you mumble in your sleep? You dream in a language that I can’t understand and I want to know every side of you. I love you that much.”

You leaned in for a kiss, his care and adoration to you leaking out of him like honey and you were a bee unable to resist the sweetness. “That’s sweet of you, Spencer,” you pulled back and studied his hazel doe eyes as if they hold the key to the universe. “But I have to ask, does this also have something to do with my mom and dad flying in for a visit?”

He nodded. Last month you mentioned to him that your parents were visiting for four days before they fly to New York, where your other sibling was located. “I want them to get to know me and like me as your boyfriend and—and I can’t do that if we can’t understand each other.”

“They can speak English, granted it’s very much broken, but I can translate for you, Spencer, it’s no problem at all.” You assured him. “Plus, you’re a federal agent, that already makes you great in their books. My dad feels relieved that his own daughter is dating someone who could protect her and my mom already likes you—trust me on this. She hears how happy I am when I talk about you.”

“Are you sure?” He clarified again, clearly he was nervous in making a good impression. You were his first girlfriend and he wanted the relationship to last for a long time—forever really, if you’d let him.

“Yes, Spence. If you want, I can teach you the basics just to get you by. Duolingo isn’t really that accurate,” you mentioned as you pulled him out of the bedroom and into the dining. “Now, let’s eat. I’m hungry and the pasta has turned cold.”

He laughed, nodding his head, watching you prep the table as he reheated the pasta based exactly on the packaging instructions.

And on the first night of your parent’s arrival, your mother pulled you aside and smiled. “He’s a keeper, Y/N. Don’t let him get away.”

You laughed as you watched Spencer try his best to communicate with your father in his broken grammar and questionable pronunciation. “I won’t, Mom. I think he’s it for me, really.”