#AI in workforce optimization

[precallai]

Top AI Features Powering Next-Gen Contact Centers

Introduction

The evolution of contact centers from traditional call hubs to intelligent customer engagement platforms is being driven by artificial intelligence (AI). In a hyper-connected world where customers expect fast, personalized, and efficient service, AI is playing a transformative role. From automating routine tasks to offering real-time analytics and sentiment analysis, AI is redefining the standards of customer support. Modern contact centers, powered by AI, are becoming more responsive, proactive, and insightful—enhancing both customer satisfaction and operational efficiency.

This article explores the top AI features that are revolutionizing next-generation contact centers and how they are helping businesses stay competitive in today’s digital landscape.

1. AI-Powered Chatbots and Virtual Assistants

Perhaps the most visible AI application in contact centers is the use of chatbots and virtual assistants. These tools are capable of handling thousands of customer queries simultaneously across various platforms, including websites, mobile apps, and social media.

Key Benefits:

24/7 availability

Immediate responses to FAQs

Reduced workload for human agents

Seamless integration with CRM systems

Advanced AI chatbots use Natural Language Processing (NLP) and Machine Learning (ML) to understand customer queries better and improve over time. They also support multilingual interactions, expanding a business’s global reach.

2. Intelligent Call Routing

Traditional call routing systems use basic algorithms like round-robin or skill-based routing. AI takes this to the next level with predictive routing, which uses historical data and real-time analytics to match customers with the most suitable agents.

Example: If a customer previously had a billing issue and rated a certain agent highly, AI can route future related calls directly to that agent, ensuring a personalized experience.

Benefits:

Enhanced customer satisfaction

Reduced average handling time

Better utilization of agent expertise

3. Speech and Sentiment Analysis

AI-driven sentiment analysis tools assess the tone, pitch, and language of customer conversations in real-time. This allows agents to adapt their approach based on the emotional state of the caller.

Key Capabilities:

Detect frustration or satisfaction

Real-time alerts for supervisors

Contextual response suggestions for agents

This not only helps in de-escalating potential conflicts but also contributes to training and performance reviews.

4. Real-Time Agent Assistance

AI can provide live suggestions, answers, and prompts to agents during customer interactions. Known as Agent Assist or Co-Pilot systems, these features boost agent efficiency and reduce error rates.

Use Cases:

Auto-suggesting answers based on past tickets or knowledge base

Providing legal or compliance language for regulated industries

Offering upsell/cross-sell suggestions during the call

This enables even less-experienced agents to perform like experts, thereby maintaining service consistency.

5. Predictive and Prescriptive Analytics

Modern AI systems can analyze historical customer data to predict future behaviors and offer prescriptive actions. For example, AI can forecast customer churn and suggest personalized retention strategies.

Key Features:

Trend identification

Churn prediction

Customer lifetime value estimation

Product recommendation modeling

These analytics turn contact centers from reactive to proactive units that can anticipate customer needs and take preventive measures.

6. Automated Quality Monitoring

Quality assurance (QA) in traditional contact centers involves manual listening to a random sample of calls. AI changes this by automatically analyzing 100% of customer interactions for compliance, tone, and performance metrics.

Advantages:

Scalable and unbiased QA process

Immediate feedback loops

Identification of training opportunities

This ensures consistent service quality and helps businesses remain compliant with industry standards and regulations.

7. AI-Driven Self-Service

Customers increasingly prefer solving issues on their own. AI enables robust self-service solutions through intelligent FAQs, voice assistants, and dynamic help centers.

Core Components:

AI-curated knowledge bases

Interactive voice response (IVR) systems

Visual IVRs with dynamic menus based on customer behavior

These systems can deflect a significant volume of queries, saving time and reducing contact center costs.

8. Workforce Optimization (WFO)

AI enhances workforce optimization by analyzing call volumes, customer demand patterns, and agent performance to create optimized schedules and workloads.

Capabilities Include:

Forecasting peak interaction times

Automating shift scheduling

Identifying training needs through performance data

This ensures that the right number of agents with the right skills are available at the right time.

9. Multilingual Support

With global customer bases, multilingual support is essential. AI translation engines powered by NLP enable real-time language translation, allowing agents to assist customers in multiple languages.

Benefits:

Expanded market reach

Consistent support quality

Reduced need for native-speaking agents

Advanced systems even recognize regional dialects and slang, further enhancing communication accuracy.

10. Omnichannel AI Integration

Today’s customers expect consistent service across phone, email, chat, social media, and more. AI enables omnichannel support by centralizing data and ensuring continuity in customer interactions.

Features Include:

Unified customer profiles

Context-aware responses

Seamless channel transitions (e.g., chat to call)

This creates a cohesive customer experience and provides agents with the full context of past interactions, reducing redundancy and frustration.

Conclusion

AI is not just an enhancement to traditional contact center operations—it is a fundamental driver of their transformation. From handling repetitive tasks to offering deep insights into customer behavior, AI is redefining what’s possible in customer service.

By leveraging AI-powered features like chatbots, intelligent routing, sentiment analysis, and predictive analytics, next-generation contact centers are achieving higher efficiency, better customer satisfaction, and lower operational costs. The focus is shifting from handling calls to delivering experiences, and AI is at the heart of that shift.

Businesses that invest in AI capabilities today will be better positioned to adapt to the growing demands of tomorrow’s customers. As AI continues to evolve, contact centers will become smarter, faster, and more human than ever before—setting a new standard for customer engagement in the digital era.

[daintilyultimateslayer]

mobile service management

Installation and commissioning of Network Infrastructure

Deploying new network infrastructure like fiber optic cables, base stations, and switching centers can be a complex and time-consuming process. But Etaprise Field Service Management (FSM) software can significantly streamline and optimize this process, leading to faster deployments, improved accuracy, and increased efficiency. 

By leveraging Etaprise FSM software, telcos can transform the installation and commissioning of network infrastructure from a complex logistical challenge into a streamlined and efficient process, ultimately leading to faster service delivery, improved customer satisfaction, and cost savings. Here is how.

Planning and Scheduling:

Efficient work order management: Etaprise FSM software creates and tracks work orders, ensuring technicians have the right tasks, equipment, and materials assigned to them.

Optimized scheduling: Algorithms consider technician skill sets, location, and workload, minimizing travel time and maximizing resource utilization.

Real-time updates: Changes in plans or delays are instantly communicated to all stakeholders, ensuring everyone is on the same page.

Improved Field Operations:

Mobile access to work orders and documentation: Technicians have instant access to all necessary information on their mobile devices, eliminating paper-based processes and reducing errors.

Digital checklists and procedures: Etaprise FSM software guides technicians through installation and commissioning steps with step-by-step checklists, ensuring consistency and accuracy.

Automated reporting and data capture: Data on progress, equipment usage, and potential issues is automatically collected for reporting and analysis.

Enhanced Collaboration and Communication:

Real-time communication with supervisors: Technicians can easily report progress, request assistance, and share updates with supervisors in real-time.

Improved customer communication: Customers can track technician arrival times and receive updates on the installation progress through self-service portals and notifications.

Streamlined knowledge sharing: Etaprise FSM platforms can integrate with knowledge bases and training materials, enabling technicians to easily access relevant information and best practices.

We’re here to help

Call us at:

+1 669 777 5279

Email us:

info@etaprise.com

[not-terezi-pyrope]

When I think reflectively about it, I realize that pretty much nothing has damaged my outlook on the world and hope for the future more than the recognition over the past few years that the seeming majority opinion is that automation is bad and meaningfully transforming our economy is a lost cause, to the extent that the working class will actively and aggressively lobby against technological improvements (even beyond the example of AI/ML...)

I don't agree with this assessment and have argued against it lots, but it's clear that I'm in a small minority among my political allies (i.e., leftists, who recognize that there is a problem with our current lives in the first place). Just a few years ago I was naive enough to assume that "automation leads to less work allows us to collectively downsize the workforce and socialize human needs while increasing abundance" was an obvious and noncontroversial progression.

Now even I'm doubting if we'll ever get there, and that's despite trying my damnedest to argue from a position of optimism. Because the fact is that if nobody believes we can make change then change won't happen, and the recognition that that's a plausible future has made me noticeably more cynical and misanthropic. I don't think that society is a lost cause, but the way that leftist orthodoxy is becoming capitalist realism and scapegoating of the technology sector is driving me there fast.

I can't stop thinking that the potential for a better future I see out there is beautiful, but I can sure as hell get more angry and callous as people refuse to reach out for what's there to be taken. Honestly if we do fail to make the leap then maybe that is proof positive of a fatal flaw in humanity.

I recognize that the issue most people are struggling with is the short term labour market disruption from things like automation, but honestly I don't believe that "dismantling capitalism first" is really a feasible option, society only responds to extant pressure and sometimes I am just like, "the best and maybe only way to make change is to create the conditions where it is intolerable not to follow the path of least resistance, by making the impact of not doing that tangible". Maybe an interim period where a bunch of people lose jobs is worth that, revolution has never exactly been easy. Or maybe I'm just being callous, but my point is that it's harder not to be these days. I thought this would all be so much easier before it came to the point of it.

I hope I don't become doompilled past the point of no return. I need to believe there's a better way for the world to be. I need to believe that we can improve people's lives more than we might damage them in the attempt.

I need to believe we can not re-elect the fucking Tories this year for once.

[aaliya-b]

The Rise of AI-Powered SaaS Products in Indian Tech

India's SaaS ecosystem is evolving fast, and AI is now at its core. From customer service to HR to sales, AI-enabled software is automating tasks that once needed full teams.

Ultimez Technology has developed a modular AI product for digital teams—helping clients monitor, analyze, and optimize customer engagement.

Freshworks, headquartered in Chennai, is now globally known for its AI-powered customer support tools. Likewise, Zuper, a SaaS player from Bangalore, uses AI for field workforce optimization.

"SaaS + AI is the new startup formula in India" 

This shift positions Indian SaaS companies as global players, offering affordable, scalable, and intelligent platforms.

[hoursofreading]

Reintroduce friction: We’ve mistaken convenience for progress. Instead of making everything easier, we need to make important things appropriately difficult in the digtital world. Right now, the system rewards the performance of expertise rather than its development. AI makes that worse. We likely will have to rethink education - AI will be a part of it, and right now, the system rewards output over understanding, so of course students are Chat-GPTing essays. Some things will have to be harder than they currently are - maybe getting a college degree should require more than the ability to prompt an AI effectively. (maybe governing a country should require more than generating attention on social media.) Treating attention as infrastructure rather than a market to be optimized. Right now, we treat attention like a commodity. It’s something to be mined, optimized, and sold to the highest bidder. But attention is infrastructure! It’s the invisible highway that ideas, identities, and institutions travel on. We have to invest in it like we would a highway. We’ve built a digital ecosystem that optimizes for engagement, not understanding. As boring as it is, we need new algorithms and to treat attention as a shared utility. Boring things. Most of the ideas that will save us are boring at first. We need to rewire our entire grid, retrain an industrial workforce, and build factories We need more of the Manhattan Project, the Interstate Highway System, DARPA, etc - the confluence of public investment (and probably private investment at this point) and educational training systems that directly train people for the phenomenal task of rebuilding the physical infrastructure of the United States. Rebuild systems that restore stakes. A functioning economic and political system doesn’t need to give everyone the same outcome, but it must give them a stake in the game. Today, vast swaths of the public don’t see policy as cause-and-effect. They see it as a performance, a branding exercise, a series of decisions made for someone else. That’s why Kansas farmers vote for a leader who guts their food export program. And it’s why young people identify as socialists- what’s the alternative? As Peter Thiel once noted: people without a stake in capitalism will rationally turn against it. What we're seeing now is that process playing out. Reestablishing stakes doesn’t mean giving everyone money in the S&P or whatever. It means showing them that effort leads to change and that policy is cause-and-effect.

From Dollar Dominance to the Slop Machine - by kyla scanlon

[edwindanao]

The AI Revolution: Is the Philippines Ready?

By Edwin Danao

The world is buzzing about Artificial Intelligence (AI). From self-driving cars to sophisticated chatbots, AI is rapidly reshaping industries and daily life. But what about the Philippines? Is our nation prepared for this technological revolution, or are we facing a digital divide that could widen the gap between the haves and have-nots?

The potential benefits of AI for the Philippines are immense. Imagine AI-powered tools improving healthcare diagnostics in rural areas, optimizing agricultural yields to boost food security, or creating more efficient and personalized education systems. AI could also drive innovation in our burgeoning BPO industry, creating higher-paying jobs and boosting global competitiveness.

However, the path to realizing this potential is fraught with challenges. Firstly, infrastructure remains a significant hurdle. Reliable internet access is still a luxury for many Filipinos, particularly in rural communities. Without robust connectivity, widespread AI adoption is simply not feasible.

Secondly, data is king in the AI world. The quality and accessibility of data are crucial for training effective AI models. The Philippines needs to invest in data infrastructure and establish clear data governance frameworks to ensure responsible and ethical AI development.

Thirdly, talent is a critical factor. We need to cultivate a skilled workforce capable of developing, deploying, and maintaining AI systems. This requires significant investment in education and training programs, focusing on STEM fields and AI-specific skills.

Finally, ethical considerations cannot be ignored. AI algorithms can perpetuate existing biases, raising concerns about fairness and equity. The Philippines needs to establish strong ethical guidelines to ensure that AI is used responsibly and benefits all citizens.

The Philippines has the potential to be a leader in the AI revolution. Our young, tech-savvy population and growing digital economy provide a fertile ground for innovation. However, we must address the challenges of infrastructure, data, talent, and ethics head-on. Investing in these areas is not just about keeping pace with global trends; it’s about ensuring that the benefits of AI reach every Filipino, bridging the digital divide and building a more inclusive and prosperous future. The question isn't if AI will transform the Philippines, but how we will ensure that transformation is equitable and beneficial for all.

[jcmarchi]

Could AI slow science?

New Post has been published on https://thedigitalinsider.com/could-ai-slow-science/

Could AI slow science?

AI leaders have predicted that it will enable dramatic scientific progress: curing cancer, doubling the human lifespan, colonizing space, and achieving a century of progress in the next decade. Given the cuts to federal funding for science in the U.S., the timing seems perfect, as AI could replace the need for a large scientific workforce.

It’s a common-sense view, at least among technologists, that AI will speed science greatly as it gets adopted in every part of the scientific pipeline — summarizing existing literature, generating new ideas, performing data analyses and experiments to test them, writing up findings, and performing “peer” review.

But many early common-sense predictions about the impact of a new technology on an existing institution proved badly wrong. The Catholic Church welcomed the printing press as a way of solidifying its authority by printing Bibles. The early days of social media led to wide-eyed optimism about the spread of democracy worldwide following the Arab Spring.

Similarly, the impact of AI on science could be counterintuitive. Even if individual scientists benefit from adopting AI, it doesn’t mean science as a whole will benefit. When thinking about the macro effects, we are dealing with a complex system with emergent properties. That system behaves in surprising ways because it is not a market. It is better than markets at some things, like rewarding truth, but worse at others, such as reacting to technological shocks. So far, on balance, AI has been an unhealthy shock to science, stretching many of its processes to the breaking point.

Any serious attempt to forecast the impact of AI on science must confront the production-progress paradox. The rate of publication of scientific papers has been growing exponentially, increasing 500 fold between 1900 and 2015. But actual progress, by any available measure, has been constant or even slowing. So we must ask how AI is impacting, and will impact, the factors that have led to this disconnect.

Our analysis in this essay suggests that AI is likely to worsen the gap. This may not be true in all scientific fields, and it is certainly not a foregone conclusion. By carefully and urgently taking actions such as those we suggest below, it may be possible to reverse course. Unfortunately, AI companies, science funders, and policy makers all seem oblivious to what the actual bottlenecks to scientific progress are. They are simply trying to accelerate production, which is like adding lanes to a highway when the slowdown is actually caused by a toll booth. It’s sure to make things worse.

1. Science has been slowing — the production-progress paradox

2. Why is progress slowing? Can AI help?

3. Science is not ready for software, let alone AI

4. AI might prolong the reliance on flawed theories

5. Human understanding remains essential

6. Implications for the future of science

7. Final thoughts

The total number of published papers is increasing exponentially, doubling every 12 years. The total number of researchers who have authored a research paper is increasing even more quickly. And between 2000 and 2021, investment in research and development increased fourfold across the top seven funders (the US, China, Japan, Germany, South Korea, the UK, and France).

But does this mean faster progress? Not necessarily. Some papers lead to fundamental breakthroughs that change the trajectory of science, while others make minor improvements to known results.

Genuine progress results from breakthroughs in our understanding. For example, we understood plate tectonics in the middle of the last century — the idea that the continents move. Before that, geologists weren’t even able to ask the right questions. They tried to figure out the effects of the cooling of the Earth, believing that that’s what led to geological features such as mountains. No amount of findings or papers in older paradigms of geology would have led to the same progress that plate tectonics did.

So it is possible that the number of papers is increasing exponentially while progress is not increasing at the same rate, or is even slowing down. How can we tell if this is the case?

One challenge in answering this question is that, unlike the production of research, progress does not have clear, objective metrics. Fortunately, an entire research field — the “science of science“, or metascience — is trying to answer this question. Metascience uses the scientific method to study scientific research. It tackles questions like: How often can studies be replicated? What influences the quality of a researcher’s work? How do incentives in academia affect scientific outcomes? How do different funding models for science affect progress? And how quickly is progress really happening?

Left: The number of papers authored and authors of research papers have been increasing exponentially (from Dong et al., redrawn to linear scale using a web plot digitizer). Right: The disruptiveness of papers is declining over time (from Park et al.).

Strikingly, many findings from metascience suggest that progress has been slowing down, despite dramatic increases in funding, the number of papers published, and the number of people who author scientific papers. We collect some evidence below; Matt Clancy reviews many of these findings in much more depth.

1) Park et al. find that “disruptive” scientific work represents an ever-smaller fraction of total scientific output. Despite an exponential increase in the number of published papers and patents, the number of breakthroughs is roughly constant.

2) Research that introduces new ideas is more likely to coin new terms. Milojevic collects the number of unique phrases used in titles of scientific papers over time as a measure of the “cognitive extent” of science, and finds that while this metric increased up until the early 2000s, it has since entered a period of stagnation, when the number of unique phrases used in titles of research papers has gone down.

3) Patrick Collison and Michael Nielsen surveyed researchers across fields on how they perceived progress in the most important breakthroughs in their fields over time — those that won a Nobel prize. They asked scientists to compare Nobel-prize-winning research from the 1910s to the 1980s.

They found that scientists considered advances from earlier decades to be roughly as important as the ones from more recent decades, across Medicine, Physics, and Chemistry. Despite the vast increases in funding, published papers, and authors, the most important breakthroughs today are about as impressive as those in the decades past.

4) Matt Clancy complements this with an analysis of what fraction of discoveries that won a Nobel Prize in a given year were published in the preceding 20 years. He found that this number dropped from 90% in 1970 to 50% in 2015, suggesting that either transformative discoveries are happening at a slower pace, or that it takes longer for discoveries to be recognized as transformative.

Share of papers describing each year’s Nobel-prize winning work that were published in the preceding 20 years. 10-year moving average. Source: Clancy based on data from Li et al.

5) Bloom et al. analyze research output from an economic perspective. Assuming that economic growth ultimately comes from new ideas, the constant or declining rate of growth implies that the exponential increase in the number of researchers is being offset by a corresponding decline in the output per researcher. They find that this pattern holds true when drilling down into specific areas, including semiconductors, agriculture, and medicine (where the progress measures are Moore’s law, crop yield growth, and life expectancy, respectively).

The decline of research productivity. Note that economists use “production” as a catch-all term, with paper and patent counts, growth, and other metrics being different ways to measure it. We view production and progress as fundamentally different constructs, so we use the term production in a narrower sense. Keep in mind that in the figure, “productivity” isn’t based on paper production but on measures that are better viewed as progress measures. Source: Bloom et al.

Of course, there are shortcomings in each of the metrics above. This is to be expected: since progress doesn’t have an objective metric, we need to rely on proxies for measuring it, and these proxies will inevitably have some flaws.

For example, Park et al. used citation patterns to flag papers as “disruptive”: if follow-on citations to a given paper don’t also cite the studies this paper cited, the paper is more likely to be considered disruptive. One criticism of the paper is that this could simply be a result of how citation practices have evolved over time, not a result of whether a paper is truly disruptive. And the metric does flag some breakthroughs as non-disruptive — for example, AlphaFold is not considered a disruptive paper by this metric.

But taken together, the findings do suggest that scientific progress is slowing down, at least compared to the volume of papers, researchers, and resources. Still, this is an area where further research would be fruitful — while the decline in the pace of progress relative to inputs seems very clear, it is less clear what is happening at an aggregate level. Furthermore, there are many notions of what the goals of science are and what progress even means, and it is not clear how to connect the available progress measures to these higher-level definitions.

Summary of a few major lines of evidence of the slowdown in scientific progress

There are many hypotheses for why progress could be slowing. One set of hypotheses is that slowdown is an intrinsic feature of scientific progress, and is what we should expect. For example, there’s the low-hanging fruit hypothesis — the easy scientific questions have already been answered, so what remains to be discovered is getting harder.

This is an intuitively appealing idea. But we don’t find this convincing. Adam Mastroianni gives many compelling counter-arguments. He points out that we’ve been wrong about this over and over and lists many comically mis-timed assessments of scientific fields reaching saturation just before they ended up undergoing revolutions, such as physics in the 1890s.

While it’s true that lower-hanging fruits get picked first, there are countervailing factors. Over time, our scientific tools improve and we stand on the tower of past knowledge, making it easier to reach higher. Often, the benefits of improved tools and understanding are so transformative that whole new fields and subfields are created. New fields from the last 50-100 years include computer science, climate science, cognitive neuroscience, network science, genetics, molecular biology, and many others. Effectively, we’re plucking fruit from new trees, so there is always low-hanging fruit.

In our view, the low-hanging fruit hypothesis can at best partly explain slowdowns within fields. So it’s worth considering other ideas.

The second set of hypotheses is less fatalistic. They say that there’s something suboptimal about the way we’ve structured the practice of science, and so the efficiency of converting scientific inputs into progress is dropping. In particular, one subset of hypotheses flags the increase in the rate of production itself as the causal culprit — science is slowing down because it is trying to go too fast.

How could this be? The key is that any one scientist’s attention is finite, so they can only pay attention to a limited number of papers every year. So it is too risky for authors of papers to depart from the canon. Any such would-be breakthrough papers would be lost in the noise and won’t get the attention of a critical mass of scholars. The greater the rate of production, the more the noise, so the less attention truly novel papers will achieve, and thus will be less likely to break through into the canon.

Chu and Evans explain:

when the number of papers published each year grows very large, the rapid flow of new papers can force scholarly attention to already well-cited papers and limit attention for less-established papers—even those with novel, useful, and potentially transformative ideas. Rather than causing faster turnover of field paradigms, a deluge of new publications entrenches top-cited papers, precluding new work from rising into the most-cited, commonly known canon of the field.

These arguments, supported by our empirical analysis, suggest that the scientific enterprise’s focus on quantity may obstruct fundamental progress. This detrimental effect will intensify as the annual mass of publications in each field continues to grow

Another causal mechanism relates to scientists’ publish-or-perish incentives. Production is easy to measure, and progress is hard to measure. So universities and other scientific institutions judge researchers based on measurable criteria such as how many papers they publish and the amount of grant funding they receive. It is not uncommon for scientists to have to publish a certain number of peer-reviewed papers to be hired or to get tenure (either due to implicit norms or explicit requirements).

The emphasis on production metrics seems to be worsening over time. Physics Nobel winner Peter Higgs famously noted that he wouldn’t even have been able to get a job in modern academia because he wouldn’t be considered productive enough.

So individual researchers’ careers might be better off if they are risk averse, but it might reduce the collective rate of progress. Rzhetsky et al. find evidence of this phenomenon in biomedicine, where experiments tend to focus too much on experimenting with known molecules that are already considered important (which would be more likely to lead to publishing a paper) rather than more risky experiments that could lead to genuine breakthroughs. Worryingly, they find this phenomenon worsening over time.

This completes the feedback loop: career incentives lead to researchers publishing more papers, and disincentivize novel research that results in true breakthroughs (but might only result in a single paper after years of work).

If slower progress is indeed being caused by faster production, how will AI impact it? Most obviously, automating parts of the scientific process will make it even easier for scientists to chase meaningless productivity metrics. AI could make individual researchers more creative but decrease the creativity of the collective because of a homogenizing effect. AI could also exacerbate the inequality of attention and make it even harder for new ideas to break through. Existing search technology, such as Google Scholar, seems to be having exactly this effect.

To recap, so far we’ve argued that if the slowdown in science is caused by overproduction, AI will make it worse. In the next few sections, we’ll discuss why AI could worsen the slowdown regardless of what’s causing it.

How do researchers use AI? In many ways: AI-based modeling to uncover trends in data using sophisticated pattern-matching algorithms; hand-written machine learning models specified based on expert knowledge; or even generative AI to write the code that researchers previously wrote. While some applications, such as using AI for literature review, don’t involve writing code, most applications of AI for science are, in essence, software development.

Unfortunately, scientists are notoriously poor software engineers. Practices that are bog-standard in the industry, like automated testing, version control, and following programming design guidelines, are largely absent or haphazardly adopted in the research community. These are practices that were developed and standardized over the last six decades of software engineering to prevent bugs and ensure the software works as expected.

Worse, there is little scrutiny of the software used in scientific studies. While peer review is a long and arduous step in publishing a scientific paper, it does not involve reviewing the code accompanying the paper, even though most of the “science” in computational research is being carried out in the code and data accompanying a paper, and only summarized in the paper itself.

In fact, papers often fail to even share the code and data used to generate results, so even if other researchers are willing to review the code, they don’t have the means to. Gabelica et al. found that of 1,800 biomedical papers that pledged to share their data and code, 93% did not end up sharing these artifacts. This even affects results in the most prominent scientific journals: Stodden et al. contacted the authors of 204 papers published in Science, one of the top scientific journals, to get the code and data for their study. Only 44% responded.

When researchers do share the code and data they used, it is often disastrously wrong. Even simple tools, like Excel, have notoriously led to widespread errors in various fields. A 2016 study found that one in five genetics papers suffer from Excel-related errors, for example, because the names of genes (say, Septin 2) were automatically converted to dates (September 2). Similarly, it took decades for most scientific communities to learn how to use simple statistics responsibly.

AI opens a whole new can of worms. The AI community often advertises AI as a silver bullet without realizing how difficult it is to detect subtle errors. Unfortunately, it takes much less competence to use AI tools than to understand them deeply and learn to identify errors. Like other software-based research, errors in AI-based science can take a long time to uncover. If the widespread adoption of AI leads to researchers spending more time and effort conducting or building on erroneous research, it could slow progress, since researcher time and effort are wasted in unproductive research directions.

Unfortunately, we’ve found that AI has already led to widespread errors. Even before generative AI, traditional machine learning led to errors in over 600 papers across 30 scientific fields. In many cases, the affected papers constituted the majority of the surveyed papers, raising the possibility that in many fields, the majority of AI-enabled research is flawed. Others have found that AI tools are often used with inappropriate baseline comparisons, making it incorrectly seem like they outperform older methods. These errors are not just theoretical: they affect the potential real-world deployment of AI too. For example, Roberts et al. found that of 400+ papers using AI for COVID-19 diagnosis, none produced clinically useful tools due to methodological flaws.

Applications of generative AI can result in new types of errors. For example, while AI can aid in programming, code generated using AI often has errors. As AI adoption increases, we will discover more applications of AI for science. We suspect we’ll find widespread errors in many of these applications.

Why is the scientific community so far behind software engineering best practices? In engineering applications, bugs are readily visible through tests, or in the worst case, when they are deployed to customers. Companies have strong incentives to fix errors to maintain the quality of their applications, or else they will lose market share. As a result, there is a strong demand for software engineers with deep expertise in writing good software (and now, in using AI well). This is why software engineering practices in the industry are decades ahead of those in research. In contrast, there are few incentives to correct flawed scientific results, and errors often persist for years.

That is not to say science should switch from a norms-based to a market-based model. But it shouldn’t be surprising that there are many problems markets have solved that science hasn’t — such as developing training pipelines for software engineers. Where such gaps between science and the industry emerge, scientific institutions need to intentionally adopt industry best practices to ensure science continues to innovate, without losing what makes science special.

In short, science needs to catch up to a half century of software engineering — fast. Otherwise, its embrace of AI will lead to an avalanche of errors and create headwinds, not tailwinds for progress.

AI could help too. There are many applications of AI to spot errors. For example, the Black Spatula project and the YesNoError project use AI to uncover flaws in research papers. In our own work, we’ve developed benchmarks aiming to spur the development of AI agents that automatically reproduce papers. Given the utility of generative AI for writing code, AI itself could be used to improve researchers’ software engineering practices, such as by providing feedback, suggestions, best practices, and code reviews at scale. If such tools become reliable and see widespread adoption, AI could be part of the solution by helping avoid wasted time and effort building on erroneous work. But all of these possibilities require interventions from journals, institutions, and funding agencies to incentivize training, synthesis, and error detection rather than production alone.

One of the main uses of AI for science is modeling. Older modeling techniques required coming up with a hypothesis for how the world works, then using statistical models to make inferences about this hypothesis.

In contrast, AI-based modeling treats this process as a black box. Instead of making a hypothesis about the world and improving our understanding based on the model’s results, it simply tries to improve our ability to predict what outcomes would occur based on past data.

Leo Breiman illustrated the differences between these two modeling approaches in his landmark paper “Statistical Modeling: The Two Cultures”. He strongly advocated for AI-based modeling, often on the basis of his experience in the industry. A focus on predictive accuracy is no doubt helpful in the industry. But it could hinder progress in science, where understanding is crucial.

Why? In a recent commentary in the journal Nature, we illustrated this with an analogy to the geocentric model of the Universe in astronomy. The geocentric model of the Universe—the model of the Universe with the Earth at the center—was very accurate at predicting the motion of planets. Workarounds like “epicycles” made these predictions accurate. (Epicycles were the small circles added to the planet’s trajectory around the Earth).

Whenever a discrepancy between the model’s predictions and the experimental readings was observed, astronomers added an epicycle to improve the model’s accuracy. The geocentric model was so accurate at predicting planets’ motions that many modern planetariums still use it to compute planets’ trajectories.

Left: The geocentric model of the Universe eventually became extremely complex due to the large number of epicycles. Right: The heliocentric model was far simpler.

How was the geocentric model of the Universe overturned in favor of the heliocentric model — the model with the planets revolving around the Sun? It couldn’t be resolved by comparing the accuracy of the two models, since the accuracy of the models was similar. Rather, it was because the heliocentric model offered a far simpler explanation for the motion of planets. In other words, advancing from geocentrism to heliocentrism required a theoretical advance, rather than simply relying on the more accurate model.

This example shows that scientific progress depends on advances in theory. No amount of improvements in predictive accuracy could get us to the heliocentric model of the world without updating the theory of how planets move.

Let’s come back to AI for science. AI-based modeling is no doubt helpful in improving predictive accuracy. But it doesn’t lend itself to an improved understanding of these phenomena. AI might be fantastic at producing the equivalents of epicycles across fields, leading to the prediction-explanation fallacy.

In other words, if AI allows us to make better predictions from incorrect theories, it might slow down scientific progress if this results in researchers using flawed theories for longer. In the extreme case, fields would be stuck in an intellectual rut even as they excel at improving predictive accuracy within existing paradigms.

Could advances in AI help overcome this limitation? Maybe, but not without radical changes to modeling approaches and technology, and there is little incentive for the AI industry to innovate on this front. So far, improvements in predictive accuracy have greatly outpaced improvements in the ability to model the underlying phenomena accurately.

Prediction without understanding: Vafa et al. show that a transformer model trained on 10 million planetary orbits excels at predicting orbits without figuring out the underlying gravitational laws that produce those orbits.

In solving scientific problems, scientists build up an understanding of the phenomena they study. It might seem like this understanding is just a way to get to the solution. So if we can automate the process of going from problem to solution, we don’t need the intermediate step.

The reality is closer to the opposite. Solving problems and writing papers about them can be seen as a ritual that leads to the real prize, human understanding, without which there can be no scientific progress.

Fields Medal-winning mathematician William Thurston wrote an essay brilliantly illustrating this. At the outset, he emphasizes that the point of mathematics is not simply to figure out the truth value for mathematical facts, but rather the accompanying human understanding:

…what [mathematicians] are doing is finding ways for people to understand and think about mathematics.

The rapid advance of computers has helped dramatize this point, because computers and people are very different. For instance, when Appel and Haken completed a proof of the 4-color map theorem using a massive automatic computation, it evoked much controversy. I interpret the controversy as having little to do with doubt people had as to the veracity of the theorem or the correctness of the proof. Rather, it reflected a continuing desire for human understanding of a proof, in addition to knowledge that the theorem is true.

On a more everyday level, it is common for people first starting to grapple with computers to make large-scale computations of things they might have done on a smaller scale by hand. They might print out a table of the first 10,000 primes, only to find that their printout isn’t something they really wanted after all. They discover by this kind of experience that what they really want is usually not some collection of “answers”—what they want is understanding. [emphasis in original]

He then describes his experience as a graduate student working on the theory of foliations, a center of attention among many mathematicians. After he proved a number of papers on the most important theorems in the field, counterintuitively, people began to leave the field:

I heard from a number of mathematicians that they were giving or receiving advice not to go into foliations—they were saying that Thurston was cleaning it out. People told me (not as a complaint, but as a compliment) that I was killing the field. Graduate students stopped studying foliations, and fairly soon, I turned to other interests as well.

I do not think that the evacuation occurred because the territory was intellectually exhausted—there were (and still are) many interesting questions that remain and that are probably approachable. Since those years, there have been interesting developments carried out by the few people who stayed in the field or who entered the field, and there have also been important developments in neighboring areas that I think would have been much accelerated had mathematicians continued to pursue foliation theory vigorously.

Today, I think there are few mathematicians who understand anything approaching the state of the art of foliations as it lived at that time, although there are some parts of the theory of foliations, including developments since that time, that are still thriving.

Two things led to this desertion. First, the results he documented were written in a way that was hard to understand. This discouraged newcomers from entering the field. Second, even though the point of mathematics is building up human understanding, the way mathematicians typically get credit for their work is by proving theorems. If the most prominent results in a field have already been proven, that leaves few incentives for others to understand a field’s contributions, because they can’t prove further results (which would ultimately lead to getting credit).

In other words, researchers are incentivized to prove theorems. More generally, researchers across fields are incentivized to find solutions to scientific problems. But this incentive only leads to progress because the process of proving theorems or finding solutions to problems also leads to building human understanding. As the desertion of work on foliations shows, when there is a mismatch between finding solutions to problems and building human understanding, it can result in slower progress.

This is precisely the effect AI might have: by solving open research problems without leading to the accompanying understanding, AI could erode these useful byproducts by reducing incentives to build understanding. If we use AI to short circuit this process of understanding, that is like using a forklift at the gym. You can lift heavier weights with it, sure, but that’s not why you go to the gym.

AI could short circuit the process of building human understanding, which is essential to scientific progress

Of course, mathematics might be an extreme case, because human understanding is the end goal of (pure) mathematics, not simply knowing the truth value of mathematical statements. This might not be the case for many applications of science, where the end goal is to make progress towards a real-world outcome rather than human understanding, say, weather forecasting or materials synthesis.

Most fields lie in between these two extremes. If we use AI to bypass human understanding, or worse, retain only illusions of understanding, we might lose the ability to train new scientists, develop new theories and paradigms, synthesize and correct results, apply knowledge beyond science, or even generate new and interesting problems.

Empirical evidence across scientific fields has found evidence for some of these effects. For example, Hao et al. collect data from six fields and find that papers that adopt AI are more likely to focus on providing solutions to known problems and working within existing paradigms rather than generating new problems.

Of course, AI can also be used to build up tacit knowledge, such as by helping people understand mathematical proofs or other scientific knowledge. But this requires fundamental changes to how science is organized. Today’s career incentives and social norms prize solutions to scientific problems over human understanding. As AI adoption accelerates, we need changes to incentives to make sure human understanding is prioritized.

Over the last decade, scientists have been in a headlong rush to adopt AI. The speed has come at the expense of any ability to adapt slow-moving scientific institutional norms to maintain quality control and identify and preserve what is essentially human about science. As a result, the trend is likely to worsen the production-progress paradox, accelerating paper publishing but only digging us deeper into the hole with regard to true scientific progress.

The number of papers that use AI quadrupled across 20 fields between 2012 and 2022 — even before the adoption of large language models. Figure by Duede et al.

So, what should the scientific community do differently? Let’s talk about the role of individual researchers, funders, publishers and other gatekeepers, and AI companies.

Individual researchers should be more careful when adopting AI. They should build software engineering skills, learn how to avoid a long and growing list of pitfalls in AI-based modeling, and ensure they don’t lose their expertise by using AI as a crutch or an oracle. Sloppy use of AI may help in the short run, but will hinder meaningful scientific achievement.

With all that said, we recognize that most individual researchers are rationally following their incentives (productivity metrics). Yelling at them is not going to help that much, because what we have are collective action problems. The actors with real power to effect change are journals, universities hiring & promotion committees, funders, policymakers, etc. Let’s turn to those next.

Meta-science research has been extremely valuable in revealing the production-progress paradox. But so far, that finding doesn’t have a lot of analytical precision. There’s only the fuzzy idea that science is getting less bang for its buck. This finding is generally consistent with scientists’ vibes, and is backed by a bunch of different metrics that vaguely try to measure true progress. But we don’t have a clear understanding of what the construct (progress) even is, and we’re far from a consensus story about what’s driving the slowdown.

To be clear, we will never have One True Progress Metric. If we did, Goodhardt/Campbell’s law would kick in — “When a measure becomes a target, it ceases to be a good measure.” Scientists would start to furiously optimize it, just as we have done with publication and citation counts, and the gaming would render it useless as a way to track progress.

That said, there’s clearly a long way for meta-science to go in improving both our quantitative and (more importantly) our qualitative/causal understanding of progress and the slowdown. Meta-science must also work to understand the efficacy of solutions.

Despite recent growth, meta-science funding is a fraction of a percent of science funding (and research on the slowdown is only a fraction of that pie). If it is indeed true that science funding as a whole is getting orders of magnitude less bang for the buck than in the past, meta-science investment seems ruefully small.

Scientists constantly complain to each other about the publish-or-perish treadmill and are keenly aware that the production-focused reward structure isn’t great for incentivizing scientific progress. But efforts to change this have consistently failed. One reason is simple inertia. Then there’s the aforementioned Goodhart’s law — whatever new metric is instituted will quickly be gamed. A final difficulty is that true progress can only be identified retrospectively, on timescales that aren’t suitable for hiring and promotion decisions.

One silver lining is that as the cost of publishing papers further drops due to AI, it could force us to stop relying on production metrics. In the AI field itself, the effort required to write a paper is so low that we are heading towards a singularity, with some researchers being able to (co-)author close to 100 papers a year. (But, again, the perceived pace of actual progress seems mostly flat.) Other fields might start going the same route.

Rewarding the publication of individual findings may simply not be an option for much longer. Perhaps the kinds of papers that count toward career progress should be limited to things that are hard to automate, such as new theories or paradigms of scientific research. Any reforms to incentive structures should go hand-in-hand with shifts in funding.

One thing we don’t need is more incentives for AI adoption. As we explained above, it is already happening at breakneck speed, and is not the bottleneck.

When it comes to AI-for-science labs and tools that come from big AI companies, the elephant in the room is that their incentives are messed up. They want flashy “AI discovers X!” headlines so that they can sustain the narrative that AI will solve humanity’s problems, which buys them favorable policy treatment. We are not holding our breath for this to change.

We should be skeptical of AI-for-science news headlines. Many of them are greatly exaggerated. The results may fail to reproduce, or AI may be framed as the main character when it was in fact one tool among many.

If there are any AI-for-science tool developers out there who actually want to help, here’s our advice. Target the actual bottlenecks instead of building yet another literature review tool. How about tools for finding errors in scientific code or other forms of quality control? Listen to the users. For example, mathematicians have repeatedly said that tools for improving human understanding are much more exciting than trying to automate theorem-proving, which they view as missing the point.

The way we evaluate AI-for-science tools should also change. Consider a literature review tool. There are three kinds of questions one can ask: Does it save a researcher time and produce results of comparable quality to existing tools? How does the use of the tool impact the researcher’s understanding of the literature compared to traditional search? What will the collective impacts on the community be if the tool were widely adopted? For example, will everyone end up citing the same few papers?

Currently, only the first question is considered part of what evaluation means. The latter two are out of scope, and there aren’t even established methods or metrics for such measurement. That means that AI-for-science evaluation is guaranteed to provide a highly incomplete and biased picture of the usefulness of these tools and minimize their potential harms.

We ourselves are enthusiastic users of AI in our scientific workflows. On a day-to-day basis, it all feels very exciting. That makes it easy to forget that the impact of AI on science as an institution, rather than individual scientists, is a different question that demands a different kind of analysis. Writing this essay required fighting our own intuitions in many cases. If you are a scientist who is similarly excited about using these tools, we urge you to keep this difference in mind.

Our skepticism here has similarities and differences to our reasons for the slow timelines we laid out in AI as Normal Technology. Market mechanisms exert some degree of quality control, and many shoddy AI deployments have failed badly, forcing companies who care about their reputation to take it slow when deploying AI, especially for consequential tasks, regardless of how fast the pace of development is. In science, adoption and quality control processes are decoupled, with the former being much faster.

We are optimistic that scientific norms and processes will catch up in the long run. But for now, it’s going to be a bumpy ride.

We are grateful to Eamon Duede for feedback on a draft of this essay.

The American Science Acceleration Project (ASAP) is a national initiative with the stated goal of making American science “ten times faster by 2030”. The offices of Senators Heinrich and Rounds recently requested feedback on how to achieve this. In our response, we emphasized the production-progress paradox, discussed why AI could slow (rather than hasten) scientific progress, and recommended policy interventions that could help.

Our colleague Alondra Nelson also wrote a response to the ASAP initiative, emphasizing that faster science is not automatically better, and highlighted many challenges that remain despite increasing the pace of production.

In a recent commentary in the journal Nature, we discussed why the proliferation of AI-driven modeling could be bad for science.

We have written about the use of AI for science in many previous essays in this newsletter:

Lisa Messeri and Molly Crockett offer a taxonomy of the uses of AI in science. They discuss many pitfalls of adopting AI in science, arguing we could end up producing more while understanding less.

Matt Clancy reviewed the evidence for slowdowns in science and innovation, and discussed interventions for incentivizing genuine progress.

The Institute for Progress released a podcast series on meta-science. Among other things, the series discusses concerns about slowdown and alternative models for funding and organizing science.

[fieldez]

Boost Your Business with Field Service Management Apps by FieldEZ Technologies

In today’s fast-paced, customer-centric business landscape, ensuring efficient field operations is not just an advantage — it’s a necessity. Whether you manage service technicians, sales teams, or retail staff on the ground, optimizing their workflow and performance can directly influence your business growth. That’s where field service management apps come in — and FieldEZ Technologies leads the way with smart, intuitive solutions designed to automate, streamline, and scale your field operations.

The Power of Field Service Management Software:

Field service management software helps businesses manage their remote workforce efficiently. It encompasses features like task scheduling, dispatching, real-time location tracking, attendance management, expense reporting, and analytics. This software transforms traditional manual operations into a seamless, automated experience — improving productivity, reducing errors, and enhancing customer satisfaction.

By integrating workflow automation, FSM apps eliminate repetitive tasks, reduce paperwork, and give managers better control over every stage of a field operation. With FieldEZ Technologies, you get a comprehensive suite of tools that empower your teams and delight your clients.

Introducing FieldEZ Technologies:

FieldEZ is a trusted name in field service automation, catering to various industries such as telecom, HVAC, retail, healthcare, and more. Their innovative platforms — ServiceEZ, SalesEZ, and RetailEZ — are designed to meet specific operational needs while delivering maximum efficiency.

Let’s dive into how these apps can boost your business:

1. ServiceEZ — Streamlining Field Service Operations

ServiceEZ is a powerful field service management app built to simplify the life of service teams. From job assignments and live tracking to invoicing and customer feedback, everything is integrated into one seamless platform. ServiceEZ ensures:

Faster response times

Reduced service delays

Better workforce utilization

Transparent customer communication

With its intuitive interface and workflow automation features, ServiceEZ minimizes downtime and ensures that service technicians are always where they need to be, with the right tools and information.

2. SalesEZ — Empowering Field Sales Teams

Field sales can be unpredictable and hard to manage without the right technology. SalesEZ gives you control over sales representatives in real time. Features like route planning, geo-tracking, sales reporting, and lead management help boost sales performance significantly. SalesEZ enables:

Real-time sales data insights

Automated reporting and forecasting

Seamless lead-to-order workflows

Geo-fenced check-ins and time tracking

By automating repetitive tasks and offering mobile access to customer information, SalesEZ improves the effectiveness and accountability of your sales force.

3. RetailEZ — Enhancing Retail Execution

For retail brands with distributed teams handling merchandising, audits, or promotions, RetailEZ brings unmatched visibility and control. It helps ensure retail execution is consistent, data-driven, and timely. Key benefits include:

Real-time field activity updates

In-store compliance tracking

Promotion execution monitoring

Inventory and planogram audits

RetailEZ supports efficient retail operations while delivering valuable insights into market trends and consumer behaviors.

Why Choose FieldEZ?

What sets FieldEZ Technologies apart is its commitment to workflow automation, mobile-first design, and customizable modules. Their solutions integrate easily with your existing ERP, CRM, or HR systems, ensuring a hassle-free experience across departments. With features like offline mode, AI-driven analytics, and multilingual support, FieldEZ apps are built for real-world field challenges.

Conclusion:

Investing in field service management software like those offered by FieldEZ Technologies can be a game-changer for your business. Whether you aim to improve service response, empower your sales team, or manage your retail presence, FieldEZ’s apps — ServiceEZ, SalesEZ, and RetailEZ — offer the tools you need to succeed.

Boost your business with smarter, faster, and more connected field operations — powered by FieldEZ.

[foggyarcadenacho]

Driving Efficiency and Customer Satisfaction with Field Service Solution Providers

Introduction In today’s fast-paced and highly competitive environment, businesses that rely on field operations require advanced tools to manage their workforce, assets, and customer expectations effectively. Field service solution providers are transforming the way organizations deliver on-site services, enabling them to streamline operations, improve technician productivity, and enhance the overall customer experience.

Optimizing Operations with Smart Technology Field service solutions integrate cutting-edge technologies such as AI, IoT, and cloud computing to create smarter workflows. From real-time scheduling and route optimization to predictive maintenance and inventory management, these solutions empower businesses to operate more efficiently. Mobile-friendly platforms also ensure that field technicians can access vital information anytime, anywhere, enhancing their ability to resolve issues on the first visit.

Enhancing Customer Engagement Customer expectations are higher than ever, and field service solution providers play a critical role in meeting these demands. By offering features like live tracking, instant communication, and automated service updates, they ensure transparency and build trust with clients. Personalized service and faster response times lead to higher satisfaction levels and stronger long-term relationships.

Driving Business Growth and Innovation Field service solutions are not only operational tools but also strategic assets. The data insights they provide allow organizations to identify trends, anticipate customer needs, and make data-driven decisions. This fosters innovation and helps businesses stay ahead of competitors by offering proactive, value-added services.

Conclusion Field service solution providers are redefining service delivery models across industries by bridging the gap between operational efficiency and customer satisfaction. As businesses continue to embrace digital transformation, these solutions will remain a cornerstone of success, ensuring that field operations are seamless, responsive, and future-ready.

[dreamycircuit]

How AI is Changing Jobs: The Rise of Automation and How to Stay Ahead in 2025

AI and Jobs

Artificial Intelligence (AI) is everywhere. From self-checkout kiosks to AI-powered chatbots handling customer service, it’s changing the way businesses operate. While AI is making things faster and more efficient, it’s also making some jobs disappear. If you’re wondering how this affects you and what you can do about it, keep reading — because the future is already here.

The AI Boom: How It’s Reshaping the Workplace

AI is not just a buzzword anymore; it’s the backbone of modern business. Companies are using AI for automation, decision-making, and customer interactions. But what does that mean for jobs?

AI is Taking Over Repetitive Tasks

Gone are the days when data entry, basic accounting, and customer support relied solely on humans. AI tools like ChatGPT, Jasper, and Midjourney are doing tasks that once required an entire team. This means fewer jobs in these sectors, but also new opportunities elsewhere.

Companies are Hiring Fewer People

With AI handling routine work, businesses don’t need as many employees as before. Hiring freezes, downsizing, and increased automation are making it tougher to land a new job.

AI-Related Jobs are on the Rise

On the flip side, there’s massive demand for AI engineers, data scientists, and automation specialists. Companies need people who can build, maintain, and optimize AI tools.

Trending AI Skills Employers Want:

Machine Learning & Deep Learning

Prompt Engineering

AI-Powered Marketing & SEO

AI in Cybersecurity

Data Science & Analytics

Click Here to Know more

The Decline of Traditional Job Offers

AI is shaking up industries, and some job roles are disappearing faster than expected. Here’s why new job offers are on the decline:

AI-Driven Cost Cutting

Businesses are using AI to reduce operational costs. Instead of hiring new employees, they’re investing in AI-powered solutions that automate tasks at a fraction of the cost.

The Gig Economy is Replacing Full-Time Jobs

Instead of hiring full-time staff, companies are outsourcing work to freelancers and gig workers. This means fewer stable job opportunities but more chances for independent workers.

Economic Uncertainty

The global economy is unpredictable, and businesses are cautious about hiring. With AI improving efficiency, companies are choosing to scale down their workforce.

Click Here to Know more

Preparing for an AI-Driven Future

Feeling worried? Don’t be. AI isn’t just taking jobs — it’s also creating new ones. The key is to stay ahead by learning the right skills and adapting to the changing landscape.

1. Learn AI and Data Analytics

The best way to future-proof your career is to understand AI. Free courses on platforms like Coursera, Udemy, and Khan Academy can get you started.

2. Develop Soft Skills AI Can’t Replace

AI is great at automation, but it lacks emotional intelligence, creativity, and critical thinking. Strengthening these skills can give you an edge.

3. Embrace Remote & Freelance Work

With traditional jobs shrinking, freelancing is a great way to stay flexible. Sites like Upwork, Fiverr, and Toptal have booming demand for AI-related skills.

4. Use AI to Your Advantage

Instead of fearing AI, learn how to use it. AI-powered tools like ChatGPT, Jasper, and Canva can help boost productivity and creativity.

5. Never Stop Learning

Technology evolves fast. Stay updated with new AI trends, attend webinars, and keep improving your skills.

Click Here to Know more

Final Thoughts

AI is here to stay, and it’s changing the job market rapidly. While some traditional roles are disappearing, new opportunities are emerging. The key to surviving (and thriving) in this AI-driven world is adaptability. Keep learning, stay flexible, and embrace AI as a tool — not a threat.

Click Here to Know more

Share this blog if you found it helpful! Let’s spread awareness and help people prepare for the AI revolution.

[precallai]

Inside the AI Based Contact Center with Tools Tech and Trends

Introduction

The evolution of customer service has entered a new era with the rise of the AI based contact center. No longer just a support line, today’s contact centers are intelligent, data-driven hubs that utilize artificial intelligence to deliver personalized, efficient, and scalable customer interactions. As businesses race to stay ahead of the curve, understanding the essential tools, technologies, and emerging trends that power AI-driven contact centers becomes crucial. This article explores how AI is transforming contact centers and what lies ahead for this innovative landscape.

The Rise of the AI Based Contact Center

Traditional contact centers, though essential, have long suffered from inefficiencies such as long wait times, inconsistent service, and high operational costs. AI-based contact centers are solving these issues by automating routine tasks, predicting customer needs, and delivering omnichannel support.

AI technology, such as machine learning, natural language processing (NLP), and robotic process automation (RPA), is now integrated into contact center platforms to enhance agent productivity and customer satisfaction.

Essential Tools Driving AI Based Contact Centers

1. AI-Powered Chatbots and Virtual Agents

Chatbots are the most visible AI tool in contact centers. These virtual assistants handle customer queries instantly and are available 24/7. Advanced bots can handle complex conversations using NLP and deep learning, reducing human intervention for repetitive inquiries.

2. Intelligent Interactive Voice Response (IVR) Systems

Modern IVR systems use voice recognition and AI to route calls more accurately. Unlike traditional menu-based IVRs, intelligent IVRs can interpret natural language, making customer interactions smoother and faster.

3. Speech Analytics Tools

AI-driven speech analytics tools analyze live or recorded conversations in real time. They extract keywords, sentiments, and emotional cues, offering insights into customer satisfaction, agent performance, and compliance issues.

4. Workforce Optimization (WFO) Platforms

AI helps optimize staffing through forecasting and scheduling tools that predict call volumes and agent availability. These platforms improve efficiency and reduce costs by aligning workforce resources with demand.

5. CRM Integration and Predictive Analytics

By integrating AI with CRM systems, contact centers gain predictive capabilities. AI analyzes customer data to forecast needs, recommend next-best actions, and personalize interactions, leading to higher engagement and retention.

Core Technologies Enabling AI Based Contact Centers

1. Natural Language Processing (NLP)

NLP allows machines to understand, interpret, and respond in human language. This is the backbone of AI-based communication, enabling features like voice recognition, sentiment detection, and conversational AI.

2. Machine Learning and Deep Learning

These technologies enable AI systems to learn from past interactions and improve over time. They are used to personalize customer interactions, detect fraud, and optimize call routing.

3. Cloud Computing

Cloud platforms provide the infrastructure for scalability and flexibility. AI contact centers hosted in the cloud offer remote access, fast deployment, and seamless integration with third-party applications.

4. Robotic Process Automation (RPA)

RPA automates repetitive tasks such as data entry, ticket generation, and follow-ups. This frees up human agents to focus on more complex customer issues, improving efficiency.

Emerging Trends in AI Based Contact Centers

1. Hyper-Personalization

AI is pushing personalization to new heights by leveraging real-time data, purchase history, and browsing behavior. Contact centers can now offer customized solutions and product recommendations during live interactions.

2. Omnichannel AI Integration

Customers expect consistent service across channels—phone, email, chat, social media, and more. AI tools unify customer data across platforms, enabling seamless, context-aware conversations.

3. Emotion AI and Sentiment Analysis

Emotion AI goes beyond words to analyze voice tone, pace, and volume to determine a caller's emotional state. This data helps agents adapt their responses or triggers escalations when needed.

4. Agent Assist Tools

AI now works hand-in-hand with human agents by suggesting responses, summarizing calls, and providing real-time knowledge base access. These agent assist tools enhance productivity and reduce training time.

5. AI Ethics and Transparency

As AI becomes more prevalent, companies are increasingly focused on responsible AI usage. Transparency in how decisions are made, data privacy, and eliminating bias are emerging priorities for AI implementation.

Benefits of Adopting an AI Based Contact Center

Businesses that adopt AI-based contact centers experience a variety of benefits:

Improved Customer Satisfaction: Faster, more accurate responses enhance the overall experience.

Cost Reduction: Automation reduces reliance on large human teams for repetitive tasks.

Increased Scalability: AI can handle spikes in volume without compromising service quality.

Better Insights: Data analytics uncover trends and customer behaviors for better strategy.

Challenges in AI Based Contact Center Implementation

Despite the advantages, there are challenges to be aware of:

High Initial Investment: Setting up AI tools can be capital intensive.

Integration Complexities: Integrating AI with legacy systems may require customization.

Change Management: Staff may resist AI adoption due to fear of replacement or complexity.

Data Security and Compliance: AI systems must adhere to data protection regulations like GDPR or HIPAA.

Future Outlook of AI Based Contact Centers

The future of AI-based contact centers is promising. As technology matures, we can expect deeper personalization, more intuitive bots, and stronger collaboration between human agents and AI. Voice AI will become more empathetic and context-aware, while backend analytics will drive strategic decision-making.

By 2030, many experts predict that AI will handle the majority of customer interactions, with human agents stepping in only for high-level concerns. This hybrid model will redefine efficiency and service quality in the contact center industry.

Conclusion

The AI based contact center is transforming how businesses interact with customers. With powerful tools, cutting-edge technologies, and evolving trends, organizations are reimagining the contact center as a strategic asset rather than a cost center. By investing in AI, companies can enhance customer experiences, improve operational efficiency, and stay competitive in an increasingly digital marketplace. The time to explore and adopt AI contact center solutions is now—because the future of customer support is already here.

[fieldserviceautomation]

Field service businesses are undergoing a remarkable transformation, driven by cutting-edge technologies like Artificial Intelligence (AI) and the Internet of Things (IoT). These innovations are reshaping operations, enhancing customer satisfaction, and creating a more efficient workforce. Here’s a comprehensive look at how AI and IoT are revolutionizing the field service industry.

1. Predictive Maintenance

Traditionally, field service operations relied on reactive or scheduled maintenance, leading to downtime and unnecessary costs. AI and IoT have introduced predictive maintenance, which uses real-time data from IoT-connected devices to anticipate issues before they arise.

IoT Sensors: These devices monitor equipment health, providing data on performance, temperature, vibration, and more.

AI Analysis: AI algorithms analyze this data to predict when a failure might occur, enabling proactive repairs.

Benefits: Reduced downtime, lower maintenance costs, and extended equipment lifespan.

2. Smart Scheduling and Dispatching

Field service businesses often face challenges in managing teams and allocating resources efficiently. AI-powered tools are transforming scheduling and dispatching by automating these processes.

Dynamic Scheduling: AI considers factors like technician availability, skill sets, and location to assign tasks optimally.

Real-Time Adjustments: IoT devices provide live updates, allowing AI to reassign tasks based on changing conditions.

Benefits: Improved workforce utilization, faster response times, and enhanced customer satisfaction.

3. Enhanced Remote Support

IoT and AI are enabling technicians to diagnose and resolve issues remotely, reducing the need for on-site visits.

IoT Connectivity: Devices send real-time diagnostic data to field service teams.

AI Chatbots: AI-powered virtual assistants guide customers or technicians through troubleshooting steps.

Benefits: Cost savings, quicker problem resolution, and minimized service disruptions.

4. Inventory and Asset Management

Managing parts and tools is critical for field service efficiency. AI and IoT are streamlining inventory and asset management.

IoT-Enabled Tracking: Devices track inventory levels and asset usage in real time.

AI Optimization: AI predicts demand for parts and tools, ensuring optimal stock levels.

Benefits: Reduced inventory costs, fewer delays, and better resource planning.

5. Improved Customer Experience

Customer satisfaction is at the heart of field service businesses. AI and IoT are enhancing the customer experience by providing timely, personalized, and seamless interactions.

Proactive Communication: AI sends automated updates on service schedules and equipment status.

IoT Insights: Customers gain real-time visibility into the status of their equipment via IoT dashboards.

Benefits: Higher customer trust, loyalty, and retention.

6. Data-Driven Decision Making

The combination of AI and IoT generates vast amounts of actionable data, empowering businesses to make informed decisions.

Performance Analytics: AI identifies trends and inefficiencies in operations.

Predictive Insights: IoT data helps forecast future needs and challenges.

Benefits: Better strategic planning, resource allocation, and operational efficiency.

Conclusion

The integration of AI and IoT is revolutionizing field service businesses by improving operational efficiency, reducing costs, and delivering exceptional customer experiences. Companies that embrace these technologies are positioning themselves for long-term success in an increasingly competitive market.

By adopting AI and IoT solutions, field service businesses can move from reactive to proactive operations, paving the way for innovation and growth.

[freelycasualquill]

Microsoft Copilot AI is The Reason Behind 6,000 Layoffs 

Microsoft Copilot AI, the technology firm's AI productivity tool, is causing ripples—not merely in the sphere of innovation, but in transforming work culture as well. Rumors indicate that Microsoft's move towards AI-assisted automation, particularly through offerings such as Copilot for Microsoft 365 and GitHub, has resulted in the laying off of 6,000 workers worldwide.

Why the Layoffs

With machine learning absorbing mundane and time-consuming work like code generation, summarization of email, meeting minutes, and data analysis, most jobs in the traditional sense are redundant. The firm is now shifting effort into creating AI-based, cloud computing, and optimizing automation.

Key Highlights:

6,000+ redundancies in technology, support, and operations personnel.

Copilot AI technology is replacing human workflows in coding and collaboration in real-time.

Microsoft is investing billions in OpenAI collaborations and cloud-based AI.

The firm will recruit strongly for cloud security, AI engineering, and AI research.

What This Means:

While Microsoft Copilot AI  is a productivity booster, it is also transforming the character of work. Companies are scaling AI, and the world is redefining its workforce. Professionals are re-skilling in AI tools, data literacy, and automation management to remain in the game.

[scamornoreviews]

Workforce AI Review - Is Workforce AI Legit?

Discover how Workforce AI transforms business automation, content creation, and marketing strategies. Read this in-depth Workforce AI review to explore its features and benefits.

Workforce AI is a game-changer in AI-powered automation, offering advanced features for businesses, marketers, and content creators. This review explores its capabilities, pricing, and why it stands out.

Artificial intelligence is evolving at lightning speed, and businesses that fail to keep up risk falling behind. Workforce AI is an all-in-one AI tool designed to help businesses, marketers, and creators automate tasks, generate high-quality content, and streamline workflows. But is it worth the hype? Let’s dive in!

What Makes Workforce AI Stand Out?

Unlike many AI tools that focus only on text generation, Workforce AI offers a complete AI automation suite that integrates multiple features into a single platform. Here’s why it’s a step ahead:

✅ Multi-AI Model Switching – Unlike tools that restrict users to one AI model, Workforce AI lets users switch between multiple models for improved content quality. ✅ AI-Powered Business Automation – Automate customer service, lead generation, and marketing with AI-driven chatbots and workflow automation. ✅ One-Time Payment Option – Many AI tools require ongoing subscriptions, but Workforce AI offers a lifetime deal, making it a smart long-term investment.

Review Verdict: Workforce AI is a legitimate suite of AI tools

Visit Workforce AI Website

Workforce AI Features & Benefits

🔹 Advanced AI Content Generation

Produces SEO-friendly blog posts, social media content, and ad copies in minutes.

Helps businesses maintain a consistent online presence without hiring expensive writers.

🔹 AI Chatbots & Lead Generation

Automates customer interactions and sales inquiries 24/7.

Enhances engagement with AI-driven chatbots that learn and adapt.

🔹 Workflow Automation

Streamlines repetitive business tasks, reducing manual labor.

Allows businesses to focus on growth rather than micromanagement.

🔹 Versatile AI Capabilities

Suitable for content creators, marketers, agencies, and e-commerce businesses.

Offers tools for image generation, scriptwriting, and even ad creatives...

Full Workforce AI Review here! at https://scamorno.com/Workforce-AI-Review/?id=tumblr

Who Should Use Workforce AI?

✅ Best Suited For:

✔ Business Owners & Agencies – Automate customer service, marketing, and content generation. ✔ Content Creators & Bloggers – Generate SEO-friendly articles, video scripts, and social media content. ✔ Freelancers & Marketers – Provide AI-powered services like copywriting and chatbot development. ✔ E-commerce Sellers – Create AI-enhanced product descriptions, ad creatives, and influencer avatars. ✔ SEO Experts & Advertisers – Optimize content, sales pages, and ad campaigns.

❌ Not Ideal For:

✖ Casual Users – Those who only need ChatGPT occasionally might find Workforce AI’s extensive features unnecessary. ✖ AI Beginners – While powerful, Workforce AI’s multiple AI model options may overwhelm first-time users. However, with a little practice, it can become an invaluable tool.

FAQs About Workforce AI

1. What is Workforce AI best used for?

Workforce AI is designed for businesses, agencies, and content creators who need AI-powered automation, including content creation, lead generation, and marketing tools.

2. Is Workforce AI a one-time payment tool?

Yes! Unlike many AI tools that require monthly subscriptions, Workforce AI offers a one-time payment option, making it a cost-effective investment.

3. Can Workforce AI replace human writers?

While Workforce AI generates high-quality content, human creativity is still essential for finalizing and refining content. It works best as an AI-powered assistant rather than a full replacement.

4. Does Workforce AI support multiple AI models?

Yes! One of its standout features is multi-AI model switching, allowing users to choose the best AI model for different tasks.

5. How does Workforce AI compare to Jasper and Copy.ai?

Workforce AI surpasses Jasper and Copy.ai by offering AI-powered automation, chatbots, and workflow management in addition to text generation...

Full Workforce AI Review here! at https://scamorno.com/Workforce-AI-Review/?id=tumblr

[maximumnightwill]

What is HRMS? Benefits and the Future of Human Resource Management Systems

What is HRMS?

A Human Resource Management System (HRMS) is a suite of software applications used to manage human resources and related processes throughout the employee lifecycle. An HRMS enables a company to fully understand its workforce while staying compliant with changing tax laws and labor regulations.

From recruitment to retirement, an HRMS streamlines every HR function including:

Employee data management

Payroll processing

Attendance tracking

Recruitment and onboarding

Performance management

Training and development

Employee self-service

With platforms like HRMSNEXT, organizations can bring all employee-related data into one place, enabling smarter decision-making and improved efficiency.

Key Benefits of HRMS

1. Centralized Employee Information

HRMS consolidates all employee data into a single, easily accessible database—no more spreadsheets or paper records.

2. Improved Productivity

Automated processes such as attendance tracking, payroll calculations, and leave management reduce manual effort and errors.

3. Data-Driven Insights

Advanced analytics provide actionable insights into employee performance, engagement, and retention trends.

4. Enhanced Compliance

Stay on top of tax filings, labor laws, and HR policies with automated compliance tracking.

5. Employee Empowerment

Self-service portals let employees view payslips, apply for leave, and update personal information without HR intervention.

6. Streamlined Recruitment

Manage the entire hiring process—job postings, applications, interviews, and onboarding—in one place.

The Future of HRMS

The HRMS landscape is rapidly transforming with the integration of new technologies:

✅ AI and Machine Learning

Predictive analytics for talent acquisition, attrition forecasting, and personalized employee development plans.

✅ Cloud-Based Solutions

Anywhere-accessible HRMS platforms that support remote and hybrid work models.

✅ Mobile Accessibility

HRMS apps are enabling real-time updates and interactions, increasing convenience for both HR and employees.

✅ Integration with Other Systems

Modern HRMS platforms easily integrate with ERP, CRM, and other business tools to create a seamless digital ecosystem.

✅ Employee Experience Focus

Next-gen HRMS tools are designed with employee engagement in mind, offering intuitive interfaces and well-being features.

Conclusion

HRMS solutions like HRMSNEXT are not just tools—they’re strategic assets that drive business growth by optimizing human capital management. Whether you're a startup or a large enterprise, investing in the right HRMS will ensure your workforce is aligned, empowered, and future-ready.

[jimmy-asija]

Jimmy Asija – Revolutionizing the Call Center Industry in India

In India’s fast-paced BPO sector, one name stands out for driving real transformation Jimmy Asija. As a thought leader and innovator, he’s reshaping how Jimmy Asija Call Center operations are managed by integrating smart automation, customer empathy, and workforce empowerment. With years of experience and a future-focused mindset, Jimmy’s strategies revolve around enhancing customer satisfaction while improving backend efficiency.

What sets him apart? His ability to balance technology with the human touch. From AI-enabled communication tools to real-time customer analytics, Jimmy Asija ensures that every customer interaction is meaningful and productive. His model is being adopted by multiple contact centers across India looking to scale effectively.

If you're a business owner aiming to outsource or optimize customer service, following Jimmy Asija model could give you the edge. His website provides insights into his approach and contributions to the evolving BPO landscape.