Daily Archives: October 4, 2022

Entrenched in academia, chemist Jacob Berlin spent a decade making small molecules to treat the worlds biggest diseases. He wondered: How can this process be more efficient?

Very little about drug development is efficient. The failure rate for drugs making their way to commercialization is 90%, after which more than around $1 billion and 10 years is sunk into each one on average.

But technological advancements in data collection are propelling artificial intelligence in drug discovery, which may unlock the ability to find cures for diseases that evaded the scientific community for centuries.

So far this year, startups in drug discovery raised more than $1.4 billion, according to Crunchbase data.

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One of these startupsthem is Terray Therapeutics, an AI drug discovery platform founded by Jacob and Eli Berlin in 2018.

There are thousands of problems sitting out there that we dont know the answer for. Thousands and thousands, said Berlin. So having a platform that lets us go faster, be precise and scale can really transform the opportunities in front of us.

Current processes of drug discovery are long and tedious. Scientists in academia or pharma make molecules. They look for targets (like proteins) the molecule can swim to in the body and deliver therapy.

To do that, scientists need to make sure the molecule doesnt mistake a healthy protein for a target, otherwise a drug swimming around in the body may attach to and kill a healthy cellamounting to poison. Once scientiststhey get a target, its taken out of the body and tested against molecules in the lab to see what will stick.

But as clinical trials continue, several of those drugs fail due to unintended toxicity in the rest of the body, or the drug itself working in the lab but not in humans. With those failures, it sinks millions of dollars and years of research are lost..

Its just this huge funnel where stuff can drop out at any point in time, said Sara Choi, a partner at Wing VC who invests in health startups. And I think that the problems are very much at the very, very, very beginning of this process.

Terray and platforms like it work differently. Terray compares molecules against targets, and the AI assesses what parts of the molecule correlate strongly with the target. Terray can then make new molecules that correlate even more strongly, refining it.

Through leveraging data, drug discovery platforms can better predict outcomes of drugs at the start of the process. AI matches molecules with targets and simulates how it will work in the body, giving it a better chance of surviving clinical trials and lowering toxicity rates in patients.

At the end of the day, its about innovation and trying to find interesting, novel ways of treating some really unmet medical needs, said David Crean, a biotech investor and managing general partner at Cardiff Advisory.

AI drug discovery is still nascent, and will require interdisciplinary knowledge of chemistry, computational engineering, machine learning and biology. Data collection in drug development only became popular in 2017, a shift we see in funding: Between 2017 and 2018, funding increased by 190%.

The foundational layers in terms of data generation were just not there for a long time, Choi said. In the last few years we have not seen the breadth of it. Were just starting a data revolution.

Nevertheless, large pharma companies like Eli Lilly are betting big on this tech to accelerate the pace of drug development, raising profits and getting medicines into the market faster. Many pharma companies partner with AI drug discovery platforms. For example, Earlier this year, Amgen and Generate Biomedicines announced a partnership potentially worth up to $1.9 billion earlier this yearn.

The molecules that come out of the drug discovery as a result of AI, theres only a few in clinical development right now, Crean said. It sounds kind of Star Trek-y. Yes, it sounds exciting, but I think we just have to try and manage our expectations.

Illustration: Dom Guzman

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AI Is Making Its Way Into Drug Discovery. What Does It Mean For Biotech? - Crunchbase News

NEW YORK, Oct. 4, 2022 /PRNewswire/ -- The artificial intelligence (AI) market size in the BFSI sector is set to grow by $32.97 bn between 2021 and 2026, progressing at a CAGR of 36.68%. According to Technavio, the market is fragmented, and the degree of fragmentation will accelerate during the forecast period. As market growth over the next five years is expected to remain high, the competitive rivalry among market vendors will remain limited. To know more about the vendor landscape Read Sample PDF Report Before Purchasing.

Technavio has announced its latest market research report titled Global Artificial Intelligence (AI) Market in BFSI Sector 2022-2026

The report identifies Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc, Glia Technologies Inc, Inbenta Technologies Inc., Intel Corp., International Business Machines Corp., Lexalytics Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Palantir Technologies Inc., Salesforce Inc., ServiceNow Inc., Verint Systems Inc, ZestFinance Inc, and SAP SEare some of the major market participants. Although the Enhanced operational efficiency with AI will offer immense growth opportunities, the need for high data quality will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Segmentation

Artificial Intelligence (AI) Market in BFSI Sector is segmented as below:

The artificial intelligence (AI) market share growth in the BFSI sector by the banking segment will be significant during the forecast period. The use of cognitive technology, along with AI, helps banks to leverage digitalization and sustain competition with FinTech players. AI technologies are revolutionizing banking processes and the relationship between banks and customers. AI is expected to shape the future of the banking sector as it provides the power of advanced data analytics to fight against fraudulent transactions and improve compliance, all within seconds.

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48% of the market's growth will originate from North America during the forecast period. The early adoption and increasing investments in AI technologies by players such as IBM, Google, Microsoft, and AWS in the region will facilitate the artificial intelligence (AI) market growth inBFSI sector in North America over the forecast period. This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans. Our artificial intelligence (ai) market in BFSI sector report covers the following areas:

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Vendor Analysis

We provide a detailed analysis of around 25 vendors operating in the Artificial Intelligence (AI) Market in BFSI Sector, including some of the vendors such as vendors Backed with competitive intelligence and benchmarking, our research reports on the Artificial Intelligence (AI) Market in BFSI Sector are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Key Highlights

CAGR of the market during the forecast period 2022-2026

Detailed information on factors that will assist artificial intelligence (AI) market in BFSI sector growth during the next five years

Estimation of the artificial intelligence (AI) market in BFSI sector size and its contribution to the parent market

Predictions on upcoming trends and changes in consumer behavior

The growth of the artificial intelligence (AI) market in BFSI sector

Analysis of the market's competitive landscape and detailed information on vendors

Comprehensive details of factors that will challenge the growth of the artificial intelligence (AI) market in BFSI sector vendors.

Related Reports:

Artificial Intelligence (AI) Market In BFSI Sector Scope

Report Coverage

Details

Page number

120

Base year

2021

Forecast period

2022-2026

Growth momentum & CAGR

Accelerate at a CAGR of 36.68%

Market growth 2022-2026

$32.97 billion

Market structure

Fragmented

YoY growth (%)

33.99

Regional analysis

North America, APAC, Europe, Middle East and Africa, and South America

Performing market contribution

North America at 48%

Key consumer countries

US, Canada, China, Japan, and UK

Competitive landscape

Leading companies, competitive strategies, consumer engagement scope

Companies profiled

Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc, Glia Technologies Inc, Inbenta Technologies Inc., Intel Corp., International Business Machines Corp., Lexalytics Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Palantir Technologies Inc., Salesforce Inc., ServiceNow Inc., Verint Systems Inc, ZestFinance Inc, and SAP SE

Market Dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and future consumer dynamics, and market condition analysis for the forecast period.

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table Of Contents :

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by End-user

6 Customer Landscape

7 Geographic Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contact

Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: media@technavio.comWebsite: http://www.technavio.com/

Global Artificial Intelligence (AI) Market in BFSI Sector 2022-2026

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SOURCE Technavio

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Artificial Intelligence (AI) Market in BFSI Sector to Record USD 32.97 Billion growth between 2021 and 2026; Major Opportunities with Alphabet Inc....

AI has the potential to deliver enormous business value for organizations, and its adoption has been sped up by the data-related challenges of the pandemic. Forrester estimates that almost 100% of organizations will be using AI by 2025, and the artificial intelligence software market will reach $37 billion by the same year.

The Problem With Biased AIs (and How To Make AI Better)

But there is growing concern around AI bias situations where AI makes decisions that are systematically unfair to particular groups of people. Researchers have found that AI bias has the potential to cause real harm.

I recently had the chance to speak with Ted Kwartler, VP of Trusted AI at DataRobot, to get his thoughts on how AI bias occurs and what companies can do to make sure their models are fair.

AI bias occurs because human beings choose the data that algorithms use, and also decide how the results of those algorithms will be applied. Without extensive testing and diverse teams, it is easy for unconscious biases to enter machine learning models. Then AI systems automate and perpetuate those biased models.

For example, a US Department of Commerce study found that facial recognition AI often misidentifies people of color. If law enforcement uses facial recognition tools, this bias could lead to wrongful arrests of people of color.

Several mortgage algorithms in financial services companies have also consistently charged Latino and Black borrowers higher interest rates, according to a study by UC Berkeley.

Kwartler says the business impact of biased AI can be substantial, particularly in regulated industries. Any missteps can result in fines, or could risk a companys reputation. Companies that need to attract customers must find ways to put AI models into production in a thoughtful way, as well as test their programs to identify potential bias.

Kwartler says good AI is a multidimensional effort across four distinct personas:

AI Innovators: Leaders or executives who understand the business and realize that machine learning can help solve problems for their organization

AI Creators: The machine learning engineers and data scientists who build the models

AI Implementers: Team members who fit AI into existing tech stacks and put it into production

AI Consumers: The people who use and monitor AI, including legal and compliance teams who handle risk management

When we work with clients, Kwartler says, we try to identify those personas at the company and articulate risks to each one of those personas a little bit differently, so they can earn trust.

Kwartler also talks about why "humble AI" is critical. AI models must demonstrate humility when making predictions, so they don't drift into the biased territory.

Kwartler told VentureBeat, "If I'm classifying an ad banner at 50% probability or 99% probability, that's kind of that middle range. You have one single cutoff threshold above this line, and you have one outcome. Below this line, you have another outcome. In reality, we're saying there's a space in between where you can apply some caveats, so a human has to go review it. We call that humble AI in the sense that the algorithm is demonstrating humility when it's making that prediction."

According to DataRobots State of AI Bias report, 81% of business leaders want government regulation to define and prevent AI bias.

Kwartler believes that thoughtful regulation could clear up a lot of ambiguity and allow companies to move forward and step into the enormous potential of AI. Regulations are particularly critical around high-risk use cases like education recommendations, credit, employment, and surveillance.

Regulation is essential for protecting consumers as more companies embed AI into their products, services, decision-making, and processes.

When I asked Kwartler for his top tips for organizations that want to create unbiased AI, he had several suggestions.

The first recommendation is to educate your data scientists about what responsible AI looks like, and how your organizational values should be embedded into the model itself, or the guardrails of the model.

Additionally, he recommends transparency with consumers, to help people understand how algorithms create predictions and make decisions. One of the ongoing challenges of AI is that it is seen as a black box, where consumers can see inputs and outputs, but have no knowledge of the AIs internal workings. Companies need to strive for explainability, so people can understand how AI works and how it might have an impact.

Lastly, he recommends companies establish a grievance process for individuals, to give people a way to have discussions with companies if they feel they have been treated unjustly.

I asked Kwartler for his hopes and predictions for the future of AI, and he said that he believes AI can help us solve some of the biggest problems human beings are currently facing, including climate change.

He shared a story of one of DataRobots clients, a cement manufacturer, who used a complex AI model to make one of their plants 1% more efficient, helping the plant save approximately 70,000 tons of carbon emissions every year.

But to reach the full potential of AI, we need to ensure that we work toward reducing bias and the possible risks AI can bring.

To stay on top of the latest on the latest trends in data, business and technology, check out my books Data Strategy: How To Profit From A World Of Big Data, Analytics And Artificial Intelligence, and make sure you subscribe to my newsletter and follow me on Twitter, LinkedIn, and YouTube.

Original post:

The Problem With Biased AIs (and How To Make AI Better) - Forbes

AUSTIN, Texas, Oct. 4, 2022 /PRNewswire/ --Striveworks and Protopia AI are excited to announce their partnership to jointly support the need for timely, relevant and trusted AI/ML models in highly regulated industries.

"The shift from Model-Centric AI to Data-Centric AI is one of the top trends in 2022. By partnering with Striveworks, we enable fast access to high quality real-world data, which has traditionally been a major challenge for Data-Centric AI in highly regulated environments," according to Eiman Ebrahimi, CEO and Co-founder of Protopia AI. "By combining the power of our patented, privacy-enabling technology with Striveworks' platform to build and deploy models at scale, we not only provide Data-Centric AI governance and protection, but also simplify MLOps across the entire ML lifecycle."

Data transformed by Protopia AI's Stained Glass Transform Solution ensures the protection of customers' sensitive AI data across the entire ML lifecycle and can be rapidly ingested into Chariot, Striveworks' MLOps platform. After models are trained and deployed in Chariot, the patent-pending Chariot Data Lineage system ensures that models remain fully auditable and reproducible. Data owners maintain full control over sensitive data throughout the ML lifecycle, a particularly important feature for all companies doing business in highly regulated industries.

Striveworks CEO Jim Rebesco said, "Organizations looking to embrace data-centric AI need to equip their data professionals with both access to operationally relevant data and the tools to rapidly build, deploy and monitor models that use that data. The partnership between Protopia and Striveworks gives our customers the ability to securely and responsibly do both."

Protopia AI was recently recognized as a Cool Vendor in AI Governance and Responsible AI by Gartner and Striveworks was recognized as number three on the 2022 CRN Fast Growth 150. Digital transformation in highly regulated environments rests on the ability to safely and responsibly build, deploy, monitor and audit models built on sensitive data. This partnership will allow businesses to leverage Protopia's capabilities in data protection in Striveworks' MLOps environment.

Striveworks is a pioneer in operational data science for national security and other highly regulated spaces. Striveworks' flagship MLOps platform is Chariot, built to solve the "Day 3" problem of model remediation. Founded in 2018, Striveworks was highlighted as an exemplar in the National Security Commission for AI 2020 Final Report. For more information visit http://www.striveworks.com.

Founded by Endowed Chair Professor of Computer Science at UCSD Hadi Esmaeilzadeh (CTO) and Ex-NVIDIA Scientist Eiman Ebrahimi (CEO), Protopia AI's patented datatype-agnostic solutions accelerate time-to-value from data to unlock the benefits of ML that are typically lost due to lengthy governance protocols and procedures. For more information, visit http://www.protopia.ai or watch the brief video here: Introducing Protopia AI Stained Glass Transform.

Media contact:Tracy Shank[emailprotected]805-874-2650

SOURCE Striveworks, Inc.

Link:

Striveworks and Protopia AI Enter into Strategic Partnership to Empower Data-Centric AI in Highly Regulated Environments - PR Newswire

Webinar Nothing about building an enterprise-class AI infrastructure stack is a walk in the park. Rather it is a lope through a maze of thorny trees in the dark with unseasonal fog.

To avoid getting lost among the roots, it helps to have a map which can help developers and engineers identify the resources they need to deliver your AI projects, efficiently and economically, no matter their complexity, and to meet all your enterprise needs.

How much better to be able to make that journey with confidence? To free your data scientists from the burden of leading you through the thickets to deliver the results you expect and to discover the very best way to create your own AI infrastructure stack.

Join Michael Balint of Nvidia and Gijsbert Janssen Van Doorn from Run:ai on 12th October at 5pm BST/12pm EDT/9am PDT as they share their experience of using Run:ai Atlas alongside Nvidia's suite of hardware solutions with our very own Tim Phillips.

Together they'll show you the way to build a cloud-native AI infrastructure platform that gives you return on investment from model training to deployment. The trio will also consider why so many AI projects come unstuck, and what you need to create the right foundations to take AI into production at scale.

Register for our "Build an enterprise-class AI infrastructure stack" webinar here and we will send you a reminder.

Sponsored by Run:ai

Link:

Scaling the apex of efficient AI - The Register

As antimicrobial-resistant infections are set to overtake cancer as the top global health risk by 2050, the world needs to quickly find a solution that will help in detecting and reducing its risk of mortality while bringing down the economic cost.

During the keynote session, "Digital Transformation of Health Care: Intelligent Anti-Microbial System (iAMS)," at HIMSS22 APAC, Dr Der-Yang Cho, professor and superintendent of China Medical University Hospital, emphasised the importance of developing a new generation of antibiotics using emerging technologies.

"Antimicrobial stewardship and development of a next-generation of antibiotics are very important and very critical," he said, noting that AMRs claim 4.7 million lives in Asia each year.

He noted from his presentation that drug-resistant infections lead to more extended hospital stays and potentially greater complications with mortality rising by 7.6% for every hour of delay in administering treatment. Unfortunately, he said, 30%-50% of existing antibiotics are inappropriately prescribed.

Moreover, there have been no new registered classes of antibiotics for human treatment for over four decades, Dr Cho mentioned.

In this regard, CMUH developed the Intelligent Anti-Microbial System (iAMS) which integrates four platforms into one. Two platforms on the information side are a clinical decision support system and personalised antibiograms, which shows the antimicrobial susceptibility profile of a targeted microorganism. On the AI-powered risk prediction side is the MALDI-TOF mass spectrometry which predicts both antimicrobial susceptibility of a bacteria and risks of sepsis and mortality.

"We put them together into one platform for automating the antimicrobial treatment," explained Dr Cho.

The AI technology has a current accuracy of over 80% and has brought down reporting time from 72 hours to an hour.

Read this article:

Using AI to fight drug-resistant infections - Healthcare IT News

The prevalence and technical relevance of machine learning algorithms have increased over the years, making predictive decisionmaking tools part of the everyday lives of online users. Today, it is harder to discern what decisions are made by humans, and the others that rely upon the cognition of machines. Most users are unaware of the widespread and normalized use of automated decisionmaking, making them completely oblivious to when machines start, and humans take over, or vice versa. Equally concerning are when online decisions make determinations about ones eligibility for credit, housing, employment, health care, and educational opportunities.

On this new episode of the Tech Tank podcast, Darrell West is joined by Nicol Turner Lee, senior fellow and the director of the Center for Technology Innovation at Brookings who authored a new chapter that is part of the forthcoming book, AI Governance Handbook (Oxford University Press, 2022). The compiled edition of the handbook offers various perspectives on the current state, and future of governance of AI and related technologies.

Responding to the current debates around the trustworthiness and fairness of AI systems, Dr. Turner Lees chapter, Mitigating Algorithmic Biases through Incentive-Based Rating Systems, explores how to improve upon informed consumer choice in the use of machine learning algorithms. Given that AI systems can sometimes mimic and often amplify existing systems of inequalities, there is a need to bring consumers more agency over their trust in and engagement with these models. The chapter explores the need for greater governance and accountability, suggesting a proposed Energy Star rating, or incentive-based rating system that is more risk-aversive and reliant on increased consumer feedback to improve the performance and optimization of these online tools. Dr. Turner Lee also shares a checklist of questions that developers and companies that license and distribute these models should use to ensure more responsible and inclusive tech.

You can listen to the episode and subscribe to the TechTank podcast on Apple, Spotify, or Acast.

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TechTank Podcast Episode 54: Can AI developers be incentivized to debias their algorithms? - Brookings Institution

Park & Battery Partners with Harkness.ai

OAKLAND, Calif. (PRWEB) October 04, 2022

Park & Battery today formalized a strategic partnership with Harkness.ai in which the agency will leverage the breakthrough Harness.ai conversational analytics technology to support both our internal communications and learning and development initiatives, as well as to powerfully enhance its qualitative research capabilities. P&B will also strategically consult on the Harkness.ai brand and product.

Harkness.ai uses cutting-edge AI and natural language processing (NLP) technologies to analyze conversations in any audio or video recording, revealing insights into group and individual dynamics for any group discussion meetings, classes, and more. The platform surfaces key insights into the interactions, topics, speech patterns, language choices, behaviors, and participation levels of everyone involved, raising awareness of habits, strengths, weaknesses, and potential biases. And it recommends additional training and content related to areas for improvement.

High performance in hybrid work requires new tools and techniques. When we were presented with the Harkness.ai platform, we were blown away by the possibilities for our clients and our people across the country and around the world, said Michael Ruby, Park & Batterys President and Chief Creative Officer.

Ruby added: Were also using the Harkness.ai platform to generate insights on key trends, team cohesion, and messaging insights within qualitative research for our clients. From customer and stakeholder interviews to focus groups and team workshops to reviewing pre-recorded webinars and videos, Harkness.ai gives us a powerful new way to accelerate discovery and reporting, validate strategic assumptions, and surface other insights that may have otherwise eluded us.

Harkness.ai was founded by Jason Brooks, Evan Ellison, and Keenan Hale, bringing together their collective experience in education, machine learning, and team performance from their backgrounds at Harvard and Microsoft, on Capitol Hill, as well as from competing as elite Division I athletes. The Harkness.ai platform has been deployed across several use cases and industries, including training, human resources, sales, marketing, software development, and content development.

"Were using incredibly powerful tools that we hope will usher in a new era of civility, service, and culture in business, stated Jason Brooks, CEO, and co-founder of Harkness.ai. We want to bring out the best in human culture, helping people transcend time zones and languages. That's something that we're really excited about."

Working with like-minded, big-hearted people is core to our philosophy at Park & Battery, commented Park & Batterys Michael Ruby. We couldnt be prouder to be teamed up with Harkness.ai as we grow our organizations and capabilities together.

About Park & Battery

Park & Battery is a global brand, marketing, and content agency that harnesses perspectives to create value for brands and businesses globally. Headquartered in Oakland, CA with hubs in New York City, Salt Lake City, and Miami, Park & Battery specializes in creating brands, launches, and experiences that deliver big impact, from strategy and messaging through to creative/design, content, and media/go-to-market. Learn more at ParkandBattery.com

About Harkness.aiHarkness.ai uses deep learning to analyze participant interactions during virtual meetings, transforming conversations from analog data into actionable intelligence across the enterprise. The tool delivers automated, objective feedback to each speaker that ensures equitable discussions and helps distributed teams resolve small issues before they threaten organizational health. In doing so, Harkness.ai help teams optimize individual contributions and identify key language choices that drive meeting outcomes. Learn more at Harkness.ai

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Park & Battery Partners with Harkness.ai to Enhance Agency Strategic Process and Organizational Performance - PR Web

ZeroTime Liberates Employees From Manual Time Tracking to Boost Data Accuracy and Employee Productivity

REDWOOD CITY, Calif., Oct. 4, 2022 /PRNewswire/ -- Replicon announced the launch of ZeroTime, a new revolution in time tracking, that enables organizations to modernize their time tracking process with AI/ML technology. It liberates employees from manual timesheets by automatically capturing their work and time data across more than 100 work applications, thus maximizing employee productivity and customer engagement.

Freedom from Traditional Timesheets

The widespread adoption of hybrid work and the resulting proliferation of digital apps has deepened the need for automating the time capture process. With solutions powered by ZeroTime, organizations can rapidly respond to changing business needs with granular, real-time visibility into their workforce planning and performance.

ZeroTime provides the ability to:

Automatically collect employee time data from collaboration and productivity tools by Microsoft, Google, Zoom, Jira, Slack, Asana, Adobe, and more.

Assemble pre-filled timesheets that employees can review and submit.

Custom reconstruct employees' work week across multiple projects for accurate project costing.

Accurately capture 100% of your employees' work time across projects for accurate billing and payroll management.

Gain granular, real-time visibility into their current projects for better resource management.

Leverage accurate time data to confidently forecast resource capacity to undertake new projects and optimize project bidding.

"ZeroTime is an organically built innovation designed to liberate employees from traditional timesheets,"Lakshmi Raj, Co-CEO at Replicon, said. "It seamlessly connects with Replicon products to automate the time capture process using AI and ML technology. At Replicon, we believe that employee time is a strategic asset and ZeroTime liberates your employees from filling timesheets manually," she added.

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Global businesses use Replicon to benefit from:

A single source of truth for time with global governance

A cloud-based solution that is secure, reliable and scalable to one million employees

Modern interfaces and user experience tailored to each group and location

Native mobile apps for employee tracking and manager approvals

Full real-time visibility into project time data for billing across all groups

Out-of-the-box support for statutory pay rules in 250+ jurisdictions

A global solution with multi-lingual and multi-currency support

Speed of delivery and ability to integrate with any ecosystem

Raj Narayanswamy, Co-CEO at Replicon, said, "ZeroTime enables organizations to transform their hybrid workforce into engaged, agile and high-performing teams. It creates a compelling value proposition for organizations looking to improve their employee experience and customer success in the new normal."

About Replicon

As a pioneer in Time Intelligence, Replicon elevates people as a strategic asset within organizations, improving operational productivity, performance, and profitability. Replicon's award-winning cloud-first solutions for enterprise time management, project time tracking and workforce management are in use by many Fortune 500 companies across the globe. Replicon also offers Polaris, the self-driving Professional Services Automation (PSA) and Project Portfolio Management (PPM) solutions, which help project-driven organizations optimize resource utilization for increased productivity and profitability.

With over 25 years of industry leadership, Replicon continues to innovate new approaches to effectively manage people's time, their skills, projects and resource utilization. Most recently, Replicon launched the world's first Knowledge Workforce Management Solution, which enables organizations to seamlessly collect and process data related to people, projects, skills and time, ensuring a single source of truth for harmony across business functions. Our innovative solution is powered by ZeroTime to harness the power of a single source of accurate data in real time and eliminate all manual processes.

To learn more, please visit http://www.replicon.com.

Liberate your employees from manual timesheets

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SOURCE Replicon

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Replicon Unveils ZeroTime: Revolutionary AI-powered Automatic Time Tracking for the Global Workforce - Yahoo Finance