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The Baruch Group, San Marcos, CA (2026)

08/31/2026

The CEO of HCLTech on Pivoting for the AI Era
by C. Vijayakumar
From the Magazine (September–October 2026)
Mackenzie Stroh
Summary. When gen AI emerged, HCLTech recognized that it would fundamentally alter the economics of IT services, breaking the traditional link between revenue growth and workforce expansion. CEO C. Vijayakumar responded by driving an enterprisewide transformation centered on mindset change, aggressive investment in AI platforms and training, and transparent communication about the future of work. The company is now equipping employees to become AI builders and superusers while helping clients accelerate adoption through scalable, industry-specific solutions.

When generative AI became widely available, in late 2022, we at HCLTech quickly recognized that it would have enormous implications for our organization and our industry. For decades IT services have operated via linear models, with staffing and revenue growing in tandem. Every major technology wave, from the internet to digitalization to the cloud, created demand for more engineers, more consultants, and larger teams. Advances in AI have changed that equation: Vast amounts of knowledge work can now be completed dramatically faster with much less human effort. The technology is not just improving productivity around the edges; it’s altering the economics of our business.

We have opted to embrace AI wholeheartedly, and it has already changed how we operate internally, create value for clients, and measure success. Our AI strategy rests on several important objectives: proactively transforming our services; building differentiated intellectual property (IP) to accelerate AI adoption in enterprises; creating new AI-led services like Physical AI and AI Factory, new AI ecosystem partnerships, and new tools spanning the entire tech stack; and most important, turning our people into AI builders, AI superusers, and human-in-the-loop decision-makers.

A History of Reinvention

In recent years we have been working with some of the world’s top enterprises to deliver advanced engineering and R&D services, which in many respects paved the way for the AI systems reinventing industries today. About a decade ago our big bet was to expand more meaningfully into enterprise software and platforms that helped clients innovate and grow. That work spanned the technologies, platforms, and research initiatives that underpin the next generation of AI solutions. While the pace of progress has been extraordinary, we are still in the early stages of an AI-led transformation that will require navigating new technological, operational, and societal challenges with the same discipline and ingenuity that brought us to where we are now. We believe this is an inflection point and a once-in-a-lifetime opportunity to use technology to amplify both human potential and enterprise value.

Shifting the Mindset
Executing such a large transformation starts with facilitating a mindset shift. Nearly everyone in our business came of age in a world where larger teams meant greater impact and higher revenues. But now teams should adopt AI to increase their output and do more with fewer people.

Speed is also of paramount importance. No one should hesitate because of a desire to preserve the old ways or protect the current cash flow. We need to be comfortable transforming and even cannibalizing parts of our existing business, confident that we are putting ourselves in a stronger position for the future.

As I’ve shared publicly, over the next few years we are targeting a fundamentally different operating model, one in which growth is no longer tied to an increase in head count but is instead driven by enabling our team members to become AI builders and AI superusers, amplifying their potential through AI-augmented, platform-led delivery.

How did we bring everyone into this new way of thinking? With conviction and communication.

When I realized the power of AI, I chose not to beat around the bush with our teams. That’s why I publicly announced an audacious goal: to double our revenue with half the number of people. The bigger strategic message was that we all needed to prepare for a world in which some current roles would no longer exist, emphasizing the urgency of upskilling. Translating that goal into baby steps, our near-term ambition is to deliver at least 5% revenue growth without having to add more people by enhancing our existing workforce with AI. Of course, incremental revenue growth after that 5% will likely require additional talent. But the message is clear: “We are no longer dealing with business as usual. AI changes everything. And your company is encouraging you to stay ahead of the curve.” https://buff.ly/qDoVNjB

When gen AI emerged, HCLTech recognized that it would fundamentally alter the economics of IT services, breaking the traditional link between revenue growth and workforce expansion. CEO C. Vijayakumar responded by driving an enterprisewide transformation centered on mindset change, aggressive invest...

08/31/2026

USC Computer Scientist Answers Five Common Questions About AI
Venice Tang | August 21, 2026
Through AI interpretability research, Robin Jia answers five questions about AI– from emotions and consciousness to learning, decision-making and speed

Artificial intelligence (AI) has advanced rapidly and has generated excitement, but its “black box” nature continues to raise questions about how it works and whether it can be trusted.

Jia’s research sheds light on how AI actually works — from how models learn and generalize to how AI thinks and makes decisions.

Drawing on his research, Jia answers five questions many AI users have.

Does AI have emotions?
“I don’t believe models have emotions,” Robin Jia said. However, he emphasized that AI has learned to reason about human emotions with remarkable accuracy.

Detailed in the paper “Mechanistic Interpretability of Emotion Inference in Large Language Models,” the researchers used mechanistic interpretability (MI) techniques to “open the black box” and identify exactly how models arrive at emotional labels for text.

The study found that emotion processing is not spread evenly throughout the model but is localized within its middle layers. These units gather emotional information from across a sentence and integrate it into the model’s internal representation of the final token. The units then evaluate a situation based on dimensions such as “pleasantness” and “self-agency” to infer how a person would feel.

Jia explained that LLMs contain internal representations of concepts such as “pleasantness” and “self-agency.” While a model can accurately conclude that a story about failing a difficult exam should result in emotions such as sadness or guilt based on its internal evaluation of the text, that does not mean the “creature” in the data center actually feels sad.

There is a clear distinction between reasoning and experiencing emotions. While mechanisms allow AI to infer how a human might feel in a situation, the model itself feels nothing.

How do AI models make decisions?
Instead of viewing an AI model as operating with a single “mind,” Jia proposes thinking of it as a “democratic society” of independent internal components to describe its mathematical decision-making process.

Inside an AI model, internal units known as attention heads and multilayer perceptrons (MLPs) act like independent individuals with specialized roles.

These “workers” communicate through a hidden state called the residual stream, where each reads information from earlier layers and contributes its own update, which Jia compares to a “shared bulletin board.”

The model’s final decision is reached through a “voting” process. The messages written by these independent workers are summed together at the end of the model, and the word, represented internally as a token, with the most “votes” is what the model predicts next.

This process occurs during every decision the model makes. For example, when you’re using ChatGPT, every word and sentence the model generates follows this process.

Jia’s paper, “When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models,” explains the concept behind the LLM decision-making process.

Does AI have a sense of self or consciousness?
Building on his explanation of how AI models make decisions, Jia explains that, unlike humans, who possess a relatively stable set of beliefs, LLMs are an amalgamation of data from across the internet. This leads to “prompt sensitivity,” meaning they may contradict themselves or appear to change their “beliefs” based on minor changes in wording.

Jia also notes that, unlike humans, AI typically lacks the judgment to question contradictory or nonsensical prompts and instead tends to “go along” with them. Humans, by contrast, have the self-awareness to recognize and clarify inconsistent instructions.

“AI doesn’t have true thoughts, opinions or a sense of self like conscious humans do,” Jia said.

Because LLMs are built from internet data rather than a stable set of beliefs, they are sensitive to prompts and may contradict themselves instead of questioning inconsistent instructions.

Rather than viewing AI as “a bodiless human,” Jia describes it as “a new type of creature” that imitates language through fundamentally different internal mechanisms.

Does AI learn the same way humans do?
“Humans learn in a very structured way,” Jia said. He explains that humans follow a curriculum with different stages of learning, mastering basic concepts first and building on them over time.

Unlike humans, AI learning is largely unstructured. He highlights that models attempt to learn everything simultaneously by predicting the next token across vast amounts of internet data. This learning process also gives AI an extraordinary breadth of knowledge across nearly every topic represented on the internet, far exceeding the knowledge of any individual human. https://bit.ly/4wKdjqL

Through AI interpretability research, Robin Jia answers five questions about AI– from emotions and consciousness to learning, decision-making and speed

08/30/2026

Rising Cyberattacks on U.S. Water Infrastructure: Federal Guidance and Next Steps for Operators

Key Points:
Cyberattacks on water utilities are on the rise, with utility companies in at least seven states reporting incidents affecting water operations to the Federal Bureau of Investigation (“FBI”) since July 27, 2026.
The FBI, Environmental Protection Agency (“EPA”), and Cybersecurity and Infrastructure Security Agency (“CISA”) recommend taking immediate action: remove programmable logic controllers (“PLCs”) from direct internet exposure, change default passwords, and route remote access through virtual private networks (“VPNs”) or gateways.
Utilities should review their incident reporting obligations under various regulatory regimes, including the Safe Drinking Water Act and pending regulations under the Cyber Incident Reporting for Critical Infrastructure Act (“CIRCIA”), which are expected to be finalized in September 2026.
Recent guidance issued by Federal agencies warns that malicious cyber actors are targeting internet-facing operational technology (“OT”) used by water and wastewater utilities, particularly PLCs. Since July 27, 2026, water and wastewater utilities in at least seven states have reported incidents to the FBI, and some of that activity has degraded water operations. CISA separately stated that it sees a significant increase in threat actors targeting PLCs in the water and wastewater systems sector and that the cyberattacks have caused water systems to issue boil water notices and implement manual operations.

The reported attack pattern has been the same in each instance; it is straightforward but operationally serious. Attackers remotely access exposed PLCs, change IP addresses and passwords, and thereby cause loss of monitoring, loss of control functionality, operator lockout, and in some cases, loss of function of connected equipment. The FBI reported operational effects including loss of pressure and flooding, and warned that pressure loss could potentially allow untreated groundwater to seep into pipes. CISA has warned that internet-exposed OT assets face increased risk of defacement, configuration changes, operational disruption, and, in severe cases, physical damage. CISA’s broader PLC advisory describes related activity in which attackers modified or deleted project-file logic, manipulated human machine interface (“HMI”) and supervisory control and data acquisition (“SCADA”) display data, and disabled critical shutdown and alarm logic, creating unsafe conditions without notifying operators. https://bit.ly/4ga9xBX

Recent guidance issued by Federal agencies warns that malicious cyber actors are targeting internet-facing operational technology (“OT”) used by water...

08/30/2026

Research: How Remote Work Is Narrowing Early-Career Opportunities
by Zhenyu Liao, Letian (LT) Zhang and Shinan Wang

August 7, 2026
Virgiliu Tilea/500px/Getty Images

Summary. Remote work is quietly reshaping early-career opportunity. A study that included analysis of 50 million European job postings and experiments with 1,200 hiring managers found that remote roles demand roughly 25% more skills, plus more experience and credentials than otherwise identical in-person jobs. This change is driven by larger applicant pools, harder-to-verify fit, and the perceived cost and burden of onboarding new talent remotely. This last reason, in particular, has major implications for leaders managing their talent pipelines. As organizations find it harder to develop inexperienced employees remotely, they increasingly hire people who require less development. That, in turn, leaves fewer opportunities for the next generation to gain the very experience employers now demand, while weaker mentoring, slower learning, and greater isolation further erode young talent’s development and wellbeing. To maintain a strong pipeline of talent, leaders must recalibrate remote hiring criteria, systematically rebuild mentoring and training infrastructure, foster deliberate social connection, and evaluate performance on outcomes, not visibility.close
Imagine that somewhere, a hiring manager faces a decision between two candidates for a remote position. One is a recent graduate: competent, motivated, and full of potential. The other has accumulated five years of experience and can hit the ground running. Not long ago, managers would regularly take a chance on the graduate, confident that the organization could turn raw potential into exceptional performance. The right team, strong mentors, and moments of informal learning can transform a promising newcomer into a high-performing professional.

In our recent research, published in Administrative Science Quarterly, we found that, for remote positions, companies have become far less willing to take a chance on rookie candidates.

We analyzed more than 50 million job postings across 28 European countries and conducted 37 in-depth interviews with hiring managers and executives. We found that remote positions consistently required about 25% more skills, more years of work experience, and higher educational credentials than otherwise identical non-remote positions from the same employer, with the same job title, in the same year. The only difference is where the work is done. We replicated this pattern in a controlled experiment with 1,200 hiring managers recruited online, who set requirements for the same job randomly described as either remote or in-person. The pattern held: simply labeling a job remote pushed companies to demand more skills and more experience.

Our study found that the shift in requirements is primarily driven by three reinforcing forces: larger applicant pools, an increased reliance on measurable credentials, and the training challenges inherent in remote work. https://bit.ly/4q7sXe0

Remote work is quietly reshaping early-career opportunity. A study that included analysis of 50 million European job postings and experiments with 1,200 hiring managers found that remote roles demand roughly 25% more skills, plus more experience and credentials than otherwise identical in-person job...

08/29/2026

Research: The Innovation Problems AI Can’t Solve
by Julian De Freitas, Ayelet Israeli, Gideon Nave, Artem Timoshenko and Olivier Toubia

August 14, 2026

Tatyana Lavrova/Stocksy
Summary. Generative AI can accelerate innovation, but the article argues that its default use often reinforces the human bottlenecks it is meant to solve. In ideation, models steer teams toward familiar ideas and make people more fixated on them. In screening, polished AI-generated pitches can be mistaken for better ideas, while AI recommendations may encode existing bias. In consumer research, simulated customers can speed testing but miss the irrational behaviors that shape real adoption. After launch, AI can summarize vast feedback, yet still help teams justify prior beliefs. The authors’ central advice is diagnostic: before applying AI, leaders should ask whether the bottleneck is informational, judgment-based, or incentive-driven, and preserve direct contact with customers where it matters most for grounding.

Every innovation team now has the same tools: the same foundation models, similar prompt libraries. Yet the results are wildly uneven. Some teams report a creative renaissance while others report a flood of homogenized, forgettable ideas that all sound like they came from the same person. The reason isn’t which model you’re using. It’s that generative AI acts on the human bottlenecks buried inside that process. These bottlenecks respond to AI in different—sometimes opposite—ways.

That’s the core argument of a new working paper, invited by the International Journal of Research in Marketing, that we—researchers at Harvard, Wharton, Northwestern, and Columbia—have written. Instead of asking what generative AI can do, we ask a more durable question: What are the cognitive, social, and organizational constraints that have always slowed innovation down, and how does AI interact with each one?

To answer this, we synthesized recent empirical studies on generative AI and innovation—on idea diversity, AI‑simulated consumers, algorithmic screening of ideas, and post‑launch analysis—against decades of established research on creativity, judgment under uncertainty, and consumer behavior. For each of the four stages below, we asked the same three questions: What is the underlying human constraint, how does generative AI affect it when used “naively,” and what would a more deliberate redesign look like? That consistent structure allowed us to apply the same diagnostic question to bottlenecks that look nothing alike on the surface, such as a screening committee’s bias against novelty, or a consumer’s inability to say what they want before they’ve actually experienced it. Is AI dissolving this bottleneck or quietly making it worse?

The answer matters more than innovation managers currently appreciate. Some of these constraints, it turns out, generative AI can genuinely dissolve. Others, it makes worse while looking like it’s helping. And a few, no plausible future model is going to touch.

The Problem with Assuming AI Will Fix Innovation
Most conversations about AI and innovation treat AI as uniformly good at removing friction in the innovation process. Usually, the assumption is it will achieve this by completely displacing the roles of human innovators: more ideas, faster testing, cheaper research, quicker learning. Our framework pushes back on that assumption. We trace bottlenecks through four stages of the innovation pipeline—ideation, screening, consumer insight, and market learning—and find that at nearly every stage, AI’s default effect (used “naively,” without deliberate redesign) is to deepen the underlying bottleneck, not shrink it.

Here’s why: Most innovation bottlenecks are human problems, not technology ones. They’re issues of how people get anchored to familiar ideas, how panels develop tribal instincts about what counts as “promising,” how consumers can’t tell you what they want until they’ve seen it. Generative AI, trained on the aggregate of existing human output, tends to reproduce exactly the tendencies that caused the bottleneck in the first place; just faster and at scale.

Here are the ways that generative AI deepens human bottlenecks at each stage of the innovation process—and what to do about it. https://buff.ly/P7o18pN

Generative AI can accelerate innovation, but the article argues that its default use often reinforces the human bottlenecks it is meant to solve. In ideation, models steer teams toward familiar ideas and make people more fixated on them. In screening, polished AI-generated pitches can be mistaken fo...

08/29/2026

How extreme weather is changing nature’s role in our lives
ENVIRONMENTAL SCIENCES

A new Perspective article by researchers from ETH Zürich, University of Colorado Boulder, and The Nature Conservancy highlights the urgent need to better understand how weather and climate extremes can disrupt the natural systems we depend on.
10.08.2026 by Sarah Hülsen

Nature provides the vital foundation for human well-being: From providing clean air and water and protecting human societies from natural hazards, to providing a sense of identity and cultural value. However, intensifying extremes such as heatwaves, droughts, storms, and floods are increasingly disrupting ecosystems. For example, marine heatwaves can trigger coral bleaching, reducing coastal protection and fisheries, while droughts can weaken forests, increasing wildfire risk and carbon loss. These changes propagate to society, altering nature’s ability to provide benefits to people.

At the same time, working with nature can support us in meeting many of today’s challenges, including mitigating and adapting to climate change. Restoring wetlands can reduce flood impacts, while diverse forests can sequester carbon and moderate local climates.

However, current knowledge is often siloed. Studies either focus on the impacts of extremes on ecosystems or on the role of ecosystems in reducing disaster risks. Failing to account for the feedbacks between these processes can lead to underestimating how risks escalate when ecosystems are degraded, and overestimating the benefits nature can provide under increasing climate stress.

“Realizing these benefits requires understanding how climate extremes, ecosystems, and human systems interact,” says Sarah Hülsen, lead author of the publication. “This means bringing together insights from several research fields.” The article synthesizes knowledge from climate risk modelling, ecology, social-ecological vulnerability, and Nature’s Contributions to People1 studies to better understand how nature-based solutions can support climate adaptation and mitigation.

“Our perspective highlights the needs and opportunities for interdisciplinary research that considers climate risk to nature-based solutions for biodiversity and human well-being. We are excited about expanding collaboration between the climate risk modeling community with scientists focused on understanding values of ecosystems to people,” says Dr. Laura Dee, co-author and Director of the Nature and People Action CoLab at University of Colorado Boulder.

Reference

Hülsen S, Amaral C, Bresch DN, Hayden MT, Kropf CM, LoPresti A, Manley K, McDonald RI, McIntosh TL, Nomura KJ, Ohlert TJ, Siegel KJ, Steinmann C, and Dee LE. “An interdisciplinary agenda to assess risks of climate extremes to nature’s contributions to people”. Environmental Research: Climate (2026). external pagehttps://doi.org/10.1088/2752-5295/ae861e https://bit.ly/4zaY0cY

A new Perspective article by researchers from ETH Zürich, University of Colorado Boulder, and The Nature Conservancy highlights the urgent need to better understand how weather and climate extremes can disrupt the natural systems we depend on.

08/28/2026

The Readiness Gap: Why Organizations That Test the Plan Still Struggle in Crisis

By Flecher Feng, CPP, PSP, CFE, MSI 17 August 2026 Focus on Crisis Communications
At 8:40 a.m. on a routine Tuesday, a regional manufacturing facility fell silent. A key supplier had suddenly halted shipments, stopping the automated production line midcycle. Within hours, employees flooded the HR hotline seeking guidance. By midday, major customers were inquiring about delayed deliveries. By evening, the issue had attracted attention from local media, while social media were already circulating conflicting and often inaccurate accounts.

Inside the organization, the real crisis was not operational but decisional.

What exactly happened?

Are our people safe?

What is the immediate business impact?

Who is in charge?

Should we activate the crisis management team?

Do regulatory notifications apply?

Is this a contained supply-chain issue or the start of a broader reputational crisis?

The company had a documented crisis plan, assigned roles, and conducted training the previous year. Yet escalation dragged, coordination broke down, and teams quickly discovered that knowing the plan is not the same as executing it under pressure.

As Dwight D. Eisenhower observed, “Plans are useless, but planning is indispensable.”

The gap rarely stems from a lack of effort. It usually results from insufficient progression in how preparedness is built, especially when organizations rely on isolated exercises that are not part of a deliberate developmental sequence.

Many security and risk professionals are familiar with the widely recommended crawl-walk-run approach to crisis exercising. While that foundation remains valuable, the persistent readiness gap reveals a deeper issue: Even organizations that conduct exercises sometimes treat them as compliance checkboxes rather than opportunities to build progressive capability.

This gap leaves leadership teams unprepared for the real decisional challenges that define crisis outcomes.

The Illusion of Having “Tested the Plan”
Testing can mean anything from a document review to a tabletop discussion or an evacuation drill. According to the Business Continuity Institute’s Crisis Management Report 2024, 75.1 percent of organizations activated their crisis teams in the prior year, yet only 22.4 percent rated their overall capabilities as “excellent.” The disconnect is clear: Exercises are happening, but they frequently fail to translate into reliable performance when disruption strikes.

This problem usually traces to one root cause—exercises lack a structured progression that moves teams from basic awareness to confident ex*****on. Crisis competence is not built through isolated simulations alone; it develops layer by layer.

Build Awareness First
Every strong program begins with shared foundational understanding. New team members must internalize governance structures and decision frameworks. Senior leaders need clear escalation thresholds. Functional departments require defined responsibility boundaries.

Awareness sessions and plan reviews answer a critical question: Does everyone truly understand what is expected when disruption hits?

At this stage, misconceptions often surface—especially among executives who assume a written plan equals capability, that assigned roles equal accountability, or that seasoned leaders will instinctively rise to the occasion.

In practice, crisis decisions occurring under incomplete information and extreme time pressure rarely feel intuitive. Security professionals who facilitate these early sessions frequently encounter resistance rooted in overconfidence.

Validate Procedures in Practice
Once awareness is in place, the next layer is making procedures operationally viable. Paper plans often hide flaws that emerge only when teams walk through them step by step:

Who notifies whom, via which channel?

How long does real-world escalation take across distributed or hybrid teams?

Who tracks employee accountability during a disruption?

This validation phase uncovers duplicated efforts, conflicting workflows, unrealistic timelines, and overlooked dependencies. Addressing these issues early prevents later exercises from wasting time on basic design flaws. It also builds the procedural confidence necessary before testing higher-level judgment. https://buff.ly/EqzzUnX

Attempts to facilitate crisis readiness growth often face resistance rooted in organizational overconfidence.

08/28/2026

4 Steps to Transform the “Middle Office” with AI
by H. James Wilson, Chetna Sehgal, Michael Zimmerman, Ali Arsanjani, Blaise Abderholden and Jimmy Priestas
August 20, 2026
Yaroslav Kushta/Getty Images

Summary. Companies are spending heavily on AI, but many are still struggling to turn that investment into measurable business results. One reason is that they’re overlooking the middle office, where work such as contract review, compliance, risk management, and accounts payable still requires a lot of human judgment. Accenture and Google research suggests this is where some of the biggest near-term opportunities lie. Companies can capture them by finding processes where difficult cases still get handed to experts, building AI systems that learn from those experts as they work, and measuring whether AI actually reduces those escalations. The final step is making sure the time AI saves gets put to good use, freeing experts to focus on complex decisions, relationships, and higher-value work.close
Recent evidence on AI adoption has revealed a sobering paradox. Economists who linked large-scale adoption surveys to administrative payroll records found that although most employers had rolled out AI tools in areas where AI should have the highest probability of impact, the effect on earnings and hours two years on was statistically indistinguishable from zero. Despite capable technology and workers reporting productivity gains, the promised benefits failed to materialize on the bottom line.

Why? The researchers found that the time the tools saved was eaten by the work of running them. Employees spent their “freed up” time responding to the model’s output and manually wiring it into existing systems and decisions. The gains never surfaced as earnings or reclaimed hours because the models were not truly integrated. The companies lacked the integration that turns them into results.

Based on our proprietary cross-industry analysis and structured experiments in live operations over the past year, we (Accenture and Google) have identified four steps leading companies take to close the gap between adoption and benefit and transform the middle office. This gap between AI adoption and benefit is widest and most costly in the middle office, the largely overlooked domain between so-called front-office customer-facing functions and back-office routine administration. This is where companies handle non-routine, exception-heavy work such as contract reviews, risk management, and compliance. Decisions often depend heavily on human judgment to resolve ambiguities as business conditions change. And because, according to our analysis, the middle office accounts for more than four-in-10 working-hour tasks across 18 industries, it represents the enterprise’s largest single layer of work and one of its biggest untapped sources of AI value.

Yet most AI roadmaps jump from the back office, where automation is mature and the gains are thinning, straight to ambitious front-office bets like dynamic pricing or AI-assisted dealmaking, whose payoff always seems a year away. The middle office, holding the plurality of the work and the nearest large returns, gets passed over.

That’s a mistake. The key to closing the adoption-to-benefit gap is improving intelligence integration: the work of turning a capable model into a profitable one by wiring it into how decisions actually get made. The results can be dramatic. For instance, in a live experiment we conducted, improving intelligence integration in a typical middle-office workflow lifted overall success from 42 percent to 80 percent. Resolution of the hardest cases rose from 36 percent to 99 percent, while freeing experts for higher-value work. To help your organization achieve stronger process performance and benefit from humans taking on higher-value work, we recommend the following four steps https://bit.ly/4gmy31A

Companies are spending heavily on AI, but many are still struggling to turn that investment into measurable business results. One reason is that they’re overlooking the middle office, where work such as contract review, compliance, risk management, and accounts payable still requires a lot of huma...

The Baruch Group

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