SAP NLP Search Solutions: Adding Intelligent Search to Your SAP Environment

The Data Access Problem Most SAP Shops Have Stopped Talking About

The data is in SAP. Everyone knows it is there. But getting to it requires knowing which transaction code to use, which fields to filter, and often which table names to query — knowledge that lives in a small group of power users and SAP consultants, not in the operations team, the supply chain planner, or the plant manager who actually needs it.

The result is a predictable pattern: analysts spend hours pulling reports. Decisions wait for data. The people closest to the operational problem rely on spreadsheet exports that are already 24 hours stale by the time they reach the right desk.

SAP NLP search solves this at the access layer. It lets users ask questions in plain language and get answers drawn from live SAP data — without transaction codes, without filter configurations, and without a power user in the loop.

USM Business Systems is a CMMi Level 3, Oracle Gold Partner Artificial Intelligence (AI) and IT services firm based in Ashburn, VA. We design and deploy SAP NLP search solutions for manufacturers, pharma companies, logistics operators, and other enterprises where the gap between SAP data and operational decision-making is costing time and accuracy.

What SAP NLP Search Actually Is?

SAP NLP search is a natural language interface layered on top of SAP data. A user types or speaks a question — ‘Which suppliers are running more than 5 days late on open POs this week?’ or ‘What is the current inventory for material X across all plants?’ — and the system retrieves the relevant SAP data and returns a plain-language answer or a structured result.

The technical architecture underneath involves three components working together:

• A retrieval layer that connects to SAP Datasphere views, HANA models, or structured data extracts and fetches the records relevant to the query
• An LLM (large language model) that interprets the natural language question, reasons about the retrieved data, and formulates a response the user can act on
• A user interface layer, typically embedded in SAP Fiori or a standalone web application, that surfaces the interaction in a format the team already uses

This architecture is known as retrieval-augmented generation (RAG). It is the standard pattern for enterprise AI search because it grounds the LLM’s responses in your actual data rather than its training knowledge — which means the answers are accurate to your environment, not generic.

Where SAP NLP Search Delivers Measurable Value?

• Supply Chain and Procurement

Supply chain teams field constant questions about supplier performance, open purchase order status, inventory positions, and demand deviations. In a typical SAP environment without NLP search, each of these questions requires a different transaction, a different filter configuration, and often a trip to the analyst team.

With NLP search on SAP Ariba and S/4HANA data, a supply chain planner asks the question directly and gets the answer in under 30 seconds. Forrester research found that enterprises deploying AI-assisted data access in supply chain operations reduced average data retrieval time by 68% within 90 days of deployment.

• Manufacturing Operations

Plant managers and production supervisors need fast access to quality data, work order status, equipment maintenance history, and production schedule adherence. In SAP PP and SAP PM, this data exists but requires navigation through multiple transaction codes.

NLP search allows a plant manager to ask ‘What is the current first-pass yield for line 3 this week compared to last week?’ and get an answer pulled from SAP QM data — in the moment, on a tablet on the shop floor. The decision that used to wait for an end-of-day report happens in real time.

• Finance and Compliance

Finance teams use SAP NLP search to answer variance questions, retrieve specific transaction histories, and surface exceptions without constructing custom reports. Compliance teams in regulated environments use it to pull audit-relevant data on demand — a capability that previously required either a SAP power user or a scheduled report.

• Procurement and Sourcing

Buyers and category managers use NLP search to surface contract terms, pricing history, and supplier qualification status from SAP Ariba without navigating the full Ariba interface. A buyer preparing for a supplier negotiation asks what the last five purchase prices were for a given material category and gets the answer directly from SAP contract and PO data.

How does NLP search on SAP handle questions the system cannot answer?

A well-designed SAP NLP search system will indicate when a query falls outside its data coverage rather than generating a fabricated answer. This is controlled by the retrieval layer — if the relevant data is not in the configured Datasphere view or HANA model, the system returns a ‘data not available’ response. Configuration of the retrieval layer’s scope is a key design decision during deployment.

Can SAP NLP search be used by non-technical users without SAP training?

Yes — that is the primary value proposition. Users who have never navigated an SAP transaction code can access operational data through plain language questions. The system requires user management and access controls, but the operational interface requires no SAP knowledge. Teams report adoption rates of 80%+ within 30 days when the deployment covers data that users actively need.

What a SAP NLP Search Deployment Involves?

• Phase 1: Data Domain Scoping (Weeks 1-2)

Define which SAP data the search system will cover. This is not ‘all of SAP’ — it is a specific set of data domains aligned to the team or use case being served first. Supply chain planner access to procurement and inventory data is a typical first domain. Finance team access to transaction history and variance data is another common starting point.

• Phase 2: Data Readiness (Weeks 2-4)

Build or validate the Datasphere views or HANA models that the retrieval layer will query. This phase surfaces master data quality issues that need resolution before the NLP layer can produce reliable answers. Budget 2-4 weeks depending on the cleanliness of the target data domain.

• Phase 3: Retrieval Layer Build (Weeks 4-6)

Configure the retrieval system that connects user queries to the relevant SAP data. This includes the embedding model that converts queries and data into a format the LLM can reason about, the vector search or structured retrieval logic, and the data access controls that ensure users only see data they are authorized to access.

• Phase 4: LLM Integration and Response Configuration (Weeks 6-8)

Connect the retrieval layer to the LLM, configure the response format, and build the prompt structure that guides the model to produce useful, accurate answers rather than general responses. Test on 50-100 representative queries across the target data domain. Tune accuracy.

• Phase 5: UI Integration and Rollout (Weeks 8-10)

Deploy the interface — typically a Fiori tile, a Teams integration, or a standalone web application — and roll out to the target user group. Collect feedback on query coverage gaps and expand the data domain in the next iteration.

A first-domain deployment typically reaches productive use in 10-12 weeks. Enterprises that have invested in SAP Datasphere can move faster because the data layer is already structured.

What Separates Good SAP NLP Search From Poor Implementations?

• Scoped retrieval, not open-ended LLM access. The model must be grounded in your SAP data, not relying on its training knowledge. RAG architecture is the standard. Implementations without a proper retrieval layer produce hallucinated data.
• SAP data structure knowledge. The engineers building the retrieval layer need to understand SAP table relationships, master data objects, and SAP Datasphere modeling — not just LLM APIs. The two skill sets are both required.
• Access control from the start. SAP data carries access restrictions for good reasons. An NLP search system that allows any user to query any data field is a governance problem. Role-based data access needs to be designed into the retrieval layer from the beginning.
• Iteration planning. No first deployment covers every query the users will try. The difference between a successful deployment and an abandoned one is whether the team has a process for expanding data coverage based on user feedback.

Why USM Business Systems?

USM Business Systems is a CMMi Level 3, Oracle Gold Partner AI and IT services firm headquartered in Ashburn, VA. With 1,000+ engineers, 2,000+ delivered applications, and 27 years of enterprise delivery experience, USM specializes in AI implementation for supply chain, pharma, manufacturing, and SAP environments. Our SAP AI practice places specialized engineers inside enterprise programs within days — on contract, as dedicated delivery pods, or on a project basis.

Ready to put SAP AI into production? Book a 30-minute scoping call with our SAP AI team.

Get In Touch!

[contact-form-7]

 

FAQ

• Does SAP NLP search require SAP Datasphere, or can it work with HANA directly?

Both work. SAP Datasphere is preferred for new deployments because it provides a governed, semantically structured data layer that is well-suited to retrieval-augmented generation. HANA views and OData APIs can serve as the retrieval source for organizations that have not yet adopted Datasphere, though more custom engineering is required.

• Which LLM works best for SAP NLP search?

The answer depends on your governance requirements. Azure OpenAI (GPT-4) is the most common choice for enterprises with existing Microsoft agreements and data residency requirements. Anthropic Claude and AWS Bedrock models are increasingly common in regulated industries that require stronger content controls. The LLM selection is less important than the retrieval layer architecture.

• How is accuracy measured for SAP NLP search?

The primary accuracy metric is the rate at which the system returns a correct answer to queries tested against known SAP data. A second metric is the rate of ‘I cannot answer this’ responses versus hallucinated answers — the former is acceptable; the latter is not. Measure both during the testing phase and set minimum thresholds before production rollout.

• Can SAP NLP search write data back to SAP, or is it read-only?

Most initial deployments are read-only — the system retrieves and presents data but does not modify SAP records. Write-back capability, where the system can initiate a SAP workflow or update a field based on a user instruction, is the next level and requires agentic architecture rather than pure NLP search.

• What user adoption approach works best for SAP NLP search?

Start with the team that has the most acute data access pain and the most frequent need to query SAP. Supply chain planners, procurement buyers, and plant managers are typically the highest-value early adopters. Get that team productive, collect their feedback on query gaps, and use their results as the business case for expanding to the next team.

Founded: 2023Achievements: 28 wins in the district, with a win rate of over 60%.Latest competitions: WPI competitionsWebsite: https://neiacademy.org/robotics/YouTube: https://www.youtube.com/@neiarobotics Post By: A. Tuter

We often imagine robots as machines with rigid arms, rotating joints, and targeted mechanical movements. The famous Optimus Prime and Bumblebee from the "Transformers" movies appear to fit these criteria. However, such robots would be unable to function in environments that are too confined and cramped.

As manufacturing, logistics, energy, and infrastructure sectors accelerate digital transformation initiatives, cellular-connected devices are replacing isolated machines with continuously communicating systems capable of real-time coordination and remote management.

DNA robots are emerging as tiny programmable machines that could one day deliver drugs, hunt viruses, and build molecular-scale devices. By borrowing ideas from traditional robotics and combining them with DNA folding techniques, scientists are creating structures that can move and act with precision. These robots can be guided using chemical reactions or external signals like light and magnetic fields.

The Mori3 modular origami robot. Image credit: EPFL. Reproduced under CC-BY-SA.

By Celia Luterbacher

If the goal of a robot is to perform a function, then minimizing the possibility of failure is a top priority when it comes to robotic design. But this minimization is at odds with the robotic raison d’être: systems with multiple units, or agents, can perform more diverse functions, but they also have more different parts that can potentially fail.

Researchers led by Jamie Paik, head of the Reconfigurable Robotics Laboratory (RRL) in EPFL’s School of Engineering, have not only circumvented this problem, but flipped it: they have designed a modular robot that actually lowers its odds of failure by sharing resources among its individual agents.

“For the first time, we have found a way to reverse the trend of increasing odds of failure with increasing function,” Paik explains. “We introduce local resource sharing as a new paradigm in robotics, reducing the failure rate with a larger number of modules.”

In a paper published in Science Robotics, the team showed how exploiting redundant resources and sharing them locally enabled a modular origami robot to successfully navigate a complex terrain, even when one module was completely deprived of power, sensing, and wireless communication.

Sharing is caring

The RRL team took inspiration for their innovation from nature, where the problem of failure is often solved collectively. Birds share local sensing information through flocking behavior, some trees communicate threats to neighbors using airborne signals, and cells continuously transport nutrients across their membranes so that the death of any individual doesn’t significantly impact the overall organism.

Modular robots, which are composed of multiple units that connect to form a complete system, are analogous to multicellular or collective organisms, but until now, their design has been a source of vulnerability: the failure of one module often disables some, if not all, of the robot’s ability to perform tasks. Some modular robots get around this problem with built-in backup resources or self-reconfiguration abilities, but these approaches usually don’t completely restore functionality.

For their study, the RRL team used something called hyper-redundancy: the sharing of all critical power, communication, and sensing resources across all modules, without any change to the robot’s physical structure.

“We found that sharing just one or two resources was not enough: if each resource had an equal chance of failure, system reliability would continue to drop with an increasing number of agents. But when all resources were shared, this this trend was reversed,” Paik says.

In a locomotion task experiment with the Mori3 robot, which is composed of four triangular modules, the team experimented with cutting battery power, wireless communication, and sensing to the central module. Normally, this ‘dead’ central module would block the articulation and movement of the other three, but thanks to hyper-redundancy, the neighboring modules fully compensated for its lack of resources. This allowed the Mori3 to successfully ‘walk’ toward a barrier and contort itself effectively to pass underneath it.

“Essentially, our methodology allowed us to ‘revive’ a dead module in a collective and bring it back to full functionality. Our local resource-sharing framework therefore has the potential to support highly adaptive robots that can operate with unprecedented reliability, finally resolving the reliability-adaptability conflict,” summarizes RRL researcher and first author Kevin Holdcroft.

The researchers say that future work could focus on applying their resource sharing framework to more complex systems with increasing numbers of agents. In particular, the same concept could be extended to robotic swarms, with hardware adaptations that allow swarm members to dock to each other for energy and information transfer.

References

Scalable robot collective resilience by sharing resources, Holdcroft, K., Bolotnikova, A., Monforte, A.J., and Paik, J., Science Robotics (2026).

The Mori3 modular origami robot. Image credit: EPFL. Reproduced under CC-BY-SA.

By Celia Luterbacher

If the goal of a robot is to perform a function, then minimizing the possibility of failure is a top priority when it comes to robotic design. But this minimization is at odds with the robotic raison d’être: systems with multiple units, or agents, can perform more diverse functions, but they also have more different parts that can potentially fail.

Researchers led by Jamie Paik, head of the Reconfigurable Robotics Laboratory (RRL) in EPFL’s School of Engineering, have not only circumvented this problem, but flipped it: they have designed a modular robot that actually lowers its odds of failure by sharing resources among its individual agents.

“For the first time, we have found a way to reverse the trend of increasing odds of failure with increasing function,” Paik explains. “We introduce local resource sharing as a new paradigm in robotics, reducing the failure rate with a larger number of modules.”

In a paper published in Science Robotics, the team showed how exploiting redundant resources and sharing them locally enabled a modular origami robot to successfully navigate a complex terrain, even when one module was completely deprived of power, sensing, and wireless communication.

Sharing is caring

The RRL team took inspiration for their innovation from nature, where the problem of failure is often solved collectively. Birds share local sensing information through flocking behavior, some trees communicate threats to neighbors using airborne signals, and cells continuously transport nutrients across their membranes so that the death of any individual doesn’t significantly impact the overall organism.

Modular robots, which are composed of multiple units that connect to form a complete system, are analogous to multicellular or collective organisms, but until now, their design has been a source of vulnerability: the failure of one module often disables some, if not all, of the robot’s ability to perform tasks. Some modular robots get around this problem with built-in backup resources or self-reconfiguration abilities, but these approaches usually don’t completely restore functionality.

For their study, the RRL team used something called hyper-redundancy: the sharing of all critical power, communication, and sensing resources across all modules, without any change to the robot’s physical structure.

“We found that sharing just one or two resources was not enough: if each resource had an equal chance of failure, system reliability would continue to drop with an increasing number of agents. But when all resources were shared, this this trend was reversed,” Paik says.

In a locomotion task experiment with the Mori3 robot, which is composed of four triangular modules, the team experimented with cutting battery power, wireless communication, and sensing to the central module. Normally, this ‘dead’ central module would block the articulation and movement of the other three, but thanks to hyper-redundancy, the neighboring modules fully compensated for its lack of resources. This allowed the Mori3 to successfully ‘walk’ toward a barrier and contort itself effectively to pass underneath it.

“Essentially, our methodology allowed us to ‘revive’ a dead module in a collective and bring it back to full functionality. Our local resource-sharing framework therefore has the potential to support highly adaptive robots that can operate with unprecedented reliability, finally resolving the reliability-adaptability conflict,” summarizes RRL researcher and first author Kevin Holdcroft.

The researchers say that future work could focus on applying their resource sharing framework to more complex systems with increasing numbers of agents. In particular, the same concept could be extended to robotic swarms, with hardware adaptations that allow swarm members to dock to each other for energy and information transfer.

References

Scalable robot collective resilience by sharing resources, Holdcroft, K., Bolotnikova, A., Monforte, A.J., and Paik, J., Science Robotics (2026).

Enterprise AI Engineers for SAP: What to Look For, What They Cost, and How to Get Them Fast?

The Staffing Problem That Is Slowing Every SAP AI Program

The program is approved. The use case is scoped. The SAP landscape is documented. And then the staffing process begins — and it stalls.

Standard AI engineering talent is available. Standard SAP consultants are available. But the engineer who understands both SAP data architecture and modern AI frameworks, who has deployed something in SAP AI Core before, who knows what OData looks like on the other side of a BTP integration — that person is scarce, expensive, and usually already committed to another program.

This article covers what enterprise AI engineers for SAP actually need to know, what they cost in today’s market, and what your options are for getting them deployed quickly.

USM Business Systems is a CMMi Level 3, Oracle Gold Partner AI and IT services firm headquartered in Ashburn, VA, with 1,000+ engineers and a specialized SAP AI practice. We place SAP BTP AI developers, SAP AI Core engineers, and enterprise LLM integration specialists on contract, as dedicated delivery pods, and on project-based engagements.

The Four Role Types That Matter for SAP AI Programs

• SAP AI Core Engineer

This is the role most programs understaff. SAP AI Core is the managed runtime where models are deployed, versioned, and governed inside the SAP ecosystem. An AI Core engineer configures the runtime environment, manages model lifecycle, handles the API connections between AI Core and external model providers, and sets up the monitoring and logging that auditors will ask about.

A general ML engineer can learn AI Core, but the learning curve runs 6-10 weeks in a live SAP environment. A program that needs AI Core production-ready in 8 weeks does not have that time.

• SAP BTP AI Developer

BTP developers build the application layer on top of SAP’s Business Technology Platform — the APIs, the integration flows, the Fiori extensions, and the AI Foundation services that connect the AI capability to SAP data and workflows. BTP AI developers need to know both SAP’s integration patterns and modern AI API integration. This combination is genuinely rare.

• Enterprise LLM Integration Engineer

This engineer connects external LLM providers — Azure OpenAI, Anthropic, AWS Bedrock — to the SAP environment through BTP Integration Suite and SAP AI Core’s generative AI hub. They manage authentication, data formatting, latency requirements, and the retrieval layer that ensures the model is reading the right SAP data. They also understand the governance requirements that determine what data can leave the SAP boundary.

• SAP Data Architecture Specialist

AI capabilities are only as good as the data they read. The SAP data architecture specialist structures SAP Datasphere views, HANA models, and data pipelines to give AI systems clean, semantically meaningful access to enterprise data. This role is often the first bottleneck — programs that try to deploy AI without involving a SAP data architect first spend weeks discovering master data quality problems they should have found in week one.

What These Engineers Cost in 2026?

Hourly bill rates for specialized SAP AI engineers reflect both the scarcity of the combined SAP and AI skill set and the urgency that drives most hiring decisions in this space.

Role	US Contract Rate	Typical Availability	Ramp Time (SAP env)
SAP AI Core Engineer	$160-$210/hr	3-6 week search	1-2 weeks
SAP BTP AI Developer	$140-$180/hr	2-5 week search	1-2 weeks
Enterprise LLM Integration Engineer	$150-$200/hr	3-6 week search	2-3 weeks
SAP Data Architecture Specialist	$130-$170/hr	2-4 week search	1 week
Enterprise AI Solution Architect	$200-$260/hr	4-8 week search	2-3 weeks

Rates reflect US market data as of Q1 2026. Rates for offshore or nearshore resources range 40-60% lower for equivalent technical profiles.

Why is it so hard to find engineers with both SAP and AI skills?

SAP expertise is typically built over years of working inside enterprise SAP programs — it is not a technology you learn from documentation alone. AI engineering has moved fast in the opposite direction, attracting engineers who have not worked in traditional enterprise software environments. The overlap between the two talent pools is small and has not kept pace with demand as SAP AI programs have accelerated.

Three Ways to Staff a SAP AI Program

• Option 1: Direct Hire

Best for organizations building a permanent internal SAP AI capability. Timeline to a qualified hire: 12-20 weeks for senior roles. Cost includes recruiting fees (20-30% of first-year salary for specialized roles), onboarding time, and the risk that the hire is not the right profile for the specific program.

• Option 2: Contract Staffing Through a Specialized Partner

Best for programs with a defined timeline and a specific skill gap. A specialized staffing partner with an existing bench of SAP AI engineers can place a qualified resource in 1-3 weeks. The engineer is already credentialed, has SAP environment experience, and ramps in days rather than weeks. Contracts typically run 3-12 months with extension options.

The key qualifier: specialized. A general IT staffing firm will not have SAP AI Core engineers on their bench. The right partner sources specifically from the SAP AI talent pool and has placed these roles in live programs before.

• Option 3: Dedicated AI Delivery Pod

Best for programs that need a full delivery capability rather than individual contributors. A pod typically includes one solution architect, two to three SAP AI engineers, and one LLM integration specialist. The pod operates as an embedded unit inside the client program, with the staffing partner responsible for team composition, continuity, and delivery quality.

Pods reach productive output faster than assembled teams of individual contractors because the team members have worked together before. For system integrators running large SAP programs with tight delivery milestones, this is often the fastest path to predictable output.

How Fast Can You Place?

This is the first question every system integrator asks, and the honest answer depends on the role and the depth of the partner’s existing bench.

• SAP Data Architecture Specialist: 5-10 business days for a contract placement from an active bench
• SAP BTP AI Developer: 7-14 business days
• SAP AI Core Engineer: 10-15 business days
• Enterprise LLM Integration Engineer: 10-18 business days
• Dedicated AI Delivery Pod (3-5 person): 2-4 weeks for full team mobilization

USM maintains an active bench of SAP AI engineers across these role types. If your program has a specific role requirement and a near-term start date, reach out directly for a bench availability check: usmsystems.com/services/sap-ai-engineering-talent.

Why USM Business Systems?

USM Business Systems is a CMMi Level 3, Oracle Gold Partner AI and IT services firm headquartered in Ashburn, VA. With 1,000+ engineers, 2,000+ delivered applications, and 27 years of enterprise delivery experience, USM specializes in AI implementation for supply chain, pharma, manufacturing, and SAP environments. Our SAP AI practice places specialized engineers inside enterprise programs within days — on contract, as dedicated delivery pods, or on a project basis.

Ready to put SAP AI into production? Book a 30-minute scoping call with our SAP AI team at usmsystems.com.

Get In Touch!

[contact-form-7]

 

FAQ

What certifications should a SAP AI engineer have?

SAP offers certifications in SAP BTP, SAP AI Core, and SAP Integration Suite that are relevant. For the LLM and agentic framework layer, certifications from major cloud providers (Microsoft, AWS, Google) combined with hands-on project experience in SAP environments are more indicative of capability than credentials alone.

Can SAP AI engineers work remotely on enterprise programs?

Yes. Most SAP AI engineering work — integration configuration, model deployment, API development — is done remotely. Periods of on-site collaboration are common during initial environment access, architecture review, and production go-live. Hybrid models work well for programs with security-cleared or regulated environments.

How do you assess whether a SAP AI engineer has the right skills for a specific program?

The most reliable assessment is a structured technical review covering the specific platforms involved — SAP AI Core, BTP Integration Suite, SAP Datasphere — combined with a review of prior program experience that matches your environment. Ask specifically about production deployments, not proofs of concept.

What is a SAP AI delivery pod and how is it different from a contract team?

A delivery pod is a pre-assembled, small team — typically 3-5 people — with defined roles and prior working experience together. A contract team is assembled from individual resources who may not have worked together before. Pods are faster to productive output because team formation and working pattern development have already happened.

What engagement length makes sense for contract SAP AI engineers?

Initial contracts of 3-6 months cover most first-deployment programs. Extensions of 6-12 months are common when the engineer is embedded in an ongoing program. Project-based engagements with fixed deliverables and defined end dates work well for enterprises that prefer milestone-based contracting.

 

Is it better to hire SAP engineers and train them on AI, or AI engineers and train them on SAP?

The answer depends on the role. For SAP AI Core and BTP work, starting with a strong SAP BTP developer and adding AI integration skills is faster — the SAP platform knowledge takes longer to build than the AI API skills. For the LLM integration and agentic framework layer, starting with a strong AI engineer and adding SAP data access patterns is often faster. The data architecture role almost always needs a dedicated SAP specialist.

 

A research team, including Huanyu "Larry" Cheng, James L. Henderson Jr. Memorial Associate Professor of Engineering Science and Mechanics at Penn State, is using pressure sensors—tiny devices, roughly the size of a paperclip, that can measure the force applied over an area—to design a highly sensitive electronic "skin" to use alongside robots and prosthetic limbs.

Robots are increasingly being used in manufacturing, agriculture and health care. But programming a team of robots to carry out individual tasks raises a question: How can robots learn from other robots if they are built differently? A multi-institutional team including Chongjie Zhang, an associate professor of computer science and engineering at WashU McKelvey Engineering, have developed a new method that enables robots to achieve intentions shown by their peers.

The fields of manufacturing, logistics, and even restaurants are increasingly moving toward automation, with robots being employed for a wide range of tasks. One of the most critical applications of robots is material handling, where grippers are used to move objects, such as automotive parts, logistics packages, food ingredients, and restaurant dishes. This reduces the burden on human workers while lowering the risk of accidents, thereby improving workplace safety.

More than a production figure, the achievement marks a significant step forward for the robotics industry, signaling a transition from early-stage validation to scalable, real-world deployment.

The Fully Automated Researcher

Writer Will Douglas Heaven reports that the next, major enhancement of ChatGPT is focused on transforming the AI into an incredibly in-depth researcher.

Observes Heaven: “The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself.”

Look for an entry level, ‘intern version’ of this system by September – followed by a fully automated, multi-AI-agent powered upgrade by 2028, Heaven adds.

In other news and analysis on AI writing:

*AI Gifted? You’re a Preferred Employee at Social Media Network Reddit: Young college grads with AI chops are the top choice as employees by Steve Huffman, CEO, Reddit.

Observes writer Emma Burleigh: “While some CEOs marvel over the abilities of chatbots and AI agents, recent graduates are actually ripe for the new tech-driven world of work: the digital natives grew up with the internet, and spent most of their higher education in the ChatGPT era.

“They’re deeply familiar with the technology and are much more apt to leverage it in their work.”

Word to the wise.

*Publisher Yanks Horror Novel for Suspected AI Use: “Shy Girl,” a walk on the spooky side, has been pulled from publication for suspected use of AI in its creation.

Observes writer Alexandra Alter: “The cancellation of the novel reveals the challenges the book world is navigating as the adoption of AI becomes more widespread.

“Readers and many writers remain ferociously opposed to the use of the technology for writing — which they regard as cheating or a form of theft.”

*AI as Journalist: At Fortune Magazine, It’s De Rigueur: As many fiction and nonfiction media outlets express outrage over AI-generated content, others are embracing it unabashedly.

Case-in-point: Fortune Magazine, where nearly 20% of all articles are generated in part by AI, according to writer Isabella Simonetti.

Most of those articles are penned – with the help of AI – by journalist Nick Lichtenberg, who has “produced more stories in six months than any of his colleagues at Fortune delivered in a year,” according to Simonetti.

*Thanks But No Thanks: Microsoft Lightens Up on AI in Windows 11: In response to popular demand, Microsoft is paring down the presence of its AI assistant – Copilot – in Windows’ latest version.

Observes writer Ross Kelly: “Microsoft has faced criticism over its persistent integration of Copilot features across the operating system — a strategy it has pursued for over 18 months now.”

Apparently, many users are put-off by the Redmond Titan’s desire to transform Windows 11 into an ever-evolving, ‘agentic’ operating system.

*OpenAI Kills Its Sora Video App: An AI video-maker that once struck fear in the hearts of Hollywood filmmakers has been scrapped.

Writer Connie Loizos reports that maker OpenAI pulled-the-plug on Sora. The reason: Sora was simply too unprofitable.

Observes Loizos: “The app was burning through roughly $1 million every day — not because people loved it, but because video generation is so costly to run.”

*OpenAI Puts ‘Adult Mode’ on Ice: OpenAI has abandoned the release of an ‘adult mode’ for ChatGPT, which it has been mulling for many months.

Observes writer Alina Maria Stan: “The feature was announced with confidence, delayed twice, and ultimately abandoned after pushback from staff, advisors, and investors.”

Also a factor: OpenAI’s widely reported decision to pare-down side projects and redouble its efforts on enhancing the core functions of ChatGPT.

*Google Experimenting With ‘Auto-Reply to a Review’ Tool: Business owners tongue-tied when faced with a negative – or positive — review may want to check-out an auto-reply tool now in beta in Google Business Profile.

The new AI-powered helper is designed to automatically serve-up responses to a customer review, which can be assessed and edited by the business – and then manually submitted.

Observes writer Danny Goodwin: “Availability is inconsistent across accounts and reviews. The feature has been spotted in the U.S., Brazil, and India, but not widely in Europe.”

*63% of Mid-Sized Law Firms Now Use AI: New survey finds that a healthy majority of mid-sized law firms are all in on AI use.

Equally eyebrow raising: 94% of those users predict that AI will spike revenue and enhance customer service.

Observes writer Bob Ambrogi: “Mid-sized firms are moving beyond experimentation into operational integration, the report says. Common implementations include automation of document creation (70%), email filing (60%), and data extraction (53%).”

*AI Pioneer Grammarly Hit With Class Action Suit: AI editing and writing tool Grammarly has been hit with a lawsuit, which accuses the firm of using writers’ identities without their permission.

Essentially, more than a few authors and writers are angry that Grammarly’s now-abandoned AI ‘Expert Review’ feature analyzed users’ writing — then attributed that analysis those scribes without their permission.

Observes Top Class Actions: Plaintiff Julia Angwin “Grammarly users were able to upload their writing and receive real-time comments on how to improve their prose from Angwin, (Stephen) King and other acclaimed writers for $12-a-month.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

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A planetary gearbox is a compact mechanical transmission used to reduce speed and increase torque in motor-driven systems. It consists of a central sun gear, multiple planet gears that rotate around it, and an outer ring gear, allowing load to be shared across several contact points. This configuration provides high torque capacity, good efficiency, and […]

No matter how sophisticated they are, robots can often be indecisive and struggle with multi-step chores in the real world. For example, if you tell a robot to tidy a messy room, it might understand the goal but not know where to grab each object. It could even end up inventing steps. To address these common mistakes, Microsoft and a group of academics have developed an AI benchmark system to improve the accuracy of robot planning. The details of their work are published in a paper on the arXiv preprint server.