Category robotics

LMI Technologies, in partnership with STT Systems and Stemmer Imaging, implemented an automated quality inspection system to detect missing bolts on automotive blanks. The system integrated multiple Gocator 3D sensors and an RFID tracking system for real-time analysis.

Multirotor drones are used across a wide range of applications today. As their roles and operating environments become more diverse, performance requirements place increasing demands on design choices. Multirotor design involves several key factors, including maneuverability, stability, payload capacity, flight duration, safety, and reliability. These factors are closely interconnected, and improving one often requires trade-offs […]

Researchers have developed artificial tendons for muscle-powered robots. They attached the rubber band-like tendons (blue) to either end of a small piece of lab-grown muscle (red), forming a “muscle-tendon unit.” Credit: Courtesy of the researchers; edited by MIT News.

Our muscles are nature’s actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate “biohybrid robots” made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers.

But for the most part, these designs are limited in the amount of motion and power they can produce. Now, MIT engineers are aiming to give bio-bots a power lift with artificial tendons.

In a study which recently appeared in the journal Advanced Science, the researchers developed artificial tendons made from tough and flexible hydrogel. They attached the rubber band-like tendons to either end of a small piece of lab-grown muscle, forming a “muscle-tendon unit.” Then they connected the ends of each artificial tendon to the fingers of a robotic gripper.

When they stimulated the central muscle to contract, the tendons pulled the gripper’s fingers together. The robot pinched its fingers together three times faster, and with 30 times greater force, compared with the same design without the connecting tendons.

The researchers envision the new muscle-tendon unit can be fit to a wide range of biohybrid robot designs, much like a universal engineering element.

“We are introducing artificial tendons as interchangeable connectors between muscle actuators and robotic skeletons,” says lead author Ritu Raman, an assistant professor of mechanical engineering (MechE) at MIT. “Such modularity could make it easier to design a wide range of robotic applications, from microscale surgical tools to adaptive, autonomous exploratory machines.”

The study’s MIT co-authors include graduate students Nicolas Castro, Maheera Bawa, Bastien Aymon, Sonika Kohli, and Angel Bu; undergraduate Annika Marschner; postdoc Ronald Heisser; alumni Sarah J. Wu and Laura Rosado; and MechE professors Martin Culpepper and Xuanhe Zhao.

Muscle’s gains

Raman and her colleagues at MIT are at the forefront of biohybrid robotics, a relatively new field that has emerged in the last decade. They focus on combining synthetic, structural robotic parts with living muscle tissue as natural actuators.

“Most actuators that engineers typically work with are really hard to make small,” Raman says. “Past a certain size, the basic physics doesn’t work. The nice thing about muscle is, each cell is an independent actuator that generates force and produces motion. So you could, in principle, make robots that are really small.”

Muscle actuators also come with other advantages, which Raman’s team has already demonstrated: The tissue can grow stronger as it works out, and can naturally heal when injured. For these reasons, Raman and others envision that muscly droids could one day be sent out to explore environments that are too remote or dangerous for humans. Such muscle-bound bots could build up their strength for unforeseen traverses or heal themselves when help is unavailable. Biohybrid bots could also serve as small, surgical assistants that perform delicate, microscale procedures inside the body.

All these future scenarios are motivating Raman and others to find ways to pair living muscles with synthetic skeletons. Designs to date have involved growing a band of muscle and attaching either end to a synthetic skeleton, similar to looping a rubber band around two posts. When the muscle is stimulated to contract, it can pull the parts of a skeleton together to generate a desired motion.

But Raman says this method produces a lot of wasted muscle that is used to attach the tissue to the skeleton rather than to make it move. And that connection isn’t always secure. Muscle is quite soft compared with skeletal structures, and the difference can cause muscle to tear or detach. What’s more, it is often only the contractions in the central part of the muscle that end up doing any work — an amount that’s relatively small and generates little force.

“We thought, how do we stop wasting muscle material, make it more modular so it can attach to anything, and make it work more efficiently?” Raman says. “The solution the body has come up with is to have tendons that are halfway in stiffness between muscle and bone, that allow you to bridge this mechanical mismatch between soft muscle and rigid skeleton. They’re like thin cables that wrap around joints efficiently.”

“Smartly connected”

In their new work, Raman and her colleagues designed artificial tendons to connect natural muscle tissue with a synthetic gripper skeleton. Their material of choice was hydrogel — a squishy yet sturdy polymer-based gel. Raman obtained hydrogel samples from her colleague and co-author Xuanhe Zhao, who has pioneered the development of hydrogels at MIT. Zhao’s group has derived recipes for hydrogels of varying toughness and stretch that can stick to many surfaces, including synthetic and biological materials.

To figure out how tough and stretchy artificial tendons should be in order to work in their gripper design, Raman’s team first modeled the design as a simple system of three types of springs, each representing the central muscle, the two connecting tendons, and the gripper skeleton. They assigned a certain stiffness to the muscle and skeleton, which were previously known, and used this to calculate the stiffness of the connecting tendons that would be required in order to move the gripper by a desired amount.

From this modeling, the team derived a recipe for hydrogel of a certain stiffness. Once the gel was made, the researchers carefully etched the gel into thin cables to form artificial tendons. They attached two tendons to either end of a small sample of muscle tissue, which they grew using lab-standard techniques. They then wrapped each tendon around a small post at the end of each finger of the robotic gripper — a skeleton design that was developed by MechE professor Martin Culpepper, an expert in designing and building precision machines.

When the team stimulated the muscle to contract, the tendons in turn pulled on the gripper to pinch its fingers together. Over multiple experiments, the researchers found that the muscle-tendon gripper worked three times faster and produced 30 times more force compared to when the gripper is actuated just with a band of muscle tissue (and without any artificial tendons). The new tendon-based design also was able to keep up this performance over 7,000 cycles, or muscle contractions.

Overall, Raman saw that the addition of artificial tendons increased the robot’s power-to-weight ratio by 11 times, meaning that the system required far less muscle to do just as much work.

“You just need a small piece of actuator that’s smartly connected to the skeleton,” Raman says. “Normally, if a muscle is really soft and attached to something with high resistance, it will just tear itself before moving anything. But if you attach it to something like a tendon that can resist tearing, it can really transmit its force through the tendon, and it can move a skeleton that it wouldn’t have been able to move otherwise.”

The team’s new muscle-tendon design successfully merges biology with robotics, says biomedical engineer Simone Schürle-Finke, associate professor of health sciences and technology at ETH Zürich.

“The tough-hydrogel tendons create a more physiological muscle–tendon–bone architecture, which greatly improves force transmission, durability, and modularity,” says Schürle-Finke, who was not involved with the study. “This moves the field toward biohybrid systems that can operate repeatably and eventually function outside the lab.”

With the new artificial tendons in place, Raman’s group is moving forward to develop other elements, such as skin-like protective casings, to enable muscle-powered robots in practical, real-world settings.

This research was supported, in part, by the U.S. Department of Defense Army Research Office, the MIT Research Support Committee, and the National Science Foundation.

AI tools designed to diagnose cancer from tissue samples are quietly learning more than just disease patterns. New research shows these systems can infer patient demographics from pathology slides, leading to biased results for certain groups. The bias stems from how the models are trained and the data they see, not just from missing samples. Researchers also demonstrated a way to significantly reduce these disparities.

In-House AI Development vs. Hiring a Custom AI Software Development Company

When your company decides to implement AI, one critical question dominates the conversation: should you build an in-house team or partner with an external custom AI software development company? Both paths can lead to success, but they require vastly different investments, timelines, and internal capabilities.

Before diving into the details, here’s a high-level comparison to help you quickly assess which approach aligns with your current business situation:

Quick Decision Framework

Decision Factor	In-House Development	External AI Company	Best For
Upfront Investment	$1M-$2M+ annually	$50K-$500K project-based	Companies needing predictable budgets
Time to First Deployment	9-18 months	3-6 months	Speed-critical implementations
Access to Expertise	Limited to hired talent	Multidisciplinary teams immediately	Diverse AI capabilities needed
Control & IP Ownership	Complete control, 100% IP	Shared control, negotiable IP	Regulated industries, proprietary tech
Scalability	Slow, fixed capacity	Rapid, flexible scaling	Fluctuating project demands
Long-Term Innovation	Builds institutional knowledge	Project-based, limited transfer	AI as core competitive advantage
Data Security	Direct control	Requires strong protocols	Highly sensitive data
ROI Timeline	18-24+ months	12-18 months	Companies needing faster returns

When your company is ready to implement AI, whether for predictive analytics, process automation, intelligent decision-making, or data optimization, one critical question emerges: Should you build an in-house AI team or partner with a custom AI software development company?

While AI adoption is on the rise, many organizations struggle to move their AI initiatives from pilot programs to full-scale production. The difference between success and stagnation often comes down to choosing the right development approach.

In this guide, we’ll compare in-house AI development against hiring a specialized custom AI software development company across 8 critical factors, and highlight 7 leading AI development firms to help you make the best decision for your organization.

Understanding the Two Approaches

In-House AI Development means recruiting data scientists, ML engineers, AI architects, and DevOps specialists, then investing in infrastructure, tools, training, and ongoing management. You maintain complete control over strategy, execution, and intellectual property.

Best for: Companies where AI is core to long-term competitive advantage, with sufficient capital and time to build institutional expertise.

Hiring a Custom AI Software Development Company gives you immediate access to specialized talent, proven methodologies, and scalable resources, without the overhead of full-time hires.

Best for: Companies needing rapid AI deployment, specialized expertise, or flexible scaling without long-term fixed commitments.

The 8 Critical Comparison Factors

We evaluated both approaches across 8 weighted factors (totaling 100%) to help you determine which model aligns with your business goals.

1. Upfront Cost & Total Investment (20% Weight)

Cost Component	In-House	External Partner
AI Engineer Salaries	$150K-$318K per engineer annually	$0 (included in project fee)
Infrastructure	$50K-$200K+ annually	$0 (vendor manages)
Recruiting Costs	$15K-$30K per hire	$0
Total First-Year (5-person team)	$1M-$2M+	$50K-$500K project-based

Winner: External development for cost-conscious companies needing predictable budgets.

2. Time-to-Market & Speed (15% Weight)

• In-House: 6-12 months to hire team + 3-6 months onboarding = 9-18 months to first production model
• External: Immediate start with pre-assembled teams = 3-6 months to first production model (60-70% faster)

Winner: External development for companies where speed-to-market is a competitive advantage.

3. Access to Specialized Expertise (15% Weight)

• In-House: Limited to talent you can attract; requires ongoing training; gaps in niche skills (Generative AI, Computer Vision, NLP, MLOps).
• External: Instant access to multidisciplinary teams; exposure to diverse industries; stays current with latest AI frameworks (TensorFlow, PyTorch, LangChain, GPT-4).

Winner: External development for companies needing diverse, cutting-edge capabilities.

4. Control & IP Ownership (10% Weight)

• In-House: Full control over roadmap and priorities; 100% IP ownership; direct oversight; no third-party dependencies.
• External: Shared control requiring strong communication; negotiable IP ownership (most contracts grant clients full IP rights); vendor dependency for updates.

Winner: In-house development for companies prioritizing absolute control and proprietary IP protection.

5. Scalability & Flexibility (10% Weight)

• In-House: Slow to scale up (recruiting, onboarding delays); difficult to scale down (layoffs, severance); fixed capacity regardless of needs.
• External: Rapid scaling (increase/decrease team size within weeks); project-based flexibility; no unused capacity costs.

Winner: External development for fluctuating AI project demands.

6. Long-Term Innovation Capability (10% Weight)

• In-House: Builds institutional knowledge; fosters continuous innovation culture; reduces long-term vendor dependency; supports ongoing iteration.
• External: Project-based engagement; limited knowledge transfer unless structured; best when combined with internal champions.

Winner: In-house development for companies committing to AI as a core, long-term strategy.

7. Data Security & Compliance Risk (10% Weight)

• In-House: Direct control over data access, storage, governance; easier compliance maintenance (HIPAA, GDPR, SOC 2); lower risk of third-party breaches.
• External: Requires strong NDAs and security protocols; reputable firms offer SOC 2, ISO 27001, HIPAA compliance; data can remain on-premise or client-controlled cloud.

Winner: In-house for highly regulated industries—but external partners with proven compliance frameworks are viable.

8. Hidden Costs & ROI Predictability (10% Weight)

• In-House: Hidden costs include employee turnover (which can be as high as 20-30% annually in tech roles), unused capacity, failed experiments, benefits, and training. ROI can be unpredictable, with some industry reports suggesting that a high percentage of AI models never reach production in less mature teams.
• External: Transparent pricing (fixed-price or milestone-based); shared risk through outcome-based agreements; faster ROI, with some enterprises reporting significant operational cost reductions and productivity gains within 12-18 months.

Winner: External development for predictable budgeting and faster ROI realization.

Scoring Summary

Factor	Weight	In-House	External	Winner
Upfront Cost & Investment	20%	4/10	9/10	External
Time-to-Market	15%	4/10	9/10	External
Access to Expertise	15%	5/10	9/10	External
Control & IP Ownership	10%	10/10	6/10	In-House
Scalability & Flexibility	10%	4/10	9/10	External
Long-Term Innovation	10%	9/10	5/10	In-House
Data Security & Compliance	10%	9/10	7/10	In-House
Hidden Costs & ROI	10%	4/10	9/10	External
TOTAL WEIGHTED SCORE	100%	5.7/10	8.2/10	External

Conclusion: For most companies, partnering with a custom AI software development company delivers faster ROI, lower risk, and greater flexibility, especially in the early stages of AI adoption.

Top 7 Custom AI Software Development Companies (2026)

Tier 1: Enterprise-Grade Leaders

1. IBM Consulting

IBM Consulting leads global AI transformation initiatives with its Watson AI platform, serving Fortune 500 companies with proven enterprise-scale deployment capabilities. The firm brings decades of experience across multiple industries, offering end-to-end AI strategy, implementation, and managed services. Their Watson suite includes pre-built AI applications for various business applications.

While IBM’s enterprise focus and proven track record at scale make it a trusted choice for large organizations, companies should expect premium pricing, long implementation timelines, and engagement models designed primarily for enterprises with $5M+ AI budgets. Smaller mid-market companies may find their offerings less agile than specialized boutique firms.

Location: Armonk, New York
Year Founded: 1911
Price Range: $$$$$
Average Review Score: 4.1/5.0
Services Offered: Enterprise AI strategy, Watson AI platform, industry-specific AI solutions, AI governance, change management

Summary of Online Reviews

Clients praise IBM’s “deep industry expertise” and “proven track record at scale,” noting strong governance frameworks and global support infrastructure, though some cite “high costs and slower execution timelines” compared to agile competitors.

2. Accenture AI

With over 40,000 AI practitioners, Accenture AI specializes in comprehensive AI transformation across all industries, combining strategy consulting, implementation, and change management. The firm leverages proprietary AI platforms and partnerships with leading technology providers to deliver enterprise-wide AI solutions. Their cross-industry experience spans multiple sectors including logistics, retail, finance, and healthcare.

Accenture excels at managing complex, large-scale AI transformations that require organizational change management and executive alignment. However, mid-market companies may encounter long sales cycles, high fees, and engagement structures better suited to Fortune 1000 organizations than fast-moving companies seeking rapid pilots.

Location: Dublin, Ireland (Global)
Year Founded: 1989
Price Range: $$$$$
Average Review Score: 4.0/5.0
Services Offered: AI strategy and transformation, industry-specific AI platforms, change management, responsible AI frameworks, enterprise-scale implementation

Summary of Online Reviews

Reviewers highlight Accenture’s “massive team capacity” and “comprehensive transformation approach,” appreciating their strategic consulting combined with technical execution, though some mention “enterprise-only focus and slower speed-to-market.”

3. Deloitte AI

Deloitte AI serves as a trusted advisor for regulated industries including finance, healthcare, and government, bringing deep compliance expertise and risk management frameworks to AI implementations. The firm’s strengths lie in navigating complex regulatory environments, establishing AI governance structures, and ensuring enterprise-level security and compliance (HIPAA, SOC 2, GDPR, FedRAMP).

For companies in highly regulated sectors or those requiring air-tight compliance, Deloitte offers unmatched credibility and risk mitigation. However, organizations prioritizing speed and cost-effectiveness may find Deloitte’s methodical, audit-first approach slower and more expensive than specialized AI development firms.

Location: London, United Kingdom (Global)
Year Founded: 1845
Price Range: $$$$$
Average Review Score: 4.2/5.0
Services Offered: AI strategy for regulated industries, risk and compliance frameworks, AI ethics and governance, secure AI implementation, data privacy solutions

Summary of Online Reviews

Clients value Deloitte’s “regulatory expertise” and “trusted brand reputation,” citing strong governance and compliance frameworks, though note “higher fees and longer timelines” compared to pure-play AI specialists.

Tier 2: Mid-Market Specialists

4. USM Business Systems

USM Business Systems specializes in custom AI solutions, combining 25+ years of IT services experience with cutting-edge AI capabilities. Founded in 1999, the firm focuses on mid-to-large organizations seeking AI-driven solutions for operational optimization, predictive analytics, and intelligent automation. Their technical stack includes Agentic AI, Generative AI, and custom machine learning models tailored to business workflows.

USM differentiates itself through deep industry expertise and an agile R&D approach that delivers faster time-to-value than enterprise consultants. The firm offers transparent milestone-based pricing and maintains a partnership model that balances enterprise-grade capabilities with personalized attention. However, companies requiring global scale or multi-industry experience may find larger firms like IBM or Accenture offer broader resources.

Location: Ashburn, Virginia
Year Founded: 1999
Price Range: $$$
Average Review Score: 4.7/5.0
Services Offered: Custom AI solutions, Agentic AI, IoT integration, predictive analytics, AI strategy consulting

Summary of Online Reviews

Clients consistently highlight USM’s “deep industry knowledge” and “faster delivery timelines,” appreciating their balance of technical sophistication and focused expertise, though some note “smaller team size compared to global firms.”

5. RTS Labs

RTS Labs delivers AI-driven software engineering with a strong focus on measurable ROI and rapid deployment cycles. The firm specializes in logistics, finance, and real estate, offering custom AI platforms, LLM integrations, and outcome-based engagement models. Their technical expertise spans modern AI frameworks including GPT-4, LangChain, and custom neural networks built for specific business problems.

RTS Labs stands out for milestone-driven projects and transparent pricing structures that tie payment to results. Their agile methodology enables faster pivots and course corrections during development. However, the firm has limited vertical-specific case studies in some industries, which may require longer discovery phases for specialized applications.

Location: Los Angeles, California
Year Founded: 2015
Price Range: $$$
Average Review Score: 4.6/5.0
Services Offered: Custom AI platforms, LLM integration, outcome-based AI projects, rapid prototyping, AI-powered analytics

Summary of Online Reviews

Reviewers praise RTS Labs’ “outcome-based agreements” and “rapid delivery,” noting strong technical execution and modern tech stack, though some mention “less vertical specialization in certain industries.”

6. LeewayHertz

LeewayHertz delivers custom AI platforms and enterprise-scale solutions, having completed over 160 digital projects across diverse industries. The firm combines AI with emerging technologies including blockchain and Web3, offering unique solutions for data traceability, decentralized AI models, and secure data sharing across enterprise networks.

LeewayHertz’s strength lies in integrating cutting-edge technologies to solve complex business problems, particularly where transparency, security, and decentralization matter. However, their heavy blockchain focus may not align with traditional organizations seeking straightforward AI implementations without distributed ledger complexity.

Location: San Francisco, California
Year Founded: 2007
Price Range: $$$
Average Review Score: 4.5/5.0
Services Offered: Custom AI development, blockchain + AI convergence, enterprise AI platforms, decentralized AI solutions, data transparency

Summary of Online Reviews

Clients appreciate LeewayHertz’s “innovative technology convergence” and “100+ enterprise solutions delivered,” valuing their forward-thinking approach, though note “blockchain emphasis may overcomplicate simpler AI needs.”

7. Intellectsoft

Intellectsoft partners with Fortune 500 companies to deliver large-scale digital transformation initiatives with AI components embedded throughout. The firm offers comprehensive technology services including custom software development, cloud migration, IoT platforms, and AI-powered analytics. Their experience spans healthcare, logistics, fintech, and retail with proven delivery of complex, multi-year enterprise programs.

Intellectsoft excels at managing large, complex engagements requiring cross-functional teams and long-term partnerships. However, their generalist approach means less deep specialization in specific industries compared to vertical-focused firms, potentially requiring more discovery and knowledge transfer time.

Location: Palo Alto, California
Year Founded: 2007
Price Range: $$$$
Average Review Score: 4.4/5.0
Services Offered: Enterprise AI integration, digital transformation, custom software with AI, IoT + AI convergence, cloud-based AI solutions

Summary of Online Reviews

Reviewers highlight Intellectsoft’s “proven enterprise delivery” and “comprehensive tech stack,” praising scalable teams and project management rigor, though some mention “generalist positioning rather than industry-specific expertise.”

Making Your Decision: A Simple Framework

Choose In-House AI Development If:

• AI is central to your long-term competitive strategy
• You have a $2M+ annual budget for team, infrastructure, and tooling
• You can afford 12-18 months to build internal capability
• Data security and IP control are non-negotiable
• You’re committed to building a culture of continuous AI innovation

Choose a Custom AI Software Development Company If:

• You need AI solutions deployed in 3-6 months
• Your budget is under $1M for initial AI projects
• You lack internal AI expertise and can’t afford 6-12 months of hiring
• You want predictable costs and shared risk
• You need flexibility to scale AI resources up or down

The Hybrid Approach

Many successful companies start with an external AI development partner to rapidly deploy initial use cases and prove ROI, then gradually transition ownership to an in-house team for long-term maintenance and iteration.

 

Final Takeaway

For most companies, hiring a custom AI software development company delivers faster ROI, lower risk, and greater flexibility compared to building in-house, especially in the critical early stages of AI adoption.

The right partner depends on your specific needs: enterprise-scale organizations with complex compliance requirements may prefer established consultancies like IBM, Accenture, or Deloitte; mid-market companies seeking industry expertise and agile delivery may find specialized firms like USM Business Systems, RTS Labs, or LeewayHertz offer better speed and value.

Evaluate potential partners based on industry expertise, proven delivery speed, transparent pricing models, technical capabilities aligned with your use cases, and cultural fit with your organization’s pace and decision-making style.

Ready to explore AI solutions for your operations? Schedule consultations with 2-3 firms from this list to compare approaches, timelines, and costs specific to your business challenges.

 

Frequently Asked Questions

Q: How much does it cost to hire a custom AI software development company?

A: Project-based pricing typically ranges from $50K-$500K depending on complexity, scope, and the firm’s positioning. Mid-market specialists generally offer more competitive rates than Big 4 consultancies, with transparent milestone-based pricing structures.

Q: How long does it take to deploy a custom AI solution?

A: With an experienced partner, initial AI pilots can launch in 6-12 weeks, with full production deployment in 3-6 months—60-70% faster than building an in-house team from scratch.

Q: Will I own the IP if I hire an external AI development company?

A: Yes. Reputable firms structure contracts to ensure clients retain full ownership of all custom AI models, algorithms, and intellectual property. Always clarify IP ownership terms before signing agreements.

Q: Can I transition from external to in-house AI development later?

A: Absolutely. Many companies use a hybrid model: partner with an external firm for rapid deployment, then gradually build internal teams with knowledge transfer and training support from the vendor.

Q: How do I ensure data security when working with an external AI partner?

A: Choose partners with SOC 2, ISO 27001, or HIPAA compliance certifications. Ensure contracts include robust NDAs, data handling protocols, and options for on-premise or client-controlled cloud deployment.

References

[1] The state of AI in 2023: Generative AI’s breakout year – https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

[2] About Us – USM Business Systems – https://usmsystems.com/about-us/

[3] USM Business Systems – LinkedIn – https://www.linkedin.com/company/usm-business-systems

[4] USM Business Systems – Crunchbase – https://www.crunchbase.com/organization/usm-business-systems

[5] AI Engineer Salary Guide 2025 – https://www.refontelearning.com/salary-guide/ai-engineering-salary-guide-2025

[6] ML / AI Software Engineer Salary – Levels.fyi – https://www.levels.fyi/t/software-engineer/focus/ml-ai

[7] Machine learning engineer salary – Indeed – https://www.indeed.com/career/machine-learning-engineer/salaries

[8] Average Turnover Rate By Industry (2025 Update) – https://www.corporatenavigators.com/articles/recruiting-trends/average-turnover-rate-by-industry-in-2024/

[9] Developer Attrition Reduction – Fullscale – https://fullscale.io/blog/developer-attrition-reduction-framework/

[10] Why 85% Of Your AI Models May Fail – Forbes – https://www.forbes.com/councils/forbestechcouncil/2024/11/15/why-85-of-your-ai-models-may-fail/

[11] The Production AI Reality Check – Medium – https://medium.com/@archie.kandala/the-production-ai-reality-check-why-80-of-ai-projects-fail-to-reach-production-849daa80b0f3

[12] AI Cuts Costs by 30% – ISG – https://isg-one.com/articles/ai-cuts-costs-by-30—but-75–of-customers-still-want-humans—here-s-why

[13] How Does AI Reduce Costs? – Master of Code – https://masterofcode.com/blog/how-does-ai-reduce-costs

[14] Accenture Technology Vision 2023 – https://newsroom.accenture.com/news/2023/accenture-technology-vision-2023-generative-ai-to-usher-in-a-bold-new-future-for-business-merging-physical-and-digital-worlds

[19] Two-thirds of surveyed enterprises in EMEA report significant productivity gains from AI – IBM – https://newsroom.ibm.com/2025-10-28-Two-thirds-of-surveyed-enterprises-in-EMEA-report-significant-productivity-gains-from-AI,-finds-new-IBM-study

[20] About Us | LeewayHertz – https://www.leewayhertz.com/about-us/

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The post Plumbing the AI Revolution: Lenovo’s Strategic Pivot to Modernize the Enterprise Backbone appeared first on TechSpective.

Modern militaries are steadily integrating ground robots—often called robotic combat vehicles (RCVs) or autonomous ground systems (AGS)—as force multipliers that enhance the reach, endurance, and situational awareness of human units. These platforms handle hazardous or burdensome tasks, allowing squads and platoons to operate more safely and focus on mission execution. Ukraine’s extensive use of unmanned […]

If you lead an enterprise data science team or a quantitative research unit today, you likely feel like you are living in two parallel universes.

In one universe, you have the “GenAI” explosion. Chatbots now write code and create art, and boardrooms are obsessed with how large language models (LLMs) will change the world. In the other universe, you have your day job: the “serious” work of predicting churn, forecasting demand, and detecting fraud using structured, tabular data. 

For years, these two universes have felt completely separate. You might even feel that the GenAI hype rocketship has left your core business data standing on the platform.

But that separation is an illusion, and it is disappearing fast.

From chatbots to forecasts: GenAI arrives at tabular and time-series modeling

Whether you are a skeptic or a true believer, you have most certainly interacted with a transformer model to draft an email or a diffusion model to generate an image. But while the world was focused on text and pixels, the same underlying architectures have been quietly learning a different language: the language of numbers, time, and tabular patterns. 

Take for instance SAP-RPT-1 and LaTable. The first uses a transformer architecture, and the second is a diffusion model; both are used for tabular data prediction.

We are witnessing the emergence of data science foundation models.

These are not just incremental improvements to the predictive models you know. They represent a paradigm shift. Just as LLMs can “zero-shot” a translation task they weren’t explicitly trained for, these new models can look at a sequence of data, for example, sales figures or server logs, and generate forecasts without the traditional, labor-intensive training pipeline.

The pace of innovation here is staggering. By our count, since the beginning of 2025 alone, we have seen at least 14 major releases of foundation models specifically designed for tabular and time-series data. This includes impressive work from the teams behind Chronos-2, TiRex, Moirai-2, TabPFN-2.5, and TempoPFN (using SDEs for data generation), to name just a few frontier models.

Models have become model-producing factories

Traditionally, machine learning models were treated as static artifacts: trained once on historical data and then deployed to produce predictions.

That framing no longer holds. Increasingly, modern models behave less like predictors and more like model-generating systems, capable of producing new, situation-specific representations on demand. 

We are moving toward a future where you won’t just ask a model for a single point prediction; you will ask a foundation model to generate a bespoke statistical representation—effectively a mini-model—tailored to the specific situation at hand. 

The revolution isn’t coming; it’s already brewing in the research labs. The question now is: why isn’t it in your production pipeline yet?

The reality check: hallucinations and trend lines

If you’ve scrolled through the endless examples of grotesque LLM hallucinations online, including lawyers citing fake cases and chatbots inventing historical events, the thought of that chaotic energy infiltrating your pristine corporate forecasts is enough to keep you awake at night.

Your concerns are entirely justified.

Classical machine learning is the conservative choice for now

While the new wave of data science foundation models (our collective term for tabular and time-series foundation models) is promising, it is still very much in the early days. 

Yes, model providers can currently claim top positions on academic benchmarks: all top-performing models on the time-series forecasting leaderboard GIFT-Eval and the tabular data leaderboard TabArena are now foundation models or agentic wrappers of foundation models. But in practice? The reality is that some of these “top-notch” models currently struggle to identify even the most basic trend lines in raw data. 

They can handle complexity, but sometimes trip over the basics that a simple regression would nail it–check out the honest ablation studies in the TabPFN v2 paper, for instance.

Why we remain confident: the case for foundation models

While these models still face early limitations, there are compelling reasons to believe in their long-term potential. We have already discussed their ability to react instantly to user input, a core requirement for any system operating in the age of agentic AI. More fundamentally, they can draw on a practically limitless reservoir of prior information.

Think about it: who has a better chance at solving a complex prediction problem?

• Option A: A classical model that knows your data, but only your data. It starts from zero every time, blind to the rest of the world.

• Option B: A foundation model that has been trained on a mind-boggling number of relevant problems across industries, decades, and modalities—often augmented by vast amounts of synthetic data—and is then exposed to your specific situation.

Classical machine learning models (like XGBoost or ARIMA) do not suffer from the “hallucinations” of early-stage GenAI, but they also do not come with a “helping prior.” They cannot transfer wisdom from one domain to another. 

The bet we are making, and the bet the industry is moving toward, is that eventually, the model with the “world’s experience” (the prior) will outperform the model that is learning in isolation.

The missing link: solving for reality, not leaderboards

Data science foundation models have a shot at becoming the next massive shift in AI. But for that to happen, we need to move the goalposts. Right now, what researchers are building and what businesses actually need remains disconnected. 

Leading tech companies and academic labs are currently locked in an arms race for numerical precision, laser-focused on topping prediction leaderboards just in time for the next major AI conference. Meanwhile, they are paying relatively little attention to solving complex, real-world problems, which, ironically, pose the toughest scientific challenges.

The blind spot: interconnected complexity

Here is the crux of the problem: none of the current top-tier foundation models are designed to predict the joint probability distributions of several dependent targets.

That sounds technical, but the business implication is massive. In the real world, variables rarely move in isolation.

• City Planning: You cannot predict traffic flow on Main Street without understanding how it impacts (and is impacted by) the flow on 5th Avenue.
• Supply Chain: Demand for Product A often cannibalizes demand for Product B.
• Finance: Take portfolio risk. To understand true market exposure, a portfolio manager doesn’t simply calculate the worst-case scenario for every instrument in isolation. Instead, they run joint simulations. You cannot just sum up individual risks; you need a model that understands how assets move together.

The world is a messy, tangled web of dependencies. Current foundation models tend to treat it like a series of isolated textbook problems. Until these models can grasp that complexity, outputting a model that captures how variables dance together, they won’t replace existing solutions.

So, for the moment, your manual workflows are safe. But mistaking this temporary gap for a permanent safety net could be a grave mistake. 

Today’s deep learning limits are tomorrow’s solved engineering problems

The missing pieces, such as modeling complex joint distributions, are not impossible laws of physics; they are simply the next engineering hurdles on the roadmap. 

If the speed of 2025 has taught us anything, it is that “impossible” engineering hurdles have a habit of vanishing overnight. The moment these specific issues are addressed, the capability curve won’t just inch upward. It will spike.

Conclusion: the tipping point is closer than it appears

Despite the current gaps, the trajectory is clear and the clock is ticking. The wall between “predictive” and “generative” AI is actively crumbling.

We are rapidly moving toward a future where we don’t just train models on historical data; we consult foundation models that possess the “priors” of a thousand industries. We are heading toward a unified data science landscape where the output isn’t just a number, but a bespoke, sophisticated model generated on the fly.

The revolution is not waiting for perfection. It is iterating toward it at breakneck speed. The leaders who recognize this shift and begin treating GenAI as a serious tool for structured data before a perfect model reaches the market will be the ones who define the next decade of data science. The rest will be playing catch-up in a game that has already changed.

We are actively researching these frontiers at DataRobot to bridge the gap between generative capabilities and predictive precision. This is just the start of the conversation. Stay tuned—we look forward to sharing our insights and progress with you soon. 

In the meantime, you can learn more about DataRobot and explore the platform with a free trial. 

The post The brewing GenAI data science revolution appeared first on DataRobot.

Over the past decades, roboticists have introduced a wide range of systems that can effectively tackle some real-world problems. Most of these robots, however, often perform poorly on tasks that they were not trained on, particularly those that entail manipulating previously unseen objects or handling objects that were encountered before in new ways.

The shift from prototyping to having agents in production is the challenge for AI teams as we look toward 2026 and beyond. Building a cool prototype is easy: hook up an LLM, give it some tools, see if it looks like it’s working. The production system, now that’s hard. Brittle integrations. Governance nightmares. Infrastructure wasn’t built for the complexities and nuances of agents. 

For AI developers, the challenge has shifted from building an agent to orchestrating, governing, and scaling it in a production environment. DataRobot’s latest release introduces a robust suite of tools designed to streamline this lifecycle, offering granular control without sacrificing speed.

New capabilities accelerating AI agent production with DataRobot

New features in DataRobot 11.2 and 11.3 help you close the gap with dozens of updates spanning observability, developer experience, and infrastructure integrations.

Together, these updates focus on one goal: reducing the friction between building AI agents and running them reliably in production. 

The most impactful areas of these updates include:

• Standardized connectivity through MCP on DataRobot
• Secure agentic retrieval through Talk to My Docs (TTMDocs) 
• Streamlined agent build and deploy through CLI tooling
• Prompt version control through Prompt Management Studio
• Enterprise governance and observability through resource monitoring
• Multi-model access through the expanded LLM Gateway
• Expanded ecosystem integrations for enterprise agents

The sections that follow focus on these capabilities in detail, starting with standardized connectivity, which underpins every production-grade agent system.

MCP on DataRobot: standardizing agent connectivity

Agents break when tools change. Custom integrations become technical debt. The Model Context Protocol (MCP) is emerging as the standard to solve this, and we’re making it production-ready. 

We’ve added an MCP server template to the DataRobot community GitHub.

• What’s new: An MCP server template you can clone, test locally, and deploy directly to your DataRobot cluster. Your agents get reliable access to tools, prompts, and resources without reinventing the integration layer every time. Easily convert your predictive models as tools that are discoverable by agents.

• Why it matters: With our MCP template, we’re giving you the open standard with enterprise guardrails already built in. Test on your laptop in the morning, deploy to production by afternoon.

Talk to My Docs: Secure, agentic knowledge retrieval

Everyone is building RAG. Almost nobody is building RAG with RBAC, audit trails, and the ability to swap models without rewriting code. 

The “Talk to My Docs” application template brings natural language chat-style productivity across all your documents and is secured and governed for the enterprise.

• What’s new: A secure, governed chat interface that connects to Google Drive, Box, SharePoint, and local files. Unlike basic RAG, it handles complex formats from tables, spreadsheets, multi-doc synthesis while maintaining enterprise-grade access control.
  
• Why it matters: Your team needs ChatGPT-style productivity. Your security team needs proof that sensitive documents stay restricted. This does both, out of the box.

Agentic application starter template and CLI: Streamlined build and deployment

Getting an agent into production should not require days of scaffolding, wiring services together, or rebuilding containers for every small change. Setup friction slows experimentation and turns simple iterations into heavyweight engineering work.

To address this, DataRobot is introducing an agentic application starter template and CLI, both designed to reduce setup overhead across both code-first and low-code workflows.

• What’s new: An agentic application starter template and CLI that let developers configure agent components through a single interactive command. Out-of-the-box components include an MCP server, a FastAPI backend, and a React frontend. For teams that prefer a low-code approach, integration with NVIDIA’s NeMo Agent Toolkit enables agent logic and tools to be defined entirely through YAML. Runtime dependencies can now be added dynamically, eliminating the need to rebuild Docker images during iteration.

• Why it matters: By minimizing setup and rebuild friction, teams can iterate faster and move agents into production more reliably. Developers can focus on agent logic rather than infrastructure, while platform teams maintain consistent, production-ready deployment patterns.

Prompt management studio: DevOps for prompts

As prompts move from experiments to production assets, ad hoc editing quickly becomes a liability. Without versioning and traceability, teams struggle to reproduce results or safely iterate.

To address this, DataRobot introduces the Prompt Management Studio, bringing software-style discipline to prompt engineering.

• What’s new: A centralized registry that treats prompts as version-controlled assets. Teams can track changes, compare implementations, and revert to stable versions as prompts move through development and deployment.

• Why it matters: By applying DevOps practices to prompts, teams gain reproducibility and control, making it easier to transition from prototyping to production without introducing hidden risk.

Multi-tenant governance and resource monitoring: Operational control at scale

As AI agents scale across teams and workloads, visibility and control become non-negotiable. Without clear insight into resource usage and enforceable limits, performance bottlenecks and cost overruns quickly follow.

• What’s new: The enhanced Resource Monitoring tab provides detailed visibility into CPU and memory utilization, helping teams identify bottlenecks and manage trade-offs between performance and cost. In parallel, Multi-tenant AI Governance introduces token-based access with configurable rate limits to ensure fair resource consumption across users and agents.

• Why it matters: Developers gain clear insight into how agent workloads behave in production, while platform teams can enforce guardrails that prevent noisy neighbors and uncontrolled resource usage as systems scale.

Expanded LLM Gateway: Multi-model access without credential sprawl

As teams experiment with agent behavior and reasoning, access to multiple foundation models becomes essential. Managing separate credentials, rate limits, and integrations across providers quickly introduces operational overhead.

• What’s new: The expanded LLM Gateway adds support for Cerebras and Together AI alongside Anthropic, providing access to models such as Gemma, Mistral, Qwen, and others through a single, governed interface. All models are accessed using DataRobot-managed credentials, eliminating the need to manage individual API keys.

• Why it matters: Teams can evaluate and deploy agents across multiple model providers without increasing security risk or operational complexity. Platform teams maintain centralized control, while developers gain flexibility to choose the right model for each workload.

New supporting ecosystem integrations

Jira and Confluence connectors: To power your vector databases, DataRobot provides a cohesive ecosystem for building enterprise-ready, knowledge-aware agents.

NVIDIA NIM Integration: Deploy Llama 4, Nemotron, GPT-OSS, and 50+ GPU-optimized models without the MLOps complexity. Pre-built containers, production-ready from day one.

Milvus Vector Database: Direct integration with the leading open-source VDB, plus the ability to select distance metrics that actually matter for your classification and clustering tasks.

Azure Repos & Git Integration: Seamless version control for Codespaces development with Azure Repos or self-hosted Git providers. No manual authentication required. Your code stays centralized where your team already works.

Get hands-on with DataRobot’s Agentic AI 

If you’re already a customer, you can spin up the GenAI Test Drive in seconds. No new account. No sales call. Just 14 days of full access inside your existing SaaS environment to test these features with your actual data.  

Not a customer yet? Start a 14-day free trial and explore the full platform.

For more information, please visit our Version 11.2 and Version 11.3 release notes in the DataRobot docs.

The post DataRobot Q4 update: driving success across the full agentic AI lifecycle appeared first on DataRobot.

Operational excellence used to mean efficiency. Now, it means consistency. In a volatile manufacturing environment, the winners aren’t those with the best machines or biggest budgets — they’re the ones who can execute the same playbook flawlessly, every day, on every line.

Gemini 3 Flash offers frontier intelligence built for speed at a fraction of the cost.

Step inside the Soft Robotics Lab at ETH Zurich, and you find yourself in a space that is part children's nursery, part high-tech workshop and part cabinet of curiosities. The lab benches are strewn with foam blocks, stuffed animals—including a cuddly squid—and other colorful toys used to train robotic dexterity. Piled up on every surface are sensors, cables and measurement devices. Skeletal fingers, on show in display cases or attached to powerful robotic arms, seem to reach out to grab you from every corner.

Step inside the Soft Robotics Lab at ETH Zurich, and you find yourself in a space that is part children's nursery, part high-tech workshop and part cabinet of curiosities. The lab benches are strewn with foam blocks, stuffed animals—including a cuddly squid—and other colorful toys used to train robotic dexterity. Piled up on every surface are sensors, cables and measurement devices. Skeletal fingers, on show in display cases or attached to powerful robotic arms, seem to reach out to grab you from every corner.