Chemical products such as medicines, plastics, soap, and paint are still often based on fossil raw materials. This is not sustainable, so there is an urgent need for ways to make a ‘materials transition’ to products made from bio-based raw materials. To achieve results more quickly and efficiently, researchers at Radboud University in the Big Chemistry programme are using robots and AI.
The material transition from fossil-based to bio-based (where raw materials are based on materials of biological origin) is a major challenge. Raw materials for products must be replaced without changing the quality of those products. This requires knowledge of the properties and behaviour of those raw materials at the molecular level. Wilhelm Huck, professor of physical-organic chemistry at Radboud University: “Moreover, you don’t want to optimize the properties of a single molecule, but of a mixture. And we can greatly accelerate that search with our robots and models.”
The difficulty, Huck explains, is that most chemistry is ‘non-additive’. ‘Whether you dissolve one sugar cube in water or ten, essentially the same thing happens with ten cubes. That is predictable. But if you know how one molecule behaves and you know how another molecule behaves, you might think: if I put them together, I’ll get the combined or the average of the two. And that’s almost never the case in chemistry. In many cases, the interaction between molecules leads to an interaction that you couldn’t have predicted.”
Because raw materials can interact with other raw materials in all kinds of ways, the number of possible interactions increases rapidly. Huck: ‘And when you consider that suppliers of ingredients for cleaning products, cosmetics, paints and coatings, ink, perfume, medicines, you name it – that they can supply tens of thousands of components. And that you can combine them in different ways. That quickly adds up to hundreds of millions of interactions that you can’t possibly study all. So you need a model that can predict the properties of mixtures. And to train that model, you need a lot of data, which you collect in experiments.”
This fall, three grants were awarded to projects by Radboud researchers within the larger Big Chemistry program of the National Growth Fund. Led by chemists Wilhelm Huck, Mathijs Mabesoone, and Peter Korevaar, the programme involves collaboration with companies to conduct research into the properties of bio-based raw materials for paints and soaps, among other things.
Peter Korevaar will be conducting research into paints together with Van Wijhe Verf. These are often still (partly) based on oil, because they have to be waterproof. And that is just one of the requirements that paint has to meet: ‘Paint has to mix well. That mixture has to remain stable. It must not be too watery or too viscous. It has to be washable, but it shouldn’t wash off your house when it rains. It simply has to be good stuff. If you try to design that based on new, bio-based ingredients, you need a lot of experimental data.”
Mathijs Mabesoone will be conducting research into soaps together with the company Croda International. ‘If you have a pure soap solution, it has a certain cleaning capacity, for example. But in mixtures of soaps, that same property can suddenly occur at a hundred times lower concentration. That is also very difficult to predict, so we are going to take a lot of measurements. We will create a large database of informative measurement points, which we can then use to train a model to better predict the interactions.”
The third project that received funding this fall deals with polymers on a more fundamental level: large molecules that often occur in mixtures. Huck: “For most polymers, there is insufficient data for theoretical calculations. For the development of new, bio-based polymers, we will collect more data in collaboration with TNO and Van Loon Chemical Innovations (VLCI), so that we can train AI models to make better predictions.”
Generating unique data, and lots of it, is the goal of all three projects. And the scientists are doing this with the help of robots. A large robot lab at Noviotech Campus in Nijmegen will follow in the fall of 2026. But the researchers are already working with robots the size of a small refrigerator that continuously take measurements. Mabesoone: “You supply such a robot with a few samples of basic solutions, and then you put it to work testing, mixing, and measuring. The robot decides which are the best samples to make, and you only need to supply a small amount to obtain a lot of data.”
Will consumers notice anything from this research, and if so, when? Huck: ‘If we don’t do this, you may find that at some point you can no longer get certain products because they contain substances that are no longer permitted or available. But if we do it right, you won’t notice much. You had good stuff and you want to keep good stuff. Only, in the long run, those good products will be more often biodegradable. And we can probably make the good products even better—with robotics and AI, we can try out so many more combinations than we ever thought possible that we are sure to discover completely new properties.”
Generations in Dialogue: Bridging Perspectives in AI is a podcast from AAAI featuring thought-provoking discussions between AI experts, practitioners, and enthusiasts from different age groups and backgrounds. Each episode delves into how generational experiences shape views on AI, exploring the challenges, opportunities, and ethical considerations that come with the advancement of this transformative technology.
In the third episode of this new series from AAAI, host Ella Lan chats to Professor Roberto Martín-Martín about taking a screwdriver to his toys as a child, how his research focus has evolved over time, how different generations interact with technology, making robots for everyone, being inspired by colleagues, advice for early-career researchers, and how machines can enhance human capabilities.
Roberto Martín-Martín is an Assistant Professor of Computer Science at the University of Texas at Austin, where his research integrates robotics, computer vision, and machine learning to build autonomous agents capable of perceiving, learning, and acting in the real world. His work spans low-level tasks like pick-and-place and navigation to complex activities such as cooking and mobile manipulation, often drawing inspiration from human cognition and integrating insights from psychology and cognitive science. He previously worked as an AI Researcher at Salesforce AI and as a Postdoctoral Scholar at the Stanford Vision and Learning Lab with Silvio Savarese and Fei-Fei Li, leading projects in visuomotor learning, mobile manipulation, and human-robot interaction. He earned his Ph.D. and M.S. from Technische Universität Berlin under Oliver Brock and a B.S. from Universidad Politécnica de Madrid. His work has been recognized with best paper awards at RSS and ICRA, and he serves as Chair of the IEEE/RAS Technical Committee on Mobile Manipulation.
Ella Lan, a member of the AAAI Student Committee, is the host of “Generations in Dialogue: Bridging Perspectives in AI.” She is passionate about bringing together voices across career stages to explore the evolving landscape of artificial intelligence. Ella is a student at Stanford University tentatively studying Computer Science and Psychology, and she enjoys creating spaces where technical innovation intersects with ethical reflection, human values, and societal impact. Her interests span education, healthcare, and AI ethics, with a focus on building inclusive, interdisciplinary conversations that shape the future of responsible AI.
While keeping pace with the seemingly endless parade of AI tools can be exhausting, getting crystal clear on the raw, new power embedded in Google’s new Nano Banana Pro image generator is well worth a huff-and-puff.
In a phrase, Nano Banana Pro (NBP) – released a few weeks ago – is the new, gold standard in AI imaging now, capable of rendering virtually anything imaginable.
Essentially: Writers now have a tool that can auto-generate one or more supplemental images for their work with a precision and power that currently has no rival.
Plus, unlike other image generators, NBP has an incredible amount of firepower under-the-hood that is simply not available to the competition.
For example: NBP is an exquisite image generator in its own right.
But it is also powered by Google’s Gemini 3.0 Pro, now widely considered the gold standard in consumer AI.
And, NBP can also be easily combined with Google Search, the world’s number one search engine.
Like many things AI, the secret to achieving master prowess with NBP is to sample how countless, highly inspired human imaginations are already working with the tool – and then synthesize that rubber-meets-the-road knowledge to forge your own method for working with NBP.
Towards that end, here are ten excellent videos on NBP, complete with detailed demos, of how imaginative folks are artfully using the AI – and surfacing truly world-class, head-turning images:
*Quick Overview: NBP Key Features: This 15-minute video from AI Master offers a great intro into the key new capabilities of NBP – complete with captivating visual examples. Demos include:
–blending multiple images into one
–converting stick figures into an image-rich scene
–experimenting with visual style changes on the same
image
–working with much more reliable text-on-images
*A Torrent of NBP Use Cases: This incredibly organized and informative 11-minute video from Digital Assets dives deep in the wide array of use cases you can tap into using NBP. Demos include:
–Historical event image generator, based on location,
date and approximate time (example: conjure Apollo moon landing)
–multi-angle product photography
–Alternate reality generator (example: depict architecture of ancient Rome as immersed in a futuristic setting)
–Hyper-realistic, 3D-diorama generation
*Another Torrent of NBP Use Cases: Click on this 27-minute video from Astrovah for a slew of more mind-bending use cases, including:
–Text-on-image analysis of any photo you upload, including its context and key facts to know about the image
–How to make an infographic in seconds
–How to inject season and weather changes to any image
–Making exploded-view images of any product
–Auto-generated blueprints of any image
*Generating Hyper-Realistic Photos With NBP: This great, 22-minute video from Tao Prompts offers an inside look at how to ensure any image you generate with NBP is hyper-photorealistic – right down to the brand of photo film you’re looking to emulate.
*Infinite Camera Angles on Tap: Getting just the right camera angle on any image is now child’s play with NBP. This 11-minute video from Chase AI serves-up demos on how to be the director of any image you create with NBP. Included is a detailed prompt library you can use featuring the same camera angle descriptions used by pro photographers.
*Swapping a Face in Seconds: Short-and-sweet, this 4-minute video from AsapGuide offers a quick, down-and-dirty way to transplant any face onto any image you provide.
*Aging/De-Aging a Person in Seconds: Another great collection of use cases, this 16-minute video from Atomic Gains includes an easy-to-replicate demo on making a person look younger, or vice-versa. Also included are demos on instantly changing the lighting in an image, changing the position of a character in an image and surgically removing specific details from any image.
*NBP: Getting Technical: Once you’ve played with NBP informally, you can pick up some extremely helpful, technical tips on how to manipulate NBP with this 29-minute video from AI Samson. Tricks include how to zoom in/out on an image, how to maintain character consistency and how to use complex cinematic stylings.
*Amplifying NBP With Google AI Studio: This 58-minute video from David Ondrej recommends using NBP in the free Google Studio interface. The reason: Google AI Studio will give you much more granular control over your results, including precise image size, creating accurate slides with text and using NBP with Google Search. Caveat: To use Google AI Studio, you need to switch to a special Google Gemini API subscription.
*Working with NBP in Photoshop: Adobe has already integrated NBP into its toolset. And this is the perfect video (8 minutes from Sebastien Jefferies) to check-out how to combine the power of NBP with the incredible precision of Photoshop. Included are lots of great demos that answer the question: NBP and Photoshop: What’s the long-term impact?
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.
The post AI Image Generation: On Genius appeared first on Robot Writers AI.
Based on comprehensive analysis of the latest industry research from CloudZero’s 2025 State of AI Costs report (surveying 500 engineering professionals), Zylo’s 2025 SaaS Management Index, and additional enterprise data, this report examines AI software pricing trends, budget allocation patterns, hidden cost drivers, and industry-specific expenses facing manufacturing and supply chain leaders in 2025.
The landscape of AI software costs has grown increasingly complex. CloudZero’s research reveals that average monthly AI spending will reach $85,521 in 2025, a 36% increase from 2024’s $62,964 [1]. For manufacturing executives evaluating AI investments, understanding these cost dynamics and identifying hidden expenses is essential for accurate budgeting, maximizing ROI, and maintaining competitive advantage in an AI-driven industrial landscape.
At USM Business Systems, we specialize in helping manufacturing leaders navigate these financial complexities, particularly as they evaluate Agentic AI systems that promise autonomous operation capabilities. This analysis provides transparent benchmarks to inform your AI investment decisions.
Organizations across all sizes are accelerating AI investments, with spending patterns varying significantly based on company scale, operational maturity, and strategic AI priorities.
| Organization Size | Monthly AI Budget 2025 | Annual AI Investment 2025 | YoY Growth Rate | Primary Investment Drivers |
|---|---|---|---|---|
| 250-500 employees | $30,000 – $40,000 | $360K – $480K | 24-28% | Pilot projects, basic automation, cloud platforms |
| 501-1,000 employees | $55,000 – $70,000 | $660K – $840K | 28-35% | Scaling successful pilots, departmental rollouts |
| 1,001-5,000 employees | $90,000 – $110,000 | $1.08M – $1.32M | 30-38% | Multi-site deployments, integration complexity |
| 5,001-10,000 employees | $150,000 – $190,000 | $1.8M – $2.28M | 38-45% | Enterprise platforms, custom development |
| 10,000+ employees | $240,000 – $280,000 | $2.88M – $3.36M | 35-40% | Organization-wide transformation, governance systems |
Source: Derived from CloudZero State of AI Costs 2025 [1]
Key Insights:
Understanding where AI budgets flow helps manufacturing enterprises benchmark their own spending patterns and identify optimization opportunities across infrastructure, applications, and security. The following table represents manufacturing-specific investment priorities for the next 24 months based on Deloitte’s 2025 Smart Manufacturing and Operations Survey of 600 manufacturing executives [2].
| Category | Investment Priority | Strategic Importance for Manufacturing |
|---|---|---|
| Process Automation (RPA, Agentic AI) | 46% | Alleviating skilled labor shortages and maximizing productivity through production scheduling and autonomous quality control |
| Factory Automation Hardware | 41% | Driving increased automation and monitoring the manufacturing environment with sensors and robotics |
| Data Analytics & BI Solutions | 40% | Advancing on the smart manufacturing maturity curve with supply chain visibility and demand forecasting |
| Active Sensors | 34% | Enabling data capture and prerequisites for advanced analytics and IoT sensor integration |
| Cloud Computing Platforms (AWS, Azure, GCP) | 29% | Supporting scaled deployments, ML workloads, and global infrastructure necessary for training and deploying AI models |
| AI/Machine Learning Platforms | 29% | Establishing AI foundations for MLOps, model training, and specialized manufacturing AI applications |
| Vision Systems | 28% | Enhancing quality control, defect detection, and visual inspection capabilities |
| Industrial IoT (IIoT) | 27% | Connecting operational and enterprise data for real-time monitoring and predictive maintenance |
Source: Deloitte 2025 Smart Manufacturing and Operations Survey [2]
Key Insights:
AI vendors employ increasingly complex pricing strategies that directly impact total cost of ownership, budget predictability, and the ability to scale AI initiatives cost-effectively.
| Pricing Model | Market AdoptionCost Predictability RatingBudget Variance Risk | Best Suited For | Avg Enterprise Cost | Contract Negotiation Tip | ||
|---|---|---|---|---|---|---|
| Subscription (per-seat) | 58% | ★★★★★ High | ±5-10% | Stable headcount, predictable usage | $30-$200/user/month | Negotiate multi-year discounts |
| Usage-based (consumption) | 47% | ★★☆☆☆ Low | ±30-50% | Variable workloads, API-driven AI | $0.002-$0.12/token or call | Demand usage caps and alerts |
| Hybrid (subscription + usage) | 49% | ★★★☆☆ Medium | ±20-30% | Enterprise platforms with scaling needs | $50K-$150K/month | Request detailed usage forecasting tools |
| Value-based (ROI-linked) | 22% | ★★★☆☆ Medium | Varies by outcome | Strategic transformations, proven use cases | Negotiated | Tie payment to measurable KPIs |
| Flat-rate enterprise | 31% | ★★★★★ Very High | ±5% | Organization-wide deployments | $100K-$500K/year | Lock in rates for 3+ years |
| Freemium with paid tiers | 35% | ★★★☆☆ Medium | Can spike quickly | Testing, gradual team adoption | $0-$20K/month | Understand upgrade triggers clearly |
Sources: Zylo AI Cost Report 2025 [2], High Alpha SaaS Benchmarks [2]
Key Insights:
Beyond advertised pricing, manufacturing enterprises encounter substantial hidden expenses that can inflate total AI ownership costs by 200-400% compared to initial vendor quotes.
| Hidden Cost Category | Impact on Total Cost | When Costs Hit | Common Sources | Typical Cost Range | Mitigation Strategy |
|---|---|---|---|---|---|
| Infrastructure scaling | 15-25% | Months 3-6 | GPU/TPU compute, storage expansion, bandwidth | $15K-$75K | Reserved cloud instances, usage monitoring dashboards |
| Data preparation & quality | 15-20% | Months 1-3 | Collection, cleaning, labeling, governance, integration | $10K-$90K | Invest in automated data quality tools upfront |
| Integration & customization | 20-30% | Months 2-5 | API development, legacy system connections, middleware | $20K-$100K | Modular architecture, phased integration approach |
| Training & change management | 10-15% | Months 2-8 | User enablement, workflow redesign, resistance management | $8K-$50K | Structured adoption programs with executive sponsorship |
| Compliance & governance | 5-10% | Months 1-6 | GDPR/HIPAA adherence, audit trails, explainability frameworks | $5K-$40K | Select vendors with built-in compliance features |
| Ongoing maintenance & retraining | 10-15% annually | Year 2+ | Model drift correction, performance monitoring, version updates | $10K-$80K/year | Implement MLOps platforms, automated monitoring |
Sources: Industry analysis, Coherent Solutions AI Development Cost Research
Key Insights:
AI software pricing varies dramatically across industries due to differences in operational complexity, regulatory compliance requirements, implementation scope, and the sophistication of use cases. Based on comprehensive research across multiple authoritative sources, healthcare has the highest AI software costs, followed by manufacturing and financial services [3] [4] [5].
| Industry | Annual AI Software Cost | Primary Cost Drivers | ROI Timeline | Common Use Cases | Compliance Premium |
|---|---|---|---|---|---|
| Healthcare | $300K – $1M+ | HIPAA compliance, diagnostic accuracy validation, patient data privacy | 18-24 months | Clinical decision support, medical imaging analysis, patient monitoring | 20-25% |
| Manufacturing | $400K – $800K+ | Predictive maintenance, quality control, IoT sensor integration, digital twins | 12-18 months | Process optimization, defect detection, supply chain analytics, autonomous scheduling | 10-15% |
| Financial Services | $300K – $800K+ | SOC 2/regulatory compliance, fraud detection algorithms, security frameworks | 14-20 months | Risk assessment, algorithmic trading, AML/fraud prevention, credit decisioning | 15-20% |
| Supply Chain & Logistics | $350K – $700K+ | Route optimization, real-time tracking, demand prediction, warehouse automation | 12-18 months | Fleet management, automated warehousing, delivery optimization, load planning | 8-12% |
| Technology & Software | $250K – $600K+ | Continuous innovation cycle, developer productivity tools, code assistance | 8-12 months | AI-assisted development, automated testing, security scanning, DevOps optimization | 5-10% |
| Retail & E-commerce | $200K – $500K+ | Recommendation engines, inventory optimization, personalization at scale | 10-14 months | Customer segmentation, demand forecasting, dynamic pricing, chatbots | 5-8% |
Sources: Industry analysis, Standard Bots Manufacturing AI Report 2025 [3], eMarketer Healthcare AI Analysis [4], Sommo Retail AI Cost Breakdown [5]
Key Insights :
Healthcare organizations are implementing AI at more than twice the rate (2.2x) of the broader US economy, positioning the sector as a clear leader in enterprise AI adoption. With 63% of healthcare professionals already actively using AI and another 31% piloting or assessing initiatives, healthcare significantly outpaces other industries, which average just 50% AI uptake [6]. This accelerated adoption is driven by compelling ROI metrics and the sector’s unique operational imperatives.
Healthcare AI investments are delivering measurable returns faster than anticipated. According to NVIDIA’s 2025 State of AI in Healthcare and Life Sciences report, 81% of healthcare organizations reported increased revenue from AI implementations, with nearly half achieving ROI within one year of deployment [6]. Additional benefits include:
These outcomes are particularly significant in an industry where over $5 trillion in annual spending has historically failed to deliver proportional patient outcomes compared to other wealthy nations. AI is emerging as a critical tool to address systemic inefficiencies while simultaneously improving clinical effectiveness.
Healthcare organizations are directing AI investments toward three primary areas: new AI use cases (47%), workflow optimization (34%), and hiring AI experts (26%). The top AI workloads currently deployed include data analytics (58%), generative AI (54%), and large language models (53%), with applications varying by healthcare segment. Medical technology companies prioritize medical imaging and diagnostics (71%), pharma and biotech focus on drug discovery and development (59%), digital healthcare emphasizes clinical decision support (54%), and payers and providers concentrate on workflow automation and documentation (48%) [6].
Why Healthcare Costs Are Higher: Healthcare’s position as the highest-cost industry for AI implementation reflects several unique factors. The sector faces more stringent regulatory requirements (HIPAA, FDA) adding 20-25% cost premiums compared to 10-15% for manufacturing. Healthcare requires extensive validation protocols, patient data protection mandates, and explainable AI capabilities for clinical decision-making contexts. Integration complexity is substantial, requiring connections with existing electronic health records ($7,800-$10,400 per integration), medical device APIs ($10,000+), and complex clinical workflows. Despite these higher costs, 86% of healthcare organizations state AI is critical to their future success, and 83% believe AI will revolutionize healthcare and life sciences within the next three to five years [6].
Agentic AI, autonomous systems capable of goal-directed decision-making without continuous human oversight, represents the next frontier in manufacturing intelligence. However, this autonomy comes with distinct cost implications that manufacturing leaders must understand.
| Agentic AI Component | Cost Range | Why It Costs More | Manufacturing Application | Expected ROI Multiplier |
|---|---|---|---|---|
| Autonomous agent development | $40K – $150K per agent | Advanced reasoning, multi-step planning, safety constraints | Production scheduling, autonomous quality inspection | 4-8x |
| Agent orchestration platforms | $60K – $200K/year | Coordinates multiple agents, manages dependencies | Factory-wide optimization, supply chain coordination | 3-6x |
| Safety & governance frameworks | $30K – $100K | Human-in-loop fallbacks, audit trails, explainability | Ensures safe autonomous operations in regulated environments | Risk mitigation |
| Real-time decision infrastructure | $50K – $180K/year | Low-latency compute, edge processing, 99.9% uptime | Millisecond production adjustments, predictive interventions | 5-10x |
Sources: Biz4Group Agentic AI Development Cost Guide 2025 [4], BCG Agentic AI Report [5], Cleveroad AI Agent Development Cost Analysis [6]
Key Insights:
This AI cost data becomes actionable when manufacturing executives use it to construct data-driven business cases for AI investments. Follow this framework:
Step 1: Benchmark Your Current Position
Step 2: Account for Hidden Costs Early
Step 3: Select the Right Pricing Model
Step 4: Calculate Realistic ROI Timelines
Step 5: Build a Phased Investment Plan
This research reflects USM Business Systems’ commitment to transparent AI cost analysis and strategic implementation guidance for manufacturing enterprises. Our team specializes in helping production leaders navigate AI investments, from accurate initial estimates to long-term TCO planning that ensures sustainable ROI.
Unlike generic AI consultants, USM brings deep manufacturing domain expertise, particularly in Agentic AI systems for production scheduling, predictive maintenance, and autonomous quality control. We understand the unique cost drivers, integration challenges, and ROI opportunities specific to manufacturing environments.
Schedule Your Free AI Cost & ROI Assessment: Our experts will analyze your specific use case and provide a preliminary budget estimate in a complimentary 30-minute strategy call. [Contact USM Business Systems]
Download the Complete 2025 AI Cost Report: Get the full analysis with 5 bonus worksheets for internal budgeting, ROI calculation templates, and vendor evaluation scorecards. [Request PDF]
References:
[1] CloudZero. (2025). The State Of AI Costs In 2025. https://www.cloudzero.com/state-of-ai-costs/
[2] Deloitte. (2025). 2025 Smart Manufacturing and Operations Survey. https://www.deloitte.com/us/en/insights/industry/manufacturing/2025-smart-manufacturing-survey.html
[3] Zylo. (2025). AI Pricing: What’s the True AI Cost for Businesses in 2025? https://zylo.com/blog/ai-cost/
[4] eMarketer. (2025). AI spending in healthcare outpaces the overall US economy. https://www.emarketer.com/content/ai-spending-healthcare-outpaces-overall-us-economy-
[5] Sommo. (2025). Generative AI for retail: options and costs for 2025. https://www.sommo.io/blog/generative-ai-for-retail
[6] RSI Security. (2025). 2025 AI Trends in Healthcare & Life Sciences | Key Insights. https://blog.rsisecurity.com/trends-in-healthcare-life-sciences/