Closing the AI Value Gap with redSling

Artificial intelligence is rapidly transforming how organisations operate, innovate, and compete. According to a PwC global report, AI adoption could boost global GDP by up to 15 percentage points by 2035, illustrating the enormous economic potential of the technology when successfully deployed.¹ Yet despite widespread interest and investment, many organisations are still struggling to convert AI adoption into clear, measurable business value.

A recent global survey by PwC found that 56% of companies report no measurable benefit from their AI investments so far, with only a small proportion achieving both cost reduction and revenue growth from AI applications.² This gap between AI adoption and business value, often referred to as the AI value gap is one of the most significant challenges facing enterprises today.

Why AI Is Failing to Deliver Expected Business Value

Even as organisations invest heavily in AI, several structural and operational barriers prevent them from realising ROI at scale:

1. Integration Complexity and Technical Barriers
   AI systems don’t operate in isolation and they must integrate with existing data, processes, and both legacy and modern systems. Without seamless integration, models remain siloed and are unable to influence real world workflows or decisions.
2. Talent Shortages and Skills Gaps
   Building and deploying AI requires specialised skills in data science, machine learning engineering, and production deployment. With demand out stripping supply, many projects stall or never move beyond pilot. At the recent World Economic Forum, business leaders noted that implementing AI alone is insufficient and that achieving meaningful outcomes requires significant investment in employee skills and training.³
3. Strategy, Culture, and Readiness
   While many organisations are experimenting with AI, fewer have developed comprehensive strategies and governance practices needed for widespread integration into core business functions.
4. IT-OT Divide in Industrial Settings
   In industrial sectors such as manufacturing, logistics, and facilities management, enterprise IT systems and operational technology (OT) systems (e.g., sensors, programmable logic controllers, robotics) frequently operate in silos. This divide makes real time data access, AI inference, and automated action extremely difficult with traditional development approaches.

No-Code Platforms: Unlocking AI Value Faster

The biggest barrier to AI delivering real business value is rarely the technology itself — it’s the speed and flexibility with which organisations can experiment, iterate, and deploy solutions. Traditional software development slows AI pilots with months of coding, testing, and integration, leaving business opportunities on the table. No-Code platforms solve this by bringing business and engineering teams together in a shared visual environment, enabling rapid creation of functional prototypes and pilots that can be refined and scaled in real time.

Key benefits include:

1. Rapid prototyping: Build AI pilots in days or weeks instead of months.
2. Seamless collaboration: Business teams define workflows while engineers integrate models, APIs, and data sources without hand-coding.
3. Faster iteration: Test multiple approaches, refine based on real-world feedback, and scale only the most effective solutions.
4. Immediate impact: Integrate AI directly into workflows for actionable insights like predictive triggers, real-time dashboards, and continuous optimisation.
5. Risk reduction: Multiple pilots reduce the chance of large-scale failures while accelerating learning.
6. Cross-functional alignment: Business, IT and Engineering teams work in parallel, balancing technical feasibility with operational practicality.

By enabling this speed, collaboration, and agility, No-Code platforms transform AI from a distant strategic initiative into a practical, business-driven capability. Organisations can move rapidly from experimentation to operational impact, unlocking measurable business value faster than traditional approaches ever could.

redSling: No-Code That Delivers Industrial Grade AI Solutions

While many No-Code solutions are tailored to simple workflows or departmental automation, redSling is purpose built to address the hardest enterprise and industrial AI challenges where operational complexity, real time data, and scalable deployment matter most.

Here’s what sets redSling apart:

1. Platformless, Container Native Architecture
   Unlike cloud only or proprietary platforms, redSling generates Docker container based applications that can be deployed anywhere whether cloud, private data centre, or edge environments, without infrastructure vendor lock-in. This containerised approach ensures portability, performance, and compliance with enterprise and industrial IT policies.
2. Bring Your Own AI (BYO-LLM & Models)
   redSling supports integration with any AI model or large language model (LLM) of choice, giving organisations the flexibility to use best in class AI engines rather than being locked into a single vendor ecosystem.
3. Seamless IT-OT Integration
   redSling is designed to bridge the gap between enterprise IT data and operational technology systems on the factory floor or in the field. This makes it possible for AI insights to directly influence real time operational decisions, a critical capability in industrial AI scenarios that traditional platforms struggle to deliver.

Industrial AI in Action: Proven Use Cases with redSling

The real value of AI emerges when it solves actual business problems. Here are two compelling examples where redSling has enabled production grade industrial AI solutions:

• Fraunhofer IPA: AI-Driven Bin Picking Automation

Robotic bin picking — where robots identify and select randomly oriented parts in bins — has long been a complex challenge, especially in high-mix, low-volume (HMLV) production environments and for SMEs, where the investment risk can be a barrier. Fraunhofer IPA, in collaboration with redSling, has developed a full AI-powered Bin Picking @ Industrial Metaverse solution that transforms how manufacturers approach feasibility, simulation, and deployment.

Key capabilities of this solution include:

1. Visual 3D simulation and virtual commissioning of robotic workflows, allowing manufacturers to test and optimise systems virtually before any hardware investment.
2. AI-driven object detection, motion planning, and KPI forecasting, including cycle time, grasping success rate, bin clearing rate, and placement precision.
3. Autonomous optimisation of gripper design and cell layout, ensuring safe handling of various parts and maximum operational performance.
4. Automatic configuration for new parts, enabling factory workers to upload CAD models and deploy fully configured bin picking solutions to real robots with just a few clicks all via a web-based, intuitive interface.

This solution removes risk from automation, accelerates deployment timelines, and reduces engineering effort, giving manufacturers the confidence to adopt AI-driven bin picking solutions quickly and efficiently. By combining virtual simulation, AI optimisation, and platformless No-Code deployment, Fraunhofer IPA and redSling are making next-generation bin picking automation accessible, scalable, and ready for real-world factory implementation.

• Jetron: AI-Optimised Industrial HVAC Systems

Traditional HVAC systems rely on fixed setpoints and manual adjustments, making it difficult to respond to dynamic conditions such as production changes, shift transitions, or seasonal fluctuations. Jetron, in collaboration with redSling, developed an AI-driven HVAC optimisation solution that replaces static control with continuous sensing, prediction, and self-optimising control.

The system integrates real-time IoT sensor data across temperature, humidity, and differential pressure, dynamically identifying regional load changes and adjusting zones for on-demand, fine-grained control, preventing over-cooling and energy waste.

Key features include:

1. 1. 1. Predictive, process-first control: By combining production schedules, historical energy data, and operational plans, the system forecasts load peaks, optimises chilled-water and temperature setpoints, and adjusts fan and pump speeds to maintain energy efficiency while stabilising critical process parameters.
      2. Targeted environmental control: For sensitive areas such as cleanrooms or drying/curing zones, the system maintains precise pressure differentials and air-change rates, reducing quality risks from environmental fluctuations.
      3. Equipment health and efficiency monitoring: Continuous tracking of chillers, cooling towers, fan-coil units, and air-handling units allows detection of fouling, valve sticking, sensor drift, or blockages, generating actionable alerts and optimisation recommendations to extend equipment life and ensure reliable operation.
      4. Intuitive operator interface: Dashboards and automated controls enable operators to monitor and manage performance seamlessly, making advanced AI-driven optimisation accessible at all levels.

redSling’s platformless No-Code architecture, with IT-OT integration capabilities, makes this level of industrial AI deployment practical and scalable. AI models, control logic, and dashboards are deployed as containerised applications directly into operational environments, without being locked into proprietary platforms. The result is faster deployment, lower integration risk, and measurable improvements in energy efficiency, reliability, and operational performance.

Conclusion: From Adoption to Business Value

AI’s potential to transform business is real, but widespread adoption alone is not enough. Organisations must integrate AI deeply into workflows, align investments with strategic goals, and overcome barriers like technical complexity and talent shortages.

No-Code platforms like redSling help close the AI value gap by enabling rapid solution delivery, empowering business and engineering teams, and supporting industrial grade deployment with strong IT-OT integration and containerised architectures that deploy on any infrastructure.

With real use cases in robotic automation and industrial AI systems, redSling shows how AI can move beyond pilot projects and produce measurable results in real operational environments.

References:

¹ PwC. (2025). AI adoption could boost global GDP by an additional 15 percentage points by 2035. PwC Global. https://www.pwc.com/gx/en/news-room/press-releases/2025/ai-adoption-could-boost-global-gdp-by-an-additional-15-percentage.html
² PwC. (2026). PwC 29th Global CEO Survey: Leading through uncertainty in the age of AI. PwC Global. https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html
³ World Economic Forum. (2026). Why AI for business means investing in its people. World Economic Forum Annual Meeting 2026. https://www.weforum.org/stories/2026/01/why-ai-for-business-means-investing-in-its-people/