## Problem Statement

At a large aircraft manufacturing company, during robotic drilling and Wing assembly operation, key product characteristics were measured only at predefined intervals to reduce cycle time. However, any anomaly detected by this approach was 'reactive to the event' and the production had to be halted till solution to fix anomaly was identified and implemented. Hence a more comprehensive, automated and proactive quality inspection process was required to predict and prevent production line disturbance.

## Solution

We employed a holistic approach of tracking and processing every aspect of manufacturing process (IoT data). Supervised machine learning algorithm was developed to deduce insights by correlating historical manufacturing key process characteristics (IoT process data) with corresponding key product characteristics (inspection data). The algorithm was then able to detect anomalies and make proactive predictions about the product characteristics.


## Key Achievements

- Fast and easy analysis of IoT and metrology data by manufacturing engineers.
- The machine learning model highlighted the specific manufacturing parameters driving quality issues.
- Prescriptive analysis highlighted parameters resulted in 10% reduction in the cost of quality.
- The results were achieved with in 4 weeks (rather than 6 months) by involving lean six sigma practitioners rather than expensive data scientist.
- Promoted a culture of data curation and data harvesting within the business.
- Reduced/eliminating inspection step.
- Improved yield and production efficiency.

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Automotive

Energy

Defence

Aerospace

Boost manufacturing productivity with AiBoost®

Industrial application of Artificial Intelligence

Industrial Inspection with AI

## Problem Statement

At a large aircraft manufacturing company, during Wing assembly stages, small gaps were seen between the interfacing components. These gaps had to be filled by a manual ‘overhead operation’ called Shimming. This operation was a significant disruption to enable the desired ramp rate and increased the manufacturing costs. Hence the objective of this activity was to understand the root cause leading to gapping and extract non-intuitive insights to reduce shimming process.

## Solution Main Activities

- Collected key product characteristics data for all the interfacing components (i.e. Wing Covers, Ribs etc.), Jig calibration data and Wing deflection
- Combined previously isolated ‘historical data set’ with the corresponding ‘events in real life’ i.e. gaping during assembly
- Developed supervised machine learning algorithm to identify the root cause characteristics leading to gaping

## Key Achievements

- Predictive data analytics that enabled the aircraft manufacturer to reduce and eliminate cost of non-quality i.e. reduce amount of gap-filling per wing assembly and enable production ramp up
- Prediction algorithm predicting gap filling cases with 95 % accuracy to enable accurate workforce deployment planning
- Spread awareness about benefits of data analytics techniques applicable to shop floor environment
- Promote a culture for data curation and data harvesting within the business

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Explainable AI (XAI)

MLOps

Root Cause Analysis with AI


## Problem Statement

Record demand for commercial aircrafts (prior to the pandemic and hopefully the demand will resume shortly) had led to huge backlog of orders (greater than 7 years in case of single aisle aircrafts). This had resulted into pressure to ramp up the production rate and reduce the delivery lead times.

However, simultaneously, aerospace manufacturers were also faced with a challenge to reduce the energy consumption at their facilities. This was a result of  commitment towards net zero emissions and increasing industrial sustainability.

Combination of the above two factors had led to the need for investigating novel methods for identifying opportunities that could lead to reduction in energy consumption whilst meeting the business objective of increasing production rate at high quality.

We developed an Artificial Intelligence solution to identify novel opportunities to reduce carbon foot print in high volume manufacturing environment.

Some of the salient features of our solution are as below.

## Solution Main Activities

- Manufacturing process parameters data was captured for drilling operations 
- Corresponding energy consumption data captured
- Performed data pre-processing and visualisation, feature engineering and subsequent down selection
- Development, evaluation and down selection of various supervised machine learning models

## Key Achievements

- Key parameters driving increased energy consumption were identified.
- Revised machine routing and parameter fine tuning led to reduced energy consumption, whilst still delivering the necessary quality and production rate.
- Energy consumption was reduced by 5% - a huge impact for an initial assessment.
- Spread awareness about benefits of data analytics techniques applicable to shop floor environment
- Promoted a culture for data curation and data harvesting within the business

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Construction

Transport

Boost industrial energy efficiency & sustainability

Recruitment

Boost energy efficiency and sustainability with AiBoost

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The emergence of Artificial Intelligence (AI) technologies and the growth in big data have opened up novel and exciting digital transformation opportunities. Yet, very few companies have been successful in adopting and exploiting the full potential of AI and so gaining a competitive edge. One of the most significant barriers to this is the shortage of ‘industry relevant’ AI skills, followed by additional barriers such as unclear starting point, AI hype, information overload, fear of the unknown, software and hardware complexities, and potentially substantial financial investment without clear ROI etc.


IntelliumAI, a company based in the southwest of the UK, has developed a solution to address these challenges and accelerate the adoption of AI both in large OEMs and SMEs. Leveraging their expertise in the high value manufacturing industry as well as cutting-edge AI technologies, IntelliumAI has developed a cloud-based enterprise AI platform called ‘AiBoost®’ to accelerate the adoption of AI in the industry. The platform aims to create ‘citizen data scientists’ within businesses by giving AI ‘power to people’. This enables users with a wide range of skill levels to get started on the AI journey in a governed, automated and scalable manner.


Some of the main features of the AiBoost® platform are; ‘AI Workbench’: A virtual workbench to standardise and automate end-to-end AI workflow covering model training, evaluation, inspection and prediction, ‘Rules Factory’: An intelligent algorithm to generate easy-to-understand rules and insights from large volumes of data and ‘Explainable AI’: A framework for robust, fair and transparent AI to build trust and confidence in the AI-based automated decision-making process.


SMEs are especially poised to benefit from the AiBoost® platform as it abstracts the software and hardware intricacies from the end users and allows them to focus on their core expertise. This results in significant productivity gains at a substantially lower financial investment. Typical advantages of the AiBoost® platform include; accelerated product design cycles, faster manufacturing maturity, reduced manufacturing cost, lower operational costs, increased productivity and quicker time to market.

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Power of AI in your hands with AiBoost®!

AutoML

AiBoost® : A toolkit for the 21st century engineers.

Bristol based deep tech company Intellium AI is supporting BAE Systems through an Innovate UK sponsored project to reduce material waste and carbon footprint across the supply chain. A novel Artificial Intelligence (AI) solution is set to be implemented to connect suppliers remotely whilst preserving supply chain data privacy.

Intellium AI is working with BAE Systems and other industry partners including Codegate, Digital Catapult, Maher and MTC on  Innovate UK’s Manufacturing Made Smarter project, INTELI. The initiative forms part of BAE Systems’ Factory of the Future, a developmental facility which is developing and testing industry 4.0 technologies that will transform and enhance manufacturing for the future. 

BAE Systems is working with UK industry to drive a digitally led, affordable solution for Tempest, the UK’s next generation combat air system.   The INTELI project is addressing a key challenge facing the manufacturing industry that relies on sharing complex and sensitive data between supply chain partners.

The INTELI project is targeting the manufacture of a prototype aircraft component produced by Maher, a tier 1 supplier to BAE Systems using a novel manufacturing process. BAE Systems’ INTELI lead Ismail Master said “Intellium AI’s privacy preserving AI solution seeks to enable our supply chain to safely and efficiently share real-time and remote insights to ensure that the aircraft product quality is right first time. By harnessing the power of accurate, secure and connected data in this way, we will be able to reduce the amount of manufacturing rework required, which directly links to savings in material waste.  Not only will this drive time and cost savings, but, as importantly it supports the Company’s commitment to achieve net-zero greenhouse gas emissions across its operations by 2030, and across a wider supply chain by 2050.”

Kiran Krishnamurthy, CEO of Intellium AI, has previous experience with these challenges in the aerospace industry where he has used AI to overcome the amount of manual rework required in assembling a long range aircraft wing. 

Kiran said “Although the external organisations involved in the supply chain agreed that data should be leveraged to improve process efficiency, there were barriers in sharing data to achieve this. Concerns about data sharing are understandable when protecting Intellectual Property and managing data correctly is so important.”

Drawing from their experience, Intellium AI is developing a solution that generates AI driven supply chain insights, whilst preserving privacy, as business data never leaves the boundaries of an organisation. 

Kiran continued, “Such privacy preserving AI technology is already used in other industries. For example, we all want predictive texting on our mobile keypads to be accurate to save time and effort, yet we don’t want our private messages to be accessed by any third party to train AI models. Privacy preserving predictive texting has been developed to address this challenge and is already in use in some mobile devices”. 

### About Intellium AI

Intellium AI is an AI-for-Engineering deep tech company based in Bristol-UK working with leading blue chip companies in the Manufacturing industry. Kiran Krishnamurthy, CEO Intellium AI, previously worked at Airbus UK for 18+ years and has strong interest in developing technology that accelerates decarbonisation of the manufacturing industry and support industry achieve the Net Zero targets. 

Contact us via info@intellium.ai or www.intellium.ai/contact to evaluate your Net Zero status and actions.

### About BAE Systems

At BAE Systems, our advanced defence technology protects people and national security, and keeps critical information and infrastructure secure. We search for new ways to provide our customers with a competitive edge across the air, maritime, land and cyber domains. We employ a skilled workforce of 89,600 people in more than 40 countries, and work closely with local partners to support economic development by transferring knowledge, skills and technology.


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Bristol based deep tech company Intellium AI is supporting BAE Systems through an Innovate UK sponsored project to reduce material waste and carbon footprint across the supply chain. A novel Artificial Intelligence (AI) solution is set to be implemented to connect suppliers remotely whilst preserving supply chain data privacy.

Intellium AI collaborate with BAE Systems to exploit the use of AI in the Supply Chain

If you were leading an innovative business transformation initiative in your organisation and had the choice between developing a *complex technology of high business value per user but having a lower user base* or a *simpler technology of lower business value per user but having a wider user base*, which option would you select?  

The choice is never easy. However, it could determine the speed of technology adoption in your organisation. I have had the privilege of being involved in both scenarios, which have heavily influenced my thinking and corporate strategy.

More than 20 years ago, when I was working at Airbus on creating the A380 aircraft, I helped develop software that automatically generated manufacturing instructions for robots based on the part's design. The software was a *technological success* that automated a business process and resulted in significant time savings per use, realising cost savings for the Company. The only caveat was that due to the product complexity, the software developers ended up being the primary end users, leading to the software being regarded as an *adoption failure*.

Having learnt this lesson on A380, we made increasing the software user base a business requirement whilst designing the software architecture for the subsequent aircraft, the Airbus A400M. The software for the same application was simplified to make it more business user friendly, resulting in widespread adoption. Features that resulted in all-or-nothing benefits were removed and gave a chance for the end users to reap micro benefits, even in situations where full exploitation of software automation was not possible.

The increased adoption of simplified software created an increasing community of technology evangelists who helped to identify further opportunities to make business processes more efficient, leading to cost and time savings. 

> The community aspect of technology creation has an amazing leveraging effect.

I am now seeing a similar trend in the industry with AI adoption. Many companies are exploring the latest and greatest AI algorithms and trying to automate the entire business process with AI. The AI skills and the data literacy to undertake complex digital transformation don't yet exist in these organisations. This leads to a gap in communication and expectation, which could eventually lead to failure in AI adoption.

Conversely, organisations focusing on increasing digital skills while initially choosing simpler AI use cases are co-creating inclusive community-based technology. 

> The ROI of initial AI projects is an increase in data and AI literacy across the community.

This creates a community of *technology evangelists* who go on to identify further digital transformation opportunities to make business processes more efficient, realising significant cost and time savings.

So, to answer my original question, technology that is co-created by involving a community of business and technical users has a higher chance of delivering value resulting in successful business adoption. 

> Creating or leveraging a community of practice is the secret sauce for successful technology adoption.


Written By,
Kiran Krishnamurthy
CEO, Intellium AI

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The secret sauce of successful AI adoption

Intellium AI has appointed Ian Risk (pictured) as a new non-executive director (NED)

In his new role, Ian — who is a highly experienced technology and innovation professional — will provide strategic advice to the board on Intellium’s product development and customer success. 

Ian commented on his appointment, “I am delighted to join the team at the Bristol based start-up Intellium AI. Digital transformation should be an essential part of the strategy for 
every company to ensure they remain or become more competitive, but for many is out of reach due to a lack of skills. The technology under development at Intellium addresses this need, it is far more inclusive for those lacking a data science background, thereby enabling more widespread adoption of artificial intelligence throughout organisations. By democratising the access to data and providing the tools to everyone to exploit it, we can address the critical issues facing us all, such as more efficient use of energy to meet our net zero targets.”

Kiran Krishnamurthy, CEO of Intellium AI, commented, “I have long known Ian since our days at the Airbus Innovations team. Between us, we have more than 60 years of industry domain expertise. This is crucial to successfully adopt technology like AI, where a deep understanding of the industry background is paramount. I am very excited to have Ian on board in our mission to make AI technology widely accessible across the business user community.

Ian joins Intellium on the 3rd of October, 2022.

About Ian Risk

Prof. Risk has more than 40 years of experience in the development, management and execution of collaborative research projects, primarily within the aerospace sector. He has created and managed some of the largest UK industrial R&D projects and many within the EU Framework Programme. He has held senior positions with UK industrial and research organisations and brings a wealth of operational and technological knowledge to Intellium AI. 

About Intellium

Intellium AI is a deep tech company based in Bristol-UK, making complex AI technology accessible to all Engineers working in the high value design and manufacturing industry.


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Intellium AI has appointed Ian Risk to the Board

Ian Risk joins forces with Intellium AI

**Introduction**

Predictive maintenance is a powerful strategy for optimizing the performance of industrial equipment and reducing downtime. By using data and machine learning algorithms, businesses can predict when maintenance is needed and schedule it before failure occurs. This allows them to avoid unexpected outages and maintain a high level of productivity.

**Business Pain Points**

Traditional maintenance strategies are often reactive, meaning they are only performed after equipment has failed. This can result in unplanned downtime, which can be costly and disruptive to business operations. In addition, scheduled maintenance can be time-consuming and costly, as it requires equipment to be taken offline during regular working hours.

**AI Solution**

Predictive maintenance uses data and machine learning algorithms to predict when equipment is likely to fail. This allows businesses to schedule maintenance at a convenient time, before failure occurs. This can significantly reduce downtime and improve productivity. In addition, predictive maintenance can reduce the need for scheduled maintenance, as it allows for a more targeted and efficient approach.

**Benefits**

The benefits of predictive maintenance are numerous. By reducing downtime, businesses can maintain a high level of productivity and avoid the costs associated with unplanned outages. In addition, predictive maintenance can reduce the need for scheduled maintenance, resulting in lower costs and less disruption to business operations. Overall, predictive maintenance is a powerful tool for optimizing the performance of industrial equipment and improving business performance.

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Reduce downtime and improve productivity with AI

How can AI help with predictive maintenance?

**Introduction**

AI-driven supply chain optimization is a powerful strategy for improving the efficiency and performance of a business's supply chain. By using data and machine learning algorithms, businesses can optimize their supply chain operations and achieve a competitive advantage.

**Business Pain Points**

Traditional supply chain management can be complex and time-consuming, requiring manual processes and decision-making. This can lead to inefficiencies and a lack of agility, which can impact a business's ability to respond to changes in demand or the market. In addition, managing the supply chain can be costly, as it requires a significant investment in resources and infrastructure.

**AI Solution**

AI-driven supply chain optimization uses data and machine learning algorithms to automate and optimize supply chain operations. This allows businesses to make more accurate and timely decisions, resulting in a more efficient and agile supply chain. In addition, AI-driven supply chain optimization can reduce the need for manual processes and decision-making, resulting in lower costs and improved productivity.

**Benefits**

The benefits of AI-driven supply chain optimization are numerous. By improving the efficiency and agility of the supply chain, businesses can achieve a competitive advantage and respond quickly to changes in demand or the market. In addition, AI-driven supply chain optimization can reduce costs and improve productivity, resulting in higher profits and a more sustainable business model. Overall, AI-driven supply chain optimization is a powerful tool for improving the performance of a business's supply chain.

Optimize your supply chain with AI-driven solutions: Improve efficiency, reduce costs, and gain a competitive advantage

How can AI help with Supply Chain optimisation?

**Introduction**

AI-driven industrial inspection automation is a powerful strategy for improving the efficiency and accuracy of industrial inspections. By using data and machine learning algorithms, businesses can automate their inspection processes and reduce the need for manual labor.

**Business Pain Points**

Traditional industrial inspection processes are often labor-intensive and time-consuming, requiring skilled workers to manually inspect equipment and identify defects. This can be costly and can lead to a lack of consistency and accuracy in the inspection process. In addition, manual inspection processes can be dangerous, as they require workers to be in close proximity to potentially hazardous equipment.

**AI Solution**

AI-driven industrial inspection automation uses data and machine learning algorithms to automate the inspection process. This allows businesses to identify defects and anomalies in equipment more quickly and accurately, without the need for manual labor. In addition, AI-driven inspection automation can reduce the need for workers to be in close proximity to hazardous equipment, improving safety and reducing the risk of accidents.

**Benefits**

The benefits of AI-driven industrial inspection automation are numerous. By automating the inspection process, businesses can reduce the need for manual labor and improve the efficiency and accuracy of their inspections. In addition, AI-driven inspection automation can improve safety by reducing the need for workers to be in close proximity to hazardous equipment. Overall, AI-driven inspection automation is a powerful tool for improving the performance of industrial inspection processes.

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Transform your industrial inspection process with AI-driven automation

How can AI help with automating Industrial Inspection?

**Introduction**

Anomaly detection in manufacturing environments is a critical task for ensuring the smooth and efficient operation of production lines. Traditionally, this has been done manually by skilled technicians, who must constantly monitor the various systems and processes in place to identify any potential issues. However, as manufacturing becomes increasingly complex and automated, it is becoming increasingly difficult for human operators to keep up with the sheer volume of data that must be analyzed.

**Business Pain Points**

One of the biggest challenges facing manufacturers today is the need to quickly and accurately detect anomalies in their production processes. Failure to do so can result in a wide range of problems, including production delays, quality issues, and even safety hazards. Additionally, manually monitoring systems and processes can be time-consuming and costly, diverting resources away from more productive tasks.

**AI Solution**

To address these challenges, many manufacturers are turning to AI-driven anomaly detection solutions. These systems use advanced machine learning algorithms to analyze data from various sources, such as sensors, control systems, and production logs, to identify potential anomalies in real-time. This allows manufacturers to quickly and accurately identify any potential issues, enabling them to take corrective action before problems arise.

**Business Benefits**

The use of AI-driven anomaly detection in manufacturing environments has a wide range of benefits. By automating the process of detecting anomalies, manufacturers can reduce the time and cost associated with manual monitoring, freeing up resources for more productive tasks. Additionally, by identifying potential issues early on, manufacturers can avoid costly production delays, quality issues, and safety hazards, ultimately improving their bottom line. Overall, AI-driven anomaly detection can help manufacturers to improve the efficiency and effectiveness of their operations, enabling them to better compete in today's highly competitive market.

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Maximizing Efficiency with AI-Driven Anomaly Detection in Manufacturing Environments

How can AI help with manufacturing anomaly detection?

**Introduction**

Boosting productivity in manufacturing environments is a critical task for ensuring the success of any business in this industry. Traditional methods of improving productivity, such as optimizing processes and streamlining operations, can only take a business so far. To truly take their productivity to the next level, manufacturers must embrace new technologies, such as artificial intelligence (AI).

**Business Pain Points**

One of the biggest challenges facing manufacturers today is the need to continuously improve their productivity. This can be difficult, as there are many factors that can impact productivity, such as changes in market demand, new regulations, and shifts in the global economy. Additionally, many manufacturers struggle to keep pace with the rapid pace of technological change, making it difficult to take advantage of new tools and techniques that can improve productivity.

**AI Solution**

To address these challenges, many manufacturers are turning to AI-powered solutions to boost their productivity. These systems use advanced machine learning algorithms to analyze data from various sources, such as sensors, control systems, and production logs, to identify opportunities for improvement. By leveraging this data, manufacturers can optimize their processes, streamline their operations, and increase their overall efficiency.

**Business Benefits**

The use of AI to boost manufacturing productivity has a wide range of benefits. By identifying opportunities for improvement, manufacturers can increase their output, reduce their costs, and improve their bottom line. Additionally, by staying on the cutting edge of technology, manufacturers can remain competitive in today's fast-paced market, enabling them to better serve their customers and grow their business. Overall, AI-powered solutions are the key to improving productivity in the manufacturing industry.

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Maximizing Manufacturing Productivity with AI: The Future of Industry

How can AI help with boosting manufacturing productivity?

Artificial intelligence can potentially transform businesses and societies, but many organisations struggle to implement AI technologies effectively. One solution to help accelerate AI adoption is identifying and empowering "AI champions" within organisations.

AI champions are employees who are passionate about the potential of AI and act as a catalyst in their companies to integrate AI successfully. They can help in several ways:

- Identifying use cases. AI champions are closely familiar with their companies' products, services, and processes. This puts them in a great position to spot opportunities where AI could drive real value. They can help identify promising use cases and applications of AI that could benefit their organizations.
- Educating others. There is still a lot of mystery and confusion surrounding AI, its capabilities, and its limitations. AI champions can help educate their colleagues about what AI is, how it works, and its potential applications and benefits for their industry and company. This education and awareness building is crucial to gaining buy-in and adoption.
- Experimenting. The best way to learn how AI can help is by experimenting. AI champions can help run pilot projects and experiments applying AI to problems to see what works well and what doesn't. They can closely monitor experiments and share lessons learned to determine how to implement the best AI going forward.
- Addressing challenges. The process of implementing AI will inevitably surface challenges, issues, and blockers. AI champions can help address these challenges, such as data gaps or regulations around data and AI use. Their passion and expertise can help companies figure out solutions to implement AI.

In summary, AI champions can help blaze the trail for AI adoption at their organizations by identifying opportunities, educating colleagues, running experiments, and addressing challenges. Identifying and supporting AI champions is a simple but effective strategy to accelerate the integration of AI and realize its potential to transform businesses.


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Importance of AI Champions to accelerate AI adoption

AI Champions: The Key to Unlocking Enterprise AI Success

Intellium AI, a leading provider of innovative AI solutions for the advanced engineering sector, today announced the appointment of Dean Taylor as a Non-Executive Director.

Dean brings decades of experience in the defence industry, successful business growth and a passion for human-centric digital transformation. His extensive industry knowledge and strong track record of scaling businesses make him an invaluable asset to Intellium AI's board.

"We are thrilled to welcome Dean Taylor to our Board of Directors," said Kiran Krishnamurthy, CEO of Intellium AI. "Dean's proven leadership and in-depth understanding of the defence industry will be instrumental in guiding our strategic direction."

Ian Risk, Non-Executive Director of Intellium AI, said, "Dean Taylor's passion for inclusive business transformation will help us achieve our mission to accelerate AI adoption in the advanced engineering sector."
Dean expressed his delight to join Intellium AI, stating, "I am honoured to join the board of Intellium AI and work alongside a talented team dedicated to making AI adoption seamless and accessible. I look forward to contributing my experience and insights to support the company's continued growth and success."

Intellium AI's core software, AiBoost, is a no-code Enterprise AI platform enabling businesses to increase productivity and save costs. With the addition of Dean Taylor to the Board of Directors, the company aims to expand its market reach further, enhance its product offerings, and drive innovation in the advanced engineering sector.

About Intellium AI

Intellium AI is a pioneer in delivering state-of-the-art, no-code AI technology that accelerates digital transformation and promotes cost-effective AI adoption across the advanced engineering sector. With a mission to empower existing workforces and eliminate barriers to AI adoption, Intellium AI's innovative AiBoost platform offers tailored AI applications for a variety of applications in the advanced engineering sector. 

For more information, visit www.intellium.ai.

Media Contact:

Intellium AI

info@intellium.ai


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Dean Taylor joins Intellium AI's Board of Directors

Dean Taylor joins forces with Intellium AI

Intellium AI, a deep tech company specialising in Artificial Intelligence (AI) and Machine Learning (ML) solutions for the Advanced Engineering sector, is pleased to announce the appointment of Graham Complin as its new Strategic Development Partner.

About the Appointment

With a distinguished career spanning leadership roles at Oracle, Sopra Steria, Thales, and the British Army, Graham brings a wealth of experience in sales, business development, and strategic growth. His visionary approach aligns seamlessly with Intellium AI's commitment to providing industry-specific solutions that bolster productivity security and expedite decision-making processes.

“As we continue to expand our innovative solutions, Graham's robust expertise in the Defence sector will be instrumental in navigating complex client accounts, accelerating our business growth, and fortifying our market leadership,” said Kiran Krishnamurthy, CEO of Intellium AI.

Key Responsibilities

In his new role as Strategic Development Partner, Graham will be intricately involved in high-level strategy formulation aimed at amplifying Intellium AI's business impact within the Defence sector. Drawing on his deep insights into customer-centric strategies, Graham will spearhead initiatives around Intellium AI's flagship products: AiBoost, a no-code AI platform for structured data insights, and BoostBot, a chatbot adept at mining unstructured data. Furthermore, he will play a vital role in strengthening Intellium AI's partnerships ecosystem, aligning with the company's overarching vision for human-centric transformation and commitment to ethical AI practices.

About Graham Complin

Graham Complin holds an MBA and is a Member of the Institute of Engineers and Technologists (MIET) and the Chartered Institute of Personnel and Development (MCIPD). His legacy of closing significant deals with Defence organisations such as the Ministry of Defence (MoD) speaks volumes about his proficiency in bringing transformative solutions to market.

About Intellium AI

Based in Bristol, UK, Intellium AI specialises in providing specialised AI and ML solutions for the Advanced Engineering sector. The company has established strong capability in AI for Logistics and Supply Chain, Manufacturing, Cyber Security, and Material Design, among other domains. Their ecosystem approach prioritises workforce empowerment and ethical practices in AI, making them a trusted client partner.

For Media Inquiries:
info@intellium.ai

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Graham Complin appointed as a Strategic Development Partner

Graham Complin joins forces with Intellium AI


In an era where environmental responsibility is paramount, Intellium AI, a scale-up company in AI and ML space within the Aerospace and Defence sectors, has taken a significant leap forward with the launch of its ambitious Carbon Reduction Plan. This initiative marks a new chapter in the company's commitment to sustainability, aligning its advanced technological capabilities with eco-friendly practices.

Strategic Approach to Carbon Footprint Reduction

At the heart of Intellium AI's strategy is a detailed assessment of its current carbon footprint, utilising its proprietary AiBoost technology. This in-depth analysis provides a clear picture of emissions across various operations, laying the groundwork for targeted actions. The company has set forth aggressive yet achievable carbon reduction targets, aiming to significantly decrease emissions by a specified percentage within a set timeframe.

Innovative Use of AI and ML

Leveraging its core strengths, Intellium AI has introduced several innovative strategies to reduce carbon emissions. This includes optimising energy efficiency in operations and data centres through AI-driven solutions, developing energy-efficient AI models, and streamlining supply chains to minimise emissions related to logistics. The plan also highlights the use of AI in predictive maintenance within the Aerospace and Defence sectors, ensuring efficient energy use and minimising waste.

Sustainable Partnerships and Employee Engagement

Recognising the power of collaboration, Intellium AI is engaging with industry partners to co-develop AI solutions for carbon reduction. This ecosystem approach fosters knowledge sharing and joint innovation. Internally, the company empowers its workforce, encouraging employees to participate actively in sustainability initiatives and adopt eco-friendly practices.

Monitoring Progress and Ensuring Transparency

A key component of the plan is regularly monitoring carbon emissions using advanced AI tools, ensuring that Intellium AI can measure its progress accurately. This commitment to transparency extends to its stakeholders, with the company providing clear and detailed reporting on its journey towards its carbon reduction goals.

Continuous Improvement for a Sustainable Future

Intellium AI's Carbon Reduction Plan is not a static document but a living strategy that will evolve with technological advancements and insights. This dynamic approach underlines the company's dedication to continuous improvement and innovation in the field of AI, ensuring that its operations not only advance technologically but also grow more sustainably.

Summary

Intellium AI's Carbon Reduction Plan exemplifies how companies in the tech sector can lead the way in environmental stewardship. By aligning its operational excellence in AI and ML with a strong commitment to reducing its carbon footprint, Intellium AI is setting a benchmark in the industry, demonstrating that technological innovation and environmental responsibility can go hand in hand. This initiative is a call to action, inviting collaboration and support from stakeholders in the journey towards a more sustainable future.


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Intellium AI Pioneers Sustainability with Its Comprehensive Carbon Reduction Plan

Intellium AI’s Carbon Reduction Plan

Artificial Intelligence now affects every aspect of our lives, in the workplace, methods of study, even in selecting what we stream on TV. This technology is everywhere, but how much do we trust it, and why should we? 

It is no surprise that the origins of trust lie in the human ability to determine if someone or something is telling them the truth. Our progress as a race has been heavily influenced by our ability, or desire, to cooperate with others: from hunting for food, to landing on the moon, we do this in teams. The more we collaborate, generally the better we perform. To do that successfully, one must trust others to do what you expect of them, and that clearly cannot be guaranteed. So, what makes you trust someone or something? Ultimately, it is a balance of risk versus reward. Given the variety in human nature, everyone approaches this differently, some enjoy the excitement of uncertainty, while others are more adverse. Trust as a concept is not clear cut, it is complex and far from black and white.

From this starting point, if one is designing an AI system with which a user is going to collaborate, how can reassurance be given to them so that they build trust in the system and any advice provided? 

This series of three articles will assess some of the key factors that impact our ability to trust:

- Firstly, the basics, identifying the underlying features that create trust.
- Then, emerging issues in digital technology that can affect that trust over time.
- Finally, how we should design systems to address our basic human concerns.   

As we move into the era of digital technology, there are increasing fears over cyber space and most organisations are trying to provide reassurance to customers over their [digital trust](https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-digital-trust-truly-matters) credentials. Many articles have been written on this topic and most focus on the functional aspects of the system, cyber security, governance and alike which are generally procedural in nature. Sadly, few address the more fundamental issues of human belief and how the design of products should evolve to address these often subconscious concerns.

A starting point for this process has to be a clear recognition of the fact that artificial intelligence systems do not possess [sentience](https://www.bitlaw.com/ai/hallucinations-and-AI.html), even if we may desire or expect our digital assistant to act that way. So, although such systems cannot lie, it has been widely shown they cannot be relied on to tell the truth. With this in mind, as designers we must consider how to protect our customers whilst considering how useful is a tool that is often, indeed frequently, being [creative](https://www.sciencedirect.com/science/article/pii/S2949719123000213?via%3Dihub) with the facts? In essence, we must carefully address the challenge of how to advise users of the reliability of a response, whilst ensuring that the system retains utility. 

From a psychological perspective much of our interactions with others depends on our [expectations](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/37051611/) of them. Skills, reliability and openness are some of the underlying attributes that we look for when evaluating people with whom we intend to collaborate. If we view an AI assistant in that light, these are the features that we will be searching for. To build trust, AI developers must ensure that their customers clearly understand the capability of a system, as for some expectations may far exceed capability, as is often the case with human co-workers. People need to [interact](https://www.linkedin.com/pulse/why-culture-matters-ai-adoption-paul-edwards) with the tools they are using, learn their traits and shortcomings to understand the limits of what can be achieved, otherwise they will be readily discarded.

[Culture is](https://www.quora.com/What-are-the-cultural-and-societal-factors-influencing-the-acceptance-and-adoption-of-AI-in-human-interactions) another critical factor in the creation of trust, it can impact the degree to which a customer will perceive the potential benefits of a technology versus the perception of their social peer group or management structure. There is no silver bullet in terms of how to address this, the more support and [transparency](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023880/) one can provide in the product, the easier it will be to establish trust. This may increase software development costs by building in additional layers of ‘on-boarding’ support, but it will create enhanced reputational trust and provide a wider client base in the long run.

These fundamental features of trust therefore need to form the basis of the approach to designing AI systems, but that is not the end of the story. Our next article will focus on some of the emerging concerns that can impact our trust in digital technologies and what we must do as developers and users to realise their vast potential, whilst protecting ourselves.

In this series Ian Risk, Non-Executive Director of Intellium AI is joined by Psychologist Charlotte Preen to assess the importance of the human approach to establishing trust with this emerging technology. This article was produced using solely human endeavours, you will have to trust us on that one.

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Trust: The Foundation of the Relationship Between Humans and AI - The Basics

The Human Side of Trusting AI

Developing strong relationships is a basic human need, they are essential to our long term wellbeing. [Studies](https://www.apa.org/monitor/2023/06/cover-story-science-friendship) have shown that those of us who are lucky enough to have stable, continuous friendships are generally healthier and live longer. Unfortunately, in a world where we are more connected, many feel more isolated and are therefore seeking dialogue with something more accessible, technology. This second paper of our trust trilogy will therefore focus on the emerging psychological issues from recent developments in digital technology that should impact our thinking around the design of more sustainable AI systems.  

Many developers exploit the basic human need for friendships by anthropomorphising their products, as establishing personality traits with which the user is familiar accelerates the process of building trust. Indeed, someone is more likely to trust a robot that has human characteristics than one that doesn’t. For the developer this may seem an appealing approach, it is however a double edged sword. Utilising [ethopoeia](https://en.wikipedia.org/wiki/Ethopoeia) to build a system ‘persona’ can in one sense, create trust but in another, risk losing it all together. If trust in someone or something you have befriended is lost through failing to meet expectations, then one's emotional ‘fall’ is far greater. Although this may not seem at all rational, in the hands of an inexperienced user, the [emotional](https://crestresearch.ac.uk/comment/emotional-over-trust-in-ai-technology/) element of trust can be far stronger due to a lack of understanding of the technology. This forms the basis of a major issue for developers, overtrust. Once compromised, this can build adversarial feelings in the mind of the user, actively causing them to sideline the system or even blocking implementation.

The recent [Horizon](https://www.instituteforgovernment.org.uk/comment/lessons-government-post-office-scandal) scandal is a clear example of too much trust being placed in digital technology that has not undergone sufficient testing. There are [many](https://www.sciencedirect.com/science/article/abs/pii/B9780128018736000054) more lessons we need to learn even when tragedies like this demonstrate the danger of stakeholder overtrust in technology. Designers must incorporate this human weakness in their approach particularly when  implementing services in which we are required to trust, such as those [provided](https://www.telegraph.co.uk/business/2024/01/24/official-british-government-chatbot-chatgpt-answers-french/) by the government or the health service. 

At the heart of this problem lies the human tendency to trust in technology where our own knowledge is [lacking](https://www.frontiersin.org/articles/10.3389/frobt.2021.554578/full) as we assume others have tested on our behalf. Overtrust is more likely to occur at the edges of our own capabilities and in safety critical scenarios, which are often the most dangerous. Whilst it is clear that AI systems cannot lie, their conclusions can be wrong and so the widespread implementation of such products without careful consideration has the potential for disaster. In the rush to exploit AI, products are arriving in the market in an immature state, something clearly illustrated by the number of them [withdrawn](https://www.theguardian.com/technology/2023/feb/01/chatgpt-maker-openai-releases-ai-generated-content-detection-tool) due to observed limitations. Vendors need to be explicit as to the capability of their products, but more importantly their limits as otherwise our trust in some large digital brands will be severely undermined.  

One of the most concerning issues arising from the use of generative AI is the issue of hallucinations. As humans we learn by using information and observations. If we are fed the wrong data then we may unwittingly not perceive the truth, creating a hallucination rather than a lie. AI is no different. Such systems are simply looking for patterns in data, so if that source of information is compromised it is unlikely that the intended purpose of the system will be achieved. Such false perceptions of reality are the source of major concerns for those observing how AI is used, particularly when decisions taken or influenced by such content could be life threatening. The more critical those decisions, the greater the care we need to take in design and that starts with data. 

The challenge for AI is the amount of data required to develop real confidence in analysis and predictions that form the basis of decision making. AI systems such as large language models will primarily scrape the internet for data which is inherently [biased](https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/) by those populating the information source. If unchecked, this can lead to highly undesired outcomes from any analysis performed. A situation made critically worse as the volume of disinformation is exponentially increasing. Alternatively, if too little information exists, the AI system cannot have a full awareness of potential outcomes and again, outcomes will be compromised. This situation is exacerbated within [commercial](https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/?sh=7ca48c3c6078) organisations who are highly sensitive to data being publicly released which compromise the protection of their intellectual property. Such precautions can force systems to operate on a far more restricted dataset of internal information for training algorithms, which can be trusted, but exhibit proximity bias. These approaches will only amplify internal bias and lead to reduced reliability in decision making.

Spotting what is and isn’t real is challenging at the best of times, but with recent advances in the development of imagery from AI, the picture becomes even more confusing. Historically, art or photographs have been faked to mislead observers into believing the creators desired view of reality. Until now this ability was limited to those skilled in the field, but today this capability is in the hands of everyone, undeniably it is a marketing feature of most advanced mobile phones. Unlike the written word, from which our senses find it is hard to immediately decipher fact from fiction, our visual senses are far better tuned to spotting fakes. Developers of immersive environments and humanoid robots have become well aware of a phenomena known as the ‘[uncanny valley](https://www.verywellmind.com/what-is-the-uncanny-valley-4846247#:~:text=The%20uncanny%20valley%20is%20a,robots%20that%20are%20highly%20realistic.)’ where the closer something becomes to reality, the more our senses are heightened to identify something is not correct. This can cause observers discomfort or even revulsion towards an artefact, and what is problematic for the AI designer, it is wired into our DNA, it cannot be bypassed. So, seeing is not always [believing](https://www.nytimes.com/2023/02/07/technology/artificial-intelligence-training-deepfake.html). 

If, however, we are fooled by such techniques and faked images do cause offence or even worse, then issues of liability are going to continue to be raised. As a consequence, social media [companies](https://www.bbc.co.uk/news/technology-68215619) are now investing heavily to address this challenge, but many are sceptical that this approach will work. One thing they do agree on, is that spotting fake outcomes from generative AI tools such as ChatGPT, is a bridge too far. That, it seems, is something we will have to adopt old school methods to solve, we will simply need to check, check and check again any information we receive. Our next article will look at how better design can help us address these issues. 

In this series, Ian Risk, Non-Executive Director of Intellium AI, is joined by Psychologist Charlotte Preen to assess the importance of the human approach to establishing trust with this emerging technology. This article was produced using solely human endeavours, you will have to believe us on that one.


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Trust: The Foundation of Relationship Between Humans and AI

Trust Is Earned Not Given

In an age of disinformation and fake news there is naturally a rising concern that generative artificial intelligence is only going to make things [worse](https://www.thetimes.co.uk/article/deepfakes-pose-serious-threat-to-democracy-says-google-chief-lbmd87pdw). As with any new technology, there will be those who use it for its intended purpose and those who choose to exploit it. With this in mind, designers tasked with bringing AI systems into use must therefore be cognisant of their [responsibility](https://bootcamp.uxdesign.cc/how-is-trust-and-credibility-designed-in-ux-fc0ed84a1697) towards misuse, just as much as the benefits. The digital world has for years been plagued by issues of cyber security, fraud and malware, but the scale, speed and frequency at which certain actors employing AI can achieve their deceptions is now a primary concern for all stakeholders. These new malpractices are a particular worry given the human tendency to place trust in technology rather than seek the truth. The old adage of ‘don’t believe anything you hear and only half what you see’ without verifying the source has never been more true, but it is getting harder now to see the wood for the trees. This final article in our trilogy will focus on what we, as developers, need to do so that users realise the potential of AI and avoid the possible pitfalls.

For the designer, the absolute goal is to ensure the user establishes a trusted relationship with a new product. One might say a utopic outcome for this process would be that a completely intuitive interface is realised. Here the user seamlessly engages, their expectations are exceeded, and trust is created. Unfortunately, when the hype in AI is so great, this may make the notions of what can be achieved unrealistic. Hence expectations must be carefully managed. 

At Intellium AI, our ethos is first to understand where customers are on their ‘[journey](https://userpilot.com/blog/user-trust/) of discovery’ and, where possible, to tailor solutions to meet their needs. Potential users are rarely at the same waypoint, so to build something completely generic is unlikely to achieve positive results for either party. We firmly believe that it is a combination of approaches that makes the difference when it comes to design. These can be grouped into three types: the fundamentals, best practices and trust centric activities.  

From a business perspective, there are certain essentials that one must have in place for customers to establish trust in a company: legal registrations, transparent accounting, reputable owners and alike. In a highly competitive market, first impressions really do count, and this process can [happen](https://uxstudioteam.com/ux-blog/increase-trust-through-ux-design/) in a fraction of a second. To ignore such basic elements would immediately place a business at risk of non-compliance or complete rejection. In the digital arena, other factors, specifically data protection and digital risk management, become more prominent. Organisations must therefore offer transparency around data usage and security policies to establish legitimacy and provide reassurance that sensitive information will be protected. Third-party certifications, accreditations and prominent customer reviews help to establish [credible](https://www.interaction-design.org/literature/article/trust-building-the-bridge-to-our-users) authority over these concerns. They present regulatory and social evidence of trust in an organisation and its brand. Details of after sales support and ethical practices, continue the theme of confirming that the fundamentals are in place even before a purchase is made. 

Adhering to best practice considers how a product fits in with the life of the customer, what other products they utilise and how accustomed they are with certain features. The more familiar they are to them, the more rapidly adoption can occur. Aspects such as colour coding, menu design and navigation mechanisms can make a user interface highly intuitive especially if aligned to common practice in other mainstream products. User feedback on the achievement of goals or tasks, re-affirm they are operating appropriately. Intertwining this approach with frequent safety reminders prior to performing [irreversible actions](https://medium.com/@h_locke/14-ways-that-good-design-builds-trust-562ffaddaf1f) can prevent issues such as data loss, helping to ease understanding of what can be quite complex tasks, without placing inexperienced users at risk. 

These initial steps help to build trust through the periods of software acquisition and training but as the user gains experience and exercises the capabilities of the system, their exposure to risk increases. It is here that trust centric practices come to the fore. 

On-boarding tutorials and tooltips integrated into the software form the backbone of these and should reinforce both the intended uses and limitations to prevent overtrust being created. The more transparent the vendor is, the more rapidly credibility and trust will be established, in exactly the same manner it would with a human counterpart. If a co-worker says they don’t have the ability to complete a task you can’t blame them if they fail, so if an AI system states it has low confidence in a particular result, you should proceed with caution. As others engage with the output of a system, ensuring openness about data used for training algorithms allows a wider user community to establish if they believe the datasets are adequate, or if they are inappropriately biased. Process transparency is critical to make the system [explainable](https://www.smashingmagazine.com/2021/02/building-user-trust-in-ux-design/) in order to determine how outcomes were generated, reinforced by confidence scores to help build a picture of reliability. Some [research](https://www.tandfonline.com/doi/abs/10.1080/10447318.2022.2049134) has however shown that in certain circumstances, providing low confidence scores can have a negative impact on trust, but in reality one has to play the [long game](https://www.nngroup.com/articles/communicating-trustworthiness/). These measures backed up with data citations help establish the validity of any outcomes. Finally, providing advanced users the ability to customise or personalise displays or the level of support provided builds a sense of ownership, achievement and pride in the capabilities they have realised.

The degree to which trust can be established and more importantly, retained is not something that is tangible, it is a concept that is fluid. As the digital community learns how to push the boundaries of these systems we must continually adapt support as the manner in which they are implemented may go far beyond what the designer may have envisaged. A trusted [partnership](https://bootcamp.uxdesign.cc/trust-in-ai-through-ux-design-e1a1a86d5c32) is needed to collectively chart a course to the desired destination and to establish this has to be our goal.

In conclusion, we must first reflect on one key observation concerning AI: we are all part of a very large trial, potentially the largest global experiment ever conducted. We are still learning about this technology and it is learning about us. Yet it is mainstream, marketed as the next big thing, without the impact being fully understood or regulated. We must proceed with caution, trust is a precious commodity, it must be earned and never taken for granted. This multi-faceted, dynamic human attribute is deep rooted in our emotions and is the key to success or failure of a product. For Intellium AI it forms the core of everything we do built on principles of knowledge, transparency, diversity, integrity and reliability. 


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Designing for Trust and Responsible AI

Bristol, UK – [02/04/2024] - Intellium AI, a leading artificial intelligence company, is pleased to announce the appointment of [Ben A](https://www.linkedin.com/in/benresearch/) as Intelligence and Security Advisor. With over 25 years of experience in the UK National Security community, Ben brings a wealth of technology, intelligence, and security expertise to his new role.

Ben's distinguished career includes leading major technical programmes and directing large multi-disciplinary teams in developing world-leading technologies. As a senior responsible owner for multi-million-pound investment programmes, he has forged innovative partnerships with industry and academia to tackle complex national security challenges.

Since joining Heligan Group as a Partner and Head of Research in March 2023, Ben has been instrumental in building the future-focused investment thesis and providing deep insights at the intersection of technology, intelligence, and security.

"We are thrilled to welcome Ben A to the Intellium AI team," said Kiran Krishnamurthy, CEO of Intellium AI. "His extensive experience, deep understanding of the intersection of technology, intelligence, and security, and proven track record of driving innovation will be instrumental in shaping our future growth in the Secure Government sector."

Ben's appointment reinforces Intellium AI's commitment to advancing AI and ML technologies and addressing the unique challenges faced by the Defence and Security sector. Under his leadership, Intellium AI is poised to forge new paths in innovation, enhance operational efficiencies, and deliver secure, sustainable solutions that meet its clients' evolving needs.

About [Intellium AI](https://www.intellium.ai/)

Intellium AI is a rapidly growing technology company dedicated to developing safe, secure, and responsible AI solutions. Focusing on innovation and committing to leveraging the power of AI, Intellium AI helps its clients boost productivity and drive growth in an increasingly digital world.

Contact:

info@intellium.ai

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Intellium AI Appoints Former UK National Security Expert as Intelligence and Security Advisor

Intellium AI Appoints Former UK National Security Expert

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