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

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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 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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Defence

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.

Industrial application of Artificial Intelligence

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

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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Energy

Automotive

Aerospace

Power of AI in your hands with AiBoost®!

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Explainable AI (XAI)

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


## 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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## 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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Boost manufacturing productivity with AiBoost®

Root Cause Analysis with AI

## 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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Industrial Inspection with AI

Intellium AI

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Deployment **Options**

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AiBoost® is a no-code cloud-based AI software enabling engineers to improve industrial operations' resource and energy efficiency.