-by Geraldo Luiz Rochocz
THE AI BOOM
Take a simple test: type "Artificial Intelligence" into any internet search engine. At the time of writing, the term already amassed hundreds of millions of results, an impressive number for a technology that only recently reached the mainstream. Despite the recent boom, driven by tools like OpenAI's ChatGPT and Google's Bard (Gemini), AI's history dates back years. Its roots lie in advances in deep learning, a field familiar to scientists and researchers for quite some time. Few may recall, but as early as the early 2000s, the topic was already being discussed, including the release of the movie A.I. - Artificial Intelligence, directed by Steven Spielberg in 2001. But even that does not express the true age of this technology: The concept of Neural Networks appeared in 1943!
Though the boundaries of this technology and its potential remain uncertain, unlike other recent innovations such as the metaverse, AI has quickly gained popularity in its use and concepts- and the numbers don’t lie.
WHAT IS THE POTENTIAL IMPACT OF AI?
Gartner predicts AI Software will grow to $297 Billion By 2027. And this is only the beginning. Gartner forecasts the market will grow at a 19.1% compound annual growth rate in the next six years. Generative AI specifically (GenAI software spending) is expected to boom from 8% in 2023 to 35% by 2027.
The industry´s digital transformation is expected to continue at an accelerated pace. But, if the plans for the future of this technology appear grand, the challenges are no smaller: AI adoption is not simple or out of the box - companies need clear access to clean data to create effective AI solutions, and IT needs to bridge the gap between technology and business to ensure that data and AI lead to operational improvements.
It is essential that industry players carefully analyze how AI fits into their current digital strategies and prepare for the pitfalls that exist when attempting to fully leverage its potential.
THE ROLE OF DATA IN AI
The potential of AI to drive growth and reduce costs cannot be overestimated, especially in energy and manufacturing, where data dependence and analysis are crucial for innovation. These sectors are built on increasingly complex and subtle processes and AI, specifically GenAI, provides a potential for a much-needed reduction in complexity.
It’s one of the many reasons firms like GlobalData are predicting that, "AI can generate $2.4 trillion in value for the energy sector by 2035, representing an 11% increase in sector revenue."
Achieving the benefits of AI requires that companies define AI, recognize the challenges in effectively implementing these solutions, and develop the roadmap and strategies needed to overcome these challenges.
Defining AI
According to Gartner, AI is the process of applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions.
The key to this definition is the view that AI adds actionable intelligence to good data, empowering decision-making, and operational performance.
At Radix, we leverage this approach to AI to help our clients and customers, specifically in sectors that generate an enormous amount of data, achieve Asset Performance Management (APM) insights that:
Drive production and productivity.
Boost operational efficiency, sustainability, and safety.
Scale better to meet demand, uncover, and remove bottlenecks.
From sensor logs and engineering reports, to work orders and maintenance records, we help our customers implement AI solutions and programs such as generative AI to better understand and leverage their rich data sources to optimize operations and make better decisions.
The Challenges to Effective AI
Some of the main pitfalls when implementing AI include:
Definition of terms and explainability around what AI systems are, how they function, and the expected outcomes and potential challenges.
Select the proper AI techniques (there are dozens of them) for each use case, avoiding current fads.
Efficient access to the right data in real-time.
An approach to building a multi-agent intelligent-based platform that allows the integration of AI models and tools from third-party providers.
Improving the ability to monitor controls continuously, throughout model and application development, testing and deployment, and ongoing operations.
A need for new methods to test, validate, and improve AI-powered workflows.
Addressing the changing regulatory landscape, from the EU AI Act to regulatory frameworks in North America.
What can A.I. with Radix Do for Me?
The short answer is converting your asset performance to asset excellence across your entire enterprise and at every stage of your operational lifecycle.
Recently, Radix leveraged Generative AI, along with other solutions, to optimize operations in a large industrial client looking to transition from manual tools and spreadsheets to a digital platform – on a global scale. With help from Radix, they leveraged A.I. to boost asset performance management in over 100 plants globally.
The results of partnering with Radix for A.I. integration led to:
Streamlined and more accurate financials that netted millions in annual savings.
Decreased electricity and utilities costs obtained from energy optimization in plant equipment and better decisions based on operational insights from real-time forecasting of electricity usage for real-time price monitoring during price hikes to prevent overspending.
Savings of over $5 MM yearly with maintenance by mitigating unexpected downtime and optimizing planned downtime (revenue increase not accounted).
Accurate real-time materials balancing model for real-time tracking of production, inventory, raw materials, production, waste, and unaccounted losses. The model runs with less 0.5% deviation and allows real-time forecasts of monthly production.
Reduction of $ 2 MM per year in production losses with real-time prediction of physical properties of polymers and enhanced control strategies.
How to Start?
Companies need to partner with firms to help them through the journey of successful AI integration. At Radix we not only solve the challenges above, but we also design and validate the target architecture and include the program and project rigor that provides communication and governance planning.
AI Implementation should be driven by business needs. The same thinking applies to governance. Value streams prioritize specific tasks and details after establishing the first set of guidelines and security boundaries.
Radix already offers advanced AI solutions for companies worldwide by making intelligent and strategic use of this technology. We help navigate all these challenges to optimize asset integrity, improve maintenance efficiency, accelerate new product discovery, create virtual consultants, and assist and optimize production.
We have a record of accomplishment of helping our customers successfully implement AI so that they can realize the following Gartner benefits:
By 2026, organizations that operationalize AI transparency, trust and security will see their AI models achieve a 50% improvement in terms of adoption, business goals and user acceptance.
By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number and time it takes to operationalize AI models by at least 25%.
If you want to learn more about AI and how we can help you leverage this solution for your business results, follow the lead of some of the world's largest companies and contact Radix today at www.radixeng.com.
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