Today I will be discussing Artificial Intelligence, A Catalyst in Digital Transformation, and Compliance. First, I’m going to cover who we are as a company so that you can learn more about Arbour Group and Pharmalex. Then I will discuss technology innovation, Artificial Intelligence (AI), driving economic value, AI for digital transformation and compliance, and the associated implications, use cases, and capabilities.
I lead the digital transformation and compliance business segments for Arbour and have over thirty years of experience. I have spent more than two decades in senior leadership roles. I have worked with entities across the healthcare and life sciences value chain during my career in both transformation and compliance initiatives. This has recently included much work associated with AI and machine learning, the cloud, cloud operating models, cloud financial management, and Analytics. Emerging technologies like AI and machine learning have become paramount since disruptive technologies have a real impact on business model transformation, regulatory standards, methods, and practices.
Arbour Group Overview
Arbour Group has customers in every sector of the Life Sciences industry, including pharmaceutical, medical device, biotechnology, and diagnostic. We are also working with major Cloud service providers such as Google, Microsoft, and Amazon. As business models continue to evolve, we are focused on meeting new goals of transformation and regulatory requirements.
Arbour’s US headquarters are in Chicago, and our global headquarters is in Mannheim, Germany. We have offshore locations in Dubai and Manilla, where development and testing take place. Our focus areas include regulatory and validation, and our digital growth areas of compliance and transformation. We also stand behind our work with clients from an Insurance perspective. Our comprehensive products and service offerings include AI, a solution critical in digital transformation, and compliance business segments. We also focus on technologies that are critical to data integrity and privacy. Arbour continues to see growth in the use of Emerging Technologies such as Software as a Medical Device.
Artificial Intelligence, A Catalyst in Digital Transformation and Compliance
We are in an unprecedented period of technology innovation. Looking at technology trends going back seventy years, Mainframe Technology peaked and then declined with the Y2K events. Client-server and PC technology has been taking off. Web eCommerce and Web Cloud/Mobile, with entities like Amazon and Apple, have taken the market by storm even during a global pandemic. We are in a confluence of IoT and Smart Machines, Artificial Intelligence, Big Data, Analytics, and Visualization. Quantum Computing is receiving investment and in its beginning stages. This technology is an exponential leap ahead of what we are capable of today.
What is Artificial Intelligence?
Artificial Intelligence is the ability to perform tasks that normally require human intelligence, including digital perception, speech recognition, and decision making. We often reference Artificial Intelligence, a Modern Approach, a leading textbook used in over 1300 universities and over 110 countries.
Artificial Intelligence is the biggest technology revolution the world has ever seen. Technologies have evolved to create foundational skills in software. The skill of sense is to perceive the world by acquiring and processing language, image, and sounds. Examples may include video feeds, speech recognition, natural language processing, emotion detection, language translation, machine vision, and facial recognition. Neural networks are used during training to improve and refine the models to be more accurate.
The second skill is to comprehend, make logical inferences, and analyze information based on a given knowledge base to draw logical conclusions. For an autonomous vehicle, the radar and visual sensors can identify signs, and the knowledge allows the AI to know what the sign means.
The third skill is to act in the physical world based on comprehension and understanding. Action can be communicated back as a response and can control functions like physical actuators or controlling virtual actuators. The autonomous vehicle acting based on the speed limit slows the vehicle down if it exceeds the speed limit.
The catalyst essential to Artificial Intelligence is the ability to learn. The initial learning is training Artificial Intelligence to improve based on training data. That data is structured so that AI can adjust underlying machine learning algorithms to validate the results of change with the most accurate model based on rules provided. For example, for handwriting recognition, the neural network can incorporate pixels to develop a model that can be accurate up to 95%. It can accurately identify the letters written, and the training is done without human involvement.
Artificial Intelligence, The New Driver of Economic Value
Artificial Intelligence growth is driving economic value with increased satisfaction, value, and productivity. Intelligent automation is based on robotic process automation capabilities layered with machine vision, self-learning, and adaptive capabilities to enhance process accuracy, flexibility, and autonomy. It is centered on software robotics plus machine and deep learning to respond autonomously to a defined task. Examples include customer service processes that involve answering questions and providing recommendations, insurance claims processes that involve how to process a claim and adapting the activity based on performance, and allowing it to be proactive to handle and minimize exceptions and mistakes.
Labor and capital augmentation use machine learning capabilities to provide proactive recommendations and personalization that enhance performance and productivity. This may include inference engines, predictions, or expert systems. AI can optimize the use of raw materials, excess, and capital for an organization through analysis and actions or recommend optimal solutions to buyers’ procurement and operations lead.
Innovation diffusion is the ability to propel innovations as Artificial Intelligence diffuses through the economy. This involves new products based on or rely on Artificial Intelligence and may include autonomous cars and everyday items like Alexa.
Artificial Intelligence for Digital Transformation
Utilizing Artificial Intelligence for Digital Transformation is being done today with current combinatorial technologies making AI affordable, doable, and available. The democratization of Artificial Intelligence skills is the diffusion of AI capabilities and skills where more organizations can adopt and adapt to AI. The open-sourced aspect of AI means that it is easier to take courses to gain access to leading thinking and implementing solutions.
High-performance computing power is readily available with cloud computing evolution, making it more accessible for high-performance requirements. With growth and recent success with high-performance aspects like GPUs, or graphical processing units, this has increased the potential for further improvements that support faster training, improved real-time responsiveness, and the ability to run and handle more complex machine learning solutions that can handle increasingly complex activities.
Data to train and fuel AI is more accessible with recent investments in Internet of Things, Digital Strategies, Big Data, and Analytics. It has created new sets of information that can make AI more successful. Artificial Intelligence relies on information to develop accurate and successful predictions.
The proliferation of Artificial Intelligence products and solutions are quickly installed and provide near-time value. Application programming interface services and other solutions can provide image recognition and speech recognition services through the cloud. As the systems improve, the accuracy improves.
Artificial Intelligence Technologies in Gartner’s Hype Cycle for Emerging Technologies
In the face of quick changes and decentralization, organizations need to shift to be more agile and have more responsive architectures, so they are already implementing Artificial Intelligence to keep up. A composite architecture comprises business capability packages built on a flexible data fabric, allowing the enterprise to respond rapidly to changing business requirements. For example, a composable enterprise backed by a composite architecture proposes increased business resilience. This modular design allows organizations to recompose when needed, like during a global pandemic or periods of economic recession.
Algorithmic trust involves increased amounts of consumer data exposure like false news information, where biased Artificial Intelligence has caused organizations to shift away from trusting central authorities like government registrars and clearinghouses to trusting algorithms. Algorithmic trust models ensure data privacy and security, the providence of assets, and people and places’ identities.
Moore’s law predicts that the quantity of transistors in a densely integrated circuit will double every two years. But technology is quickly reaching Silicon’s physical limits, leading to the evolution of new advanced materials with enhanced capabilities designed to support smaller and faster technologies. For example, DNA computing and storage and the use of DNA and biochemistry in place of Silicon or Quantum Architectures perform computation or store data. The data is encoded into synthetic DNA strands for storage, and enzymes provide the processing capabilities through chemical reactions.
Artificial Intelligence is capable of dynamically changing to respond to a situation. As technology integrates with people, there are more opportunities to create digital versions of ourselves. These digital models represent humans in both the real and virtual worlds. For example, Bidirectional brain-machine interfaces are brain-altering wearables that allow for two-way communications between a human brain and a computer or machine interface. They can be wearables or implants that monitor activity in the brain and individuals’ mental states. These examples further demonstrate how AI is present in our world today.
Computers can now be trained to support better decisions. Machine learning is a subset of the concept of Artificial Intelligence and consists of both supervised and unsupervised learning and the development of models that allow for deep learning based on the data. It is done by developing linear or multilinear regression models in neural networks. The algorithms are developed, and then an iterative training approach is conducted based on the data collection process. This data can take many forms, from databases involving structured and unstructured data to sensors, videos, and images. Outcomes are developed with a recommendation, and corporate decisions are made.
Artificial Intelligence in a Regulated Environment
Humans should be at the center of consideration when committing to Artificial Intelligence initiatives, considering healthcare and life sciences’ regulatory and political environment. There are many regulatory implications with Artificial Intelligence. Economic policy and regulatory compliance have disrupted how participants in industries modify the rigs accordingly. Arbour Group has found that one focus is on algorithms and how they predict and produce data and outcomes. Additionally, PHI or Personal Health Information and their implications ensure the solutions comply with appropriate HIPAA and GDPR requirements. Education and awareness are key, especially when you look at things from a legal perspective.
Use Cases for Life Sciences
Our research has identified 100 plus use cases of the most impactful technologies for life science and healthcare. Looking at Emerging technology to Mature technology, Artificial Intelligence is steadily climbing. Life sciences and healthcare will be changing for the next few years, looking at Artificial Intelligence, an autonomous factory that involves moving from manual processes to robotics. You also see real-time product safety by performing vigilant inspection and defect identification for finished goods coming off a production line. Predictive analytics are also key in forecasting for pharmaceutical companies. Other Artificial Intelligence test use cases include detecting or diagnosing infectious diseases, and some of the technology we use today that involves virtual nursing assistants.
Remaining Competitive in Artificial Intelligence
To remain competitive continued action is a necessity. Low barriers to Artificial Intelligence signify that not only can your enterprise realize the benefits, but competitors will as well. When you look at open-sourced capabilities, the quickest option that Artificial Intelligence can release from Application Programming Interfaces (APIs) and products increases the adoption of AI solutions that can drive significant value to an organization. Your competition is building solutions and changes the nature of competition.
The exponential aspects of Artificial Intelligence involve the ability to increase performance as cost decreases and learn and improve based on regular use and improvement from data that has made the strategy of being a fast follower potentially outdated. This is due to combinatorial technologies. Therefore, high-performing organizations are adopting Artificial Intelligence solutions and using them in their products today, and it is not easy to catch up if an investment does not catch up but exceeds. When we discuss Artificial Intelligence, we speak to a lot of information; at the end of the day, AI is a game-changer.
Arbour Drives Digital Transformation
Arbour uses the following framework, whether we are doing Artificial Intelligence for digital transformation or digital compliance. We start with the human at the center and work with you on strategy, process transformation, partner on products, and focus on the data. When we discuss data, we like to speak to it as unlocking dark data value, which essentially means analyzing known and unknown data. Many organizations can capitalize on making more intelligent decisions based on even unknown data that they may have within their organization. Whatever your focus or use case, we have the capabilities to work with you. It may take us into machine learning and technologies. Arbour specializes in regulatory and compliance areas that provide a force multiplier with your solution.
Arbour’s Artificial Intelligence Compliance Sample Approach
Arbour Group’s compliance methodology reflects our experience in innovating with clients and product vendors to deliver high-performance AI solutions. We can work with you on value targeting, pilot development, deployments, and opp support. We have flexibility based on your particular solution regarding key activities associated with phases, potential deliverables, and cost structures.
Arbour Group’s typical approach to Artificial Intelligence compliance starts with assessing the current state, creating a target state, defining a roadmap, and ends with implementation with a varied timeline. Arbour Group works with you on evaluating your goals, defining a scope definition that is typically focused on algorithms, and providing appropriate deliverables.
If you would like to discuss further Arbour Group’s approach to an Artificial Intelligence roadmap, contact us today.