The Background
In the 70 years since Alan Turing released Computing Machinery and Intelligence, Artificial Intelligence (AI) has gone from a theoretical concept to an integral part of our everyday lives.
From recommending what to watch next on Netflix to revolutionizing the way we diagnose life-threatening illnesses such as cancer, there are few areas of our lives that AI has improved or, at the very least, impacted.
Businesses of all sizes and types are also leveraging AI to not only develop new products and services, but optimize back office processes and boost productivity everywhere from newsrooms to the manufacturing floor.
What exactly is Applied AI?
The term “Applied AI” encompasses all the activities underlying the operationalization of AI from experimentation to production. In other words, applied AI is not just about the theoretical coverage of AI/ML, but it runs on your actual data and delivers real-world results and outcomes.
AI is making machines around us smarter. While this is common knowledge, what many don’t understand is that artificial intelligence is more of a concept, and applied artificial intelligence is what puts AI to work in reality. Applied AI leverages the capabilities of software applications and powers machine learning, making it highly accurate and adaptable. It is applied AI that is transforming business processes as well the modern society.
Applied AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Having a robust analytical framework in place is critical for ensuring data flows in and out of the AI are fully integrated, highly accurate and rapidly flowing.
Why Applied AI is important and relevant
Applied AI doesn't have to be 'exclusive' only to those who are technically competent or experts in AI/ML programming languages. Low-code/no-code machine learning platforms allow non-AI experts to create AI applications from predefined components. Such platforms are based on an intuitive graphical user interface in designing the application and visual programming as opposed to hard-coded programming techniques. When it comes to developing AI solutions, increasingly, more people will be able to create AI applications and use AI technology, freeing up highly trained coders to focus on the hardest problems.
Did you know that by 2024, more than 65% of applications will be developed using the low-code/no-code development approach. Also, by the same year, it is expected that more than 75% of large enterprises will use at least four low-code/no-code development tools.
During the global coronavirus crisis, the need for software development has driven growth in demand for low-code/no-coding development platforms. As a result, the worldwide low-code development technologies market is projected to total $13.8 billion in 2021.
Examples of Applied AI projects in practice
At our Centre for AI Innovation (CEAI), we help facilitate non-technical but experienced senior executives and managers design, develop and implement AI solutions on a no-code/low-code platform. This is done over a 3-month Certification in Applied AI programme (CAAI), that allows project owners to own their IP asset and sandbox in their organisation with their own prototype (TRL 4 - 6).
Examples of the Applied AI projects from Cohort 1 of the CAAI programme are listed below:
1. Talent Management
- Veterinary students personalised learning
- Personalised coaching systems
- Employability skills tailored for job-seekers
- Student and academic programme data management
- Postgrad students progress monitoring
- Lesson plan and teaching skills
- Staff talent assessment
2. Green Technology
- Building and Equipment monitoring : predictive maintenance
- Harmful algae bloom early warning signals
3. Market Insights
- Profiling businesses and people and matching needs in events
- Supply chain insights to optimise marketing decisions
4. Investments & Social Finance
- Shariah compliant risk-return analysis for equity securities
- Evaluate startups to look for potential unicorn elements
- Evaluation and monitoring of donor recipients / social impact
- Sustainable reporting insights for PLCs with ESG parameters
5. Socio Political Decision System
- Assess sentiments and fundamentals associated with a country's social & political issues
6. Healthcare and Well-being
- Tracking and monitoring the health care status for the elderly through IOT / Analytics
- Digital dental diagnostics and automating the personalised oral health of patients
7. Project Planning & Management
- Cost-benefit analysis of projects with risk-based scoring and recommendations
- Management of a large portfolio of assets across the value chain
8. Governance, Risk & Compliance
- Anti-bribery / fraud risk assessment system
- Assess food standards via Food Safety analytics:
- Predicting the likelihood of contractual breach between two or more parties on a transaction/project
Important Note: The above listings are non-exhaustive and some cases have been grouped together because of similar clusters or/and these are handled by more than one individual.
Benefits of Applied AI
Several benefits like accuracy, cost-saving, and better decision-making come bundled with Applied artificial intelligence. In a dynamic business landscape, these are the benefits applied AI offers to industries:
1. Better Decision Making: A lot of tasks would happen smoothly if machines had human-like judgment capabilities and that is exactly what applied AI brings to the forefront. It ensures reduced errors, predicts close to accurate outcomes, achieves end-to-end process automation, and creates a smart ecosystem.
2. Accuracy and Precision: Applied AI bridges the gap between the digital world and the machine world while reducing errors, social ethics, and human bias in the process.
3. Sharp Efficiency: Throughout all the stages of business processes, Applied AI accelerates efficiency while saving time, effort, and money.
4. Augmenting Human Skills: Applied AI helps in automating mundane and repetitive tasks to free up employees. While computer systems powered by AI handle tedious tasks, employees can devote their time and effort elsewhere to increase the business ROI.
5. Increased Profitability: Applied AI boosts profitability by identifying and solving complex business problems, faster than humans, through its machine learning capabilities.
What’s next?
Low-code/No Code Applied AI solutions help organisations significantly reduce the time to market of AI solutions, allow significant cost savings on hiring software and AI/ML experts, yet, it offers the speed, simplicity, and flexibility of ready-made software solutions in a customised manner.
An interesting perspective is that no/low-code AI platforms can help any organisational set-up to kick off an AI initiative; especially for the majority of organisations that lack a formalized AI practice. Such available platforms help them build better applications and solutions quickly without any coding experience or infrastructure, at a much lower cost and more visible impact.
Learn more about related topics:
- What you need to know about the Centre for AI Innovation (CEAI)
- A 2-minute video overview on CAAI
- Read about Certification in Applied AI (CAAI)