Artificial Intelligence (AI) enables machines to simulate human intelligence and perform tasks like learning from data and automating repetitive tasks. AI systems can identify patterns from data, summarize content, and generate video, audio, written, and code content.
Over the past seven decades — beginning with Alan Turing's breakthrough, the Turing test — several subsets of AI have emerged. During this time, the subsets — machine learning, deep learning, and the latest, generative AI have progressed by leaps and bounds.
From consumer tools on mobile phones and self-driving cars to using (generative AI) to augment human work, AI is increasingly gaining prominence in both domestic and business life.
But as the buzz around the integration of AI in business takes off, conversations around its applications, importance, benefits, and ethics become critically important.
In the context of business, AI is a transformative tool that can help organizations streamline operations, boost efficiency, and extract insights from extensive datasets. According to the Infosys Data + AI Radar 2022, an effective AI strategy can improve user satisfaction and help enterprises contribute to a combined profit growth of nearly $467 billion. AI has made a valuable contribution to the following industries:
Manufacturing:
Manufacturers use AI to elevate quality control, anticipate maintenance requirements, and refine production processes. It can help ensure product quality by identifying defects, while predictive maintenance minimizes downtime. Using robotics, businesses can optimize supply chains for efficient resource allocation, and manufacturing facilities can enhance precision and productivity.
Finance:
Finance professionals are increasingly using AI in areas such as fraud detection, algorithmic trading, credit scoring, and risk assessment. These models and tools also allow the finance industry to automate tasks like underwriting and claims processing. Additionally, machine learning models can spot suspicious transactions in real time, while algorithmic trading enhances the speed and accuracy of trade executions.
Education:
Technologies such as conversational AI can help tailor learning experiences, furnish instant feedback to students, and help educators scrutinize student performance and pinpoint areas for improvement. Generative AI can also offer personalized education via adaptive learning systems and virtual teachers, thus making education more accessible to individuals in remote or disadvantaged
regions.
Healthcare:
The use of AI has helped the healthcare sector scrutinize medical information, recognize patterns, aid in diagnosing illnesses, create personalized treatment strategies, and also advance drug discovery. Wearable devices and IoT-enabled health monitoring systems help gather continuous patient data, including blood pressure, heart rate, and glucose levels. This data allows healthcare providers to monitor and manage chronic conditions effectively.
Retail:
AI is the bedrock of efficient and automated personalized customer experiences. Recommendation algorithms, optimizing supply chain management with predictive analytics, and improving inventory management through demand forecasting, have remarkably helped the retail industry.
Security:
Law enforcement agencies and cybersecurity firms deploy computer vision to monitor security cameras, analyze potential threats, and extract data from security logs for improved safety.
Insurance:
AI can help identify fraudulent claims, refine risk evaluation, and personalize insurance policies based on customer behavior and preferences.
Hospitality:
Businesses integrate AI to provide individualized guest experiences, automate check-in and check-out procedures, enhance agent experience, and improve overall hotel management.
Transportation:
AI streamlines route planning, improving fuel efficiency through driver assistance systems.
As organizations integrate artificial intelligence into their operations, they unlock new opportunities for growth, optimization, and strategic evolution. Companies benefit by viewing AI in terms of business capabilities rather than just technologies for sustainable advantage. The advantages of artificial intelligence lie in addressing these four key business requirements:
Automated processes: It is essential to automate routine tasks, including data collection, data entry, email responses, software testing, and invoice generation. It also aids in automating processes, predicting maintenance requirements, and minimizing downtime. This automation can enhance end-to-end efficiency and productivity, enabling employees to concentrate on tasks that demand human skills.
Accurate data insights for smart decision-making: AI can help researchers and data scientists analyze patterns, predict outcomes, and make adjustments in significantly less time than traditional methods. This integration helps businesses make faster and better decisions.
Improved customer experience: Continuous machine learning enables businesses to gain deeper customer insights, facilitating hyper-personalization. AI systems collect insights for tailored recommendations to facilitate customer interactions in e-commerce, digital marketing, and entertainment. Using natural language processing and prediction software, online chatbots can simplify issue resolution, expediting help desk routing and real-time information curation.
Enhanced safety and security: AI can enhance security through deep learning techniques and encryption software. It addresses the rising need for threat identification and safeguarding sensitive data, bolstering safety in both public spaces and private organizations.
According to a recent report, AI technology has the potential to generate a revenue boost exceeding $15 trillion in the coming decade. Additionally, 83% of companies state that this integration is critical to their business strategies. But what are the main AI technologies that businesses should capitalize on?
Weak and Strong AI
While these are not separate technologies, the path to understanding the progression and history of AI must address these forms of AI models. Weak AI performs very specific tasks within defined boundaries. The specialized or narrow AI functions mimic human intelligence. It addresses well-defined problems based on user input, such as creating content, translating language, or transcribing speech. Weak AI examples include conversational bots, Google search, and smart assistants like Siri and Alexa.
In contrast, strong AI, or artificial general intelligence, refers to a machine that performs in human-like ways. It learns and aims to replicate human understanding and problem-solving by learning and potentially teaching itself to develop a human-like consciousness instead of imitating it. It is theoretical at present and unlikely to be developed soon. Critics emphasize the need for caution in developing strong AI.
Machine learning is a subset of AI that focuses on creating machines (computer programs or algorithms) that can learn independently. It uses APIs, development tools, big data, and applications to train the machine on a particular subject. A series of artificial neural networks with multiple nodes (artificial neurons) is responsible for machine learning models to operate.
Through an iterative process, the machine analyzes the data, reaches conclusions, and stores them for future use. The more data the machine learns from, the better it becomes at its tasks.
Some common examples of machine learning include fraud detection, speech recognition, image recognition, medical diagnosis, and stock market trading.
Machine learning algorithms generally fall under four broad classifications, each possessing unique traits and applications: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Deep learning is a subset of machine learning that trains computers to perform a wide range of tasks like speech recognition, image identification, and predictions. It powers innovations such as driverless cars, voice recognition, and personalized shopping and entertainment.
Deep learning operates through artificial neural networks designed to mimic human thinking and learning. It is characterized by a neural network architecture with three or more layers: the input layer for data entry, hidden layers for processing and transferring data, and the output layer for making predictions.
Generative AI is a category of deep learning models capable of producing text, images, code, and audio-visual content in response to prompts. These models train on extensive datasets, learning to generate responses statistically. Generative AI can mimic some human creative abilities, providing reliable responses to requests. Notable examples include ChatGPT for text generation and DALL-E and Midjourney for image generation.
Its capacity to produce new data across various formats beyond text makes it unique. Generative AI has proven its value in developing virtual assistants with human-like responses, dynamic content for video games, and synthetic data for training other models.
Generative AI applications are making a substantial impact by fostering innovation, automating creative tasks, and enhancing personalized customer experiences. Infosys CMO Radar 2024 found that 73% of marketers have deployed or experimented with AI in marketing activities.
Some of the most prominent use cases are as follows:
Generating video, images, and audio: Generative AI models, such as GANs (Generative Adversarial Networks), can create high-quality video content, images, and music/audio tracks. This can be particularly useful for marketing campaigns.
Text-to-speech solutions: Businesses can leverage generative AI for text-to-speech solutions to enhance accessibility for visually impaired individuals. It also helps create personalized customer service experiences through automated voice assistants.
Writing assistance/content generation: Generative AI tools can help generate ideas for articles and blog posts, as well as create first drafts, product descriptions, and marketing copies.
Product design and development: GenerativeAI can aid in product design and development by generating 3D models, prototypes, and design variations based on specified parameters. This can accelerate the design iteration process and facilitate innovation in product development.
Code generation: In software development, generative AI can automate the process of writing code by generating snippets, templates, or even entire algorithms. This can help developers increase productivity and expedite the development of software.
Its capacity to produce new data across various formats beyond text makes it unique. Generative AI has proven its value in developing virtual assistants with human-like responses, dynamic content for video games, and synthetic data for training other models.
A GAN is a type of deep learning framework in which two neural networks compete to create realistic new data based on a given set of training examples. One network alters input samples to generate data, while the other network evaluates whether the generated data is genuine or artificial. This iterative process continues until the discerning network is unable to differentiate between generated and real data. GANs can produce all forms of data, including images, videos, and text, replicating the characteristics of the training set.
Natural Language Processing (NLP) combines linguistics with statistical and machine learning methods. Its goal is to enable computers and digital devices to understand, interpret, and produce human language in forms such as text and speech. NLP is crucial for a range of applications and technologies. It can help with translations, respond to voice or text commands, verify the user based on voice, summarize text data, analyze intent or sentiment in text or speech, and more.
While most individuals interact with AI algorithms through NLP in consumer products like GPS systems and virtual assistants, it also plays a role in business solutions by optimizing operations, enhancing efficiency and simplifying procedures.
About 48% of businesses employ machine learning, data analysis, and AI tools to uphold the precision of their data.
AI ethics are the guiding principles that aim to maximize the positive impacts of artificial intelligence while minimizing its potential drawbacks. These place a strong emphasis on transparency, inclusivity, sustainability, and accountability. Organizations have to align with data privacy laws, tackle algorithmic bias, and proactively inform consumers how their data is utilized. It's primarily about making AI work in a way that's responsible, fair, and transparent.
Ethical and responsible AI is important because AI has the potential to cause harm as a result of relying on biased or inaccurate data. This could lead to harmful consequences, especially for underrepresented or marginalized groups. It can also lead to deception, political suppression, disinformation, or abuse, proving detrimental to individuals, society, and the environment.
In sectors such as healthcare, where businesses deal with sensitive data and impactful decision-making, responsible AI is essential. It safeguards valuable information, enhances critical processes, and prevents potential reputational or legal repercussions. Here are some ethical concerns surrounding AI development and deployment:
Bias and discrimination: Instances of bias and discrimination have sparked ethical concerns about artificial intelligence usage. To prevent the unintentional exclusion of marginalized groups and ensure that products cater to diverse needs, companies should adopt inclusive AI practices.
Privacy: AI relies on data sourced from online purchases, internet searches, or social media, enhancing customer personalization. However, concerns arise about the lack of consent for accessing such personal information. The influence of data privacy laws is expected to regulate and limit the power of AI companies in this regard.
Impact on jobs and labor: Concerns about transparency in creating and training AI models have arisen with the emergence of digital sweatshops. The public perception of artificial intelligence revolves around the fear of job losses and replacing human creativity.
AI and the environment: AI techniques and models are resource-intensive, demanding substantial energy for training them on data. Although efforts are underway to develop energy-efficient AI methods, there is a need for greater consideration of environmental concerns in AI.
Developing ethical AI involves a thorough examination of the ramifications within policy, education, and technology. Regulatory frameworks also play a crucial role in ensuring that technologies contribute positively to society rather than causing harm.
Elements of an enterprise response strategy to the EU AI act
Implementing AI in business necessitates a comprehensive organization-wide shift involving digital transformation, a transition to data-driven business operations, and a cultural change toward digital orientation. Let's look at a few challenges of artificial intelligence that companies need to address.
Data security and privacy
The vast amount of data required for AI training and decision-making poses a significant risk if not adequately protected. This can lead to potential breaches and legal consequences.
High development costs Implementing AI in business involves substantial upfront costs, including hiring skilled professionals and investing in infrastructure.
Data preparation Preparing data for AI applications and tools requires significant effort, taking up a chunk of the workload involved in deploying it. The more fragmented or unstructured the data, the more time and resources are needed to export, clean, and prepare data.
Lack of expertise Finding and retaining skilled professionals who can develop, implement, maintain, and navigate AI systems is a major challenge for businesses. This scarcity can result in delays in its adoption and hinder the optimization of AI applications.
Inadequate infrastructure Businesses need powerful computing resources, high-speed networks, and scalable storage systems to handle the vast amounts of data required for effective implementation. Upgrading existing infrastructure to meet these demands is complex and costly.
Key challenges firms are facing in going AI-first
A digital operating model for an AI-first enterprise
The latest developments in artificial intelligence have impacted pretty much every industry. AI is poised to become increasingly ubiquitous in various aspects of our daily lives. Let's take a look at the emerging trends.
Interpretable AI
AI interpretability shows how a model reaches its decisions. It explains the inner workings of the model. For example, how it combines and weighs features to give predictions. Interpretability helps find errors, reduce biases, and meet regulations. It makes outputs clear, fair, and trustworthy. This builds confidence in the system and supports ethical use.
Explainable AI
The growing reliance on AI has resulted in a need for increased accuracy, but the lack of transparency in decision-making raises concerns about reliability. AI explainability means understanding why a model made a specific decision or prediction. By making the results easy to understand, explainability ensures models are not only accurate but also reliable and accountable.
No-code platforms The popularity of AI technologies is attributed to their accessibility, particularly with the emergence of no-code AI platforms. These have lowered barriers for small companies to adopt advanced solutions.
No-code machine learning programs simplify model building and deployment through a user-friendly drag-and-drop interface, reducing the need for extensive programming and code editing. This approach is both time and cost-efficient, providing speed and flexibility without requiring high technical expertise.
The future focus lies in the rapid application of AI solutions for complex tasks by creating models requiring fewer data and training examples. This trend towards AI-based automation aims to replicate the human brain and human interaction patterns. It employs multimodal learning and algorithms for advisory and training to reduce human errors and shift organizational focus towards creative tasks.
Explanation of outputs is crucial to establish trust in large language models (LLMs). However, current explainability methods, including perplexity, BLEU, ROUGE, and human evaluation, struggle with extensive summarization tasks and use cases that require speed and scalability. One solution is to use generative AI to evaluate other generative AI models. The key here is to employ metrics aligned with LLM architecture. Firms can couple generative AI with a responsibly designed framework to ease concerns relating to accountability, fairness, data privacy, and value and purpose alignment.
Members approved the EU AI Act of the European Parliament in March 2024. It uses a risk-based categorization system with higher-risk AI systems being subjected to greater scrutiny, and some AI systems are marked as posing an "unacceptable risk," such as those that exploit human behavior or utilize social scoring systems. Similar regulations, aligned with OECD's 2019 principles (human rights, sustainability, transparency, and strong risk management), are emerging in China and the US.
AIOps uses machine learning capabilities to collect and aggregate data generated by IT infrastructure, identifying patterns within this data related to application performance and availability. It also enables rapid response and remediation of issues.
Deepfakes can create realistic media and have evolved to develop deepfakes. GANs pit two AI models against each other, with the generated image (or other media) so realistic that the detector GAN can't distinguish between real or fake. Since then, "self-aware" AI has created deepfakes that can self-correct and learn.
AI platform architecture uses independent, layered components that form a comprehensive framework. This approach endures fast-evolving AI frameworks, product and vendor landscapes, generative AI innovations, and industry regulations. Unlike use-case-centric and siloed approaches, it encourages participation among enterprise stakeholders.
To onboard the technology quickly and at scale, firms need to make their workforce "AI aware". For example, software engineers should upskill for tools like GitHub Copilot. Firms need to ensure technologists within the firm are "AI evangelists" who understand both benefits and drawbacks. Here, responsible AI is crucial, with explainable outputs, ethical guardrails (particularly bias and IP), and documented datasets.
