Artificial Intelligence (AI) is the integration of advanced technologies, enabling machines to simulate human intelligence, learn from data, and perform advanced tasks. AI systems can identify patterns from data, summarize content, and generate video, audio, written, and code content. From consumer tools on mobile phones and self-driving cars to using generative AI to augment human work, AI is increasingly gaining prominence in daily routines.
Weak AI
Weak AI, also known as specialized or narrow AI, functions
within specific boundaries and mimics human intelligence for
particular tasks. 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.
Strong AI
In contrast, strong AI, or artificial general intelligence,
refers to a machine that performs in human-like ways. Strong AI
can learn 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.
Strong AI is theoretical at present, and unlikely to be
developed soon. Critics emphasize the need for caution with
developing strong AI.
In the business context, 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. Businesses can use AI technology to automate repetitive tasks, optimize resource allocation, and base decisions on accurate data.
But as the buzz around the integration of AI in business takes off, conversations around its importance, benefits, applications, and ethics become critically important.
Manufacturing: Companies use AI to elevate quality control standards, anticipate maintenance requirements, and refine production processes. AI 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, risk assessment, and automating financial 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, and aid in diagnosing illnesses. Wearable devices and IoT-enabled health monitoring systems help gather continuous patient data, including blood pressure, heart rate, and glucose levels, that allow healthcare providers to monitor and manage chronic conditions effectively. AI can also help create individualized treatment strategies and advance drug discovery.
Retail: AI is transforming the retail industry by enhancing personalized customer experiences through recommendation algorithms, optimizing supply chain management with predictive analytics, and improving inventory management through demand forecasting.
Security: Law enforcement agencies and cybersecurity firms implement AI to oversee 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 can be used to streamline route planning, thus 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 - AI is essential in automating routine tasks, including data collection, data entry, email responses, software testing, and invoice generation. Additionally, it 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. For instance, AI in manufacturing enables close monitoring of output and reduces production errors. Meanwhile, in the shipping industry, AI solutions help account for potential input inaccuracies, shipping delays, or lost goods, thereby minimizing revenue loss.
Accurate data insights for smart decision-making - AI can help research 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 through predictive analytics by uncovering patterns and trends in large datasets that might otherwise be overlooked.
Improved customer experience - Continuous machine learning enables businesses to gain deeper customer insights, facilitating hyper-personalization, which is beneficial in e-commerce, digital marketing, and entertainment. AI systems collect insights for tailored recommendations to facilitate customer interactions, potentially improving customer engagement and retention. Additionally, 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 integrating AI is critical in their business strategies. But what are the main AI tech that businesses should capitalize on? Let's take a look.
Machine learning is a subset of computer science and a vital part of artificial intelligence that focuses on creating machines (computers) that can learn independently. It involves using algorithms, APIs, development tools, big data, and applications to train the machine on a particular subject. Through an iterative process, the machine analyzes the data, makes conclusions, and remembers them for future use. The more data the machine learns from, the better it becomes at its tasks. These platforms are increasingly popular, especially for tasks involving categorization and prediction. Advances in technology, particularly in storage and processing power, have facilitated the development of products based on practical applications of machine learning, such as Netflix's recommendation engine and self-driving cars. Other 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 focused on training computers to perform tasks such as speech recognition, image identification, and making predictions. It enhances the computer's ability to classify, recognize, detect, and describe using data. It plays a significant role in innovations such as driverless cars, hands-free speakers, personalized shopping and entertainment, and voice recognition in various devices.
While machine learning relies on simpler concepts, 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.
They also consist of interconnected nodes in multiple layers, often with tens or hundreds of hidden layers. Neural networks analyze extensive training data and repeatedly perform tasks to enhance accuracy, in a manner similar to human skill improvement through study and practice. This allows computers to quickly observe, learn, and respond to complex situations. Deep learning has proven beneficial in tasks such as image classification, language translation, and speech recognition, offering solutions to pattern recognition problems without requiring human intervention.
A Generative Adversarial Network (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 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 language in forms such as text and speech. NLP is crucial for a range of applications and technologies that translate languages, 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 NLP through 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. Other AI technologies include natural language generation, decision management, text analytics, speech recognition, and generative AI.
Generative AI is a powerful tool enabling businesses to develop, refine, and enhance business solutions. To become an industry leader in the next five years, it is crucial to establish a well-defined and compelling generative AI strategy.
Generative artificial intelligence (AI) is a category of deep learning models capable of producing text, images, code, and audiovisual content in response to prompts. These models train on extensive datasets, learning to generate responses statistically. For instance, text-based generative AI models work on large amounts of text to respond to written prompts. Generative AI can mimic some human creative abilities, providing quick responses to requests. Notable examples include ChatGPT for text generation and DALL-E for image generation.
Its capacity to produce new data instances across various formats beyond text. This versatility makes it valuable for developing virtual assistants with human-like responses, dynamic content for video games, and synthetic data for training other AI models, especially in challenging or impractical data collection scenarios.
In the business realm, generative AI applications are making a substantial impact by fostering innovation, automating creative tasks, and enhancing personalized customer experiences. Research carried out for the Infosys Generative AI Radar Reports found that businesses plan to invest 67% more in generative AI in 2024 to drive efficiency, experience, and growth. Many businesses view generative AI as a tool for increasing automation, enhancing personalization for customers, and creating content. Some of the most prominent use cases include the following:
Generating video, images, music/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 or to 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 copy.
Product design and development: Generative AI 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.
The increasing prevalence of artificial intelligence (AI) adoption prompts ethical concerns about its design and implementation.
Ethical AI is a set of guiding principles that aims 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 will have to align with data privacy laws, tackle algorithmic bias head-on, and ensure customers are well-informed about how their data is utilized before they sign up for a product. 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 that could lead to harmful consequences, especially for underrepresented or marginalized groups and individuals. 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 associated with irresponsible decision-making.
Bias and discrimination: Instances of bias and discrimination have sparked ethical concerns about artificial intelligence (AI) usage. To address this, companies should adopt inclusive AI practices to prevent the unintentional exclusion of marginalized groups and ensure that products cater to diverse needs.
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. Additionally, 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 on data. Although efforts are underway to develop energy-efficient AI methods, there is a need for greater consideration of environmental ethical concerns in AI-related policies.
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.
The European Union pioneered with its White Paper on Artificial Intelligence in 2020, followed by the 2023 EU AI Act. In contrast, the US has taken a different approach, with no specific AI regulation but initiatives like the Executive Order on AI safety and the proposed AI Bill of Rights that outline five protections - data privacy, safe and effective systems, algorithmic discrimination protections, notice and explanation, and human alternatives, consideration and fallback. The Shanghai AI Regulations, Brazil's Bill of Law 2338, and Canada's Artificial Intelligence and Data Act are among the recent regulations. Consequently, understanding these laws is vital for businesses to build trust, access global markets, and innovate securely, while avoiding legal penalties and reputational harm.
Any organization aiming for compliance must perform a gap analysis that identifies areas where its existing governance structures, policies, metrics, and processes might be inadequate. This enables the company to efficiently address inquiries and ensure approval. However, the real challenge lies in operationalizing the necessary steps to bridge these gaps while ensuring internal alignment. Here's where the C-suite becomes instrumental in designing and overseeing AI programs, beginning with a thorough assessment to identify existing resources. This process not only aligns stakeholders but also customizes ethical frameworks to fit the organization's approach.
Elements of an enterprise response strategy to the EU AI act
Source: Infosys Knowledge Institute
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, leading 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. Small and medium-sized enterprises (SMEs) may find it particularly challenging to bear these high development costs, limiting their access to the benefits of AI.
Data preparation: Preparing data for AI applications and tools requires significant effort, taking up a chunk of the workload involved in deploying AI. The more fragmented or unstructured the data, the more time and resources are needed to export, clean, and prepare data.
Lack of AI experts: 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 AI 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 AI implementation. Upgrading existing infrastructure to meet these demands can be complex and costly.
Integration with existing systems: Compatibility issues, resistance to change, and the need for training programs for existing staff pose hurdles to the successful integration of AI into business operations.
Interpretable AI: Understanding and interpreting the outcomes of AI models is vital for gaining the trust of customers, stakeholders, and regulatory bodies.
Key challenges firms are facing in going AI-first
A digital operating model for an AI-first enterprise
Source: Infosys
The latest developments in artificial intelligence have impacted pretty much every industry. From healthcare, finance, and cybersecurity to retail, manufacturing, and digital marketing, AI is poised to become increasingly ubiquitous in various aspects of our daily lives. Let's take a look at the emerging trends.
Generative AI: Generative AI's mainstream applications, such as generating text, videos, images, and human-like speech, are user-friendly, leading to widespread acceptance. The focus of future research will be on seamless integration with various platforms. The upcoming generation of generative AI tools surpasses previous chatbots and image generators. New developments include powerful and user-friendly generative video and music creators, integrated into creative platforms and productivity tools. The ability to discern between real and computer-generated content will become a crucial skill in the evolving landscape of generative AI.
Explainable AI: The growing reliance on AI has resulted in increased accuracy, but the lack of transparency in AI decision-making raises concerns about reliability. In the future, interpretability will be expected to play a crucial role in enhancing decision-making accuracy, particularly in industries such as healthcare and human resources.
No-code AI platforms: The popularity of AI technologies is attributed to their accessibility, particularly with the emergence of no-code AI platforms, which 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. No-code AI platforms are particularly sought-after in scenarios where extensive customization is not crucial, often deployed by companies for tasks such as image and object classification in computer programs.
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 human interaction patterns and employ multimodal learning and algorithms for advisory and training to reduce human errors and shift organizational focus towards creative tasks.
How can Infosys help? Infosys provides enterprises with a holistic strategy and roadmap for scaling AI across their operations. By combining the capabilities of AI, analytics, and cloud, we deliver innovative business solutions and insightful experiences that not only future-proof AI investments but also enable efficient enterprise-wide scaling.
Explanation of outputs is crucial to establish trust in large language models (LLMs). However, current explainability methods, including perplexity, BLEU, ROUGE, and human evaluation, are pregenerative AI and 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.
The EU AI Act, approved by members of the European parliament in March 2024, uses a risk-based categorization system, with higher-risk AI systems being subjected to greater scrutiny, and some AI systems marked as posing an “unacceptable risk,” such as those that exploit human behavior or utilize social scoring systems. It also defines “general purpose AI (foundation models),” requiring generative AI creators to perform intensive evaluations and ensure adequate cybersecurity. Similar AI 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, and enabling rapid response and remediation of issues. Effective solutions offer observability for monitoring and troubleshooting; predictive analytics for faster issue detection; and proactive response to forecast IT problems. Some operational workflows can embed AI as point solutions, while others require a complete overhaul of the process or workflow for significant business benefits.
Deepfakes, which uses AI to create realistic media, have evolved alongside technological advancements over the last 35 years. The better the AI, the better the deepfakes. A turning point came in 2014 with GANs, invented by Ian Goodfellow and his colleagues. They pit two AIs against each other, with the generated image (or other media) so realistic that the detector GAN couldn’t distinguish between real or fake. Since then, “self-aware” AI has created deepfakes that can self-correct and learn, bypassing even the most sophisticated defenses. This raises serious ethical dilemmas and the need for stricter regulations.
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 and simplifies the AI journey by hiding engineering complexity and enabling efficient management and rapid onboarding of new generative AI solutions through agile techniques.
The most significant fear around generative AI adoption is that it will render current work obsolete. It is important to note that though roles will change, employees will more often than not be upskilled to take on higher value, and more interesting work.
To onboard the technology quickly and at scale, firms need to make their workforce “AI aware,” as we’re doing at Infosys. 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. Astute firms should create an “AI-aware culture,” with a dedicated AI ethics team focused on trust, ethics, security, privacy, and compliance.
