The Future of AI Apps: Conversational, Personal, and Impactful

Artificial Intelligence (AI) has come a long way from being a concept confined to the realm of science fiction and large mega-caps who could afford to invest in the nascent technology. Today, through the launch of mainstream tools such as Siri or Alexa, it has become a familiar part of our lives, changing the way we interact with technology and shaping the future of applications. 

As we gaze into the horizon of technological advancements, the future of AI apps appears to be exceedingly promising, characterised by conversational interfaces, personalised experiences, and profound impact on various aspects of our daily lives.

Let’s examine each of these areas to see what the future could hold.

Conversational Interfaces: The Power of Natural Language

The future of AI apps lies in their ability to understand and interact with users in a manner that resembles human conversation. Conversational AI leverages Natural Language Processing (NLP) to comprehend user queries, enabling seamless and intuitive interactions. Gone are the days of rigid command-based systems; instead, we are witnessing the rise of AI-powered virtual assistants capable of engaging users in meaningful dialogues.

Imagine an AI app that can decipher context, tone, and intent to provide tailored responses and recommendations. This enhanced conversational capability will not only revolutionise customer support but also find applications across a broad range of industries.

We are seeing conversational interfaces becoming increasingly popular in various industries, including healthcare, finance, and retail. Conversational interfaces enable users to interact with machines using natural language, making it easier to access information and services. Conversational interfaces can be used in various applications, including chatbots, virtual agents, and voice assistants. Chatbots are computer programs designed to simulate conversation with human users. Chatbots can be used in various applications, including customer service, e-commerce, and healthcare. For example, the chatbot Woebot is a mental health chatbot that uses cognitive-behavioural therapy techniques to help users manage their mental health.

Virtual agents are similar to chatbots but are designed to perform more complex tasks, such as booking flights or scheduling appointments. For example, the virtual agent Amelia is used by various companies, including Allstate and Vodafone, to provide customer service. 

Voice assistants are AI-powered devices that can understand and respond to voice commands. Voice assistants can be used in various applications, including home automation, entertainment, and healthcare. For example, Amazon's Alexa can be used to control smart home devices, play music, and provide health advice.

The future of conversational interfaces is expected to be more natural and intuitive. Innovators are working to enhance conversational AI technology to enable machines to understand and respond to human language more accurately and efficiently. For example, Google's Duplex technology can make phone calls on behalf of users and book appointments.

The conversational AI bots of the future will be able to handle multiple entities and purposes in a single conversation and understand context from collected behaviours to appear as a personal assistant, enterprise employee, or customer service representative.

Personalised Experiences: AI Tailored to Individuals

The future of AI apps will also take on a highly individual-centric approach. As AI algorithms collect and analyse vast amounts of user data, they can deliver personalised experiences that cater to each user's unique preferences and needs. Whether it's an AI-powered shopping assistant suggesting products based on past purchases or a health app curating personalised fitness plans, AI will be a driving force in delivering more targeted and effective services.

Personalisation is becoming increasingly important in various industries, including healthcare, finance, and retail. Personalisation enables companies to provide tailored experiences to individual users, making it easier to access information and services. Personalisation can be achieved using various techniques, including machine learning and natural language processing. Machine learning is a subset of AI that enables machines to learn from data and improve their performance over time. Machine learning can be used to analyse user data and provide personalised recommendations. For example, Netflix uses machine learning algorithms to recommend movies and TV shows to users based on their viewing history.

Natural language processing is a subset of AI that enables machines to understand and respond to human language. Natural language processing can be used to analyse user feedback and provide personalised responses. For example, the chatbot Woebot uses natural language processing to provide personalised mental health advice to users.

The future of personalised experiences is expected to be more accurate and efficient. Innovators are working to enhance machine learning and natural language processing to enable machines to provide more accurate and efficient personalised experiences. For example, Amazon's Alexa can now recognize individual voices and provide personalised responses based on the user's voice.

Given the progress we have witnessed in Conversational AI, the era of voice-enabled technology may arrive sooner than expected.

Transformative Impact on Industries: New Age of Productivity

Conversational AI has the potential to transform various industries, including healthcare, finance, and retail. Conversational AI can be used to improve customer service, reduce costs, and increase efficiency. Conversational AI can also be used to improve healthcare outcomes, by providing personalised recommendations and monitoring patient health. In healthcare, conversational AI can be used to provide personalised health advice to patients. For example, the chatbot Ada can be used to provide medical advice to users based on their symptoms.

In consulting, AI analytical tools will complete data analysis, provide client recommendations, support proposal writing and assist across knowledge management processes.

In finance, conversational AI can be used to provide personalised financial advice to users. For example, the chatbot Cleo can be used to help users manage their finances and save money.

In retail, conversational AI can be used to provide personalised recommendations to users. For example, the chatbot H&M can be used to provide fashion advice to users based on their preferences and prior shopping patterns.

The future of AI apps is expected to be transformative, with AI-powered devices and applications becoming increasingly prevalent in various industries. The global conversational AI market, including chatbots and intelligent virtual assistants, is expected to grow at a CAGR of 22% during 2020–25.

By 2022, 70% of white-collar workers will interact regularly with conversational platforms, according to Gartner.

Ethical and Privacy Concerns

As AI-powered devices and applications become increasingly prevalent, ethical concerns need to be acknowledged. AI-powered devices and applications can be used to collect and analyse user data, raising concerns about privacy and security.

AI-powered devices and applications can also be used to automate jobs, raising concerns about job displacement. To address these concerns, innovators and policymakers need to work together to develop ethical guidelines for AI-powered devices and applications. Ethical guidelines should address issues such as privacy, security, and job displacement. Ethical guidelines should also address issues such as bias and discrimination, which can arise when AI-powered devices and applications are trained on biased data.

To support the ethical adoption of AI into society, many governments have developed ethical frameworks that companies can adopt, either voluntarily in countries like Australia, or follow regulations in regions like the EU, where it is mandatory to conduct impact assessments, consider internal and external reviews, adopt a risk based approach and provide for an independent over-sight body.

Data sovereignty is also a major concern that needs to be considered, especially when interacting with the government and its agencies. The European Union's General Data Protection Regulation (GDPR) is a notable example of a government taking action over data sovereignty concerns. The GDPR, implemented in 2018, aims to protect the privacy and personal data of EU citizens. It establishes strict rules for how organisations collect, store, process, and transfer personal data. The regulation applies not only to EU-based companies but also to any organisation that handles the personal data of EU citizens, regardless of where the organisation is located. The GDPR grants individuals greater control over their personal data and imposes significant fines for non-compliance, ensuring that data sovereignty is respected and protected within the EU.

Another example is China's data sovereignty laws. China has implemented strict regulations regarding the collection, storage, and processing of personal data within its borders. The Cybersecurity Law of China, enacted in 2017, requires companies operating in China to store Chinese citizens' personal data within the country and obtain consent for data collection and processing. These laws aim to protect the privacy and security of Chinese citizens' data and ensure that it remains under the control of the Chinese government.

We are also familiar with the US government’s restriction of the TikTok app due to its concerns regarding data collection and storage on offshore servers.

Conclusion

In conclusion, the future of AI apps is expected to be conversational, personalised, and impactful. Conversational interfaces, personalised experiences, and transformative impact on industries are expected to be the key features of AI apps in the future. Conversational AI has the potential to transform various industries, including healthcare, finance, and retail. However, ethical concerns need to be acknowledged and addressed to ensure that AI-powered devices and applications are developed and used in a responsible and ethical manner.