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Navigating AI: Strategic choices for the upcoming year

Punit Shah

Senior Technology Specialist , Pune, India

Artificial Intelligence

Artificial intelligence (AI) has evolved from basic rule-based systems to advanced machine learning (ML) algorithms, particularly those powered by deep learning advancements. Generative AI now spearheads this evolution, transforming industries as we know them with its wide-ranging applications from digital art to complex data analysis.

Its economic potential is immense; McKinsey & Company forecast an annual global economic boost of $2.6 trillion to $4.4 trillion. This technology is reshaping industries, economies, and business operations. Generative AI, essential for innovation and efficiency, is now a strategic necessity for forward-thinking businesses.

Generative AI is a business imperative

The data is unequivocal: Over half of employees in the US, UK, and India are using generative AI technologies in some capacity, marking a substantial shift in how work is performed and optimized. It’s also estimated that GenAI could raise Global GDP by 7%. And it’s not just the workforce that’s paying attention — Michael Wooldridge, Professor of Computer Science at Oxford University, considers 2023 to be a “watershed year” for generative AI, asserting that the technology will be as transformative as the microprocessor or a mobile phone.

What differentiates generative AI is its ability to create new data, including text, images, and code. Whether it’s expediting content creation, writing code or automating tasks that traditionally required specialized skills, the net result is a significant gain in efficiency and scalability. As it stands, the most popular use case for generative AI is crafting replies to emails, however, the possibilities span far beyond that, excelling in demand forecasting, and predictive maintenance. Its adaptability extends to healthcare, finance, and education — offering innovative solutions and enhancing decision-making in diverse fields, from personalized health recommendations to financial risk analysis and intelligent tutoring systems.

Forward-thinking trends for the year ahead

Industry leaders confront pivotal decisions on embracing Large Language Model (LLM)-based technologies, carefully selecting innovations for enduring, sustainable advantages. These choices encompass:

Evolving prompt engineering techniques

Also known as ‘AI whispering’, this refines the art of crafting the perfect question or instruction to make the AI not just understand, but excel in delivering groundbreaking results, turning seemingly ordinary queries into extraordinary outcomes. This involves the strategic use of language to guide AI towards the desired output and is critical for maximizing the potential of Large Language Models (LLMs), making the difference between mediocre results and groundbreaking functionality. Prompt engineering has revolutionized customer support, empowering chatbots with well-structured prompts to deliver instant, accurate responses. In marketing and sales, it drives personalized interactions, while in finance, prompts automate tasks like data entry and invoice processing.

Advancements in multi-model LLMs

Beyond text, multi-modal LLMs interpret and generate multimedia content, incorporating verbal, visual, and auditory data processing for enhanced, intuitive interactions. This allows the model to not only comprehend the written details but also interpret and incorporate visual elements, generating a more holistic and accurate output. In a medical setting, a multi-model LLM could analyze patient records that include text, images, and sensor data, aiding healthcare professionals in comprehensive diagnostics.

The rise of datafication AI

Leveraging previously untapped data sources to drive AI insights, datafication refers to the transformation of every aspect of our lives into data. The trend is toward turning these vast and diverse data streams into actionable intelligence, enhancing predictive analytics and personalizing user experiences.

Seamless integration of embedded finance

This is the integration of financial services into non-financial environments to make finance a seamless part of the customer journey, whether that’s through retail platforms, business software, or social media. AI plays a crucial role in this trend by personalizing financial solutions and simplifying complex financial decisions for users. Consider online marketplaces seamlessly offering instant financing or business software effortlessly automating financial tasks weaving finance into the fabric of our daily experiences.

Innovations in intelligent RegTech

Also known as digital regulatory technology (RegTech), this trend is about leveraging AI to anticipate regulatory changes and adapting accordingly to reduce the risks of non-compliance and lower operational costs. This can include AI-powered compliance monitoring systems that continuously analyze regulatory updates, allowing businesses to swiftly adapt their practices and ensure adherence to evolving requirements.

Development of autonomous AI agents

This refers to autonomous programs that can perform tasks on behalf of users. These agents are becoming more proactive and context-aware, capable of performing complex sequences of actions and learning from their interactions. The trend is towards agents that can navigate the digital world as a human would, giving recommendations based on a user’s preferences and past behaviour.

Blockchain’s convergence with AI

Although blockchain first emerged in 2008, it’s now converging with AI to enhance security, transparency, and traceability. This trend leverages blockchain’s decentralized ledger to log AI interactions and decisions, fostering more trust in AI systems. It can also play a significant role in the management and tracking of data used to train AI models, ensuring integrity and ethical use of information which are the major concerns around AI.

Quantum computing’s intersection with AI

By performing complex calculations at unprecedented speeds, quantum computing exploits the principles of quantum mechanics to process information in ways that classical computers can’t, opening up new possibilities for AI applications and algorithmic complexity.

AI success rests on meticulous planning and execution

In the coming year, embracing AI is a necessity. The trends identified, from refining AI interactions through prompt engineering to leveraging quantum computing and AI convergence, outline a roadmap for innovation. It’s crucial to adopt ethical frameworks and conduct regular risk assessments to ensure responsible use. By establishing clear, robust internal policies you can ensure consistency and transparency in AI applications.

In short, aligning strategic choices based on these trends is key to propelling your business forward and contributing positively to society at large.

The Author

Rachel Anderson, Digital Lead at Synechron UK
Punit Shah

Senior Technology Specialist

Punit Shah is a Senior Specialist in (Gen)AI, Data Science, & AI Strategies within Synechron’s FinLabs. FinLabs is Synechron’s Innovation Lab with a mission to empower our customers to accelerate their digital transformation journeys. Here, we combine innovative ideas with business knowledge and our global technology teams’ skills to deliver the best solutions possible.

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