McKinsey Technology Council’s latest report, Technology Trends Outlook 2024, highlights how cutting-edge technologies are not only surviving but thriving, despite elevated interest rates and market uncertainties.
“Gen AI has seen a spike of almost 700 per cent in Google searches from 2022 to 2023 along with a notable jump in job postings and investments,” the report notes.
This surge in interest and investment is setting the stage for groundbreaking advancements across interconnected trends such as robotics and immersive reality.
As companies navigate these trends, understanding the underlying technologies and their potential impact becomes crucial for strategic planning.
In an era defined by rapid technological evolution, staying ahead means leveraging these insights to drive future growth and innovation.
In total, McKinsey outlined 15 tech trends to watch, but we’ve pulled out the top 5 that caught our eye.
Generative AI (Gen AI) has made significant strides by using vast data sets to create outputs including text, images, music, and 3D models.
Recent advancements include text generation models like ChatGPT-4 and image tools like DALL-E 3, highlighting the technology’s potential in various industries.
Gen AI has garnered widespread interest. According to the latest McKinsey Global Survey on the state of AI, 65 per cent of organisations use Gen AI in at least one business function, up from one-third last year.
However, risks such as bias, misinformation, and deepfakes need addressing as adoption grows.
Companies are focusing on risk mitigation, operating models, talent, and technology to scale Gen AI effectively.
The surge in multimodal models and open-source developments, coupled with the expanding context window in natural language processing, underscores the importance and rapid evolution of gen AI in today’s technological landscape.
In real estate: Gen AI can be used in real estate to improve customer service through chatbots and virtual assistants, enhance marketing and immersive visual experiences, provide data-driven market insights, streamline agency operating processes and much more.
Applied AI is transforming industries by leveraging technologies like machine learning (ML), computer vision, and natural language processing (NLP) to automate processes, derive insights, and make better decisions.
McKinsey research notes it has the potential to unlock economic value estimated between $11 trillion and $18 trillion annually.
As excitement around generative AI (Gen AI) grows, the awareness and adoption of applied AI is increasing, with 67 per cent of organisations in McKinsey’s global survey on the state of AI, flagging that they are planning to invest more in AI over the next three years.
Applied AI is crucial for enhancing operational efficiency, improving decision-making, and driving innovation across sectors such as finance, healthcare, and manufacturing.
The integration of gen AI and applied AI can yield powerful results, as demonstrated by digital marketing companies using AI-generated content and performance analysis to optimise future outputs.
Despite challenges like governance conflicts, regulatory issues, and the need for skilled talent, effective change management and robust data practices can help organisations successfully adopt and scale applied AI.
In real estate: In real estate, applied AI can analyse market trends, property features, and historical data to provide accurate property valuations. Natural language processing (NLP) can improve search functionalities on real estate platforms and applied AI can predict market trends and property price movements.
Digital trust and cybersecurity are crucial for managing technology and data risks, protecting assets, and fostering innovation.
These technologies, including digital identity, privacy-enhancing technologies, and Web3, are increasingly important as organisations adopt emerging technologies like cloud computing and applied AI.
This trend matters because it enhances organisational performance and customer relationships by building trust in data and technology governance.
Despite challenges like integration issues, talent shortages, and limited consideration in value propositions, effective leadership and strategic changes can help overcome these hurdles.
In real estate: Robust digital trust and cybersecurity can protect sensitive customer data, ensure secure transactions, and maintain compliance with regulations.
Immersive reality technologies, including augmented reality (AR), mixed reality (MR), and virtual reality (VR), enable users to experience augmented or virtual worlds, creating new use cases for both consumers and enterprises.
These technologies simulate real-world environments and facilitate interactions in virtual settings using spatial computing.
Despite a dip in investment and consumer demand in 2023, notable developments like Apple’s Vision Pro headset and enterprise interest in digital twin technology highlight the trend’s resilience.
In real estate: Immersive reality is important as it offers innovative ways for industries such as real estate to enhance customer engagement through virtual property tours and AR-enhanced viewings.
The technology can also improve training, design, and operational efficiencies in various sectors.
For real estate, immersive reality can revolutionise how properties are marketed, viewed, and sold, providing a more interactive and engaging experience for buyers and sellers.
Industrialising machine learning (ML), also known as machine learning operations (MLOps), is the process of scaling and maintaining ML applications within enterprises.
This trend is important because it enables the transition from pilot projects to robust business processes, enhancing team productivity and reducing production timelines and development resources.
McKinsey says as we progress through 2024, MLOps tools are evolving, improving functionality and interoperability, which supports the scaling of analytics solutions.
The rise of generative AI (gen AI) has reshaped the AI landscape, demanding an upgrade in MLOps capabilities to handle its unique demands.
Successful industrialisation of ML ensures the sustainability of solutions and fosters closer collaboration between data scientists, ML engineers, and operations teams.
In real estate: Adopting MLOps can optimise data management, improve predictive analytics, and streamline operational efficiencies, providing a competitive edge in a data-driven market.
