Hype or Hope? Evaluating 6 Disruptive Technologies for 2024

Many companies invested in new technologies during the pandemic, but not all digital transformation initiatives have endured.

According to a 2023 report, 79% of companies feel that technology needs to be in sync with their business goals. Pew Research also highlighted that, in some cases, technology causes more problems than it solves. In my experience, this is true more often than we would like to admit.

As a business leader in 2024, is all disruptive technology worth your time, attention, and, of course, investment? Today, I cut through the clutter and distinguish the hype or the hope from up-and-coming tech trends.

1. Blockchain (hope)

While blockchain is mainly associated with digital currencies and alternative financial services models, it has a broader application across business verticals. The blockchain is a distributed ledger technology system which allows you to store data in interconnected blocks. Depending on the application, it could range from identity information to past purchase history.

Hype:

· Blockchain emerged with much hype, especially during the cryptocurrency boom. Many people associated it solely with Bitcoin and other digital currencies.

· The hype led to speculative investments in various blockchain projects, some of which didn’t deliver as promised.

· Some early blockchain projects overpromised their capabilities, leading to scepticism.

Hope:

· Blockchain’s core strength lies in its decentralised nature. It enables trust without intermediaries, making it ideal for applications like supply chain management, provenance tracking, and secure transactions.

· Smart contracts, powered by blockchain, automate processes and ensure transparency. They have applications beyond finance, including legal agreements, real estate, and logistics.

· Blockchain’s immutability ensures that altering the data once recorded is not possible. This feature is valuable for auditing, compliance, and historical records.

· Blockchain can revolutionise identity management, giving individuals control over their data.

· Tokenising assets (real estate, art, etc.) on the blockchain can democratise investment opportunities.

Enterprise Applications:

· Supply Chain: Blockchain can enhance transparency, traceability, and efficiency.

· Finance: Beyond cryptocurrencies, blockchain can streamline cross-border payments, reduce fraud, and improve settlement times.

· Healthcare: Secure patient data sharing, drug provenance, and clinical trials benefit from blockchain.

· Energy: Blockchain makes decentralised energy grids and peer-to-peer energy trading possible.

· Digital Identity: Self-sovereign identity solutions empower users to control their data.

Challenges:

· Scalability: Blockchain networks face scalability issues, especially public ones like Ethereum.

· Regulation: Legal frameworks are still evolving, impacting adoption.

· Energy Consumption: Proof-of-work blockchains consume significant energy.

· Interoperability: Different blockchains need better interoperability.

Blockchain continues to be grossly underutilised by enterprises, especially in areas like marketing and customer loyalty. Also, blockchain-driven consumer technologies like the Brave browser have quietly amassed a large following. In 2024, blockchain will get closer to becoming mainstream, and it is not mere hype for businesses who know how to use it wisely.

2. Metaverse (hype)

Interestingly, despite failing to take off. Isolated metaverse platforms such as Decentraland saw sporadic development before and during the pandemic. The term became a discussion point in boardrooms and investment conversations only with Facebook’s rebrand as Meta Platforms Inc.

Hype:

· The concept of a virtual, interconnected world where people can interact, work, and play captured imaginations; however, it has not led to significant business play.

· Movies, TV shows, and books often portray the metaverse as a futuristic, game-changing technology.

· Major companies like Facebook (now Meta), Microsoft, and others made high-profile announcements about their metaverse initiatives.

Hope:

· The metaverse represents a shift toward a more immersive, interconnected digital experience. It could revolutionise how we collaborate, learn, and socialise.

· The metaverse offers businesses new revenue streams, advertising possibilities, and customer engagement models.

· While VR adoption remains low, hardware and content creation advancements are ongoing. As VR becomes more accessible, the metaverse could gain traction.

· Blockchain and decentralised technologies play a role in the metaverse, enabling ownership of digital assets and secure transactions.

Challenges:

· Hardware Constraints: VR headsets are expensive, and not everyone can access them. Widespread adoption requires more affordable and user-friendly devices.

· Internet Infrastructure: High-speed internet is essential for seamless metaverse experiences. Uneven global infrastructure hinders progress.

· Content Creation: A thriving metaverse relies on engaging content. Creators need tools and incentives to build compelling virtual worlds.

· Privacy and Security: Balancing privacy with interconnectedness is crucial. Trustworthy systems are necessary to protect users’ data.

· Standards and Interoperability: Creating a cohesive metaverse across platforms and ecosystems is challenging.

However, the hype around the metaverse has cooled mainly since then, and despite large companies making early moves, the relegation of the technology to the back burner, for now, is for sure. Therefore, the metaverse is more a hype than a valuable investment proposition in 2024.

3. Generative AI and LLMs (hope)

Large language models, or LLMs, are a subset of generative artificial intelligence. LLMs process massive information as training data to produce startlingly human-like language outputs. ChatGPT, for instance, is trained on several terabytes of data until 2021.

Hype:

· There was immense excitement when introducing LLMs like GPT-3 (now GPT-4). Their ability to generate coherent, context-aware text seemed almost magical.

· News articles, social media, and tech forums buzzed with discussions about LLMs. Their potential applications seemed limitless.

· LLMs could write poems, stories, and code snippets. People marvelled at their versatility.

Hope:

· LLMs excel at specific tasks (such as text generation, summarisation, and question answering), but their broader applications are still evolving.

· Adapting LLMs to specific domains or enterprise needs requires fine-tuning, which can be resource intensive.

· Real challenges include bias, misinformation, and harmful outputs. Responsible AI practices are crucial.

· LLMs learn from vast amounts of data. If the training data is biased or incomplete, it affects their performance.

· LLMs struggle with deep context understanding. They lack proper comprehension and reasoning abilities.

· Advances in LLMs will address limitations and enhance their capabilities.

· Domain-specific models, i.e. tailoring LLMs for specific industries (e.g., healthcare, finance), will yield better results.

· Multimodal integration combines text with images, audio, and video to create richer AI experiences.

Enterprise Applications:

· LLMs can handle routine customer queries for customer support, thus freeing up human agents for more complex issues.

· Content generation is a good area as it assists in drafting reports, articles, and marketing content.

· LLMs summarise lengthy documents, saving time for professionals.

· One of the industry segments where content labelling and data organisation is concerned is the legal industry, which builds local LLMs that aid in contract analysis, legal research, and compliance checks.

· Customising recommendations for personas profiles and improving user experiences based on LLM-generated insights is now an ample opportunity.

Challenges:

· Cost and Resources: Training and deploying LLMs require substantial computational resources.

· Explainability: LLMs lack transparency; understanding their decisions is challenging.

· Security: Protecting LLMs from adversarial attacks and misuse is critical.

· Human-AI Collaboration: Finding the right balance between automation and human judgment is critical.

Let’s explore a three-step approach to tailor LLMs for domain specificity:

- Prompt Engineering: Prompt engineering is a swift method for extracting domain-specific knowledge from a generic LLM without altering its architecture or requiring retraining. Formulate targeted questions or prompts that direct the model to generate outputs tailored to a particular domain. These prompts act as cues, guiding the LLM to focus on specific topics or contexts relevant to the industry. You can leverage the existing LLM capabilities by crafting domain-specific prompts while ensuring relevance to your field.

- Retrieval Augmented Generation (RAG): RAG merges the strengths of information retrieval and LLMs. Combine a retrieval component (such as a search engine or a database) with an LLM. The retrieval component retrieves relevant context or documents related to the domain. The LLM generates responses based on this context, producing more domain-specific and accurate outputs. RAG enhances the LLM’s ability to incorporate external knowledge.

- Fine-tuning: Fine-tuning involves adapting a pre-trained LLM to a specific task or domain by training it on domain-specific data. Collect a dataset specific to your industry (e.g., medical records, legal documents, financial reports). Fine-tune the LLM using this domain-specific data. During fine-tuning, the model adjusts its parameters to better align with the specialised context. Fine-tuning allows the LLM to learn domain-specific nuances, jargon, and patterns.

Benefits of Domain-Specific LLMs:

- Precision and Expertise: Tailored LLMs capture domain nuances better than generic models, thus offering precision and expertise. Focused models are less susceptible to irrelevant information, thereby increasing reliability. Domain-specific LLMs can deliver trustworthy insights crucial for fields like healthcare and law. Models that speak the domain’s language lead to more gratifying interactions and a better customer experience. Smaller, fine-tuned models offer high-quality outputs at a lower cost.

In summary, LLMs are potent tools, but their true potential lies in responsible deployment, ongoing research, and collaboration with human expertise.

4. Automation (hope)

The landscape of Intelligent Automation is rapidly evolving, driven by advancements in Artificial Intelligence (AI), Low-Code Application Platforms (LCAPs), Process Intelligence, and Robotic Process Automation (RPA). These technologies are enhancing operational efficiencies and transforming various aspects of business strategies. Automation is now a mature technology and no-code/low-code platforms make automating easier than ever. Several types of automation, from bot-based RPA to more complex script-based automation, are available as investment alternatives.

Hype:

· Successful automation transformation programs will utilise a multimodal approach. Low-code application Platforms (LCAPs), RPA, Process Mining, Machine Learning, and GenAI will converge.

· This approach enhances outcomes and increases the complexity of policy, governance, and software selection.

· Organisations will continue to grapple with measuring ROI and discovering processes ripe for automation.

· Proven use cases will gain traction as companies seek tangible business gains.

Hope:

· Intelligent Automation is increasingly becoming imperative in the C-suite. Business and IT leaders must collaborate to balance risk with speed and agility. Hyperautomation — rapidly identifying, vetting, and automating as many business and IT processes as possible — gains prominence.

· Adding AI to Automated Process Automation is a top objective for executive teams. Many executives consider AI-driven automation crucial for their industry’s future.

· Generative AI (GenAI) is accepted and integrated into large transformation programs rather than isolated investments.

· Change management around AI becomes a top concern for leadership.

· Media focus is shifting from AI hype to AI policy and ethics. It will lead to genuine interest from even sceptical adopters.

· Connecting AI with specific business contexts for tangible benefits becomes a priority.

Enterprise Applications to Platform Play:

· Automation solutions will evolve beyond standalone tools to comprehensive platforms. These platforms will integrate various automation technologies, enabling seamless orchestration and scalability.

Challenges:

· Ethical Automation and ESG Compliance: As automation becomes pervasive, organisations will focus on ethical considerations. Responsible and transparent automation must align with environmental, social, and governance (ESG) principles.

· Governance and Security: Robust governance frameworks will be essential to manage complex automation ecosystems. Security measures will address data privacy, access controls, and compliance.

In summary, 2024 promises a convergence of automation modes, pragmatic AI adoption, and a shift toward comprehensive platforms. The future of Intelligent Automation looks bright as organisations strive for efficiency, resilience, and business growth.

In 2024, automation is not only a “hope,” but it is instead an enterprise staple. It can eliminate repetitive, non-value-adding work, such as accounts payable processes, data checks, alerts, notifications, etc.

5. Sustainability (hype and hope)

Sustainability has slowly but steadily become crucial to most businesses’ value propositions. Retail companies are looking to roll out sustainable clothing lines. IT departments are aiming to cut down the carbon footprint. Climate-focused venture capital firms like Avaana are also changing the game. Honeywell’s automation company recently committed 150 crore INR towards sustainability in India.

Examples of sustainability practices are: a) They improve energy management efficiency using alternative power sources and carbon accounting. b) Deploying infrastructure that reduces greenhouse gas emissions, preserves water resources, and eliminates waste. c) They operate dynamic and efficient supply chains to promote a circular economy and protect natural resources. and d) It enables sustainable development by improving resiliency and assessing risks while adhering to external regulations and development goals.

Hype and Hope: It is both hype and hope; the reason is simple. It has yet to catch the imagination of media and influencers as another technological innovation has. On the other hand, governments and non-government agencies are defining standards and raising expectations from investors.

Hype: The growing awareness around sustainability has led to increased attention, investment, and corporate commitments. Companies are eager to showcase their sustainable practices.

Hope: Hope lies in positively impacting the environment, society, and future generations. Sustainable practices can drive innovation, resilience, and long-term success.

Enterprise Applications:

· Retail companies launch sustainable clothing lines, emphasising eco-friendly materials and ethical production.

· IT departments focus on reducing their carbon footprint through energy-efficient data centres, renewable energy adoption, and responsible e-waste management, as they have such KPIs.

· Angel funds and venture capital firms invest in startups driving sustainability innovations.

Challenges:

· Complexity: Balancing economic growth, social equity, and environmental protection is intricate.

· Behavioural Change: Shifting mindsets and practices across organisations and industries require concerted effort. ESG measurement frameworks and reporting standards are converging. Reliable ESG disclosures require standardised reporting for further regulation and third-party attestation.

· Short-term vs. Long-Term is all about striking a balance between immediate financial gains and long-term sustainability goals.

· Regulatory Landscape: Navigating evolving regulations and policies related to sustainability. Governments worldwide are incentivising ESG activities. Mandating ESG-related information disclosure, regulating ESG investing, and enhancing ESG assessments in public procurement are poorly defined.

· Supply Chain Impact: Ensuring sustainability practices extend throughout the supply chain.

Sustainability will remain a key theme for technology investments in 2023 and beyond. Enterprises must build their sustainability reporting capabilities to keep up with market and regulatory pressures and customer sentiment.

6. Monolithic clouds (hype)

Finally, the days of monolithic clouds are now over. Cloud giants like Amazon, Google, and Microsoft, who once reigned supreme, are diversifying in 2024 to keep up with lesser-than-expected revenues from cloud divisions. Amazon Web Services (AWS) maintains the highest market share at 32%, followed by Microsoft Azure (23%) and Google Cloud (10%). These three giants — AWS, Azure, and Google Cloud — lead the charge in the global cloud market; however, the cloud market is fragmenting. At the same time, cloud adopters are becoming mature, moving away from monoliths and towards more hybrid and multi-cloud infrastructures. Businesses are indeed moving away from monolithic clouds and embracing hybrid cloud solutions. A staggering 82% of IT leaders have adopted the hybrid cloud, which combines public and private cloud solutions1.

Hype:

· Monolithic clouds were popular due to their simplicity and ease of deployment.

· They promised centralised management and straightforward scaling.

· Hybrid clouds are ideal for complex, dynamic workloads. Examples include e-commerce platforms, data analytics, and applications with varying resource demands.

· Hybrid clouds promise flexibility, cost optimisation, and resilience.

· Organisations can leverage the strengths of both public and private clouds.

Hope:

· Some legacy applications still rely on monolithic architectures.

· Organisations hope to modernise these applications by gradually migrating to more flexible models.

Enterprise Applications:

· Monolithic clouds are suitable for simple, self-contained applications. Examples include basic websites, small-scale e-commerce platforms, and internal tools.

· Hybrid clouds are ideal for complex, dynamic workloads. Examples include e-commerce platforms, data analytics, and applications with varying resource demands.

Challenges:

· Monolithic applications to scale horizontally can be challenging.

· Updates or changes affect the entire application, leading to downtime.

· Monolithic apps may consume more resources than necessary.

· Tight coupling between components makes replacing or upgrading individual parts harder.

· Bridging public and private clouds requires robust connectivity and complicates integration.

· Ensuring data consistency across environments can be tricky.

· Protecting data during transit and at rest across different clouds.

· It balances the costs of public and private cloud usage.

· Different workloads have varying cloud requirements. For example:

- Mission-critical workloads require high availability, reliability, and robust disaster recovery. Consider a private cloud or a hybrid approach.

- Development and Testing: Public clouds are often cost-effective for these workloads due to their scalability.

- Big Data and Analytics: Public clouds with extensive data processing capabilities may be suitable.

- IoT and Edge Computing: Consider edge clouds for low-latency processing.

- Machine Learning and AI: Public clouds offer specialised ML and AI workloads services.

This approach allows organisations to gain from best-of-breed technologies while building extra resilience. Redundant mainframes and servers can kick in during emergencies, such as another pandemic or other unforeseen events.

Avoiding this hype lets you gain from best-of-breed technologies and build extra resilience through redundant mainframes and servers that may kick in again if there were another Act of God like the pandemic.

In summary

Technology investments in 2024 are a double-edged sword. 83% of CIOs drive lean practices to do more with less and stretch budgets to their maximum. A hype-filled technology landscape threatens to pull you in different directions, only sometimes with the proportional value you expect. These six technologies will be on the top of your mind, with some hype and hope.

Blockchain technology and practical applications are emerging. Expect growth in decentralised finance (DeFi) and NFTs, while convergence with AI promises enriched user experiences.

Metaverse is gaining attention because of tech giants’ developments (e.g., Apple, Meta). Elon Musk’s Neuralink brain implant technology could influence the metaverse.

Businesses will use generative AI for synthetic customer data. Energy-efficient computational methods will optimise GenAI implementations.

Intelligent automation will improve sustainable production and design. AI and automation will extend sustainability benefits across the product lifecycle.

Sustainable Technologies is a strategic focus for organisations. CIO compensation will be linked to sustainable tech impact.

Monolithic clouds were popular but are facing challenges in cost-scalability optimisation and maintenance.

Which technology is more hype than hope in 2024? Did I miss any in this article? Please let me know your thoughts at Arvind@am-pmassociates.com or comment below.