10 emerging technologies shaping the future of innovation

1. AI trust, risk, and security management (AI TRiSM)

As AI tech advances rapidly, it will be critical to govern and secure applications with robust but flexible AI regulation. The are some undoubtable risks coming with AI, such as poisoned or biased training data, hallucination for increased creativity, or plausible truthiness instead of facts in the large language models. 

Here are some real-life examples: 

• Biased hiring tool: An AI-driven recruitment tool underwent training using resumes gathered over a span of 10 years, primarily sourced from male applicants. Consequently, the system displayed a gender bias, disproportionately favoring male candidates over their female counterparts. 
• Hallucinated legal cases: a U.S. attorney used generative AI to identify precedent cases for a current legal matter and obtained promising outcomes. However, a significant issue arose when it was discovered that at least six cases cited in the legal brief did not actually exist. Upon scrutiny of the submitted documents, the judges pointed out that the submitted cases contained false names and file numbers as well as manipulated internal citations and inverted commas. After further hearings, the lawyer received a heavy fine. 
• Incorrect medical diagnosis: When responding to medical questions, a generative AI model provided responses that appeared plausible on the surface. However, these answers were fundamentally inaccurate, stemming from reliance on flawed or misinterpreted sources. Such misinterpretations bear potential health risks and legal complications, particularly in sensitive areas such as diagnosis and treatment. 

The democratised AI access in general, as well as a few key stakeholders – be it in military scenarios, healthcare or publishing – amplify the urgency for ‘TRiSM’ controls. These controls are essential for ensuring the trustworthiness, fairness, transparency, and reliability of AI technologies. Additionally, they play a crucial role in protecting privacy and managing the broader societal impacts of AI. 

We need to create a balance between innovation and responsible use, promoting the positive contributions of AI while mitigating potential risks and harms. Without guardrails, AI models can spiral into misinformation, or lead to unintended bad consequences. TRiSM encompasses AI model operationalisation, proactive data protection, and risk controls. According to Gartner, organisations that apply AI trust, risk, and security management controls to their AI applications will use a good 50% more accurate information to reduce flawed decision making by 2026. 

TRiSM enhances bias control, promotes fairness, and enables organisations to remain competitive by ensuring transparent AI management. Gartner anticipates a 50% improvement in intended outcomes for models under AI trust, risk, and security management. And keep in mind: AI risk management practices should mitigate not only internal, but also external AI risks that you cannot directly control. Third-party AI models, e.g. search and chat, bear risks such as misinformation and errors used for decision making, unintended consequences of widespread AI adoption, or vulnerabilities in widely used AI frameworks. 

Third-party AI services subjected to TRiSM yield more accurate information, but maybe also less content overall. But without TRiSM, the risk of misinformation grows. By taking a holistic approach to AI risk management that includes both internal and external considerations, organisations can better safeguard their AI systems and contribute to the overall resilience of the AI ecosystem. Because embracing AI trust, risk, and security management is not only about risk mitigation. It's also about unlocking AI's full potential for ethical decision making in our digital era. 

Check out our guide to responsible AI to learn more about adopting safe, ethical, and sustainable practices around AI – and why this is a moral, economic, and regulatory imperative. 
 

2. Sustainable technology

Sustainable technology will remain a key strategic trend in 2024, driven by competitive pressure, customer requirements, and legal regulations in environment, social, and governance (ESG) goals.

Gartner anticipates that by 2027, 25% of CIOs will derive compensation from sustainable technology. For Gartner, the greatest potential for sustainable technology lies in digital solutions that support human rights and contribute towards long-term ecological balance. 

This includes environmental technologies for risk prevention, social technologies that enhance wellbeing, and governance technologies that strengthen business conduct.

Technologies like AI, cryptocurrency, IoT, and cloud computing raise concerns about energy consumption. That’s why business must collaborate to innovate more efficient and sustainable practices, products, and solutions and to create better outcomes – for people, planet, and profit.

In 2024, we’ll see mounting pressure for transparency in the field of sustainability. This will prompt the use of observability platforms, digital employee experience tools, and data centre infrastructure management for monitoring social, governance, and environmental aspects.

Beyond sustainability, these technologies offer valuable insights, enhancing overall IT performance. The business opportunity lies in aligning tech initiatives with ESG outcomes, meeting stakeholder demands, and ensuring a more sustainable and responsible technology landscape.

Ultimately, sustainability is about much more than sustainable technology. It’s an opportunity to do business better.

3. Industry cloud platforms

This emerging technology has huge potential for creating tailored solutions that tackle industry-specific challenges. Gartner predicts that by 2027 more than 70% of enterprises will leverage industry cloud platforms to accelerate their business initiatives. That’s a significant increase from less than 15% in 2023.

Industry cloud platforms combine Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS) into comprehensive offerings with composable capabilities, including industry data fabric and business capabilities. Together with our global partner ecosystem, we’re helping diverse organisations to shape and derive ongoing value from these composable platforms.

In the face of economic risks and uncertainty, some organisations are increasingly cautious about cloud technology investments. Industry cloud platforms can help here by providing cloud solutions tailored to specific industries and their mission-critical priorities. A great example is the world of finance, where we’re seeing the emergence of cloud-enabled financial services ecosystems.

The composable approach of these industry cloud platforms provides critical agility in a rapidly changing business environment. With approximately 270 platforms serving 20 industry groups, the business opportunity lies in leveraging these platforms to deliver unique and differentiated customer value propositions.

4. Platform engineering

Efficient application delivery is an ongoing requirement. So it’s good to see Gartner recognise platform engineering as trend that will continue into 2024. By 2026, 80% of large software engineering organisations will establish platform engineering teams – a notable increase from 45% in 2022.

Platform engineering involves creating self-service internal development platforms. According to Gartner, each is managed by a dedicated product team to optimise user experience and increase business value. Many people think 'platform engineering’ starts and ends with backend development. But the benefits of platform engineering also extend to mobile platforms – from increased productivity to higher consistency and efficiency gains. Check out the award-winning mobile investment solution we created with HSBC for inspiration.

Early adopters have demonstrated the concept with ‘home-built’ platforms. Now, platform engineering it is being adapted on a broad scale. New tools are increasing the cognitive load and improving developer experience and productivity. This trend not only streamlines application delivery, but also helps retain talent by creating an environment that fosters developer success. For organisations, the opportunity lies in adopting this developer-centric approach, increasing efficiency and driving innovation in application development.

5. Continuous threat exposure management

Threat actors are adapting at an unprecedented pace to evolving IT landscapes and digitisation initiatives. The introduction of generative AI has enabled unskilled actors to participate in cyber activities. Exploits can now be developed, tested, and executed in a fraction of the time. Additionally, the geopolitical instability in technologically advanced countries is contributing to the proliferation of cybercriminal activities. Moving beyond sporadic and isolated risk assessments, continuous monitoring is essential to transition towards a comprehensive risk inventory that dynamically adjusts to emerging threats. Gartner predicts a two-thirds reduction in security breaches by 2026 for organisations prioritising security investments based on a continuous threat exposure management program.

This systematic approach involves aligning exposure assessments with business projects, tackling patchable vulnerabilities, and addressing ‘unpatchable’ threats. Continuous threat exposure management emphasises validating priorities from the attacker's viewpoint, fostering evidence-based security optimisations and cross-team collaboration.

Beyond traditional patching, continuous threat exposure management recognises the need to mitigate risks tied to human error and supply chain dependencies.

As the attack surface expands, prioritising remediation actions becomes challenging. Continuous threat exposure management advocates a shift from compliance-driven to continuous, automated cybersecurity validation. Implementing breach and attack simulation tools marks a crucial step, enabling a systematic validation approach.

With Zühlke as a trusted cybersecurity partner, our clients benefit from cybersecurity resilience, reducing breaches, and maintaining a proactive stance against evolving security risks in the digital landscape. Discover how we collaborated with BitMEX to further enhance its security operations and bootstrap its DevSecOps practice.

6. AI-augmented development

Gartner predicts that AI-augmented development will transform the software engineering landscape. AI-augmented development involves software engineers using generative AI and machine learning technologies to help design, code, and test applications. These tools optimise development by translating legacy code, enabling design-to-code conversion, and improving testing capabilities.

By 2026, generative AI will significantly impact 70% of design and development efforts. And by 2025, over 50% of software engineering leaders will explicitly require oversight of generative AI. What's more, Gartner® anticipates that 75% of enterprise software engineers will use AI coding assistants by 2028 – a huge increase from less than 10% in early 2023.

‘A large enterprise with 12,000 developers improved productivity by 5% and saved $2M per year in costs by using (...) AI-enabled code generation’.

With increasing demand for software, and the pressure to modernise existing applications, AI-augmented software development increases productivity and allows engineers to focus on more strategic tasks. It can also improve software quality and the user experience.

As the Gartner® report points out, results can be ‘almost immediate and tangible’. They observed, for example, that a large enterprise with approximately 12,000 developers improved productivity by 5% and saved $2M per year in costs by using Github CoPilot for AI-enabled code generation.

However, advances in AI development must be supported by a responsible AI framework and AI regulation, such as the EU AI Act, to foster innovation while preventing unintended bad consequences.

7. Intelligent applications

Intelligent applications will take centre stage in 2024, driven by the strategic need to optimise decision-making processes. Intelligent applications use AI to learn and optimise themselves based on user behaviour and other experiences. With the skills shortage and increased focus on digital technology initiatives, intelligent applications are becoming a critical solution for organisations.

By 2026, 30% of new applications will use AI to enable personalised and adaptive user interfaces, according to Gartner. That’s a substantial increase from less than 5% in 2023. What's more, over 80% of independent software vendors are expected to embed generative AI capabilities in their enterprise applications by 2026 – compared to less than 1% in 2023.

More than 50% of respondents to the ‘Gartner AI Use-Case ROI Survey’ are already using smart applications. But the main obstacle to optimising employee productivity remains the lack of effective automation and tools. This represents a major innovation opportunity.

8. Democratised generative AI

Generative AI applications can produce diverse content – including images, video, audio, and text. This has wide applications which can help to automate tasks, enhance productivity, reduce costs, and open new growth opportunities. (You can dig deeper into the potential of generative AI in our article on ChatGPT business cases).

The democratisation of generative AI platforms globally brings generative capabilities to a broad spectrum of roles and functions. This is what makes it one of the most disruptive trends of the decade.

The confluence of cloud computing and open source is making AI models accessible to workers worldwide, even those without programming skills. AI applications that use machine learning technologies such as natural language conversational interfaces, democratise knowledge and skills by making vast information sources available to business users.

9. Augmented connected workforce

The augmented connected workforce is the application of intelligent technology and workforce analytics to enhance the value of ‘human’ work. By 2026, 50% of global office workers will be AI-augmented, according to Gartner, boosting productivity and improving quality and output.

This represents a double win for people and businesses. Organisations benefit from a higher performing, more efficient workforce – and they’re better positioned to meet colleagues’ evolving needs. Meanwhile, intelligent applications and analytics provide contextualised assistance, improving the employee experience, wellbeing, and skills development.

The augmented connected workforce is born of the need to close the gaps between employee skills and business needs. With digital capabilities in high demand, AI could help to accelerate time to competency, nurture talent development, and automate the workplace. A great example is LBBW. Discover how we helped the bank optimise its risk management processes using AI applications and ML use cases.

10. Machine customers

The 2024 Gartner trend report introduces a new concept – that of machine customers or ‘non-human economic actors’:

‘Machine customers purchase goods and services in exchange for payment. By 2028, 15 billion connected products will exist with the potential to behave as customers, with billions more to follow in the coming years.’

According to Gartner, more than 50% of sales and service centres will interact with machine customers by 2027. And by 2028, machine customers are expected to render 20% of digital storefronts obsolete.

This evolution, driven by Generative AI and the proliferation of connected products, presents a transformative business opportunity. It will impact trillions of dollars in purchases and eventually become more significant than the arrival of digital commerce. (Don’t miss our deep dive on the AI retail opportunity). 
 
The ability to create for and sell to machine customers will become a competitive advantage. The strategic imperative here is not just technological. It involves understanding and navigating a profound shift in the buyer landscape.