Top 10 Tech Trends 2024
gives developers and organizations the opportunity to run, develop and manage their own applications and processes.
1. AI TRiSM
AI TRiSM is a contraction of AI Trust and risk and security management. This model supports AI model governance, fairness, reliability, robustness, transparency, and data protection by combining content anomaly detection, data protection, application security, model management, and adversarial resistance.?
The results of this strategic approach? Improved model precision and consistency, but also enhanced bias control in decision and more fairness in AI-driven applications. By 2026, enterprises that apply TRiSM controls to AI applications will likely increase the accuracy of their decision-making by eliminating 80% of faulty and illegitimate information.
Key AI TRiSM actions for companies to consider
These are best practices that help you maximize the possibilities for AI TRiSM.
Setting up an organizational task force
Businesses should start setting up an organizational task force or dedicated unit to manage their AI TRiSM efforts. This task force or dedicated team should develop and implement tested AI TRiSM policies and frameworks.?
Your task force must 100% understand how they have to monitor and evaluate the effectiveness of those policies and establish procedures for responding to any changes in case of any incidents.?For example, your task force should educate employees on the implications and potential risks of using AI technologies and how to use those technologies.
Maximizing business outcomes through robust AI TRiSM
Companies should not just be focused on meeting the minimum legal requirements. Instead, they should focus on implementing measures to ensure their AI systems' security, privacy, and risk management.?This will help better manage the AI systems and maximize the business outcomes.?
For example, an AI system designed to analyze customer data should have the appropriate security measures to protect the customer data from unauthorized access or misuse.
Involving diverse experts
Since various tools and software are used to build AI systems, many stakeholders — tech enthusiasts and data scientists, business leaders and legal experts — should participate in the development process.?
You can create a comprehensive AI TRiSM program by bringing together different experts because they understand the technical aspects of AI and the legal implications. For example…
Prioritizing AI explainability & interpretability
Your company should make its AI models explainable or interpretable using open-source tools or vendor solutions.?By understanding the inner workings of models, you can ensure that the models act ethically and responsibly, which will help protect both customers and the company itself.?
For example, AI explainability tools can provide insight into which input variables are most important for a given model and indicate how a model's output is calculated.
Tailoring methods to use cases & components
Data is valuable, and AI models rely heavily on it to make accurate predictions and decisions. This means that companies must prioritize data protection to prevent unauthorized access, misuse and theft of data used by their AI systems.?
Implementing solutions such as encryption, access control and data anonymization can help keep data safe and secure while ensuring compliance with data privacy regulations. However, different use cases and components of AI models may require other data protection methods.?
By preparing to use different data protection methods for different use cases and their components, companies can ensure that their AI systems are secure and protect customer privacy and reputation.?
Ensuring data and model integrity & reliability
When building and deploying AI models, you should focus on their performance and accuracy and the potential risks they may pose to the organization. So, it's crucial to incorporate risk management into the model's AI operations.
One way to do this is by using solutions that assure model and data integrity. This means implementing security measures to protect the models and data from manipulation and ensuring that the models are accurate and reliable. For example, your organization can use automated testing to validate model accuracy and detect data anomalies or errors that can lead to inaccurate model outcomes.
Revolutionize your AI models with AI TRiSM
AI TRiSM is an emerging technology that is predicted to enhances AI models' reliability, trustworthiness, security and privacy. By using AI models more securely and safely, businesses can achieve improved goals, support various business strategies, and protect and grow their brands.?
2. Continuous threat exposure management
Continuous threat exposure management (CTEM) presents a pragmatic and systemic approach to cybersecurity optimization. CTEM provides an excellent balance between diagnosis and action. It includes scoping, discovery, prioritization (identifying which threats matter most), validation, and mobilization (taking the proper measures to battle a threat or minimize its impact).
This comprehensive approach to security aligns exposure assessment cycles with specific business projects or critical threat vectors, addresses patchable and unpatchable exposures, and facilitates evidence-based security optimizations.
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3. Sustainable technology
Sustainability is a top priority for most modern organizations. Sustainable technologies can aid in meeting environmental, social and governmental (ESG) improvement goals that support long-term ecological balance and human rights.
Gartner expects that more and more organizations will increasingly select technologies that will help drive sustainability in their industry and are identified as a priority for the business and key stakeholders. By 2027, 25% of CIOs will have compensation linked to their sustainable technology impact, says Gartner.
4. Platform engineering
Platform engineering is another top trend in 2024 and beyond. Gartner predicts that by 2026, approximately 80% of software engineering organizations will establish platform teams as internal providers of reusable services, components and tools for application delivery. Well-designed platforms have the potential to offer customers and business partners a frictionless self-service experience, allowing users to do valuable work with as little overhead as possible.
The benefits of proper platform engineering are manyfold. The practice allows you to reduce cognitive load, improve the developer experience and productivity, and give users more independence through advanced self-service options. Building platforms with reusable, composable and configurable platform components, knowledge and services also increases the flexibility and adaptability of your organization.
5. AI-augmented development
Advanced AI technologies, such as generative AI and machine learning, are becoming an ever more important part of the process of creating, testing and delivering applications and platforms. According to Gartner, in 2028 about 75% of enterprise software engineers will use AI coding assistants.?
AI-augmented development tools allow you to translate legacy code to modern languages, enable design-to-code transformation, and enhance application testing capabilities. They also improve developer productivity (developers spend less time writing code and can focus on higher-level activities) and enable development teams to address the increasing demand for business-critical software solutions.
6. Industry cloud platforms
Industry cloud platforms (ICPs) are tailored cloud proposals specific to your industry. They offer the composability that modern organizations need to respond to accelerating disruptions in their industry.
By 2027, more than 50% of enterprises will probably use industry cloud platforms to accelerate their business initiatives. This number is up from less than 15% in 2023.
7. Intelligent applications
Intelligence is rapidly becoming a foundational capacity for modern applications. Intelligent, mostly AI-augmented applications are an important trend because they allow organizations to better automate and augment work across an incredibly broad range of use cases.
Transforming the experience of users, customers and product owners, adding accurate predictions and recommendations, and advance data-driven decision-making: intelligent apps make it possible.?
8. Democratized generative AI
Democratized generative AI is another major trend, mainly because the widespread availability of generative AI is bound to democratize access to information and skills. Vast sources of information – both internal and external – can be made accessible and available to business users via natural language conversational interfaces, allowing organizations to boost productivity, reduce costs, and create and utilize new opportunities for growth.?
Gartner expects that by 2026, more than 80% of enterprises will have used generative AI APIs, models and/or deployed generative AI-enabled applications in production environments, an increase from fewer than 5% today.
9. Augmented connected workforce
The ninth trend on our list is the augmented connected workforce. This strategy allows you to optimize human performance by establishing a connective tissue that optimizes the use of intelligent technology. Augmented connected workforces enable you to work smarter and tackle complex issues through smart workplace automation.
Through 2027, 25% of CIOs will use augmented connected workforce initiatives to reduce time to competency by 50% for key roles”, says Gartner.
10. Machine customers
The last trend on Gartner’s list is both an interesting and disruptive one. For the first time in history, companies will be able to ‘make’ their own customers.
Connected products (approximately 28 billion in 2028) have the potential to behave as customers and will impact trillions of dollars in purchases by 2030. In the long run, machine customers might render 20% of human-readable digital storefronts obsolete.
Driving Efficiency with Be Informed in Platform Engineering
Platform engineering is a particularly important trend because it can accelerate the delivery of applications and significantly increase the pace at which they produce business value. Be Informed recognizes this and was founded to meet the rising need of organizations to automate complex, knowledge-intensive work.?
This resulted in the creation of our model-based intelligent automation platform. The use of declarative models and advanced goal-oriented, backward-chaining inferences to automate decisions, supports decision-makers and generates contextual processes according to the rules specific to a particular situation.
The model-driven character of the Be Informed platform accelerates development greatly and is less labor-intensive than traditional approaches. Solid, open integration solutions make the platform highly scalable, integrable, future-proof, and capable of handling all levels of complexity. Continuous development is also one of the principles that Be Informed adheres to. This means that we are constantly busy improving and optimizing the platform to create an even better development and user experience.