Each year, the Gartner Research and Consultancy company reveals ten key technology trends. These predictions are not only a fascinating vision of the future, but also a powerful tool that can accelerate business goals. At a time when artificial intelligence is gaining momentum, Gartner’s recommendations are becoming an invaluable guide for companies that want to be on the cutting edge.
We are here to provide a comprehensive look at these trends for the fourth year in a row.
Here are Gartner’s 10 trends for 2024:
AI Trust, Risk and Security Management (AI TRiSM)
AI TRiSM is not just an acronym, it’s an artificial intelligence management philosophy developed by the Gartner Research Institute. It focuses on three key aspects: trust, risk and AI security. Its goal is to ensure reliability, fairness, efficiency and privacy in AI.
At the heart of AI TRiSM is the interpretability and explainability of models, AI data protection, model operations and resilience to attacks. Gartner predicts that by 2026, companies that use TRiSM control will increase the accuracy of their decision-making, eliminating as much as 80% of erroneous and illegal information.
This trend points to a new role for artificial intelligence as a business partner. AI is being introduced into companies’ processes while taking into account the fact that it involves certain risks that must be effectively eliminated. An example is Fidelity Investments, which has successfully implemented hundreds of artificial intelligence models through a model operations structure. These operations have made it possible to monitor deployments to detect potential problems such as drift, which in turn has reduced the time to find and solve problems by 80%, from weeks to hours.
The four pillars of AI TRiSM:
- Model Explainability/Monitoring (explained below)
- ModelOps = model operations = is a set of functionalities that focus primarily on the management and full lifecycle of all AI models and decision models.
- AI application security
- Privacy
It is worth remembering that we are only at the beginning of AI development. Below are the key issues related to the risks of implementing artificial intelligence, which need to be taken into account and eliminated if possible:
- Most people face difficulties in communicating to managers, users and consumers a clear understanding of what artificial intelligence models are and what specific tasks they are capable of performing. This phenomenon, known as “explainability,” highlights the need to be aware of the functions and mechanisms of AI models, especially those used in a corporate context.
- Access to generative artificial intelligence tools, such as ChatGPT, has become easy for everyone.The development of these technologies, called GenAI, has the potential to significantly transform the way companies compete and perform. However, along with new capabilities also come new risks that cannot be effectively addressed by traditional controls. Cloud-based generative artificial intelligence applications are a particularly significant area of risk.
- This brings up another point – external artificial intelligence tools pose a threat to data confidentiality.
- AI models and applications require constant monitoring.Monitoring AI models and applications is essential to ensure their reliability and effectiveness. This requires the integration of specialized risk management processes, known as ModelOps, into AI model operations. This process ensures regulatory compliance, promotes integrity and nurtures ethics in the use of artificial intelligence.In the context of limited availability of off-the-shelf tools, the need to develop custom solutions for AI is becoming a reality. This challenge requires creativity and customizing controls on a continuous basis, covering every stage from model development to testing, deployment and daily operations.
- Detecting and stopping AI attacks requires new methods.With the growing threat of AI attacks, it is becoming necessary to develop new, effective methods for detecting and stopping such incidents. These attacks, whether in-house or embedded in third-party models, can bring severe damage to organizations, involving financial, image and intellectual property, personal data or proprietary data aspects.To effectively defend against these threats, it is necessary to implement specialized controls. In addition, it is important to implement practices that include testing, validation and continuous improvement of the reliability of artificial intelligence workflows. These measures must go beyond standard practices for other types of applications to effectively protect AI-based systems from potential threats.
- Regulations will soon define what a compliance check should look like.Current regulations, such as the EU’s Artificial Intelligence Act and regulatory frameworks in North America, China and India, impose rules for managing the risks associated with artificial intelligence applications. As a result, organizations using AI-based technology must be prepared to meet requirements that go beyond standard privacy regulations.
Implementing effective risk management procedures is becoming crucial to comply with new regulatory requirements and minimize potential consequences associated with violations. Companies should focus on strict compliance with current regulations while preparing for future changes in artificial intelligence regulations.
Continuous Threat Exposure Management (CTEM)
Continuous Threat Exposure Management (CTEM) is a key tool for detecting and proactively prioritizing the factors that can most threaten your business. Creating an effective CTEM program involves five key steps that constitute a comprehensive process for managing an organization’s risk and security:
- Defining the scope of CTEM activities is a step that should be taken first when countering cyber threats – especially those from external sources and those resulting from the use of SaaS (Software as a Service) services. At this stage, you need to define so-called vulnerable entry points, which play an important role in identifying potential threats.
Gartner recommends a special focus on assessing the security status of SaaS systems, which are becoming increasingly popular in the face of increased use of remote work. At the same time, they store a lot of critical business data. - Discovery.
Discovery processes, while originally focused on the areas specified during scoping (see Section 1.), should also identify visible and hidden assets, vulnerabilities, misconfigurations and other potential risks.
It is worth noting that confusing scoping with the discovery process is a common mistake when building a CTEM program. The goal is supposed to be not the number of assets and vulnerabilities identified, but precise scoping based on business risk and potential impact on the organization. - Prioritization: at this stage you prioritize the risks to which the company is exposed.
- Validation: checking how attacks can work and what response options systems have.
The first step is to confirm that attackers can actually exploit the vulnerability, analyze all potential attack paths against the asset, and determine whether the current response plan is fast and effective enough to protect the company. - A key step in a continuous response program to potential threats is mobilizing people and processes. This includes not only automated remediation that can be applied to obvious problems, but more importantly communicating the CTEM plan to the security team and business stakeholders, ensuring they understand the actions.
Today’s enterprise cyber-security management mainly focuses on responding to specific incidents. In the long run, however, this is not an optimal solution. A tactical approach, based on implementing a continuous response program to potential threats, is therefore becoming essential to prioritize the identification and minimization of the threats most relevant to the company. Moving from a reactive to a proactive model, based on continuous monitoring and adapting to the dynamic cybersecurity landscape, is a fundamental step toward more effective threat protection.
Sustainable Technology
Sustainable technology is again in the spotlight, especially IT services. The concept of sustainable IT involves the selective choice of tools, equipment and vendors to achieve maximum efficiency with minimum resource consumption. Key goals for sustainable IT include the reduction of Scope 2 and 3 greenhouse gas (GHG) emissions. This issue refers to both indirect emissions related to electricity used by IT, as well as emissions beyond the direct control of the company, such as those related to carbon contained in retired IT equipment. In addition, more attention should be paid to issues related to human rights, ethical sourcing and transparency in the supply chain.
Customers are increasingly pursuing their own sustainability goals, which requires providing products that are sustainable without sacrificing quality. The ultimate goal is to harmoniously combine technological progress with a responsible approach to the environment, which is slowly becoming a priority in today’s dynamic technological world.
Platform Engineering
Platform engineering is a field that includes the design and construction of complex tool chains and workflows. Its goal is to create an infrastructure that enables software engineering organizations to use self-paying services in the era of native clouds. Platform engineers provide integrated products that are most often used under the name “in-house development platform.” Such a platform covers the full range of operational needs throughout the application lifecycle.
An example of such an advanced platform is NAVIGATOR, which allows users to build their own applications.
It is forecast that by 2026, as many as 80% of large software engineering organizations will establish dedicated platform engineering teams to act as internal providers of reusable services, components and tools to support the application delivery process. Platform engineering aims to address significant collaboration issues between software developers and operators, resulting in a more efficient application development and deployment process.
AI-Augmented Development
This new trend focuses on artificial intelligence, offering software engineers numerous opportunities to immediately leverage this technology in key areas of the software development lifecycle. Here are five ways forward-thinking developers and programmers can quickly integrate artificial intelligence into their work at various stages of the software development process.
- Using generative artificial intelligence to write and understand software code.
- Implementing generative artificial intelligence as a tool for application modernization.
- Using generative artificial intelligence to explain, detect and measure technical debt and its impact.
- Meeting user expectations of artificial intelligence-based products and services.
- Using artificial intelligence throughout the software testing lifecycle.
In addition, software engineering leaders can prepare their teams for sustained integration of artificial intelligence. These activities allow for more efficient and advanced software development processes, while adding value to the enterprise.
In summary, generative artificial intelligence and coding assistants provide support for processes spanning the entire software life cycle (from design to testing). AI improves testing efficiency and shortens the product delivery cycle. To fully realize the potential of AI as a partner in the software development process, changes in the operating model, culture and skills of the team are required. Implementing these innovations can increase the efficiency of software development activities.
Industry Cloud Platforms
Industry Cloud Platforms (ICPs), tailored to specific sectors, offer comprehensive solutions including data management, analytics and software tools. Their popularity is growing as they outperform general platforms in terms of personalization, industry-specific customization and accelerating cloud adoption.
According to a Gartner survey, nearly 39% of companies in North America and Europe have already deployed ICP platforms, and an additional 14% are in the pilot phase. Growth momentum is strong, with 17% of enterprises projected to deploy these platforms by 2026. Gartner forecasts a huge shift, predicting that more than 70% of companies will use ICPs to accelerate business initiatives by 2027, a significant increase from just 15% reported in 2023.
Intelligent Applications
Intelligent applications are not only those that appear intelligent, but actually possess this characteristic. They are capable of learning, adapting, generating new ideas and results, and support automated and dynamic decision-making through the use of artificial intelligence.
It is forecast that by 2026, as many as 30% of new applications will use artificial intelligence to support personalized, adaptive user interfaces, a significant increase from the current level of less than 5%.
Democratized Generative AI
Democratized generative artificial intelligence (GenAI) is a powerful tool that has the potential to revolutionarily change the nature of work in companies, enabling them to grow faster and achieve their goals.
IT leaders need to realize GenAI’s transformative capabilities while developing policies that control its risks. This opens the door to a variety of applications, from automating routine tasks to generating creative solutions to complex problems. As a result, the company can more effectively leverage the potential of artificial intelligence.
Augmented Connected Workforce
The augmented and connected workforce concept is an integrated approach that combines augmentation technologies (i.e., technologies that integrate reality with the digital environment) with the work environment to enable employees to expand their capabilities, increase productivity and improve overall business processes and operations. The concept uses a variety of technologies, including:
- Augmented Reality (AR)
- Mixed reality (MR)
- Computer vision
- Internet of things (IoT)
- Artificial intelligence (AI)
- Exoskeletons
- Connected worker platforms
- Collaborative robots
This trend envisions synergistic collaboration between humans and modern technology solutions to achieve optimal efficiency and innovation in the workplace.
Machine Customers
Machine Customers is a growing trend in which artificial intelligence interacts with contractors and negotiates terms of business with them. AIs in this context are characterized as more rational and logical than humans. Companies will therefore need to adapt business, customer service and marketing strategies to meet the unique needs of AI customers. Automation, personalization of offers and value creation are key to staying competitive. Failure to adapt to this trend carries the risk of losing revenue and market position. Serving customer-machines is becoming a key challenge, requiring innovative approaches to business relationships.
Get to know more about Electronic Workflow, AI, Business Intelligence and No-code applications in NAVIGATOR system
Summary
With this overview of the latest technology trends according to Gartner, we note that sustainable technology, smart applications with AI, platform engineering, democratization of GenAI and integrated augmentation technology work are changing the business landscape.
Sustainable technology and smart applications are becoming the norm, and evolving engineering platforms and GenAI are opening up new opportunities. However, the concepts of augmented and connected workforce and customer-machines are also drawing attention to the changing dynamics of work and customer relationships.
Sources:
Tourism and Leisure Management graduate at IMC Fachhochschule Krems. After finishing university, she decided that digital marketing was her calling. At Archman she acts as a marketing specialist, where every day she broadens her knowledge about SEO and SEM. Mariia is keen on storytelling, and in her free time most frequently she reaches for Stephen’s King novels.
