As of 2025, AI has reached a remarkable level of maturity. The real-world applications of Artificial Intelligence have become more sophisticated, driving innovation and efficiency across industries. Despite these advancements, AI trends for 2025 reveal that there is plenty of room for further development. The focus is now shifting toward enhancing AI’s adaptability, ethics, security, and real-world impact. In this article, we at DigitalMara will explore these trends in detail, offering a comprehensive look at what’s next.
Agentic AI
After modern chatbots and co-pilots, Agentic AI is the next level. They’re built on the same foundation – Large Language Models (LLM) for intuitive interactions, synthesis of complex information, and content generation. The difference lies in the agent’s ability to act independently, break a job down into discrete steps, and complete the work with minimal human supervision or intervention. Deloitte predicts that in 2025, 25% of companies that currently use GenAI will launch Agentic AI pilots or at least a proof of concept, a percentage that will grow to 50% by 2027. Gartner suggests that 33% of enterprise software applications will include Agentic AI by 2028, enabling 15% of day-to-day work decisions to be made autonomously.
This technology is still under development and continues to evolve. Nevertheless, experts identify these basic characteristics:
- Built on the foundation of LLMs, augmented by other technologies.
- Use chain-of-thought function based on reasoning tokens. This makes the models slower, but they can be trained to plan and execute complex tasks on their own and, furthermore, correct their own errors.
- Can understand the context of the given tasks and process multimodal data, including videos, images, audio, text, and figures.
- Interact with other software and applications to perform tasks.
- Сan control the participation of other systems and bots in the task.
- Use retrieval mechanisms and databases to access short-term memory for maintaining context while performing a specific task, and long-term memory to learn and improve from experience.
Experts, drawing on market research, identify key areas for Generative AI agents, including software development, sales, marketing, and regulatory compliance. These are cross-industry cases; however, some are domain-specific. Agents have the potential to change decision-making and improve situational awareness through quicker data analysis and prediction intelligence. AI can analyze several systems simultaneously and suggest the necessary actions.
Implementing Agentic AI does come with significant challenges on numerous levels: technical, ethical, operational, and security. Technically, it can be complex to ensure the accuracy, reliability, and scalability of these systems, especially when you’re dealing with incomplete or biased data, which may lead to flawed decision-making. Ethical concerns arise around accountability, transparency, and the potential for unintended consequences, as these systems operate autonomously and may make decisions that are difficult to audit or explain.
Security poses a critical challenge, AI systems can become targets for cyberattacks, data breaches, or adversarial manipulation. That leads to negative consequences, especially for sensitive applications. Also, there may be operational hurdles, which include integrating AI into existing workflows, managing workforce transitions, and ensuring compliance with evolving regulations. Addressing these challenges requires a multidisciplinary approach that combines robust AI development, stakeholder collaboration, and a strong focus on responsible and secure AI practices.
AI governance platforms
The field of AI ethics is based on 5 main principles. That is, use of AI should be fair, explainable and transparent; secure and safe; accountable; human-centric; and socially beneficial. While these principles guide the adoption of AI, governance platforms serve as tools for management and control. An AI governance platform brings together a set of practices and technologies that perform the following functions:
- Ensuring transparency, which promotes trust, accountability, and informed decision-making.
- Assessment of potential risks, such as bias, privacy violations, and negative impact on society.
- Control over the development of ethical AI models.
- Auditing and monitoring of existing AI systems.
- Compliance management and monitoring of changes in data protection laws, GDPR, CCPA, etc.
- Identification of functions that oversee responsible AI while enabling diverse stakeholders to design, test, and develop AI systems.
Implementing an AI governance platform requires a structured approach that integrates technical and organizational measures. Central to this approach is robust data management, including ensuring data integrity, privacy, and security throughout the AI lifecycle. This involves deploying tools to monitor data usage, prevent unauthorized access, and detect anomalies. It’s equally important to establish clear internal policies that define ethical standards, data handling protocols, and accountability frameworks. Companies should also invest in comprehensive training programs to educate employees on responsible AI practices.
Building cross-functional teams that include legal, technical, and business experts can enhance governance by ensuring that diverse perspectives on AI risks and opportunities are taken into account. Legal experts ensure compliance with data protection laws, intellectual property regulations, and ethical guidelines. Technical specialists focus on the practical aspects of system design, risk assessment, and implementation of AI models. Business experts ensure that AI solutions align with the company’s strategic objectives and business needs. Together, they can consider all aspects and strike a balance.
Cybersecurity
In matters of security, AI plays on both sides. In the 2024 Deloitte-NASCIO Cybersecurity Study, experts said the cyber threat from AI was high. Gen AI-based cyberattacks have rapidly increased and are projected to grow further. These include malicious phishing emails, deep fakes, software code for malware attacks, account takeovers, and more. Attackers train their AI systems to outsmart defensive algorithms. In response, tech companies that make Generative AI tools in 2025 will be developing guardrails to prevent their malicious use.
However, AI also demonstrates its efficiency on the defense side. Special solutions can help to identify and neutralize threats in real-time. Smart algorithms analyze vast amounts of data to identify anomalies and patterns indicative of cyberattacks. Plus, predicting potential vulnerabilities enables companies to mitigate them proactively.
According to Gartner, by 2028, 50% of enterprises will have adopted products, services, or features specifically to address disinformation security. That’s 10 times more than the number who had done so in 2024. This rapid adoption is being driven by the severe consequences associated with disinformation, including reputational damage, financial loss, operational disruption, strained partnerships, and customer attrition.
Hybrid AI infrastructure
Deloitte research shows that GenAI via cloud services is the dominant option for most companies. However, a high percentage of enterprises worldwide will implement on-premises AI data center infrastructure in 2025. This strategic shift is primarily driven by the need to protect IPs (internet protocols) and sensitive data from potential breaches. This is also a means to comply with stringent data sovereignty and localization regulations, and to reduce long-term operational costs associated with cloud dependency. On-premises infrastructure offers greater control, security, and adherence to legal requirements.
The State of Generative AI in Enterprise survey found that 80% of companies with “very high” AI expertise are increasing their investments in cloud-based AI solutions, leveraging the scalability and accessibility of cloud platforms. At the same time, 61% of these companies are simultaneously investing in their own hardware and on-premises systems to support hybrid AI strategies. Such an approach reflects a growing recognition of the need to balance flexibility with control, ensuring security, compliance, and cost-efficiency in AI deployment.
Hybrid AI deployment is also being influenced by advancements in AI technologies that make on-premises solutions more feasible and cost-effective. Modern hardware and software are increasingly optimized for local deployment, enabling enterprises to achieve high performance and scalability without relying exclusively on cloud infrastructure. In addition, hybrid strategies allow companies to tailor their AI setups to specific use cases, such as processing highly confidential data on-premises while leveraging the cloud for less sensitive, computation-intensive tasks.
Generative AI limitations
Deloitte defines 2025 as a year of “closing gaps” for GenAI. Their report reveals 8 crucial points that industry and society need to overcome. We selected 5 of the most significant:
- GenAI infrastructure and monetization. Companies heavily invest in chips, data centers, creating and training models. While they see certain revenues, the investment is still higher than the return. However, experts say that the risk of underinvesting in general is higher than the risk of overinvesting.
- Generative AI environmental impact. Data centers demand enormous amounts of power, posing significant challenges to companies’ sustainability targets. With power consumption projected to surge, Deloitte estimates these facilities will account for approximately 2% of global electricity consumption by 2025. This growth stems from the need for high-density infrastructure to support massive computing and cooling requirements. The technology and energy sectors must collaborate to increase the use of carbon-free energy sources and optimize computer-intensive AI workloads to reduce their environmental impact.
- Gender gap. Women are less likely than men to use GenAI tools for both work and play. Some of this is due to a lack of trust. Experts say this can be influenced through enhancements in data security and data management practices.
- Trust. The proliferation of generated deep fakes, including images, videos, and audio, makes it harder for consumers to trust GenAI content. Concerns about content authenticity and the potential harms of fake content are growing more urgent. To address this, the ecosystem must adopt consistent and permanent labeling of generated content, while also ensuring reliable, real-time detection of fakes. Here where cybersecurity measures come in, such as using cryptographic metadata to verify the provenance of authentic media.
- GenAI usage. Companies are using technology for content production. They are cautious about the challenges with regard to intellectual property inherent to generative content. At the same time, they are keen to gain enterprise capabilities that can reduce time, lower costs, and expand their reach.
Final words
Experts agree that aligning Artificial Intelligence business strategies and applications is crucial for driving growth and enhancing resilience in today’s competitive market. Those who adapt to the latest AI advancements will be better equipped to enhance operational efficiency and unlock new opportunities. However, businesses need to address various challenges, including ethical considerations, data security, and the environmental impact of AI. There needs to be a strategic balance between innovation and governance.
As a leading IT company offering full-stack development services, DigitalMara constantly monitors emerging tech trends to ensure that our clients adopt the most effective and innovative solutions. Through custom software development, we create effective and forward-thinking solutions for our clients’ success.
