Without a doubt, 2024 became the year of AI, particularly Generative AI (GenAI). However, it was also a year of AI discoveries and revelations for investors and developers alike. It was marked by both breakthroughs and disillusionments, as well as new opportunities and challenges. This intensity affected enterprise software trends, prompting decision-makers and adopters to adjust their course and consider not just the advantages, but also the limitations of artificial intelligence and its impact on other technologies.
To keep tech executives aware of these changes and help them build the best transformation strategy for their enterprises, this article offers an in-depth exploration of top enterprise technology trends and how they will impact business environments in 2025.
- Future of enterprise: how do today's changes impact the trends of tomorrow?
- 1) Tipping the enterprise process efficiency scales with generative AI
- 2) Rise of AI governance platforms
- 3) Leveraging intelligent AI assistants
- 4) MLOps gaining scalability and impacting AI governance
- 5) Advancing of sustainable computing with cloud technology
- Top enterprise technology trends: how to keep up with the change?
- FAQ
Future of enterprise: how do today's changes impact the trends of tomorrow?
According to Gartner, the strategic technology trends for enterprises will shift from innovative hype to more practical concerns, such as exploring risks and opportunities for synergy between machine capabilities and human talent.
This transition was predictable, given the developments throughout 2024:
- Investors’ dwindling faith in AI
Investors began their AI journey with enthusiasm, optimism, and high expectations. However, while Apple, Google, Meta, and other tech giants continued to increase their AI spending, setting a course to invest $1 trillion in the technology over the coming years, the results were lackluster. Consequently, more investors started doubting AI’s ability to deliver profit and justify the cost. This change in perspective is likely to affect the overall pace and priorities of AI adoption. - Increased AI security concerns
While the bar for AI expectations was raised too high, the same cannot be said for risks and concerns regarding AI security. According to the NIST report, there are numerous ways in which cybercriminals can poison AI systems and exploit them to steal data or sabotage work across the entire enterprise. Additionally, an ethical hacker's simple experiment revealed that models such as Grok, GPT-4.0, and Llama 3 could be hacked in 30 minutes. These findings show how easily an AI system can transition from a useful enterprise helper to an unsuspecting accomplice—without enterprise teams even knowing. Therefore, enterprises need to pay as much attention to improving their security framework as they do to artificial intelligence adoption. - AGI put on ice
While OpenAI CEO Sam Altman is confident about AGI emerging as early as 2025, the recent disbandment of the OpenAI AGI Readiness team and the resignation of the senior advisor for AGI Readiness, Miles Brundage, suggest otherwise. In his farewell letter, Miles confirmed that the world is not yet ready for AGI—not when enterprises, developers, and collectives are still figuring out AI and gleaning all its benefits. This wave of departures sends a message to let go of the fantasies about achieving AI singularity and concentrate on the current reality, which involves leveraging artificial intelligence, polishing out the flaws, and unlocking its full range of applications.
Another emerging trend noticed by Gartner is the exploration of new computing frontiers, such as quantum computing. However, as of now, quantum computing is not yet real or ready for work, so we’re going to focus on the trends that can make a difference for enterprises already.
1) Tipping the enterprise process efficiency scales with generative AI
Despite not meeting high expectations, GenAI still dominates enterprise software trends. Projected to contribute up to $4.4 trillion to the global economy, the technology saw 2024 as its definitive year, where businesses aimed to separate its true value from the marketing hype. This process was accompanied by disillusionment as organizations started realizing not just the limitations of the technology but also their lack of understanding.
For instance, the abundance of possibilities made it hard for adopters to identify where to start. Additionally, they didn’t take into account that implementing GenAI doesn’t just update existing workflows but creates new ones, which involves identifying new KPIs and ways to calculate ROI.
Furthermore, C-level executives were the ones deciding to adopt AI and charged their IT departments with the task, which was the opposite of the traditional innovation adoption process where IT teams usually identify a technology for implementation and convince the C-suite to invest in it.
Many C-level executives became very excited about adopting GenAI. However, they didn’t have a clear idea of how they would be using it, which led to more complications: vague ROI, unrealistic expectations, and other issues. GenAI is an efficient tool – when you know how you’re going to apply it
Although these challenges were unexpected, GenAI managed to prove itself in several successful use cases that defined the most productive ways of using the technology:
- Business process augmentation
Key business operations are always unique, depending on the enterprise’s vertical. Therefore, when executives need to identify areas for improvement, it takes a lot of time and research to find the sweet spot. Leveraging GenAI platforms accelerates this process by gleaning important insights from the sea of data and presenting them to decision-makers. This allows them to remove productivity setbacks and identify new revenue streams. For example, 82% of insurance firms that implemented GenAI for their core workflows expect at least a 10% revenue boost. - Marketing support
GenAI delivered up to 40% savings on non-working spending for marketing organizations and departments, marking remarkable progress. Contrary to popular belief, content generation was just a small fraction of the advantages unlocked. One of the most important benefits of GenAI was personalization. By pulling up large amounts of contextual information on specific client types, GenAI platforms allowed for more adaptable marketing strategies and instant adjustments based on customer feedback. Additionally, GenAI enabled marketing experts to conduct fast market analysis, significantly impacting their decision-making. - Drug discovery
Drug discovery, a costly, time-consuming, and complicated process, has a high probability of being revolutionized by GenAI platforms. Due to its ability to pull unstructured data, GenAI allows scientists to access and navigate biomedical information at a much faster rate, transforming pipelines and accelerating drug candidate verification. Although the first drug discovered with the help of GenAI tools is yet to be announced, the number of drugs reaching phases II and III of clinical trials has increased exponentially in the past decade. GenAI has played a major role in this, prompting more pharmaceutical organizations to adopt and leverage the technology. - Cybersecurity enhancement
GenAI can be both an exploit for cybercriminals and a weapon against them. According to research by the Alan Turing Institute, large language models (LLMs) can be used by phishers to make their phishing emails more subtle and deceiving. However, LLMs can also be used to detect suspicious patterns and manipulative intent. As a result, mastering GenAI for cyberdefense becomes an "arms race" for organizations, as they must harness GenAI to improve their cybersecurity before criminals leverage the technology for malicious activities.
Considering these use cases, enterprise technology trends related to GenAI emphasize finding and onboarding teams of AI strategists and data scientists capable of developing solutions tailored to an individual organization's tech and security requirements, as well as its in-house development teams.
2) Rise of AI governance platforms
As the pace of AI adoption increases, concerns about the responsible and ethical use of the technology follow. From copyright issues to employees pushing back against AI to protect their right to privacy, there are many areas for enterprises to address if they want to enjoy all the benefits of the technology. Additionally, customers have consistently expressed their distrust of AI, with 3 in 5 customers surveyed by KPMG considering the technology untrustworthy.
It’s not up to the customers to change their mind. Brands and enterprises should work on their AI policies, developing a clear and transparent structure for when and how they use AI, what kind of data they work with, and how they address the improper use of data. Enterprises need to show full compliance with government regulations regarding AI and be part of the dialogue dedicated to ethical AI use.
To assist enterprises in building a responsible approach to AI and ensuring transparency, AI governance platforms will join future technology trends. In 2023, numerous U.S. philanthropic organizations invested $200 million in developing responsible and ethical AI initiatives.
By doing so, they pursued the following objectives:
Introducing AI global use standards
Accelerating the development of international AI norms and rules.
Keeping AI human-centric
Using AI to deliver breakthroughs relevant to the public interest and capable of improving humanity’s quality of life.
Protecting democracy and human rights
Preventing AI from infringing the rights and freedoms of people.
Empowering human talent
Focusing AI on creating better work conditions for employees and minimizing stress.
Building security and transparency
Creating interpretable and transparent AI models with comprehensive accountability.
AI governance platforms will be among the new technologies in software designed to support and execute these initiatives, meeting compliance standards and ensuring the reliability and fairness of AI models. While such platforms will be tailored to the specific models used by enterprises, it’s possible to outline a list of their features and capabilities:
- Risk assessment
AI governance platforms will evaluate all the risks connected to AI, including the probability of AI delivering biased outcomes and negative societal responses. They will also prevent cases of privacy violation by monitoring data. - Compliance management
AI governance tools will ensure that enterprise AI models comply with relevant AI regulations and current data protection laws. - AI system auditing
AI governance platforms will have tools for consistently checking AI systems and keeping them aligned with proper governance standards. - Life cycle management
Since AI models are vulnerable to hacker or third-party intervention at all stages of development, AI governance platforms will provide necessary monitoring and control, identify suspicious activity, and notify stakeholders. They will also enable developers to adhere to ethical AI engineering practices throughout the development process. - Transparency and accountability
AI governance platforms will assist stakeholders in designing, developing, and testing AI systems that are explainable, transparent, and capable of fostering trust across the organization.
3) Leveraging intelligent AI assistants
Intelligent AI assistants remain among the top enterprise software trends for 2025, as Gartner predicts the expansion of new AI-powered tools that assist various teams across organizations with their specific tasks and duties.
The intense demand for virtual AI assistants is driven by the growing user need for instant information, fast response times, and high-quality service. Whether addressing customer queries or assisting software engineers at every stage of product development, there is an innate human need for comfort that intelligent AI assistants seem capable of meeting. For example, after Bank of America launched its virtual assistant Erica, client engagement reached 2 billion interactions per day, with customers receiving their answers in an average of 44 seconds.
It’s worth noting that digital assistants have also made considerable differences for employees and organizations by streamlining their work, providing necessary insights, and optimizing their routines.
Augmented analytics
AI-powered apps can process large volumes of data from various data sources (user interactions, files, transactions) and deliver them straight to users as visualized real-time insights.
Efficient decision-making
With intelligent applications taking care of omitting irrelevant and obsolete data, leaders get more informative and comprehensive reports on enterprise processes, which allows them to make more impactful decisions.
Streamlined sales operations
Intelligent apps assist sales reps with successful lead generation and nurturing by scoring leads and identifying ones with the highest probability of conversion based on behavior data analysis.
Management shortcut discovery
When used as a part of enterprise IT technology and tapping into their advanced analytics, intelligent apps gather data on business processes and employee performance, revealing options to cut repetitiveness and improve employee engagement.
As enterprises reimagine the synergy between human talent and AI capabilities, AI digital assistants will be among the most impactful enterprise software trends. With their ability to adapt to new requirements and prompts, they are guaranteed to enhance enterprise processes and facilitate tasks for multiple teams, amplifying results and freeing up work hours.
4) MLOps gaining scalability and impacting AI governance
After making its entrance in 2024, machine learning operations (MLOps) transitioned from a new trend in IT to an enterprise fundamental. As the global MLOps market is projected to reach a $39 billion market value by 2034, it currently has its own trends that impact the future of ML development and deployment.
- Cloud-based model management
The shift towards cloud computing has also impacted MLOps. As a result, service providers are constantly pushed to expand their MLOps capabilities, enabling enterprises to deploy their machine learning models across different environments. This avoids fragmentation, secures collaboration, and ensures scalability.
- ML pipeline automation
The growing complexity of ML models leads to a longer ML lifecycle, which is far from the desired outcome. To tackle this issue, MLOps has started leveraging automated tools to accelerate data ingestion, feature engineering, model selection, and other repetitive tasks. This approach allows data scientists to operate more strategically, discover new business cases, and minimize the risk of human error.
- Leveraging MLOps for ethical AI
MLOps is directly connected to fair and responsible AI use. Since MLOps is dedicated to end-to-end model management, it provides technical stakeholders with a set of processes and practices that help them track model explainability, performance, and bias throughout the entire lifecycle. This visibility is a necessary part of AI governance, making MLOps infrastructure crucial for successfully deploying regulated, explainable, and transparent AI models.
Given that more and more large enterprises, such as PayPal, are extending their MLOps platforms to support their AI initiatives, industrialized machine learning has confidently evolved from a trend to a supporting pillar for exploring innovation. Therefore, building a successful MLOps strategy will be key to smooth solution deployment.
5) Advancing of sustainable computing with cloud technology
The rapid evolution of AI has led to increased environmental concerns, as it’s no secret that AI requires significant amounts of energy to compute. AI queries have been found to consume 10 times more electricity than Google search queries, further straining electrical grids. Considering that tech industry giants plan to continue their AI initiatives, energy consumption is expected to grow considerably, consequently increasing AI’s share in global emissions.
As a result, sustainable computing solutions are becoming trending technologies, with giants like NVIDIA promoting their energy-saving options and the U.S. National Science Foundation awarding $12 million to a multi-research initiative aimed at reimagining computer structure and introducing new standards for carbon accounting.
Enterprises also have a new responsibility: to reduce energy consumption and incorporate sustainable practices into their strategy. Due to its virtualized nature, cloud computing becomes essential for executing such a strategy. Enterprises are further assisted by providers’ commitment to sustainability, with Google, Microsoft, and AWS aiming to achieve carbon neutrality, either by switching to renewable energy or going carbon-negative by 2030
To support this initiative, data centers are also transforming by reducing their energy consumption through a number of techniques:
Virtual Machine (VM)
Creating a virtual clone of a computer system for more efficient use of IT resources.
Dynamic Voltage and Frequency Scaling (DVFS)
Scaling down the frequency and voltage to minimize the energy needed for performing necessary tasks.
Advanced algorithms
Enabling real-time energy optimization and resource allocation with the help of VM placement algorithms and policies.
Migration and VM consolidation
Performing the transfer of a virtual machine to another physical machine.
Top enterprise technology trends: how to keep up with the change?
The riveting journey of AI makes it clear that there are no straight roads to successfully mastering and adopting innovation. No matter how great the high is, it’s important to stay connected to reality, which consists of specific business needs, relevant industry pain points, and particular employee concerns.
Therefore, a major part of successfully following technology trends 2025 depends on executives’ ability to avoid the "adopt or regress" mentality. Instead, they need to focus on opportunities that matter.
- Doing researchAn overview of top enterprise technology trends can paint a rather promising picture of flawless enterprise processes, seamless adoption, and stellar ROI. However, the true adoption journey is going to be complex. New workflows will be created, and executives will need to identify new KPIs. To prepare for the road ahead, executives need to gather all necessary information in advance, communicate with stakeholders, and check in with teams.
- Making room for experiments
It’s not the most popular tech trend that defines the future of an enterprise, but the one that can help the enterprise overcome its growing pains and make progress. Investigating enterprise software trends should be deliberate and meticulous, with equal attention to all options. Doing so can help executives discover a new competitive edge and gain an upper hand in the intense business landscape. - Prioritizing long-term valueNot all enterprise trends remain relevant. Only innovations capable of maintaining strong positions over the years and offering continuous improvement options can consistently deliver the most rewarding return on investment. Therefore, when exploring trending technologies, every tech leader across the enterprise (from VP of Technology to CTO) needs to consider the kind of results they expect to gain at least five years after implementing enterprise technology.
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