Artificial intelligence (AI) is undergoing rapid transformation, redefining how organizations operate and make decisions. The adoption of Generative AI (GenAI) has skyrocketed, leading to increased cloud consumption, automation and data-driven decision-making. However, as AI becomes more powerful, businesses must balance innovation with cloud costs, ethical concerns and human-AI collaboration.
GenAI: Accelerating AI adoption and cloud costs
Generative AI has been a major driver of AI adoption across industries, particularly among SaaS consumers. AI-powered SaaS applications are now automating customer interactions, streamlining workflows and enhancing decision-making.
This is the first year we’ve included GenAI as an option for public cloud services used by all organizations in Flexera’s 2025 State of the Cloud Report—and we found that a whopping 83% of all organizations surveyed are either currently using or experimenting with GenAI. That’s the most out of any new PaaS offering listed in the history of the report. Additionally, the report shows that 49% of enterprises currently use AI or machine learning (ML). All of these options lead the pack for public cloud services that organizations are experimenting with (33% for GenAI and 30% for AI/ML).
An LLM is a type of AI trained on massive amounts of text data, allowing it to understand and generate human-like text, translate languages, answer questions and perform other natural language tasks effectively.
At Flexera, we’re using large language models (LLMs) to create tools that enable easy access to information for our internal users as well as our customers (e.g., Chatbot for Flexera documentation). We’ve also used LLMs to build solutions that require text data processing (e.g., Software Title Normalizer). Integrating this technology into our solutions not only helps boost customer experience, but also drive efficiency and make data-driven decisions to achieve strategic outcomes quicker.
Agentic AI: A New era of intelligent autonomy
One of the most transformative AI trends is agentic AI, which refers to AI systems capable of autonomous decision-making and long-term reasoning. Unlike traditional AI models that rely on human prompts, agentic AI can self-direct tasks, learn from interactions and refine its responses over time.
For organizations, this means AI can take on more complex roles, such as customer service automation or business strategy optimization. In cloud environments, agentic AI could optimize cloud resource allocation dynamically, potentially reducing costs while enhancing system efficiency. At Flexera, we’re using agentic AI to automate end of end ingest and curation workflows for our world-class catalog, Technopedia, to provide the most updated information to our customers faster.
However, the rise of agentic AI also brings challenges, particularly in governance, transparency and accountability. As AI makes more independent decisions, organizations must establish clear oversight mechanisms to prevent unintended biases and ethical pitfalls. That’s why at Flexera, we use a Human-in-the-Loop (HITL) approach to AI to ensure that there’s an oversight and control. This is an approach that integrates human input and expertise into the AI lifecycle, ensuring AI systems are not only accurate, but also aligned with human values and needs, using a continuous feedback loop to improve performance.
The soaring cloud costs of AI
AI-driven enterprises rely heavily on cloud computing for model training, inference and deployment, but this comes at a high price. GenAI and AI have driven cloud expenses 30% higher. Cloud costs for AI workloads are expected to rise significantly, driven by compute-intensive models, increased data processing and real-time AI applications. According to projections from The Business Research Company, the cloud AI market size will grow nearly 40% in the next few years.
Source: The Business Research Company’s Cloud AI Global Market Report
Key cost challenges related to AI on the cloud include:
- Compute demand spikes: AI workloads can be unpredictable across training and inferencing phases, leading to unexpected cloud expenses
- Data transfer costs: Moving large datasets between cloud regions or hybrid environments incurs significant costs
- Storage requirements: AI models require massive datasets, increasing storage costs
Organizations struggle to forecast AI cloud expenses accurately, with many relying on reserved instances to control spending. However, flexible, on-demand AI capabilities remain expensive, requiring companies to refine their budgeting strategies. Flexera is vested into defining a blueprint for FinOps for AI, and many of Flexera leaders are active contributors to FinOps for AI work group as a part of the FinOps Foundation and defining KPIs to measure, track and optimize their AI spends.
Balancing AI innovation with ethical considerations
As AI continues its rapid adoption, businesses must address critical ethical and regulatory concerns. This includes:
- Bias and fairness: AI systems can inadvertently reinforce biases, leading to unfair outcomes
- Data privacy: Stricter regulations demand greater transparency and accountability in AI decision-making
- Energy consumption: AI models are energy-intensive, raising concerns about sustainability and carbon footprints
To mitigate these risks, companies are investing in responsible AI frameworks, ensuring AI aligns with ethical standards and corporate social responsibility goals. At Flexera, our key AI strategy guiding principles include building responsible/trustworthy AI and ensuring customer transparency in terms of consent in using their data (opt-in/out) and sharing how we use their data.
The future of human-AI collaboration
Rather than replacing jobs, AI is set to augment human capabilities, freeing up time for strategic, creative and people-centric tasks. Key trends shaping AI-human collaboration include:
- AI-assisted decision-making: AI will support professionals in finance, healthcare and IT, enabling faster and data-driven insights
- Upskilling and reskilling: Organizations will need to reskill employees to work alongside AI effectively
- AI as a copilot: Instead of replacing human roles, AI will act as an advanced digital assistant, helping professionals streamline operations and drive innovation
At Flexera, many teams use AI-based tools in an assist mode to help boost engineering development productivity with code-assist tools as well as other AI tools across product, IT, HR, admin, procurement, operations, sales and marketing functions.
Striking the right balance
The future of AI is promising, but the success depends on how well organizations manage cloud costs using robust FinOps for AI measures, address ethical concerns and integrate AI with human workflows. As agentic AI reshapes industries, businesses must navigate a complex landscape of opportunities and challenges.
By optimizing cloud investments, enforcing AI governance and fostering human-AI collaboration, you can harness the power of AI while ensuring ethical and sustainable growth.
