In our digital trends guide, Looking Forward 2025, Axway’s technology, business, and industry thought leaders shared insights into how the age of artificial intelligence was just beginning. AI and machine learning continue to reshape enterprise API strategies and step in for manual job roles, while driving forecast management and planning.
AI capabilities have exploded into enterprise environments: Bain & Company reports that more than 80% of executives plan to increase AI investments in 2025, tapping into both new and existing budgets—even though many are still in the early stages of defining their broader AI strategy.
Since ChatGPT made AI accessible – and wildly popular – in late 2022, it’s safe to say the discourse on AI has already drastically changed, the most significant being a transition toward practical enterprise implementations.
As we hit the mid-year mark, this shift reveals a fundamental truth: AI is maturing into a business capability. Here’s what’s happening and where enterprise AI implementation may be heading next.
AI’s maturation beyond the hype
As Axway B2B Product Marketing Director Stass Pertsovskiy put it earlier this year: generative AI isn’t just another buzzword; it’s already transforming industries by automating tasks, creating content, and changing how we work.
“This time, AI has a tangible, practical impact. It’s here to stay, and it’s delivering that “wow” factor we’ve been waiting for. The truth is, we still don’t fully grasp how far it will go in reshaping our lives and businesses. Just like we never predicted the internet’s massive influence, AI will continue to revolutionize how we live and work in ways we can’t even imagine yet.”
Chandu Manda, Axway Managed File Transfer Field CTO, recently confirmed the trend is reaching practical applications:
“We have arrived at a tipping point in MFT operations where AI is no longer a futuristic add-on. AI is fast becoming a transformative part of how file transfer systems operate, delivering new capabilities for moving, transforming, and securing data in more efficient and effective ways.”
But as AI transitions from proof-of-concept to production, enterprises encounter complex implementation realities. How do you harness AI’s transformative power – in a responsible way – while also maintaining data security in an increasingly fragmented global landscape?
Many of today’s brightest minds are working hard to answer this very question, and we’re seeing several models emerge for practical applications of AI technologies.
Making AI enterprise-ready: integration and standards
As Emmanuel Methivier, Axway Business Program Director, predicted, a critical piece of unlocking AI’s value involves how we can integrate it into functional processes.
“AI’s incursion into virtually every aspect of our lives and work means that enterprises need to treat Large Language Models as one of their customers or partners. Meaning, much like we focus on developer experience to drive adoption of our products and services, we need to make our data more easily accessible to AI.”
Brian Otten, VP Digital Transformation, also pointed to the emergence of the Arazzo Specification: a new community-driven technical API standard that is complementary to OpenAPI, aimed at defining language-agnostic workflows and dependencies between multiple API calls.
Critically, Arazzo allows AI systems to understand and interact with APIs in a predictable manner to automate tasks like documentation, coding, and regulatory compliance checks – paving a path for agentic API consumption.
RAG: enhancing AI accuracy with real data
While integration standards address one “how” of AI implementation, data accuracy remains paramount for enterprise adoption.
Early this year, Retrieval-Augmented Generation (RAG) emerged as a powerful framework to enhance the generative capabilities of LLMs with the precision of information retrieval systems, making them more accurate, reliable, and business-relevant.
Jeroen Delbarre, Amplify AI Product Line Director, outlines how Amplify Fusion creates a bridge between your organization’s knowledge assets and genAI: Retrieval-Augmented Generation (RAG) Using Amplify Fusion
RAG is powerful when carefully applied, but it hasn’t solved all of AI’s problems yet. If you aren’t yet familiar with the fascinating case of lawyers accidentally inventing legal authorities out of thin air because they used AI to research their cases, dive in here: The AI That Lied to the Court: How Legal Professionals Worldwide Are Being Betrayed by Technology | LinkedIn
A fascinating March 2025 study assessing leading AI legal research tools found that even RAG-enhanced systems hallucinated between 17% and 33% of the time—better than general-purpose chatbots but still concerning for enterprise applications.
From RAG to MCP: the next evolution
These accuracy challenges underscore why, as Axway VP of the Center of Excellence for AI and Cloud Ingo Muschenetz noted in his 2025 predictions, AI integration must be approached thoughtfully:
“AI is in the process of being seamlessly integrated into applications to react to user needs—answering questions like ‘What happened?’ and ‘How can I fix this?’ However, fully autonomous, proactive AI will take longer to scale, as ensuring reliable model behavior remains a complex challenge,” he cautioned.
The path toward more reliable AI lies in better context, certainly, but also improved connectivity and usability.
Emmanuel Methivier stated in March that “agentic AI represents the next evolution in business process automation.” But, he warns,
“These autonomous agents require seamless access to high-quality data across organizational boundaries to function effectively.”
Enter Model Context Protocol (or MCP), which The New Stack describes as “the missing link between AI agents and APIs.”
Dive deeper: How Model Context Protocol (MCP) Enables LLMs to Take Action
MCP is an open protocol that standardizes how applications provide context to large language models. It enables LLMs to interact with your systems and retrieve relevant information or execute actions.
Even though it is still rather new, MCP is gaining a lot of traction, and we’re seeing usage explode amongst both vendors offering the technology to support this protocol and companies offering their services through an MCP server. At the same time, early implementations reveal the complexity involved—some systems require dozens of API calls for a single user query, highlighting a gap between theoretical capability and practical efficiency in agentic AI workflows.
See also: Step-by-Step Guide to Setting up an MCP Server with Amplify Fusion
This is where the groundwork laid by the Arazzo standard in late 2024 begins to show its potential—though significant practical challenges remain:
“Arazzo describes how systems, such as MCP servers and agents, should interact with APIs in a structured, machine-readable way — adding an ‘agent experience’ (AX) dimension to the documentation layer.” – via API Central
While Arazzo can theoretically annotate complex workflows requiring dozens of API calls, Ingo Muschenetz cautions that it remains to be seen whether this approach proves efficient in real-world agentic scenarios—or whether companies will need to redesign their APIs to be less granular.
The convergence of these technologies — integration standards, RAG frameworks, and MCP protocols — creates new possibilities, but also new complexities. As organizations worldwide are learning, operationalizing AI as a transparent, governable component of enterprise architecture is no small feat.
This operational challenge is precisely what drove the development of Amplify AI Gateway, which addresses the growing demand for secure, compliant, and flexible AI integration at the enterprise level.
Operationalizing AI: Axway’s AI strategy and roadmap
Given AI’s impact on virtually every solution today’s enterprises depend on, Axway is developing a thoughtful, comprehensive approach to AI integration. Ingo Muschenetz synthesized this strategy in a panel discussion at our recent Summit event, saying,
“Axway takes a pragmatic, trust-first approach to AI implementation, recognizing that customers depend on our products for business-critical operations where ‘probably correct’ isn’t sufficient.”
Axway’s AI strategy focuses on two key areas:
- Internal productivity enhancement through AI-powered development tools (while maintaining strict data segregation from customer information), and
- Practical AI-powered product features that deliver real acceleration rather than hype-driven capabilities.
AI is not quite ready to be an autonomous actor, so we position it principally as an advisor, emphasizing responsible AI practices as we securely move toward more agentic AI solutions. Ingo sums it up this way: “Customers already trust us with critical data — our AI has to keep that trust every day.”
We are making a veritable shift from experimental AI to production-ready solutions.
The following articles dive deeper into AI implementations across Axway’s main product lines:
Amplify Platform: AI Meets Amplify: Smarter API Experiences for Developers and Enterprises
Managed File Transfer: The Evolution of Managed File Transfer AI: Automation to Autonomy
B2B Integration: Unlocking the Power of AI in B2B Integration
Financial Accounting Hub: Embrace AI-Powered Autonomous Accounting to Transform CFO Roles
What’s next? Time to assess your AI integration strategies
Ingo points to Agent-to-Agent (A2A) communication as one of the most exciting (and disruptive) trends on the near horizon.
A2A enables direct communication and collaboration between different AI systems, meaning we could see scenarios where autonomous AI agents communicate and transact with each other without constant human intervention.
“We’ll eventually get to the point where we will be comfortable with moving from AIs asking us to AIs acting on our behalf, but there’s a strong need to keep humans in the loop,” cautions Ingo. “And even when AIs become more autonomous, you need to keep your visibility into the inner workings intact.”
Multi-agent AI protocols such as A2A or Agent Communication Protocol (ACP) aren’t ready for prime time yet – but they’re coming faster than most will be prepared for.
The predictions from our Looking Forward 2025 guide are becoming operational realities: organizations are moving beyond asking “Can AI help?” to “How do we implement AI responsibly and effectively?”
The convergence of mature AI governance frameworks, evolving integration protocols, and proven practical applications creates a unique window of opportunity. Companies that act now by establishing robust AI governance will gain sustainable competitive advantages.
The path forward requires balancing ambition with pragmatism. As Ingo Muschenetz noted, humans will continue to be “the deciders,” but the systems supporting those decisions are becoming exponentially more intelligent. Your next step isn’t to choose between human expertise and AI capability; it’s to architect solutions that amplify both.
On your implementation journey, make sure to assess where your organization stands on AI governance and observability, evaluate your data integration protocols, and identify the operational pain points where AI can deliver immediate value.
Axway is ready to support enterprises with tailored solutions and strategies to secure and optimize the data you already have.
Access the digital trends guide for 2025 by Axway technology, business, and industry thought leaders.