Maciej Saganowski is the Director of AI Products at Appfire.
Appfire is a leading provider of enterprise software solutions designed to enhance collaboration, streamline workflows, and improve productivity across teams. Specializing in tools that integrate with platforms like Atlassian, Salesforce, and Microsoft, Appfire offers a robust suite of apps tailored for project management, automation, reporting, and IT service management. With a global presence and a commitment to innovation, the company has become a trusted partner for organizations seeking to optimize their software ecosystems, serving a wide range of industries and empowering teams to achieve their goals efficiently.
Appfire is known for providing enterprise collaboration solutions, can you introduce us to Appfire’s approach to developing AI-driven products?
Over the past year, the market has been flooded with AI-powered solutions as companies pivot to stay relevant and competitive. While some of these products have met expectations, there remains an opportunity for vendors to truly address real customer needs with impactful solutions.
At Appfire, we are focused on staying at the forefront of AI innovation, enabling us to anticipate and exceed the evolving needs of enterprise collaboration. We approach AI integration with the aim of delivering real value rather than merely claiming “AI-readiness” only for the sake of differentiation. Our approach to developing AI-driven products centers on creating seamless, impactful experiences for our customers.
We want AI to blend into the user experience, enhancing it without overshadowing it or, worse, creating an extra burden by requiring users to learn entirely new features.
“Time to Value” is one of the most critical objectives for our AI-powered features. This principle focuses on how quickly a user—especially a new user—can start benefiting from our products.
For example, with Canned Responses, a support agent responding to a customer won’t need to sift through the entire email thread; the AI will be able to suggest the most appropriate response template, saving time and improving accuracy.
Appfire has partnered with Atlassian to launch WorkFlow Pro as a Rovo agent. What makes this AI-powered product stand out in a market filled with similar products?
This category of products is relatively uncommon. We are one of the first companies to ship a Jira-class software automation configuration assistant—and this is only the beginning.
WorkFlow Pro is an AI-powered automation assistant for Jira that is transforming how teams set up and manage their automation workflows. Powered by Atlassian’s Rovo AI, it assists users in configuring new automations or troubleshooting existing ones.
Historically, Jira automation products have been complex and required a specific level of expertise. WorkFlow Pro demystifies these configurations and enables new or less-experienced Jira admins to accomplish their tasks without spending time on product documentation, forums, or risking costly mistakes.
A new Jira admin can simply ask the agent how to perform a task, and based on the automation app installed (JMWE, JSU, or Power Scripts), the agent provides a step-by-step guide to achieving the desired outcome. It’s like having a Michelin-star chef in your kitchen, ready to answer any question with precise instructions.
At Appfire, we are committed to simplifying the lives of our customers. In the next version of WorkFlow Pro, users will be able to request new automations in plain English by simply typing the desired outcome, without the need to navigate the configurator UI or know any scripting language. Returning to our chef analogy, the next version will allow the user not only to ask the chef how to cook a dish but to prepare it on their behalf, freeing them up to focus on more important tasks.
How do you involve user feedback when iterating on AI products like WorkFlow Pro? What role does customer input play in shaping the development of these tools?
At Appfire, we stay very close to our users. Not only do our designers and product managers engage regularly with them, but we also have a dedicated user research group that undertakes broader research initiatives, informing our vision and product roadmaps.
We analyze both quantitative data and user stories focused on challenges, asking ourselves, “Can AI help in this moment?” If we understand the user’s problem well enough and believe AI can provide a solution, our team begins experimenting with the technology to address the issue. Each feature’s journey begins not with the technology but from the user’s pain point.
For instance, we learned from our users that new admins face a significant barrier when creating complex automations. Many lack the experience or time to study documentation and master intricate scripting mechanisms. WorkFlow Pro was developed to ease this pain point, helping users more easily learn and configure Jira.
Beyond WorkFlow Pro, Appfire plans to develop additional AI-driven applications. How will these new products transform the way users set goals, track work, and harness data more effectively?
AI will have a profound impact on what future knowledge workers can accomplish and how they interact with software. Organizations will evolve, becoming flatter, more nimble, and more efficient. Projects will require fewer people to coordinate and deliver. While this sounds like a bold prediction, it’s already taking shape through three key AI-powered advancements:
- Offloading technically complex or mundane tasks to AI
- Interacting with software using natural language
- Agentic workflows
We’re already seeing AI reduce the burden of mundane tasks and ease new users into these products. For instance, AI assistants can take meeting notes or list action items. To illustrate this on the Appfire example, when a manager creates a new Key Result within their OKR framework, the AI will suggest the Key Result wording based on industry best practices and the company’s unique context, easing the mental load on users as they learn to define effective OKRs.
Natural language interfaces represent a major paradigm shift in how we design and use software. The evolution of software over the past 50 years has created virtually limitless capabilities for knowledge workers, yet this interconnected power has brought significant complexity.
Until recently, there wasn’t an easy way to navigate this complexity. Now, AI and natural language interfaces are making it manageable and accessible. For example, one of Appfire’s most popular app categories is Document Management. Many Fortune 500 companies require document workflows for compliance or regulatory review. Soon, creating these workflows could be as simple as speaking to the system. A manager might say, “For a policy to be approved and distributed to all employees, it first needs to be reviewed and approved by the senior leadership team.” AI would understand this instruction and create the workflow. If any details are missing, the AI would prompt for clarification and offer tips for smoother flows.
Additionally, “agentic workflows” are the next frontier of the AI revolution, and we’re embracing this at Appfire with our agent WorkFlow Pro. In the future, AI agents will act more like human collaborators, capable of tackling complex tasks such as conducting research, gathering information from multiple sources, and coordinating with other agents and people to deliver a proposal within hours or days. This agent-run approach will go beyond simple interactions like those with ChatGPT; agents will become proactive, perhaps suggesting a draft presentation deck before you even realize you need one. And voice interactions with agents will become more common, allowing users to work while on the go.
In summary, where we’re heading with AI in knowledge work is akin to how we now operate vehicles: we know where we want to go but typically don’t need to understand the intricacies of combustion engines or fine-tune the car ourselves.
You’re also enhancing existing Appfire products using AI. Can you give us examples of how AI has supercharged current Appfire apps, boosting their functionality and user experience?
Each of our apps is unique, solving distinct user challenges and designed for various user roles. As a result, the use of AI in these apps is tailored to enhance specific functions and improve the user experience in meaningful ways.
In Canned Responses, AI accelerates customer communication by helping users quickly formulate responses based on the content of a request and existing templates. This AI feature not only saves time but also enhances the quality of customer interactions.
In OKR for Jira, for example, AI could assist users who are new to the OKR (Objective and Key Results) framework. By simplifying and clarifying this often complex methodology, AI could provide guidance in formulating effective Key Results aligned with specific objectives, making the OKR process more approachable.
Finally, WorkFlow Pro represents an innovative way to interact with our documentation and exemplifies our commitment to agentic workflows and natural language automation requests. This AI-driven approach reduces the barrier to entry for new Jira admins and streamlines workflows for experienced admins alike.
Shared AI services, such as the summarization feature, are being developed across multiple Appfire apps. How do you envision these services impacting user productivity across your platform?
At Appfire, we have a broad portfolio of apps across multiple marketplaces, including Atlassian, Microsoft, monday.com, and Salesforce.
With such a large suite of apps and diverse use cases for AI, we took a step back to design and build a shared internal AI service that could be leveraged across multiple apps.
We developed a platform AI service that allows product teams across our apps to connect to multiple LLMs. Now that the service is live, we’ll continue expanding it with features like locally run models and pre-packaged prompts.
With the rapid evolution of AI technologies, how do you ensure that Appfire’s approach to AI development continues to meet changing customer needs and market demands?
At Appfire, a product manager’s top priority is bridging the gap between technical feasibility and solving meaningful customer problems. As AI capabilities advance rapidly, we stay up to date with market trends and actively monitor the industry for best practices. On the customer side, we continually engage with our users to understand their challenges, not only within our apps but also in the underlying platforms they use.
When we identify an overlap between technical feasibility and a meaningful customer need, we focus on delivering a secure and robust AI feature. Before launching, we experiment and test these solutions with users to ensure they genuinely address their pain points.
Appfire operates in a highly competitive AI-driven SaaS landscape. What steps are you taking to ensure your AI innovations remain unique and continue to drive value for users?
Appfire’s approach to AI focuses on purpose. We’re not integrating AI just to check a box; our goal is for AI to work so naturally within our products that it becomes almost invisible to the user. We want AI to address real challenges our customers face—whether it’s simplifying workflows in Jira, managing complex document processes, or streamlining strategic planning. Ideally, using AI should feel as intuitive as picking up a pen.
Many SaaS products have traditionally required specialized expertise to unlock their full potential. Our vision for AI is to reduce the learning curve and make our apps more accessible. With the launch of our first Rovo agent, WorkFlow Pro, we’re taking an important step in this journey. Ultimately, we aim to ensure AI within our apps enables users to achieve value more quickly.
Looking ahead, what trends in AI development do you think will have the greatest impact on the SaaS industry in the coming years?
Two major AI trends that will shape the SaaS industry in the coming years are the rise of AI-powered agents and increasing concerns about security and privacy.
Some argue that agent technology has yet to live up to its hype and remains relatively immature. To these skeptics, I’d say that we often overestimate what technology will achieve in 1–2 years but vastly underestimate what it will accomplish over a decade. While current agent use cases are indeed limited, we are witnessing massive investments in agentic workflows throughout the software value chain. Foundational models from companies like OpenAI and Anthropic, along with platforms Appfire currently operates or plans to operate on, are making extensive investments in agent technology. OpenAI, for instance, is working on “System 2” agents capable of reasoning, while Anthropic has launched models capable of using regular apps and websites, emulating human actions. Atlassian has introduced Rovo, and Salesforce has launched Agentforce. Each week brings new announcements in agentic progress, and, at Appfire, we’re excited about these developments and look forward to integrating them into our apps.
At the same time, as AI capabilities expand, so do the risks associated with data security and privacy. Enterprises must ensure that any AI integration respects and protects both their assets and those of their customers, from sensitive data to broader security measures. Balancing innovation with robust security practices will be essential to unlocking AI’s full value in SaaS and enabling responsible, secure advancements.
Thank you for the great interview, readers who wish to learn more should visit Appfire.
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