Maciej Saganowski is the Director of AI Products at Appfire.
Appfire is a number one provider of enterprise software solutions designed to reinforce collaboration, streamline workflows, and improve productivity across teams. Specializing in tools that integrate with platforms like Atlassian, Salesforce, and Microsoft, Appfire offers a strong suite of apps tailored for project management, automation, reporting, and IT service management. With a world presence and a commitment to innovation, the corporate has develop into a trusted partner for organizations in search of to optimize their software ecosystems, serving a wide selection of industries and empowering teams to attain their goals efficiently.
Appfire is understood for providing enterprise collaboration solutions, are you able to introduce us to Appfire’s approach to developing AI-driven products?
Over the past 12 months, the market has been flooded with AI-powered solutions as corporations pivot to remain relevant and competitive. While a few of these products have met expectations, there stays a possibility for vendors to actually address real customer needs with impactful solutions.
At Appfire, we’re focused on staying on 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 fairly than merely claiming “AI-readiness” just for the sake of differentiation. Our approach to developing AI-driven products centers on creating seamless, impactful experiences for our customers.
We would like AI to mix into the user experience, enhancing it without overshadowing it or, worse, creating an additional burden by requiring users to learn entirely latest features.
“Time to Value” is one of the crucial critical objectives for our AI-powered features. This principle focuses on how quickly a user—especially a brand new user—can start benefiting from our products.
For instance, with Canned Responses, a support agent responding to a customer won’t must sift through the whole email thread; the AI will give you the option to suggest probably 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 crammed with similar products?
This category of products is comparatively unusual. We’re considered one of the primary corporations to ship a Jira-class software automation configuration assistant—and this is just the start.
WorkFlow Pro is an AI-powered automation assistant for Jira that’s transforming how teams arrange and manage their automation workflows. Powered by Atlassian’s Rovo AI, it assists users in configuring latest automations or troubleshooting existing ones.
Historically, Jira automation products have been complex and required a particular level of experience. WorkFlow Pro demystifies these configurations and enables latest or less-experienced Jira admins to perform their tasks without spending time on product documentation, forums, or risking costly mistakes.
A brand new Jira admin can simply ask the agent the way 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 specified consequence. It’s like having a Michelin-star chef in your kitchen, able to answer any query with precise instructions.
At Appfire, we’re committed to simplifying the lives of our customers. In the following version of WorkFlow Pro, users will give you the option to request latest automations in plain English by simply typing the specified consequence, without the necessity to navigate the configurator UI or know any scripting language. Returning to our chef analogy, the following version will allow the user not only to ask the chef the way to cook a dish but to organize it on their behalf, freeing them as much as concentrate on more essential tasks.
How do you involve user feedback when iterating on AI products like WorkFlow Pro? What role does customer input play in shaping the event of those tools?
At Appfire, we stay very near our users. Not only do our designers and product managers engage recurrently with them, but we even have a dedicated user research group that undertakes broader research initiatives, informing our vision and product roadmaps.
We analyze each quantitative data and user stories focused on challenges, asking ourselves, “Can AI assist in this moment?” If we understand the user’s problem well enough and imagine AI can provide an answer, our team begins experimenting with the technology to deal with the difficulty. Each feature’s journey begins not with the technology but from the user’s pain point.
For example, we learned from our users that latest admins face a big barrier when creating complex automations. Many lack the experience or time to check 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 latest products transform the way in which users set goals, track work, and harness data more effectively?
AI could have a profound impact on what future knowledge employees can accomplish and the way they interact with software. Organizations will evolve, becoming flatter, more nimble, and more efficient. Projects would require fewer people to coordinate and deliver. While this seems like a daring 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 latest users into these products. For example, AI assistants can take meeting notes or list motion items. For instance this on the Appfire example, when a manager creates a brand new Key Result inside their OKR framework, the AI will suggest the Key Result wording based on industry best practices and the corporate’s unique context, easing the mental load on users as they learn to define effective OKRs.
Natural language interfaces represent a serious 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 employees, yet this interconnected power has brought significant complexity.
Until recently, there wasn’t a straightforward solution to navigate this complexity. Now, AI and natural language interfaces are making it manageable and accessible. For instance, considered one of Appfire’s hottest app categories is Document Management. Many Fortune 500 corporations require document workflows for compliance or regulatory review. Soon, creating these workflows could possibly be so simple as chatting with the system. A manager might say, “For a policy to be approved and distributed to all employees, it first must 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 suggestions for smoother flows.
Moreover, “agentic workflows” are the following frontier of the AI revolution, and we’re embracing this at Appfire with our agent WorkFlow Pro. In the longer term, AI agents will act more like human collaborators, able to tackling complex tasks resembling conducting research, gathering information from multiple sources, and coordinating with other agents and other people to deliver a proposal inside hours or days. This agent-run approach will transcend easy interactions like those with ChatGPT; agents will develop into proactive, perhaps suggesting a draft presentation deck before you even realize you wish one. And voice interactions with agents will develop into 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 all know where we wish to go but typically don’t need to grasp the intricacies of combustion engines or fine-tune the automobile ourselves.
You’re also enhancing existing Appfire products using AI. Are you able to give us examples of how AI has supercharged current Appfire apps, boosting their functionality and user experience?
Each of our apps is exclusive, solving distinct user challenges and designed for various user roles. Consequently, the usage of AI in these apps is tailored to reinforce 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 additionally enhances the standard of customer interactions.
In OKR for Jira, for instance, AI could assist users who’re latest 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 revolutionary solution 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 brand spanking new Jira admins and streamlines workflows for knowledgeable admins alike.
Shared AI services, resembling 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’ve a broad portfolio of apps across multiple marketplaces, including Atlassian, Microsoft, monday.com, and Salesforce.
With such a big suite of apps and diverse use cases for AI, we took a step back to design and construct a shared internal AI service that could possibly be leveraged across multiple apps.
We developed a platform AI service that permits product teams across our apps to connect with multiple LLMs. Now that the service is live, we’ll proceed expanding it with features like locally run models and pre-packaged prompts.
With the rapid evolution of AI technologies, how do you make sure that Appfire’s approach to AI development continues to satisfy 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 not sleep thus far with market trends and actively monitor the industry for best practices. On the shopper side, we continually engage with our users to grasp their challenges, not only inside our apps but additionally within the underlying platforms they use.
Once we discover an overlap between technical feasibility and a meaningful customer need, we concentrate on delivering a secure and robust AI feature. Before launching, we experiment and test these solutions with users to make sure they genuinely address their pain points.
Appfire operates in a highly competitive AI-driven SaaS landscape. What steps are you taking to make sure your AI innovations remain unique and proceed to drive value for users?
Appfire’s approach to AI focuses on purpose. We’re not integrating AI just to envision a box; our goal is for AI to work so naturally inside our products that it becomes almost invisible to the user. We would like AI to deal with 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 scale back the educational curve and make our apps more accessible. With the launch of our first Rovo agent, WorkFlow Pro, we’re taking a crucial step on this journey. Ultimately, we aim to make sure AI inside our apps enables users to attain value more quickly.
Looking ahead, what trends in AI development do you’re thinking that could have the best impact on the SaaS industry in the approaching years?
Two major AI trends that may shape the SaaS industry in the approaching years are the rise of AI-powered agents and increasing concerns about security and privacy.
Some argue that agent technology has yet to live as much as its hype and stays relatively immature. To those skeptics, I’d say that we frequently overestimate what technology will achieve in 1–2 years but vastly underestimate what it would accomplish over a decade. While current agent use cases are indeed limited, we’re witnessing massive investments in agentic workflows throughout the software value chain. Foundational models from corporations like OpenAI and Anthropic, together with platforms Appfire currently operates or plans to operate on, are making extensive investments in agent technology. OpenAI, for example, is working on “System 2” agents able to reasoning, while Anthropic has launched models able to using regular apps and web sites, emulating human actions. Atlassian has introduced Rovo, and Salesforce has launched Agentforce. Each week brings latest announcements in agentic progress, and, at Appfire, we’re enthusiastic about these developments and look ahead to integrating them into our apps.
At the identical time, as AI capabilities expand, so do the risks related to data security and privacy. Enterprises must make sure that any AI integration respects and protects each their assets and people of their customers, from sensitive data to broader security measures. Balancing innovation with robust security practices will probably be essential to unlocking AI’s full value in SaaS and enabling responsible, secure advancements.
