Maciej Saganowski is the Director of AI Merchandise at Appfire.
Appfire is a number one supplier of enterprise software program options designed to reinforce collaboration, streamline workflows, and enhance productiveness throughout groups. Specializing in instruments that combine with platforms like Atlassian, Salesforce, and Microsoft, Appfire presents a sturdy suite of apps tailor-made for mission administration, automation, reporting, and IT service administration. With a world presence and a dedication to innovation, the corporate has turn into a trusted accomplice for organizations searching for to optimize their software program ecosystems, serving a variety of industries and empowering groups to realize their targets effectively.
Appfire is understood for offering enterprise collaboration options, are you able to introduce us to Appfire’s strategy to creating AI-driven merchandise?
Over the previous yr, the market has been flooded with AI-powered options as corporations pivot to remain related and aggressive. Whereas a few of these merchandise have met expectations, there stays a chance for distributors to actually deal with actual buyer wants with impactful options.
At Appfire, we’re targeted on staying on the forefront of AI innovation, enabling us to anticipate and exceed the evolving wants of enterprise collaboration. We strategy AI integration with the intention of delivering actual worth relatively than merely claiming “AI-readiness” just for the sake of differentiation. Our strategy to creating AI-driven merchandise facilities on creating seamless, impactful experiences for our clients.
We would like AI to mix into the person expertise, enhancing it with out overshadowing it or, worse, creating an additional burden by requiring customers to study fully new options.
“Time to Worth” is among the most important goals for our AI-powered options. This precept focuses on how rapidly a person—particularly a brand new person—can begin benefiting from our merchandise.
For instance, with Canned Responses, a assist agent responding to a buyer received’t have to sift by way of all the e-mail thread; the AI will be capable to counsel essentially the most applicable response template, saving time and enhancing accuracy.
Appfire has partnered with Atlassian to launch WorkFlow Professional as a Rovo agent. What makes this AI-powered product stand out in a market stuffed with comparable merchandise?
This class of merchandise is comparatively unusual. We’re one of many first corporations to ship a Jira-class software program automation configuration assistant—and that is solely the start.
WorkFlow Professional is an AI-powered automation assistant for Jira that’s reworking how groups arrange and handle their automation workflows. Powered by Atlassian’s Rovo AI, it assists customers in configuring new automations or troubleshooting current ones.
Traditionally, Jira automation merchandise have been advanced and required a particular stage of experience. WorkFlow Professional demystifies these configurations and allows new or less-experienced Jira admins to perform their duties with out spending time on product documentation, boards, or risking expensive errors.
A brand new Jira admin can merely ask the agent easy methods to carry out a job, and based mostly on the automation app put in (JMWE, JSU, or Energy Scripts), the agent offers a step-by-step information to attaining the specified final result. It’s like having a Michelin-star chef in your kitchen, able to reply any query with exact directions.
At Appfire, we’re dedicated to simplifying the lives of our clients. Within the subsequent model of WorkFlow Professional, customers will be capable to request new automations in plain English by merely typing the specified final result, with out the necessity to navigate the configurator UI or know any scripting language. Returning to our chef analogy, the following model will permit the person not solely to ask the chef easy methods to cook dinner a dish however to organize it on their behalf, liberating them as much as give attention to extra essential duties.
How do you contain person suggestions when iterating on AI merchandise like WorkFlow Professional? What function does buyer enter play in shaping the event of those instruments?
At Appfire, we keep very near our customers. Not solely do our designers and product managers interact usually with them, however we even have a devoted person analysis group that undertakes broader analysis initiatives, informing our imaginative and prescient and product roadmaps.
We analyze each quantitative knowledge and person tales targeted on challenges, asking ourselves, “Can AI assist on this second?” If we perceive the person’s downside effectively sufficient and consider AI can present an answer, our group begins experimenting with the know-how to deal with the difficulty. Every function’s journey begins not with the know-how however from the person’s ache level.
As an example, we discovered from our customers that new admins face a major barrier when creating advanced automations. Many lack the expertise or time to review documentation and grasp intricate scripting mechanisms. WorkFlow Professional was developed to ease this ache level, serving to customers extra simply study and configure Jira.
Past WorkFlow Professional, Appfire plans to develop extra AI-driven functions. How will these new merchandise rework the best way customers set targets, monitor work, and harness knowledge extra successfully?
AI could have a profound affect on what future data employees can accomplish and the way they work together with software program. Organizations will evolve, changing into flatter, extra nimble, and extra environment friendly. Tasks would require fewer individuals to coordinate and ship. Whereas this seems like a daring prediction, it’s already taking form by way of three key AI-powered developments:
- Offloading technically advanced or mundane duties to AI
- Interacting with software program utilizing pure language
- Agentic workflows
We’re already seeing AI scale back the burden of mundane duties and ease new customers into these merchandise. As an example, AI assistants can take assembly notes or checklist motion objects. As an instance this on the Appfire instance, when a supervisor creates a brand new Key Outcome inside their OKR framework, the AI will counsel the Key Outcome wording based mostly on trade greatest practices and the corporate’s distinctive context, easing the psychological load on customers as they study to outline efficient OKRs.
Pure language interfaces signify a serious paradigm shift in how we design and use software program. The evolution of software program over the previous 50 years has created nearly limitless capabilities for data employees, but this interconnected energy has introduced important complexity.
Till not too long ago, there wasn’t a straightforward technique to navigate this complexity. Now, AI and pure language interfaces are making it manageable and accessible. For instance, certainly one of Appfire’s hottest app classes is Doc Administration. Many Fortune 500 corporations require doc workflows for compliance or regulatory evaluate. Quickly, creating these workflows could possibly be so simple as chatting with the system. A supervisor would possibly say, “For a coverage to be authorized and distributed to all staff, it first must be reviewed and authorized by the senior management group.” AI would perceive this instruction and create the workflow. If any particulars are lacking, the AI would immediate for clarification and supply 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 Professional. Sooner or later, AI brokers will act extra like human collaborators, able to tackling advanced duties resembling conducting analysis, gathering info from a number of sources, and coordinating with different brokers and folks to ship a proposal inside hours or days. This agent-run strategy will transcend easy interactions like these with ChatGPT; brokers will turn into proactive, maybe suggesting a draft presentation deck earlier than you even notice you want one. And voice interactions with brokers will turn into extra widespread, permitting customers to work whereas on the go.
In abstract, the place we’re heading with AI in data work is akin to how we now function automobiles: we all know the place we wish to go however usually don’t want to grasp the intricacies of combustion engines or fine-tune the automobile ourselves.
You’re additionally enhancing current Appfire merchandise utilizing AI. Are you able to give us examples of how AI has supercharged present Appfire apps, boosting their performance and person expertise?
Every of our apps is exclusive, fixing distinct person challenges and designed for varied person roles. Consequently, using AI in these apps is tailor-made to reinforce particular capabilities and enhance the person expertise in significant methods.
In Canned Responses, AI accelerates buyer communication by serving to customers rapidly formulate responses based mostly on the content material of a request and current templates. This AI function not solely saves time but additionally enhances the standard of buyer interactions.
In OKR for Jira, for instance, AI may help customers who’re new to the OKR (Goal and Key Outcomes) framework. By simplifying and clarifying this usually advanced methodology, AI may present steerage in formulating efficient Key Outcomes aligned with particular goals, making the OKR course of extra approachable.
Lastly, WorkFlow Professional represents an progressive technique to work together with our documentation and exemplifies our dedication to agentic workflows and pure language automation requests. This AI-driven strategy reduces the barrier to entry for brand spanking new Jira admins and streamlines workflows for skilled admins alike.
Shared AI providers, such because the summarization function, are being developed throughout a number of Appfire apps. How do you envision these providers impacting person productiveness throughout your platform?
At Appfire, now we have a broad portfolio of apps throughout a number of marketplaces, together with Atlassian, Microsoft, monday.com, and Salesforce.
With such a big suite of apps and various use circumstances for AI, we took a step again to design and construct a shared inside AI service that could possibly be leveraged throughout a number of apps.
We developed a platform AI service that permits product groups throughout our apps to hook up with a number of LLMs. Now that the service is stay, we’ll proceed increasing it with options like domestically run fashions and pre-packaged prompts.
With the fast evolution of AI applied sciences, how do you make sure that Appfire’s strategy to AI improvement continues to fulfill altering buyer wants and market calls for?
At Appfire, a product supervisor’s prime precedence is bridging the hole between technical feasibility and fixing significant buyer issues. As AI capabilities advance quickly, we keep updated with market tendencies and actively monitor the trade for greatest practices. On the shopper facet, we frequently interact with our customers to grasp their challenges, not solely inside our apps but additionally within the underlying platforms they use.
After we determine an overlap between technical feasibility and a significant buyer want, we give attention to delivering a safe and sturdy AI function. Earlier than launching, we experiment and check these options with customers to make sure they genuinely deal with their ache factors.
Appfire operates in a extremely aggressive AI-driven SaaS panorama. What steps are you taking to make sure your AI improvements stay distinctive and proceed to drive worth for customers?
Appfire’s strategy to AI focuses on goal. We’re not integrating AI simply to test a field; our purpose is for AI to work so naturally inside our merchandise that it turns into virtually invisible to the person. We would like AI to deal with actual challenges our clients face—whether or not it’s simplifying workflows in Jira, managing advanced doc processes, or streamlining strategic planning. Ideally, utilizing AI ought to really feel as intuitive as choosing up a pen.
Many SaaS merchandise have historically required specialised experience to unlock their full potential. Our imaginative and prescient for AI is to cut back the educational curve and make our apps extra accessible. With the launch of our first Rovo agent, WorkFlow Professional, we’re taking an essential step on this journey. Finally, we intention to make sure AI inside our apps allows customers to realize worth extra rapidly.
Wanting forward, what tendencies in AI improvement do you suppose could have the best affect on the SaaS trade within the coming years?
Two main AI tendencies that may form the SaaS trade within the coming years are the rise of AI-powered brokers and growing issues about safety and privateness.
Some argue that agent know-how has but to stay as much as its hype and stays comparatively immature. To those skeptics, I’d say that we frequently overestimate what know-how will obtain in 1–2 years however vastly underestimate what it should accomplish over a decade. Whereas present agent use circumstances are certainly restricted, we’re witnessing large investments in agentic workflows all through the software program worth chain. Foundational fashions from corporations like OpenAI and Anthropic, together with platforms Appfire presently operates or plans to function on, are making in depth investments in agent know-how. OpenAI, for example, is engaged on “System 2” brokers able to reasoning, whereas Anthropic has launched fashions able to utilizing common apps and web sites, emulating human actions. Atlassian has launched Rovo, and Salesforce has launched Agentforce. Every week brings new bulletins in agentic progress, and, at Appfire, we’re enthusiastic about these developments and look ahead to integrating them into our apps.
On the identical time, as AI capabilities develop, so do the dangers related to knowledge safety and privateness. Enterprises should be certain that any AI integration respects and protects each their property and people of their clients, from delicate knowledge to broader safety measures. Balancing innovation with sturdy safety practices might be important to unlocking AI’s full worth in SaaS and enabling accountable, safe developments.
Thanks for the good interview, readers who want to study extra ought to go to Appfire.
