Maciej Saganowski is the Director of AI Merchandise at Appfire.
Appfire is a number one supplier of enterprise software program options designed to boost 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 change 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 method to creating AI-driven merchandise?
Over the previous yr, the market has been flooded with AI-powered options as firms pivot to remain related and aggressive. Whereas a few of these merchandise have met expectations, there stays a possibility for distributors to really deal with actual buyer wants with impactful options.
At Appfire, we’re centered on staying on the forefront of AI innovation, enabling us to anticipate and exceed the evolving wants of enterprise collaboration. We method AI integration with the purpose of delivering actual worth reasonably than merely claiming “AI-readiness” just for the sake of differentiation. Our method to creating AI-driven merchandise facilities on creating seamless, impactful experiences for our prospects.
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 be taught fully new options.
“Time to Worth” is among the most important goals for our AI-powered options. This precept focuses on how shortly 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 must sift by your complete e-mail thread; the AI will have the ability to counsel essentially the most acceptable response template, saving time and bettering 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 related merchandise?
This class of merchandise is comparatively unusual. We’re one of many first firms 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 remodeling how groups arrange and handle their automation workflows. Powered by Atlassian’s Rovo AI, it assists customers in configuring new automations or troubleshooting present ones.
Traditionally, Jira automation merchandise have been complicated and required a particular degree of experience. WorkFlow Professional demystifies these configurations and permits 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 methods to carry out a activity, and primarily based on the automation app put in (JMWE, JSU, or Energy Scripts), the agent supplies a step-by-step information to attaining the specified end 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 prospects. Within the subsequent model of WorkFlow Professional, customers will have the ability to request new automations in plain English by merely typing the specified end result, with out the necessity to navigate the configurator UI or know any scripting language. Returning to our chef analogy, the subsequent model will permit the person not solely to ask the chef methods to prepare dinner a dish however to arrange it on their behalf, liberating them as much as give attention to extra vital duties.
How do you contain person suggestions when iterating on AI merchandise like WorkFlow Professional? What position 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 recurrently 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 information and person tales centered 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 workforce begins experimenting with the expertise to handle the difficulty. Every function’s journey begins not with the expertise however from the person’s ache level.
As an illustration, we discovered from our customers that new admins face a big barrier when creating complicated automations. Many lack the expertise or time to check documentation and grasp intricate scripting mechanisms. WorkFlow Professional was developed to ease this ache level, serving to customers extra simply be taught and configure Jira.
Past WorkFlow Professional, Appfire plans to develop extra AI-driven purposes. How will these new merchandise remodel the way in which customers set targets, monitor work, and harness information extra successfully?
AI can have a profound impression on what future data employees can accomplish and the way they work together with software program. Organizations will evolve, turning into flatter, extra nimble, and extra environment friendly. Initiatives would require fewer folks to coordinate and ship. Whereas this appears like a daring prediction, it’s already taking form by three key AI-powered developments:
- Offloading technically complicated or mundane duties to AI
- Interacting with software program utilizing pure language
- Agentic workflows
We’re already seeing AI cut back the burden of mundane duties and ease new customers into these merchandise. As an illustration, AI assistants can take assembly notes or checklist motion objects. For example this on the Appfire instance, when a supervisor creates a brand new Key Consequence inside their OKR framework, the AI will counsel the Key Consequence wording primarily based on trade finest practices and the corporate’s distinctive context, easing the psychological load on customers as they be taught to outline efficient OKRs.
Pure language interfaces symbolize a significant 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 approach to navigate this complexity. Now, AI and pure language interfaces are making it manageable and accessible. For instance, considered one of Appfire’s hottest app classes is Doc Administration. Many Fortune 500 firms require doc workflows for compliance or regulatory evaluate. Quickly, creating these workflows may very well be so simple as talking to the system. A supervisor may say, “For a coverage to be accredited and distributed to all workers, it first must be reviewed and accredited by the senior management workforce.” AI would perceive this instruction and create the workflow. If any particulars are lacking, the AI would immediate for clarification and provide ideas for smoother flows.
Moreover, “agentic workflows” are the subsequent 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 complicated duties reminiscent of 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 method will transcend easy interactions like these with ChatGPT; brokers will change into proactive, maybe suggesting a draft presentation deck earlier than you even notice you want one. And voice interactions with brokers will change into extra frequent, 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 need to go however usually don’t want to know the intricacies of combustion engines or fine-tune the automotive ourselves.
You’re additionally enhancing present 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. In consequence, the usage of AI in these apps is tailor-made to boost particular capabilities and enhance the person expertise in significant methods.
In Canned Responses, AI accelerates buyer communication by serving to customers shortly formulate responses primarily based on the content material of a request and present templates. This AI function not solely saves time but in addition enhances the standard of buyer interactions.
In OKR for Jira, for instance, AI might help customers who’re new to the OKR (Goal and Key Outcomes) framework. By simplifying and clarifying this typically complicated methodology, AI might present steerage in formulating efficient Key Outcomes aligned with particular goals, making the OKR course of extra approachable.
Lastly, WorkFlow Professional represents an revolutionary approach to work together with our documentation and exemplifies our dedication to agentic workflows and pure language automation requests. This AI-driven method reduces the barrier to entry for brand new Jira admins and streamlines workflows for skilled admins alike.
Shared AI companies, such because the summarization function, are being developed throughout a number of Appfire apps. How do you envision these companies impacting person productiveness throughout your platform?
At Appfire, we’ve 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 inner AI service that may very well be leveraged throughout a number of apps.
We developed a platform AI service that permits product groups throughout our apps to connect with a number of LLMs. Now that the service is reside, we’ll proceed increasing it with options like domestically run fashions and pre-packaged prompts.
With the speedy evolution of AI applied sciences, how do you make sure that Appfire’s method 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 finest practices. On the shopper facet, we frequently interact with our customers to know their challenges, not solely inside our apps but in addition within the underlying platforms they use.
Once we establish 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 method to AI focuses on function. We’re not integrating AI simply to verify a field; our aim is for AI to work so naturally inside our merchandise that it turns into nearly invisible to the person. We would like AI to handle actual challenges our prospects face—whether or not it’s simplifying workflows in Jira, managing complicated 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 scale back the training curve and make our apps extra accessible. With the launch of our first Rovo agent, WorkFlow Professional, we’re taking an vital step on this journey. Finally, we purpose to make sure AI inside our apps permits customers to realize worth extra shortly.
Trying forward, what tendencies in AI improvement do you suppose can have the best impression 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 considerations about safety and privateness.
Some argue that agent expertise has but to reside as much as its hype and stays comparatively immature. To those skeptics, I’d say that we regularly overestimate what expertise 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 firms like OpenAI and Anthropic, together with platforms Appfire at the moment operates or plans to function on, are making intensive investments in agent expertise. 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 stay up for integrating them into our apps.
On the identical time, as AI capabilities develop, so do the dangers related to information safety and privateness. Enterprises should make sure that any AI integration respects and protects each their property and people of their prospects, from delicate information to broader safety measures. Balancing innovation with sturdy safety practices will probably be important to unlocking AI’s full worth in SaaS and enabling accountable, safe developments.
Thanks for the nice interview, readers who want to be taught extra ought to go to Appfire.
