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 gives a strong suite of apps tailor-made for challenge administration, automation, reporting, and IT service administration. With a world presence and a dedication to innovation, the corporate has turn out to be a trusted associate for organizations in search of 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 moderately 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 study totally new options.
“Time to Worth” is likely one of the most crucial targets 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 help agent responding to a buyer received’t must sift by means of the complete e-mail thread; the AI will be capable of 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 full of comparable 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 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 complicated and required a selected 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 how one can carry out a job, 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 be capable of 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 following model will permit the person not solely to ask the chef how one can cook dinner a dish however to arrange it on their behalf, releasing them as much as concentrate on 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 have interaction frequently 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 nicely sufficient and consider AI can present an answer, our workforce begins experimenting with the expertise to handle the problem. Every function’s journey begins not with the expertise however from the person’s ache level.
As an example, we discovered from our customers that new admins face a big barrier when creating complicated 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 purposes. How will these new merchandise rework the way in which customers set targets, monitor work, and harness information extra successfully?
AI may have a profound affect on what future information staff can accomplish and the way they work together with software program. Organizations will evolve, turning into flatter, extra nimble, and extra environment friendly. Tasks would require fewer individuals to coordinate and ship. Whereas this feels like a daring prediction, it’s already taking form by means of 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 example, AI assistants can take assembly notes or checklist motion gadgets. 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 primarily based on business greatest practices and the corporate’s distinctive context, easing the psychological load on customers as they study to outline efficient OKRs.
Pure language interfaces characterize 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 information staff, but this interconnected energy has introduced important complexity.
Till not too long ago, there wasn’t a simple option to navigate this complexity. Now, AI and pure language interfaces are making it manageable and accessible. For instance, one in every of Appfire’s hottest app classes is Doc Administration. Many Fortune 500 firms require doc workflows for compliance or regulatory assessment. Quickly, creating these workflows could possibly be so simple as talking to the system. A supervisor may say, “For a coverage to be authorized and distributed to all staff, it first must be reviewed and authorized 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 supply ideas 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 complicated duties comparable to conducting analysis, gathering info from a number of sources, and coordinating with different brokers and other people to ship a proposal inside hours or days. This agent-run method will transcend easy interactions like these with ChatGPT; brokers will turn out to be proactive, maybe suggesting a draft presentation deck earlier than you even notice you want one. And voice interactions with brokers will turn out to be extra widespread, permitting customers to work whereas on the go.
In abstract, the place we’re heading with AI in information work is akin to how we now function autos: we all know the place we wish to go however sometimes don’t want to grasp the intricacies of combustion engines or fine-tune the automotive 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 features 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 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 typically complicated methodology, AI may present steerage in formulating efficient Key Outcomes aligned with particular targets, making the OKR course of extra approachable.
Lastly, WorkFlow Professional represents an progressive option 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 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, we’ve got 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 enables product groups throughout our apps to connect with a number of LLMs. Now that the service is dwell, 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 satisfy altering buyer wants and market calls for?
At Appfire, a product supervisor’s high 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 business for greatest practices. On the client aspect, we frequently have interaction with our customers to grasp their challenges, not solely inside our apps but additionally within the underlying platforms they use.
Once we determine an overlap between technical feasibility and a significant buyer want, we concentrate on delivering a safe and strong 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 goal. We’re not integrating AI simply to verify a field; our objective 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. In the end, we purpose to make sure AI inside our apps allows customers to realize worth extra shortly.
Trying forward, what tendencies in AI improvement do you assume may have the best affect on the SaaS business within the coming years?
Two main AI tendencies that may form the SaaS business within the coming years are the rise of AI-powered brokers and rising considerations about safety and privateness.
Some argue that agent expertise has but to dwell 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 is going to accomplish over a decade. Whereas present agent use circumstances are certainly restricted, we’re witnessing huge investments in agentic workflows all through the software program worth chain. Foundational fashions from firms like OpenAI and Anthropic, together with platforms Appfire presently 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 broaden, so do the dangers related to information safety and privateness. Enterprises should make sure that any AI integration respects and protects each their belongings and people of their prospects, from delicate information to broader safety measures. Balancing innovation with strong safety practices can 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.
