Generative AI is making giant waves and stands to revolutionize how we think about creativity and problem-solving. It’s exciting and the technology’s conversational interface has proven accessible to a much broader audience than previous AI technologies. It can quickly learn patterns to generate novel and unique content with just a tiny investment of time. And while it excels in purely creative use-cases, it also holds immense potential for accelerating real-world business tasks, as noted by Nintex’s Chris Ellis in late February.
As excited as we are about generative AI technology here at Nintex, we are also grounded in the realization that what we build needs to reflect the diversity of the customers that we aim to serve each day. To that end, we’ve created a set of four responsible automation principles that will help guide our own decisions, and continue to give our customers and partners the confidence that we’re building technology responsibly.
1. People centric designs
As a company with a rich history of process automation, we know how transformative it can be. We also know that not all processes should be automated. We know that regardless of the process or its complexity, prioritizing the needs and experiences of the user above all else will result in products and services that better meet the needs of our diverse customer base. In the long-term we’re confident that this will result in increased user satisfaction, loyalty, and positive impact on society.
2. Transparency and accountability
Human process modelers and automation designers are best suited to understand the nuances of their environment. We’re committed to ensuring that our customers and partners understand how our AI solutions are built and recognize that automated decisions must be fully transparent. We’re committed to ensuring we provide clear process documentation blueprints so everyone knows how to build and re-build solutions as we all learn more.
3. Governance and ethics
Governance has been a core component of the Nintex digital process system for more than a decade. As organizations begin to explore the potential of generative AI, clear guidelines and ethical frameworks for their development and deployment will be essential to avoid inadvertently perpetuating social inequalities, or cognitive biases.
We’re committed to ensuring our customers and partners are not just aware of these risks but also providing design and decision transparency to make it easy to identify, adapt or even abandon automation solutions that are not achieving their intended outcomes or causing unintended harm.
4. Privacy and security
Our solutions are built with data protection, sovereignty, and privacy at its core. As Nintex and our customers begin to explore the potential of generative AI we will continue to adopt a privacy and security posture that’s designed to prevent misuse and protect our customers’ data.
With those responsible automation principles in mind we wanted to give you a sense of how we’re using AI today to create an infinite learning and process discovery loop, and show you some of the innovative AI solutions on our near-term roadmap.
Nintex AI today
Artificial intelligence is the next logical step in automation, saving people valuable time: something Nintex knows all about!
Here are a few examples of Nintex products utilizing AI:
1. For the past two years, Nintex Automation Cloud has offered a patent-pending feature called Intelligent PDF Forms Converter, which uses computer-vision AI to analyze and digitize PDF files, including scanned/image-based PDFs, converting them into Nintex Forms. During the conversion process, users can interact with the PDF to help improve accuracy, and once the conversion has occurred, the form is fully editable. Form designers converting their paper-based or PDF forms often have a collection of existing PDFs, and this feature can save them hours as they digitize.
2. Nintex customers benefit from discovering and documenting their existing processes so they can manage, optimize, and automate them. Our Nintex Process Discovery product (via the acquisition of Kryon) builds detailed process models and descriptions simply by observing what users do at their desktops through video and activity analysis. The system can be configured to automatically mask PII (personally identifiable information) so it doesn’t appear in the details. All of this is accomplished by using multiple AI/ML algorithms such as computer vision, clustering algorithms, NLP (natural language processing), and others, all working together.
3. Nintex customers who manage and document their processes in Nintex Process Manager, often automate those processes in Automation Cloud. To help save time, our patented Nintex Workflow Generator feature has been shipping since 2019, using NLU (Natural Language Understanding) AI to generate the starting point for a workflow definition based on the process definition.
4. Nintex recently released an Xtension for OpenAI. This Xtension interacts directly with OpenAI APIs, including both ChatGPT and Dall-E, allowing workflow designers to harness the power of these tools in their Automation Cloud workflows.
5. Automation Cloud supports an Azure Computer Vision connector that can be used when designing workflows that need OCR or other AI-based document analysis.
When generative AI emerged, Nintex employees and customers began generating so many great ideas for utilizing it, we decided to group the collective ideas into the following categories to get a better handle on them:
1. Nintex customers can use generative AI as part of their automations using Nintex no-code/low-code RPA and Workflow automation products. (Xtension for OpenAI is a perfect example of this!)
2. Generative AI can significantly enhance the experience of interacting with the Nintex Community to access Nintex news, guidelines, forums, how-to articles, technical articles, support, best practices, etc.
3. Generative AI can accelerate how Nintex develops new features and products, including documentation, software testing, and even code, allowing Nintex to bring more value to customers more quickly.
4. And, of course, AI (including generative AI) can be built into new Nintex product features, saving our customers even more time.
We’re excited about the ideas in all these categories, especially #4! Here are some AI explorations in that category currently in the oven at Nintex!
1. The Automation Cloud Forms team recently created a generative AI proof-of-concept where a form designer asks an AI assistant to create a form. The AI assistant then adjusts and enhances the results based on the designer’s continued interaction and explicit instruction. The designer can then preview and make final tweaks to the form before proceeding with their work.
2. From there, AI is used in our next proof-of-concept to help customers localize their Nintex form by automatically translating labels and configurable options into additional languages, saving the designer precious time. The designer can then test translation with a trusted employee or translator.
3. Nintex recently released an advanced private preview of our new BPMN 2.0 modeling features, which we’re hoping to share with everyone later in the summer. This exciting addition to our Process Manager product enables IT professionals to participate in their company’s process center of excellence, taking process definition to the next level and moving us closer to a single source of truth for process intelligence. We are super excited about these new modeling features, which will be released for general availability next quarter!
To help people import their previous process diagrams as BPMN models in Process Manager, we’ve been experimenting with an AI proof-of-concept to convert image files to interactive BPMN process models. The user selects an image file (PNG, JPG, etc.), and the AI detects objects in the image using computer vision algorithms. The user can enhance the conversion before making any final edits to the BPMN model, before saving it. An optional simulation step dynamically illustrates how the process flows. This conversion feature could save customers significant time as they migrate existing process documentation and diagrams, bringing them life to them in Process Manager.
4. Our final example brings us back to Process Discovery, where one of the more time-consuming requirements when completing a process capture involves someone sifting through process map elements to extrapolate the context and give meaningful names to each step.
Our AI engineering team discovered that generative AI was a perfect candidate to help do this step automatically, inferring the context based on previously gathered information about the process. The team determined that this AI feature could save approximately 30 minutes per process, and with some customers having tens or even a hundred processes, this represents significant time savings.
At Nintex, we’re exploring how generative AI will enhance our software and our lives moving forward, and we’re optimistic. We also continue to explore other forms of AI and other technologies that can enhance our customers’ experiences and save valuable time!
Ready to explore?
Nintex is committed to our mission of helping business users save time through process intelligence and automation – as AI emerges, it stands to help accelerate and enhance our customer experiences, saving all of us even more time.