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Big data, machine learning and smart machines are changing how we live and work. For example, we can buy laundry detergent via Amazon’s Alexa personal assistant, or use Chordify to learn the chords needed to play a song on a guitar. The emergence of smart machines has given rise to chat bots we can interact with to answer our questions and take action for us, based on our directions.
Machine learning as a technology is at a tipping point. In its 2016 Hype Cycle for Emerging Technologies, Gartner placed machine learning at the top of the “Peak of Inflated Expectations” and said, “Smart machine technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power.” As we look for ways to accomplish tasks with limited time, whether at home or work, we are increasingly expecting machines to know what we want, when we want it, and how to take action to clear those tasks from our list.
This is largely because we have become a mobile-centric, multitasking society. A recent survey from Deloitte LLP found that in the aggretate, Americans look at their smartphones more than 8 billion times a day. Further, Business Insider Intelligence found that more users are logging into messaging apps than social networks and are doing more than just chatting with friends. Users, particularly the up-and-coming generation of professionals, are utilizing messaging apps more rapidly every day. Why? To be more productive and interact with bots to automate more processes. The rise of Slack in the past year is proof.
With the unprecedented computing power Amazon, Microsoft and Google (among others) make available, there will be a significant opportunity to reinvent how users think about and use software solutions. So how do we take advantage of these technologies and empower people who want to solve problems in new fast ways without depending solely on data engineers or developers?
Let’s apply these technologies to something you probably do many times a day: creating a document and assembling its content from different sources. Opening Microsoft Word or Google Docs and then typing is simple but painfully manual. At K2, we see an opportunity to use natural language engines and self-constructing business policies and rules with intelligent processes to deliver a single solution to auto-generate documents based on context, the latest data, trends and patterns.
We also believe these document assembly approaches can be coupled with chat engines and machine learning to manage tasks between people, systems and machines, making them exponentially more effective. Today’s forms-based interfaces will be augmented with contextualized real-time information and, in many cases, automated conversations. This approach will speed up how we collate and present aggregated content from many sources and result in an exponentially better outcome.
Picture yourself simply saying to your computer, AI device or mobile phone “K2, create an NDA document.” K2 then kicks off a short question and answer phase, and the entire document is created automatically, in context, prepopulated with the right information and clauses in the right sections.
Take it a step further and apply this next-generation document assembly and creation to contract management — something many K2 customers spend a lot of time on. Contract management on both the buy and sell side takes a tremendous amount of time during procurement cycles. There are vast libraries of contract templates for organizations to draft and sign — thousands of contracts annually in many cases. Yet once they are completed they are filed, templates are updated and the process starts over. This is inefficient for today’s economy, and companies will need to do things faster and faster going forward.
Instead of this slow approach, imagine using machine learning to constantly analyze historical contracts, the context and nature of the agreement, the parties involved, and other changing circumstances. From this information K2 dynamically constructs a legal document with a much higher probability of getting signed in the shortest time possible, yet mitigates the risks given the circumstances of the transaction.
Once the draft is created, say “K2, send NDA for review” to finalize it. The K2 bot automatically initiates a workflow that sends a text message or an IM to those involved to interact with the bot to keep the process moving, ensuring our processes work where we are working. The essence of efficiency.
The rapid evolution of natural language systems, including auto-learning capabilities, machine learning for pattern recognition and optimization, self-authoring, and self-optimizing rules and policies, will allow us to create a next-generation mesh platform for intelligent, event-driven business processes. These natural language and conversational techniques allow people on the other side to make fast and informed decisions with the ability to ask or provide as much context as needed to complete tasks or keep them moving.
We see many new opportunities to combine these technologies into solutions that will allow our customers to run their organizations at the speed of innovation. Smart machines are key for us to empower people to rapidly assemble solutions that span devices and platforms, without the need for huge development teams, to enter a new era of running more effective and optimized businesses.
What have you seen as the most exciting smart machine-enabled technology? What do you dream about for the future and hope machines can do for us in the next year and beyond? Join the conversation on LinkedIn or Twitter using #LowCodeFuture5.