If knowledge is power, then knowledge visibility is a superpower. Full knowledge visibility requires powerful AI to continuously read, organize and retrieve all the content in your systems.
Speaker: Igor Jablokov, CEO & Founder, Pryon
Session Description: Named an “Industry Luminary” by Speech Technology magazine, our speaker founded the world’s first high-accuracy, fully automated cloud platform for voice recognition which served as the nucleus for follow-on products such as Alexa, Echo, and Fire TV and which was the precursor to Watson when he was with IBM. In this session, he discusses knowledge ops, an AI-enabled, process-oriented methodology used by business units, knowledge managers, and application developers to improve the quality and accessibility of knowledge through the enterprise. It’s an emerging, additive approach to KM challenges, including continuously capturing and making available everything the company knows across all departments in near real time so decisions can be made and actions prioritized based on the best possible information; knowing what is valuable to a person it has never interacted with; where all this knowledge should be collected and who will keep it organized as it grows and grows. AI plays a massive supporting role here, orchestrating and fusing data from any information, content, or knowledge place and file format. Our speaker emphasizes how augmenting agents with a state-of-the-art language model that understands natural language queries can help organizations eliminate steep learning curves so new agents perform like pros on Day One.
[These are my notes from the KMWorld Connect 2021 Conference. I’m publishing them as soon as possible after the end of a session so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]
- Augmented Intelligence vs Artificial Intelligence
- He prefers to focus on Augmented Intelligence rather than Artificial Intelligence. People are concerned that artificial intelligence will replace humans. But he believes the better approach is to use Augmented Intelligence, which will not displace humans but will help them work more productively.
- [All references to AI in this post are intended to be references to Augmented Intelligence.]
- Why focus on Augmented Intelligence?
- AI transforms raw data into consumable knowledge
- Once the AI is trained to be “literate”, it can “read” across all categories
- What is possible with Augmented Intelligence? To see the current capability of AI, look at the consumer world. Consumers are comfortable using Alexa and Siri.
- Next, they will expect to have similar capabilities at work.
- BUT the enterprise has to manage this at scale within typical organizational constraints (e.g., security, competitive, regulatory, etc.). So the enterprise is slow to meet this demand.
- Imagine the future of work in 2030
- Wall Street 2030 will reward the AI winners and losers
- Companies that left employees out of their AI strategy got left behind
- Companies who moved from narrow to unified platforms were winners (by working more comprehensively rather than limiting themselves to small use cases)
- 2030 org charts will include humans + AI
- humans will manage a team of AIs doing the rudimentary work, which will free humans up to deal primarily with more challenging and interesting issues
- Companies will need to increase capacity by 10X but will not be able to increase their staff by 10X. So they will make up the difference in new AIs that can plug the gap.
- AI Assistants
- AI can predict, read, summarize, find answers and experts. With this out of the way, their human colleagues can focus on the more abstract, conceptual work.
- Big tech is making heavy investments in AI assistants. Those assistants will become the dominant interface between the users and the 278 apps (and their unique passwords) on your phone. The AI assistant becomes the agent that “harmonizes” your experience of the various apps.
- Assistant to Assistant Collaboration: Soon, you will be able to say “Have your AI call my AI.”
- Wall Street 2030 will reward the AI winners and losers
- Present Day Capabilities
- If knowledge is power, then knowledge visibility is a superpower.
- Companies win with enhanced knowledge visibility. Key examples:
- Full knowledge visibility requires powerful AI to continuously read, organize and retrieve all the content in your systems. It also provides next generation experiences to employees and customers via natural language processing that is automated as much as possible.
- NO code, no skills required to create apps. You need to democratize access to knowledge and move the engineering to the edge.
- Benefits of full knowledge visibility
- with a knowledge “fabric” that is centralized and easy to access, you will be able to achieve better business outcomes and improve efficiency.
- Legacy Knowledge Management has failed us:
- Deloitte found that 91% of organizations do not have knowledge management capabilities and systems ready to take advantage of AI.
- Knowledge in an organization is scattered and siloed.
- Companies often either do custom development tailored to their content or they give up and just create a people-centric model in the hope that connecting people will help them keep up with the rapid growth in content.
- How AI solves Knowledge Management
- The first mile: it more comprehensively gathers ALL the content — it can gather, unify, normalize the content and then fill in the missing gaps in knowledge.
- The last mile: it uses NLP to deliver content that is tailored to the individual needs of users.
- Ambient Computing will leave current capabilities in the dust. Ambient computing will predict what you want and deliver it to you BEFORE you even ask for it.
- Farm-to-Table AI: look for vendors who can offer creative AI to you as soon as it is available rather than waiting for large tech companies to select, standardize and “cook” the technology for you first.
- Modern AI changes your knowledge base strategy. He suggests that we should no longer create knowledge bases. Instead, he believes that modern AI can create a better knowledge base for you, and do so more comprehensively and accurately.