Knowledge Managment

GPT To Boost Knowledge Management Adoption

At Deep Analysis, we keep up with the pace of knowledge management innovation, so you don’t have to. To the IT mainstream, KM has long been an esoteric corner where it’s difficult to pin down the actual business value – and that has held back enterprise adoption. That’s about to change with the rise of the LLM.

Box Works 2022 Report

Unlike the past two years’ flurry of game-changing product announcements such as Box Sign, Box Shield, Box Governance, and Box Shuttle, Box Works 2022 was short on “new” and long on “we’re now in GA with all the stuff we said we would do”. This will be good news for Box’s enterprise license customers who seem eager to deploy the new tools.

EX CX

Crossing the EX CX Divide

Those who have been around a while in this industry know that sales often grow fastest in the toughest of times, as efficiencies need to be made and costs saved. Even so, there is always an on-ramp and readjustment to remind and educate buyers and investors alike that to save money, you need to invest in the right places to make the necessary changes. 

KM EX

KM & EX in 2022

I’m not going to lie; I like it when we get things right at Deep Analysis 🙂 Back in 2019, we published a note on what seemed to be a re-emergence of KM (Knowledge Management), and over these past few years, it has come back with a vengeance.

microsoft viva analyst

Microsoft Viva EXP Launch – Our Thoughts

This week Microsoft launched Viva, its employee experience platform, building on the foundational work of Project Cortex. Microsoft describes Viva as an organizing layer across 365 leveraging teams. Nothing too surprising there, but where it does get interesting is its ability to co-ordinate insights, alerts, and information across the enterprise.

dotcom 2.0

Enterprise AI & a New Dotcom?

But, here’s the thing, a couple of years ago, everyone was talking about AI, but few were doing it. This past year things changed fast; now, there is a mad rush to embrace AI or get left behind. But its easier said than done, AI tools, models, and libraries are readily available, but skills, knowledge of specific user needs, and good data are not.