Over-reactions to technology stories that gain mainstream traction are not unusual (nor is the desire to grift for personal benefit from the event, sadly), but what sets this cycle apart is the relative accessibility of the technology coupled with the extreme nature of the reaction.
If we were to be speaking to two start-up peers in North America vs Europe, where everything was identical about their product, one would be citing the university program and research papers of the founders, while the other would be citing the prior company logos of the founders.
It’s perhaps natural at this phase to be overly concerned with the mechanics of how to engineer generation, rather than question what is being generated. Once that begins to be questioned, then it is naturally the legality and efficacy that follows, the nature of originality and composition fall far further down the list.
All markets are, in essence, artificial constructs [..] Lassoing a set of technologies and suggesting a core similarity, such that those similarities become a set of defining characteristics, is a big part of the traditional analyst firm business model [..] As a mechanism for understanding how these pieces might go to help an organization operate better, it sucks.
Our new long form (and free) report encompasses Process Mining, Task Mining, RPA, BPM, Workflow Automation, Decision Intelligence, Process Orchestration, Low Code and No Code platforms. As from a buyers, and users perspective, these technologies are all used to automate and monitor business process activities
When you consider the certainty of uncertainty – that no matter how diligently you prepare, you cannot predict what is around the corner – how does that affect how you think about planning your work?
Those who know me well will know that I’m a massive public transport nerd outside my weekday life. Avoid me at parties, but specifically because I’ll end up talking about this. It’s not the trains. It’s more about networks and their interconnectivity, how these networks have grown over time, often through accident rather than design, and how their development tracks against our collective social history.
Google’s cloud business – encompassing everything from computing power to desktop productivity – has suffered an identity crisis throughout its life. For a company that generates an overwhelming amount of its revenue through services outside the cloud business (92% as of Q4 2021), the cloud side of its business seemed to have little direction other than the promise of cost reduction to prospective customers. With this week’s global “Cloud Next” conferences, Google is signaling that it now sees the creation of value as the way to power its next growth phase.
Genie is the culmination of at least a decade’s worth of incremental progress building on top of the core CRM product through both organic development and a significant volume of acquisitions (of relevance here includes ExactTarget, Krux, Mulesoft as the most significant, at the head of a much longer list).
There is no shortage of opportunities for organizations and suppliers to identify those moments and create potentially significant value for each. Managing the data-heavy requirements of the AI to enable them, along with governance structures to ensure their efficacy in operation – not forgetting their accuracy in the outcome – remains a challenge.
A significant decision along the path from interesting to useful is where AI technology’s application hits the industry verticalization challenge. Lawyers want AI specific to their legal practice; healthcare insurance providers need AI that understands the complexity and details of healthcare insurance etc. For a technology vendor, the need to meet the requirements of a …
Standing up isn’t easy: Industry Verticalization of AI Read More »
Value that should be front and center, not the underlying technology, however interesting. The successful business application of AI is ultimately just as revolutionary, if not more so, than the code, algorithms, models, and processors used.
