Matt Mullen

Art materials spread across a cutting board.

The art isn’t falling apart: AI, the mechanical and the compositional

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.

Having spent much of the last six months working on the two Work Intelligence reports now available - “Work Intelligence: Improving Processes by Balancing Human Intelligence and AI” (which requires free registration) and “Work Intelligence: Market Analysis” (which requires your wallet), there are a number of notes that I jotted down while building that analysis that didn’t make the final report. Some of these are filed away to be added to the “to do” list for the next update, others are more general points that while interesting, perhaps lack the specificity for such a report. That of course doesn’t mean that they’re not worth letting you all in on, so in the next couple of analyst notes I thought I’d share some of these additional chunks of knowledge that might be new or useful (preferably, both). But first, let’s talk about markets. All markets are, in essence, artificial constructs. That is not to say that dynamics that can be identified, analyzed and reported upon around a market is false, but rather that the demarcation of a set of tools, use cases and buyers as a defined market is itself a construct that exists largely to make analysis possible. 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. And it’s a very successful and lucrative business for sure. There is good money to be made in slicing and dicing BPA from Mining, Low Code and RPA, even if they may all be used to solve the same problems within an organization. But what you end up with is a set of discreet, often overlapping, but specifically defined markets on which you can overlay a set of conventions as to what they must have to be considered a member of that market. As a mechanism for sorting things into piles, it’s great. As a mechanism for understanding how these pieces might go to help an organization operate better, it sucks. So what is Work Intelligence then, if it’s not a market? When we started to put together what became Work Intelligence, it was less to capture and define a market, but rather to identify and analyze a set of complementary technologies, with dependencies and connections that - when combined - was instructive in presenting a way for organizations to improve the way in which they work. Importantly, those sets of connections enabled the sum of the parts to become greater than any of the individual pieces. For some, it meant an extended lifecycle, for others it meant an accelerated adolescence. But for all, those interdependencies were indicative of a combined superset output, which we defined and called Work Intelligence. Which is a long winded way of saying that it’s a set of connected markets. So why is that different and why does that offer a greater insight? Our decision to frame what we were observing this way was informed by our approach to (to slightly paraphrase our initial introductory report from the beginning of 2023) “diverge deliberately from the notion of efficiency and focus on organizational health”. That is to say we looked at what organizations were beginning to incubate in terms of change and then overlaid the tools that most likely to have a positive impact in enabling that change. For one, that grounds Work Intelligence in something real and demonstrable right now - as opposed to a vague promise set years into the future - and secondly, it helps explain how and why those connected technologies can grow in usage through a form of interdependence in the pursuit of that outcome. What all of this means in terms of what we’re projecting within the Work Intelligence: Market Analysis for the next few years is that the fortunes for the technologies within the sub-markets that we’ve identified, are highly interconnected and interdependent. The rise of one sub-market is likely to hoist those related as a result, albeit with a slight delay in that being apparent in terms of revenue. For example, there is an interesting set of dependencies developing between the automation platforms referred to as Task Execution in the report and those analysis tools we term Mining Intelligence. In order to identify which parts of an organization's operations could be candidates for automation, we need to be able to define all the ways in which that operation is currently executed. In short, to consider automation, you need to first find the automatable and then decide the path that such an automation should follow. We can see that playing out in our sub-market CAGRs within the reports and as we additionally point to within our M&A analysis, it’s no great surprise that Task Execution vendors have been snapping up nascent Mining Intelligence vendors as this plays out. These interdependencies naturally don’t remain static. The ways in which the growth rates of these sub-markets change during the timeframe that we’ve looked at, demonstrate this fact. The heat being generated by the broad consideration of automation as a first instinct has already begun to fade and we’re seeing a change toward a more considered approach to automation as an important, perhaps vital tool but the only tool that matters. Vendor strategies in parallel are broadening too. Organizations have realized that not every part of every operation is identical and easily repeatable. They, the people they serve and those they employ to serve are more complex than to fit that single mode of thinking about change. And right now, of all times….. Even though it wasn’t in the express design when we first set out, even in the early part of the second half of 2022, it was clear that the tide was changing for big transformation projects. The period since, with ongoing traumatic employment consequences in the software industry has isolated nobody from its consequences. Organizations are now actively looking to reduce the number of technology partners and we’re often hearing of “zero budget” IT landscapes, where you’re told that if you want to spend money, you first have to raise it. There’s a great sensitivity to spending; distinct to some downturns in that it is much about the perception of being thrifty in an environment where the workforce at large is being expected to make do with less in many geographies. Organizations big and small alike are looking for efficiency gains, and looking for long term holistic and adaptable solutions to get them there. Additionally after the past few years of chaos, they want much better insight into what is really happening across their business processes, and work intelligence makes a great deal of sense in that respect. So what’s your next move? If this is something that your organization is in the midst of considering - whether as end users, integrators of software vendors - you’ve read the report and you’d like a neutral sounding board to work through your ideas with and plan a roadmap for change, please get in touch for a chat.

Connected Markets: Lesson one from compiling Work Intelligence

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.

Image of research notebooks and a pencil

New Report: Work Intelligence

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

A Buff-tailed bee on the flower of the Creeping Thistle

Why we need to automate our automation choices, and why we often don’t.

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.

The former range markers at RSPB Rainham

Google Cloud Next; from cost management to value creation

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.

Redwood Tree

Dreamforce 2022: Salesforce summons Genie to enable its real time, customer data wishes

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).

Common Tern

Moments of Action; applying AI to the tasks that matter

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.

Transamerica Pyramid, San Francisco CA

Standing up isn’t easy: Industry Verticalization of AI

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 …

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