The thirty-fifth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). It's a new year and the beginning of series 5 (despite what Matt says in the intro to this episode, because he lost count) and with Matt and Alan in the chairs, they catch-up on some recent blog posts on the need for specificity, not to ignore paper, how there's no new cash in IT and how to make your Information Management skill invaluable in AI projects.

In this month’s episode:

Specificity & Elephants

Alan's kicked off the new year with a blog post "Navigating 2026: The Demand for Hyper-Specificity and the Persistent Paper Elephant", which is a result of him pondering about 2025 over the festive period. First is the need to continue to focus on the specifics of the outcomes from technology, rather that the technology horizontal markets that might provide them. Focus on the fixes, the bottlenecks in the real world. Second is that there remains a lot more paper in the world that technologists. Our own quantitative research data points not only to a lot of paper being used in processes but also to that amount of paper likely to increase (you can read about this in MMI reports that are available here and here). Matt reminds us that the sole superpower that analysts have is the ability to talk to people.

Beyond the walled gardens

Matt's published a pair of blog posts; at the tail end of last year "Beyond the walled gardens; can business applications break out to be the ultimate agentic orchestrators?" and just this month a follow-up "The lengthy hikes awaiting AI and automation in 2026". As he starts to update the data model for the 2026 edition of the Work Intelligence Market Analysis, he's pondering how AI-native, business application vendors and existing automation vendors are looking to develop their businesses against an tiny inflation in IT budgets (and big renewal numbers being demanded by the core suppliers). The pair also discuss maybe why there's been a lot of big M&A for security software firms lately.

How to not lose the AI opportunity

Finally, Alan wrote an Information Management focused blog post in December "How to not lose the opportunity that AI offers Information Management". Here he discusses the ways in which IM needs to inveigle itself into AI projects to be the good stewards of data; "own the data, own the data sourcing, the cleansing pipeline, and the metadata framework for the vector store".

https://www.youtube.com/watch?v=5ZKtMA72r5A

Related Links for Series 5 Episode 1

Alan's blog post "Navigating 2026: The Demand for Hyper-Specificity and the Persistent Paper Elephant" on the need for extreme specificity and that paper isn't going away.
There a couple of MMI reports that talk about the amount of paper in contemporary processes;
Market Momentum Index: Intelligent Document

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The thirty-fourth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). This month, it's time for the annual Deep Analysis predictions, so Matt is joined by both Alan and Dan to discuss a handful of those in our extensive predictions report and catch up on how well those made 12 months have panned out now that they've made contact with reality.

In this month’s episode:

Dan: 2025 Review and 2026 Prediction

First up, Dan discusses his 2025 prediction, "To grow, intelligent document processing (IDP) companies must cross the border. He's giving himself a tick here, as he's seeing many vendors pushing the edges of both the vertical (industry) and horizontal (business function) markets that they've grown out of. For 2026, Dan is backing the prediction "The unstructured data gold rush will finally begin", which follows on from his 2025 prediction (and also Alan's 2025 prediction recapped below) that the growth in specific vertical and business functions for AI-derived technology like IDP and the broader AI agent market means a need for access to a lot more business data (and all the jeans, shovels and buckets that requires).

Alan: 2025 Review and 2026 Prediction

Alan starts with a review of his 2025 prediction "Structured data people will stop treating unstructured data like something that got stuck in their shoes" and gives himself a partial tick, in that he believes that there is general move from the big application vendors to recognise unstructured data is really useful for context, but also at the same time that it's really complicated to pick out the different data types within that big unstructured pile (and that's not just a technical challenge). For 2026, he's pitching "Edge computing will re-emerge as a strategic imperative"; specifically, that access to AI-derived applications increasingly need to take advantage of on-device processing, clever caching technology, etc., to enable remote workers (and related use cases) to operate smoothly.

Matt: 2025 Review and 2026 Prediction

Finally, it's Matt and he's keen up provide an update on his 2025 prediction "The shift to “payment on outcome” is going to lead to some awkward conversations between customers and suppliers". He explains that the business of developing consumption-based pricing models for AI agents has become increasingly complex, moving beyond the success criteria (especially Salesforce's per-conversation pricing, which remains but is joined by other options). In general, determining the economics of AI agent use remains in the early stages of being made easier to calculate. That notwithstanding, Matt is giving himself a tick here. For 2026, Matt is backing "'Doorstep adoption' of AI will be exposed as a counterproductive farce" as his chosen prediction; that vendors enforcing the bundling of AI tools into renewals doesn't make their adoption real. Matt suggests that it's akin to having a trailer (or a caravan if you prefer) welded to the back of your car without your permission, and then a bill arriving for the job for the excellent utility it provides.

https://www.youtube.com/watch?v=LulxRfzf8jk

Related Links for Series 4 Episode 12

Here's Alan's introductory blog for this year's predictions: It’s Time to Separate the Wine from the Hype.
The download page for the full awards

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The thirty-third episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). This month it's time to discuss the annual Deep Analysis Innovation Awards, so Matt is joined by both Alan and Dan to discuss this year's winners; Infrrd and LatticeFlow AI (but as ever, show notes are included just below the embedded video).

In this month’s episode:

Preamble: Why do we have Innovation Awards?

First up, Alan talks about why Deep Analysis has annual Innovations Awards and why they are different to many other awards that are out there. Firstly, you can't apply for them, and if you win one there's no fee to pay, fancy dinner to attend (and buy a table at) or winners speech to rehearse. Also there's no set number of winners each year either; the team discusses which briefings wowed them and meets the awards criteria; Does it solve a problem? Does it apply ingenuity? Does it add value? Does it show flexibility?

Winner: Infrrd

Dan introduces the first award winner, Infrrd. Having initially annoyed him with their marketing choices, he was won over with the number of patents that have been awarded for their research in document AI and their development team deserves recognition for that work. Additionally, Dan was impressed with a demo involving Autocad drawings and data extraction, retrieval and document understanding (and awarded them a mini award of their own in the Intelligent Document Processing Market Analysis 2025-2028 report for best demo). Finally Dan was also impressed with Infrrd's work on mortgage loan files; a notoriously difficult use case. Alan adds that IDP in general is featuring in virtually every briefing that Deep Analysis receives right now, as it is a vital part of how unstructured data is made available for AI and agentic technology to use and it could be said it's what is driving many of the predominant AI use cases and is fundamental to LLMs themselves.

Winner: LatticeFlow AI

Matt then talks about second 2025 award winner, LatticeFlow AI. One of the features of 2025's discourse has been that AI projects are not making it out of pilots and into production. One of the reasons that is happening is that often the hurdle that is compliance, risk etc are not baked into the development from the beginning. LatticeFlow AI is focused on ensuring that an organizations AI applications meet both internal (think GRC; governance, risk and compliance) and external (regulations, like ISO, EU AI Act etc), with a focus both on internal data quality as well as how well individual models are likely to work in conjunction with it. Alan adds that that it's another areas that underlines the increasing gap between demos and production realities; as soon as the word governance people tend to switch off, but it's a vital part of ensuring that organizations are insulated against inevitable legal challenges coming down the road for organizations that don't realise its importance.

Postamble: What are we looking for in 2026?

To wrap things up, the team nominate what they hope they'll be seeing in 2026 that will end up with them pitching briefings in for the nominations pot in 12 month's time.

Dan: Hoping to see better support for tabular information in IDP products; there's lots of start-ups working on the

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