Why We Exist.

TLDR: AI is software. Software always needs good data. Ergo, we need to fix the data.

Hi, I'm William - founder of Modern Productivity. Our philosophy boils down to one belief: over the long term, fixing the underlying issues with data is the single, most valuable investment any business can make. The AI era has made that belief urgent - not theoretical - for every senior leader signing off on the next wave of spend. We have already lived through this once, and it saddens me that many organisations are about to pay a lot of money to learn it again:

I refer to the dawn of Business Intelligence tooling, which arrived to much fanfare and corporate purse-opening. However instead of using BI to expose the major flaws in corporate data estates - e.g. the absurdity of holding 38 duplicate suppliers across four different instances of SAP - it was used predominantly as an aesthetic presentation layer. Vendors charged top dollar to create beautiful, colourful dashboards that made everything look wonderful while the underlying semantic plumbing was still leaking into the basement. These manufactured chains of hidden digital spaghetti presented their own, novel problems: now understood only by a handful of reporting professionals or worse still external vendors, further increasing friction, complexity debt, and creating significant knowledge-retention risk.

The agentic era upgrades the old adage. Garbage in, garbage out becomes garbage in - incredibly expensive and authoritative-sounding garbage out. Here is my prediction of the mistakes enterprises are about to, if not already, embark upon:

• Software and AI vendors will charge exorbitant fees to build semantic layers on overly complex, fundamentally broken core data. (It is a much easier, more lucrative sell to offer a shiny AI wrapper than a gruelling, complicated, data governance and simplification programme.)
• Because LLM and agentic workflows charge by the token, a messy data estate effectively acts as a heavy tax. Lookups that should cost fractions of a penny against clean data run to dollars per query against redundant, inconsistent structures - a multiplier that turns tolerable pilot spend into a board-level problem at scale.
• Responses take too long to be useful, costs will skyrocket, accuracy will be questionable, and users will lose confidence in the results.
• Inevitably, the expense becomes too astronomical to sustain, scope is cut, and the benefits that would have been unlocked had underlying complexity been addressed will never be realised.

At Modern Productivity, we are here to directly challenge this paradigm because it violates our key principle of addressing complexity. Specifically, our challenge is this: would it not be vastly easier - and vastly more courageous - to face the elephant in the room and tackle these structural issues head-on? I would posit that the single, true, and most effective ROI for enterprise AI is not automation or generation for its own sake - but entity resolution, duplicate / anomaly detection, and targeted reasoning to locate where the data estate is weakest - so we can fix it. We need to use AI to expose complexity and radically simplify it.

Many companies will spend millions learning that the hard way. I would rather we skip that part and get straight to delivering value. If that resonates with you - drop us a line.

Complexity is corporate cancer. I say this not because it's a statement PR professionals love, nor to be unnecessarily facetious or provocative. Complexity, like cancer, spreads from the inside out - often undetected until the damage is structural. It drains resources, erodes capability, and compromises the integrity of everything built on top of it. This has to be addressed if you want a productive, cost efficient organisation in a modern world.

This is not surprising. On the surface, we feel that simplicity should be easy. It sounds simple and it's easy to conceptualise. The sad truth is that simplicity is actually extremely challenging to achieve. Human evolution wires our default settings to be predisposed to building rather than continuously reforming. I like to imagine organisations as an army of spiders, each hell-bent on spinning their own web. Convincing them to work on the same web requires leadership, incentivisation, discipline, constant monitoring, intellectual and practical rigour, high levels of collaboration and communication, and a great deal of time explaining to each spider why it matters to stick to and enforce the central nodes. Worst of all, it involves painstakingly untangling interwoven threads - many wrapped in several others that should never have been woven in the first place, and many not visible until you tug on another.

While we untangle yesterday's threads, complexity continues accumulating in BAU. Rather than pause and confront that drift, we are told the answer is another platform, another integration layer, another AI pilot - a pattern I have watched play out often enough to recognise the ending. Good-faith responses, in other words, can accidentally compound the disorder they were meant to relieve. Trying to buy your way out of complexity before understanding what (webs) you are building on is how organisations keep spending heavily to make the problem worse.

Fixing this is in itself highly complex. It requires a smorgasbord of disciplines - gravitas, charisma, and patience to name a few. That difficulty is not an excuse to look away - but rather to lean in. Complexity left to accumulate is a signal from leadership - louder than any values statement on any wall - that people's judgment and years are not worth the untangling. If an organisation claims to value its people, simplification is how that claim is honoured.

I am acutely aware of what the large advisory firms optimise for - and it is rarely speed, intimacy, or accountability to outcomes. While I respect the intellect of many of their employees, it is undeniable how these firms often thrive commercially in ambiguous or complex environments. We are not that model, we do not pretend to be, and we do not want to be.

When we partner with you, I do not want us to behave like outsiders dispensing advice from a safe distance. I want us to treat your business as if it were our own, and do valuable, meaningful work alongside you that we can both be proud of. If an initiative does not move your bottom line, improve your operational resilience, reduce your complexity, or help your people to do excellent work, then we should not do it.

This is the principle that surprises people most, and it is the one I am most committed to.

From the first day of an engagement, we aim to build capability, processes, and clarity that work long after we have left. The more replaceable we make ourselves, the more valuable I perceive the engagement has been. If you still need us for the same problem in three years' time, then we didn't finish the job.

This is not altruism, but a belief stemming from the fact that there is always more work for people with enough integrity to democratise information and do their utmost to optimise or automate their work away.