Hype and Hiring: The AI Reality in Media Tech

Over the last 12 months, I have lost count of the number of conversations where founders, investors and senior leaders have told me some version of the same thing.

“We need an AI story.”

The challenge is that a story and a strategy are not the same thing.

Across the global broadcast, media, entertainment and sports technology industries, AI has shifted from an innovation topic to a commercial expectation. Investor decks now routinely include AI positioning. Product roadmaps are being reframed through an AI lens. Hiring mandates reference AI capability, even when businesses are still trying to define what that means in practice.

Some of this momentum is justified. AI will reshape parts of our industry, and in many areas it already is. From metadata tagging and content discovery to localisation, highlights creation, virtual production and audience personalisation, the opportunities are real and significant.

But alongside the genuine innovation, there is an increasing amount of noise.

And the industry is starting to feel the tension between the two.

Everyone Has an AI Strategy Now

A few years ago, companies could position themselves around cloud migration, remote production, IP transformation or streaming growth. Today, almost every leadership team feels pressure to demonstrate an AI narrative, whether or not the capability meaningfully exists yet.

That pressure is coming from every direction. Investors want scalable growth stories. Customers are asking questions about automation. Boards want reassurance that the business is well positioned for the next five years. Competitors are repositioning aggressively.

The result is that AI is now appearing in almost every conversation, every deck and every roadmap.

But sophisticated investors and buyers are becoming alert to the fact that not all AI positioning is equal.

There is a growing gap between businesses genuinely embedding AI into products, workflows and customer outcomes, and businesses rebranding existing functionality to stay commercially relevant. This is not always deliberate. In a market moving this quickly, the line between aspiration and reality can blur even for well-intentioned leadership teams.

But the gap is starting to be exposed.

In M&A conversations, simply saying “we use AI” is no longer enough. Buyers want to understand what problem AI is actually solving, what data advantage exists, whether adoption is real, and whether the value is measurable. Where the answer is not clear, deals slow down. In a handful of cases this year, I have seen transactions stall entirely once the AI story did not hold up under scrutiny.

Investor Pressure Is Driving the Wrong Hires

This pressure is flowing directly into hiring decisions, and that is where the cost will show up.

Some of the urgency is understandable. AI is evolving quickly, and nobody wants to be left behind. But the unintended consequence is that many businesses are becoming reactive in how they build their teams.

Some are restructuring prematurely. Some are reducing headcount before fully understanding where human expertise still creates competitive advantage. Others are rushing into AI-related hiring without clarity on what the role is actually meant to do, beyond signalling to the market that the company is taking AI seriously.

In several cases this year, I have seen companies appoint senior AI leadership simply because they felt they needed someone visible in place.

But visibility and strategy are not the same thing either.

My honest view is that a meaningful share of the AI hires being made right now under investor pressure will turn out to be the wrong hires. The cost will not be obvious immediately. It will show up in 18 to 24 months, when companies realise they hired for terminology rather than judgement, and when the leaders they actually needed have already gone elsewhere.

The strongest businesses I am speaking with are not necessarily the loudest about AI externally. They are the ones assessing where AI genuinely improves workflows, investing in infrastructure and data foundations, and thinking carefully about how people and technology work together over the longer term.

That approach may not generate the most exciting headline. But it tends to create stronger businesses over time.

Hiring for Terminology Rather Than Capability

From a recruitment perspective, this is one of the most challenging periods I have seen in years.

The pace of technological change is creating genuine uncertainty around hiring itself. Businesses are still trying to work out what capabilities they actually need, how technical those hires need to be, and whether they require AI specialists or commercially minded operators who understand how to deploy AI in real workflows.

Job descriptions are shifting constantly. Expectations are moving quickly. In some cases, businesses are hiring for terminology rather than capability.

Candidates are under similar pressure to position themselves within the AI conversation. Unsurprisingly, that can lead to inflated claims of expertise or a tendency to overstate hands-on experience.

The reality is that very few people have fully mastered this landscape yet.

And that is okay.

What matters more is adaptability, the ability to learn quickly, and the judgement to apply technology intelligently within a commercial context.

The candidates who stand out most to me right now are rarely the ones trying hardest to sound like AI experts. More often, they are the people who combine curiosity with strong judgement, communication skills, commercial understanding, and the ability to evolve alongside the market.

Those qualities remain incredibly valuable.

Human Skills Are Becoming More Important, Not Less

One of the biggest misconceptions about AI is that technology automatically reduces the value of people.

I believe the opposite is true.

As AI lowers the barrier to execution, the value of human judgement rises.

In broadcast, sports and entertainment, where storytelling, creativity, trust, timing and emotional connection matter enormously, human discernment plays a critical role. AI can assist with efficiency. It can accelerate workflows. It can process information at scale.

But it cannot replace leadership maturity, relationship-building, taste, contextual understanding, or the ability to make sound decisions under pressure.

Those qualities become more valuable as automation becomes more widespread, not less.

Communication is another area that is consistently underestimated. Leaders who can communicate clearly, align teams, simplify complexity and bring people with them are at a significant commercial advantage. Businesses still buy from people they trust.

Technology evolves quickly. Trust still compounds slowly.

That part has not changed.

What the Next 18 Months Will Reveal

It is not coming. It is already here.

In M&A processes, thin AI stories are being exposed in due diligence. In leadership searches, hires made under pressure 12 months ago are starting to show the strain. In product, the gap between what was promised and what shipped is becoming visible to customers.

The industry has been through disruption before, and it will adapt. But the next 18 months will separate the companies that treated AI as a positioning exercise from the ones that treated it as a leadership problem.

The companies that come out of this period strongest will be the ones that resisted the pressure to hire for the story, and instead hired for the judgement to build a real one.