News
- The second season of Netflix crime drama Dept Q begins production in Edinburgh, Scotland
- Core cast set to return alongside nine new faces
- No word yet on release date or the number of episodes
Netflix has confirmed that the second season of the hit crime drama Dept Q. is now in production, filming in Edinburgh, Scotland.
Our core cast is set to return, including:
- Matthew Goode as DCI Carl Morck
- Alexej Manvelov as Akram Salim
- Jamie Sives as DS James Hardy
- Leah Byrne as DC Rose Dickson
- Mark Bonnar as Stephen Burns,
- Kate Dickie as Moira Jacobson
- Aaron McVeigh as Jasper
- Sanjeev Kohli as Martin Flemming
Nine new cast members will also be seen in new episodes. While we don't know exactly what their roles will be, we do have names:
- Aisling Franciosi as Kimmie
- Greg Wise as Derek Powell
- Nicholas Rowe as Thomas Fulton
- Tony Curran as Winnie Calderwood
- Hamish Clark as Christopher Herron
- Alex Ferns as Phil Allenbeck
- Ross Anderson as Ricky Daddario
- Rebecca Root as June Lovesay
- Isla Johnston as Agnes
Currently, there's no word on exactly when Dept Q. season 2 will be released, nor how many episodes will make up the series.
However, we do have a brief idea of what the plot will involve thanks to the synopsis below.
Dept Q. season 2 set to explore a brand-new caseAs per Netflix, Dept Q. season 2 follows "DCI Carl Morck heading up the maverick Dept. Q from the basement of an Edinburgh police station, charged with cases previously deemed unsolvable.
"This darkly humorous, propulsive show delivers all the pleasures of a procedural but takes us into the complex mysteries not just of the cases but of the detectives themselves."
Unsurprisingly, this is all very vague at this point in development. However, Rob Bullock, Executive Producer, Left Bank Pictures, added in a statement, “This season, Carl and his band of misfits tackle a terrible crime hidden in the highest echelons of Scottish society. It is a story for our times: rich and powerful people who believe they are above the law."
Executive Manda Levin agreed, "The story of season 2 is as darkly delicious as you’d expect, and Carl and his glorious gang will have their work cut out pinning down the perpetrators as we launch back in for more!”
What we know for sure is that we will continue to see an adaptation of Jussi Adler-Olsen’s original novels — but how faithful the show will be remains to be seen.
Data governance is unglamorous work. It is also the reason most AI strategies stall before they scale.
Spending on models, platforms and use cases keeps growing. But the disciplines that make those investments effective – data quality, ownership and governance – often receive far less attention.
Part of the challenge is that data governance is neither “fun” nor “sexy.” It lacks the excitement of new technologies and the appeal of quick wins, so it is consistently deprioritized.
Yet as organizations scale their AI ambitions, governance is increasingly the factor that determines whether those efforts succeed or stall.
The imbalance in attention is now starting to show. While AI adoption continues to grow, many organizations still struggle to move beyond pilot stages into enterprise-scale deployment. The gap between ambition and execution is widening, and weak data governance is often at the center of it.
The issue is not awareness. Most business leaders recognize that governance matters. The challenge is that governance demands structural decisions, cultural alignment and sustained discipline – the hard parts of the job. And, unlike a new platform or tool, its value often only becomes fully apparent when it is missing.
When governance is absent, problems don’t stay smallWeak governance rarely fails loudly at first. The problems accumulate.
Early AI initiatives often prioritize delivery, with dashboards, models and applications taking precedence over governance. Silos form, data definitions diverge and access controls become inconsistent. A common pattern: two teams – one in marketing, one in data science – train separate models against different definitions of the same metric.
Both definitions look correct in isolation. In production, the predictions conflict, neither team can explain why, and the investigation takes weeks longer than building either model did. Quality issues are patched rather than fixed, and new projects begin to rely on shaky assumptions.
As complexity grows over time, confidence in the data declines.
Data dictionaries and permission frameworks are not administrative overhead – they are what makes scalable AI possible. Building them early demands investment before visible returns but postponing that effort is far costlier.
Left unchecked, governance gaps eventually land hard, resulting in delayed projects, compliance failures and decisions made on unreliable data. At that point, organizations are forced into reactive fixes – or even total rebuilds – that are far more expensive and disruptive than addressing governance from the start.
Governance is not just compliance – it enables innovationRegulators are placing increasing importance on accountability in how data is used. The UK’s Information Commissioner’s Office (ICO) has made it clear that organizations must be able to demonstrate control over data use, particularly as AI systems become more prevalent. Scotland’s new National AI Strategy also highlights that organizations must follow best practice in responsible AI governance aligned with OECD principles.
This has reinforced the perception that governance is primarily a compliance exercise – something important but not necessarily prioritized at the prototype stage. Effective governance is far more than that: it shapes how data flows through an organization, how decisions are made and how confidently teams can act. It defines accountability and sets the standards needed to maintain consistency at scale.
In that sense, governance is a design choice, and businesses need to make the right one to effectively scale their innovation ambitions.
Define ownership before you decide the modelGovernance is not one-size-fits-all - nor it is purely a technical problem to be addressed through tools or platforms alone. In fact, the harder initial challenge is often a people and accountability one. Before designing a governance model, organizations need to define the who as much as the how. Who owns the data? Who is responsible for its quality and who decides how it should be used?
In many organizations, these responsibilities are unclear. Management is shared, and ownership is (often wrongly) assumed rather than defined. But it is only once those questions have been answered – in practice as well as on paper – that businesses can turn their attention to developing a governance model that fits their structure.
Some take a centralized approach to this, with control sitting in a single function. This can provide consistency and clarity, but the model may struggle to scale across complex organizations with diverse needs.
Others adopt a federated model, combining central standards with local ownership. This can be more flexible and scalable, but only if the business is committed to those shared standards and has defined clear roles and accountability. Without them, federated models risk furthering data fragmentation.
The key is alignment. Governance models should match how teams actually use data and AI, not how they’re assumed to operate.
A practical test: ask three different teams how they define a key business metric – revenue, active users, or customer churn. If the answers differ, the governance problem already exists. The operating model question is not how to prevent that divergence in future; it is who has the authority to resolve it now.
Governance doesn’t show up in a demoGovernance is rarely the most visible part of an AI strategy. It’s detailed, structural work that often goes overlooked, but that is precisely why it matters.
For business leaders, the challenge is to move beyond acknowledging its importance and begin making early, deliberate decisions about how it is implemented. That means defining data ownership, aligning operating models and investing in the capabilities that support long-term success.
Technology choices are reversible. Data ownership decisions compound. The governance model you design – or neglect – in the next twelve months will shape what your AI strategy can actually deliver in three years.
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Operational performance is becoming just as important as investment performance in private credit.
As fundraising slows and investor expectations increase, firms are facing growing pressure to modernize the IT infrastructure supporting their portfolios.
In fact, transparency and faster reporting are becoming top priorities.
Without such capabilities, funds can’t clearly see across their own portfolio, amplifying stress when markets are less forgiving.
Especially as private credit scales and operational diligence become more central to allocation decisions, such back-office issues become something more structural.
Fortunately, managers that properly invest in their operational foundations now, will be better equipped to manage the increasing demands facing the private credit industry moving forward.
Analyzing Operational PressuresPrivate credit is operationally intensive. And many firms never built systems to match the increasing complexity of their portfolios as they grew and evolved. Instead, operations are often spread across legacy servicing platforms, spreadsheets, email-driven workflows, and disconnected internal tools, leaving firms without a single real-time view of portfolio data.
Most loan servicing operates on cycles such as monthly reconciliations, quarterly reporting, and batch-based payment processing. This model reflects the constraints of legacy systems and manual workflows. Data must be collected, validated, and processed in stages. Consequently, funds often view their portfolios through periodic snapshots rather than in real-time.
Many funds also maintain parallel spreadsheets to verify their servicer's calculations. Known as shadow booking, this redundancy has no other purpose than to increase control. Ultimately though, it’s a sign of mistrust in the data provided, and the underlying calculations are often difficult to inspect. When discrepancies arise, they are discovered after the fact and require manual investigation, often across multiple systems.
These realities of the private credit industry are now colliding with a more competitive fundraising environment. Even if their underlying deals aren't catastrophic, private credit funds begin to look fragile if they cannot perform operationally well under increasing pressure.
Not surprisingly, many private credit firms are looking to AI to address these issues. But automation software alone cannot repair broken operational foundations. The critical question becomes whether their infrastructure is actually prepared to support the AI model they choose to use.
AI Alone Is Not the AnswerAI agents can execute operational work reliably while platforms can provide real-time visibility and full auditability. The pieces are in place. But there’s a pattern in how AI projects fail in the finance industry that's worth naming and it's almost never the model that's the problem.
What’s often missing is the infrastructure surrounding the model - the systems, workflows, data access, permissions, and controls that allow AI to operate reliably inside real financial processes. Otherwise known as the harness. Generic AI tools don’t know what a rate notice is.
They don't know that a prepayment request triggers a multi-step workflow across every syndicated entity on a facility. They don't know the difference between a funded tranche and a committed-but-undisbursed revolver, or why that distinction matters for how a payoff figure should be calculated.
This lack of operational context is also one of the biggest reasons AI hallucinations occur in finance. The majority of hallucinations aren't random malfunctions, they're a context problem. The AI model wants to give an appropriate answer. When it doesn't have access to the specific data it needs, it reasons from what it does know and produces something plausible.
Private credit portfolio data isn't embedded in any public language model. If the harness doesn't provide it, through tools, memory or real-time data access, the model will fill the gap with something that sounds right. Which, in a financial operations context, is a real problem.
The fix isn’t a better model. It’s a harness that gives the agent access to the right data, at the right moment, with the right tools and controls to retrieve it.
The firms that focus on building operational systems that provide context, transparency, audibility and human oversight will receive the greatest value from its AI investments. In private credit, the long-term advantage may not come from adopting AI faster than competitors, but from building the infrastructure, or the harness, capable of supporting it responsibly and at scale.
Establishing New Competitive DifferentiatorsAs these operational systems mature and advance, they will also reshape what excellence actually looks like inside private credit firms. Responsiveness won't be a differentiator. It will be assumed. Real-time and instant delivery will be the new baseline.
This is because most routine interactions will no longer require human involvement. With real-time systems, shared data layers, and automated workflows, information will be directly accessible and continuously updated. What previously required a request and response cycle will be resolved at the source.
As a result, the role of the servicer shifts. They are no longer measured by how quickly they process or reply, but by how well they handle what cannot be automated - exceptions, edge cases, and judgment calls.
This is why the next generation of private credit leaders may look fundamentally different from the firms that defined the industry’s earlier growth period. Capital access and underwriting expertise will remain essential, but operational execution is becoming increasingly strategic.
Most funds are using general-purpose AI for ad-hoc analysis or are in a holding pattern. A small number are starting to build their own and discovering how much harder it is than first expected. The funds that move first on specialized operational infrastructure will have an advantage that compounds. Not because they picked the right model (the model will keep getting better and cheaper regardless), but because they built or adopted the right harness, trained it on the right context, and gave it the controls that make it trustworthy at scale.
In many ways, private credit firms are evolving into operational organizations as much as financial organizations. The ability to manage workflows, data, oversight, and execution will become a defining part of a firm’s competitive performance.
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