U.S. Merit Systems Protection Board: Driving Operational Exellence
Interface revisits the important work of the U.S. Merit Systems Protection Board – an independent, quasi-judicial agency in the Executive branch that serves as the guardian of Federal merit systems. CIOCraig Thomas, explains the strategy behind the completion of Phase II of its case management journey, application stabilisation challenges and the deliberate rollout of secure artificial intelligence tools.
“At MSPB we field IT that is necessary, useful, cost-effective, manageable and scalable. If the technology doesn’t add value, doesn’t help us meet our mandate, I don’t want it… We see the active deployment phase as an incubator for support methodology, data and reporting strategy, and road-mapping the next five years of your application – use the active deployment phase, don’t just firefight.”
Morgan Street Holdings: Cybersecurity for Strategic Operations
Morgan Street Holdings is a privately owned investment and operating company whose portfolio includes global supply chain specialist HAVI, marketing and sourcing firm tms, consumer products brand Stanley, and hospitality and vending business Continental Services. Collectively, the organisation employs more 10,000+ people globally across more than 50 countries. Business Information Security Officer, Douglas Darden, talks building cyber resilience to enable the business and deliver cybersecurity for strategic operations.
“It’s not in us as a cyber team to just say no. Give us the opportunity to conduct a risk assessment and the cybersecurity function can become a business enabler.”
Also in this issue, we hear from Confluent on why AI is changing what data privacy means; learn from Kore.ai on how unmonitored AI agents are becoming enterprise AI’s biggest risk; and Xsolla explain why the real AI debate in video games isn’t about jobs, it’s about creativity.
Peter Pugh-Jones, EMEA Field Chief Data Officer at Confluent, on why the challenge for companies will not be building AI systems but standing behind the decisions those systems make
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For years conversations about data privacy have centred on conventional dangers, like breaches from outside, or leaks from within. The assumption has been that the biggest risk lies in information either being inadequately protected or improperly handled.
As we move deeper into the AI era, that picture is shifting. A more complex privacy challenge that we’re now faced with comes from how data is used once it sits inside an organisation.
AI is a crucial part of everyday business operations. It automates processes and shapes decisions. Data is no longer simply stored or shared; it’s interpreted and acted on at incredible speed, in ways that are difficult to trace or explain.
Decision-makers recognise the importance of accessing this data. 80% of business decision makers say without up-to-date data, businesses can’t be fully confident in decisions.
As such, the systems and rules that dictate how AI is used have never been more important. Business leaders need to be able to understand and explain how automated decisions are reached and the data that informed it.
Governance has to sit at the centre of the privacy conversation — not as a compliance exercise, but as the framework for the responsible application of AI. Without that foundation, organisations are likely to expose themselves to regulatory and reputation risk that they’re simply not equipped to avoid.
Privacy Now Depends On Explanation
Governments worldwide recognise the need for these AI frameworks. Europe’s AI Act has drawn the most attention, but equivalent regulatory approaches are appearing in markets from South Korea and the UK.
The result is that companies deploying AI systems are held to higher standards than ever when it comes to demonstrating that they use AI in a lawful, appropriate manner. As such, many businesses are discovering a gap between deploying AI and understanding it.
Implementing a model that produces useful outputs is relatively straightforward. Explaining how those outputs were created and which data shaped them is much harder. Where that clarity is missing, privacy becomes an operational problem.
Organisations may need to pause or redesign systems already in production because they cannot explain how those systems reached their conclusions. Some companies may even find themselves withdrawing products already released into the market.
So: what challenges are these organisations facing?
The Segmentation Challenge AI Introduces
One significant obstacle is segmentation. Companies need to understand who their systems interact with and which rules apply to each, and this isn’t always clear.
Businesses have historically segmented audiences by geography, demographics or behaviours. AI creates a more demanding requirement in that the systems using it must recognise when different safeguards should apply to different groups of people.
Regulation is pushing this further especially around children and vulnerable users. Content recommendations and automated responses that are acceptable for one group can become inappropriate for another.
The difficulty is that many AI systems are trained on broad datasets gathered across the internet. That breadth makes it harder to understand whether information came from a reliable source, or whether it should appear in a particular context. Without stronger segmentation, organisations risk losing control over how AI behaves in real interactions — and if that AI behaves inappropriately, it’s likely to lead to a breach of regulation.
Why Governance Cannot Be An Afterthought
Another mistake appears repeatedly in businesses deploying AI and modern data platforms. Governance is treated as something that can be dealt with later once systems are already running, with isolated but exciting pilots running well on smaller data sets often leading businesses to try and run before they can walk.
The challenge is even sharper in real time environments where data moves constantly between applications and teams. When governance is not designed into those flows from the beginning it quickly becomes a bottleneck.
In practice, the most effective point to govern data is when it first enters the business. Once information is already moving through systems, feeding models and generating outputs introducing controls becomes far more complex.
Many companies only realise months later that they’re lacking certain controls needed to manage risk. If they were never properly built in, it’s an expensive, time-consuming, incredibly nuanced task to put the genie back in the bottle.
The Shared Responsibility For AI Decisions
As with any cybersecurity approach, the human link can also be the weakest in the chain.
Responsibility for AI risk is often treated as if it sits neatly with one team. In many companies the assumption is that specialists are handling it, whether that is data scientists, a compliance function or a single executive role. The underlying thought is: “this is not my problem — someone else is dealing with it.”
That assumption creates problems. AI systems are trained on vast datasets and their behaviour is not always easy to predict without close scrutiny. When governance is treated as a delegated task, important questions can slip through the cracks unanswered.
Decisions informed by AI still sit with the business using the technology and ultimately with its leadership. Without senior oversight and a clear understanding of how systems are built, trained and governed privacy risks tend to surface long after those systems have become part of everyday operations.
The Question Organisations Should Be Asking
Organisations have the capability to build powerful AI systems. The harder question is whether those systems can stand up to scrutiny.
That means understanding where data originates, recognising differences between users and putting governance in place before automation begins to scale. It’s not enough to just co-opt an impressive new platform and give it access to your data.
In an AI driven environment privacy does not sit somewhere at the end of the process. It shapes how systems are designed, deployed and managed from the beginning.
In the years ahead, the challenge for companies will not be building AI systems. The challenge will be standing behind the decisions those systems make.
Jack Bingham, Regional Director of Digital Native UK, Ireland & South Africa, Confluent on how data, treated properly, compounds in value to drive digital disruption
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When I talk to founders and tech leaders, one question seems to consistently come up: what separates today’s disruptors from the last decade’s? In 2010, being cloud-first was what made investors sit up and take note. In 2026, it will be streaming-first.
I’ve spent the last year or so working closely with companies that are, quite literally, building their businesses in real time. For them, real-time capability isn’t a department or a layer that supports the business. It is the business. The acid test is simple: how quickly can you capture a critical event – a payment, a login, a failed delivery – and respond with the next best action? That focus shapes how they build products, structure teams, and think about innovation.
Here’s what I’ve learned from them:
Lesson 1: Data is a Product, Not a By-Product
Many traditional companies still treat data as something to collect, store, and analyse later. The new generation of businesses, on the other hand, treats it as a reusable, governed product that everyone can access. When it’s built and shared this way, teams stop rebuilding the same foundations for every new use case. They move faster because they’re working from a single, trusted view of the truth, shortening product cycles, speeding up iteration, and spending more time solving problems that matter.
That mindset, rather than the size of the tech stack or the number of engineers, is what sets disruptive businesses apart. In these organisations, technology, data, and business strategy move in lockstep. Decisions aren’t passed up and down hierarchies, they’re made by teams who understand both the data and the customer problem in front of them.
When you can trust your data and respond in real time, innovation stops being a department. It becomes a reflex.
Lesson 2: Real-Time isn’t a Feature, it’s a Foundation
A few years ago, one of the world’s largest supermarket chains realised it didn’t have a single real-time view of its inventory. Without that visibility, omnichannel experiences were impossible. Once it shifted to a streaming architecture, every transaction became a live event that updated stock, triggered supply chains, and even made it possible to get your groceries delivered straight to your kitchen fridge – coordinated through live inventory data, smart home devices, and real-time security feeds.
That’s the practical power of streaming: it connects what happens in your business to what should happen next so you can provide products and services that take customer satisfaction to a whole other level. Real-time data stops being a reporting tool and becomes the foundation of every decision, interaction, and innovation.
I often ask businesses what they would do differently, if they knew the state of every event in their organisation. The most forward-thinking companies already have the answer. They’re using streaming to turn business events into reusable building blocks, creating new experiences by connecting the data they already have in smarter ways.
Lesson 3: Culture is the Multiplier
Being streaming-first is only half about architecture. The other half is attitude. The best digital enterprises don’t wait for permission to experiment. They map their most important business events, align teams around them, and empower people at every level to react fast and learn faster.
And the difference is visible. Feedback loops are shorter. Structures are flatter. Failure is treated as information. This culture of continuous experimentation is why these companies can move at the pace they do.
We often run ‘Event Storming’ workshops with teams to map their critical business events. The idea is to create alignment – getting people from engineering, product, and operations to agree on what really matters and how those moments connect. That process reveals a lot.
Digital disruptors go beyond simply deploying streaming architectures. They build streaming mindsets. Leadership plays a crucial role here: data must be treated as a strategic asset. If it isn’t up top, it won’t be anywhere else in the organisation either.
Lesson 4: Streaming and AI will Converge
AI is only as good as the data you feed it. Unfortunately, most enterprises are still feeding it yesterday’s data. Streaming-first companies already know this. They’re building intelligent data pipelines that give AI the context it needs to make decisions in real time.
That’s how the next generation of innovators will pull ahead: not by having bigger models, but by having cleaner, faster, more connected data. Streaming is what will let AI move from reactive to predictive… and from predictive to autonomous.
Too many organisations are cutting investment in data while pouring money into AI projects. But AI without quality data is just expensive guesswork. The companies doing this well understand that data has to be a product in its own right. And when business and technology teams design around that shared understanding, innovation follows naturally.
Lesson 5: The Mindset of the Next Disruptors
If I were starting a company tomorrow, I’d look closely at the critical events that run my business. I’d then make sure I had a way to capture those in the stream, make them reusable, and build every product and process around them.
When your business can see and act on what’s happening in the moment, you gain something no traditional architecture can give you: time. And in the next wave of disruption, that’s the only advantage that really matters.
If we look to who we can learn from in the coming months, it’s financial services and healthcare that are moving the fastest. Real-time fraud detection, patient monitoring, and risk management are becoming operational necessities – and these industries will set the benchmark for real-time data excellence.
Looking Ahead to 2026
By 2026, I don’t think we’ll talk about ‘real-time’ as a differentiator. It will simply be how modern businesses operate. Batch systems won’t disappear, but they’ll coexist within a single, streaming-first platform that delivers data whenever it’s needed.
Once every process can react instantly, the question then becomes: can it anticipate? Can it learn? That’s where AI and streaming meet and where we move from reactive to autonomous enterprises that not only respond to the present but adapt to what’s coming next.
Data, treated properly, compounds in value. The decisions you make with it become faster, sharper, and more confident. The companies that understand this will be the ones still leading when today’s titans look like yesterday’s news.
