How UI fatigue is quietly killing momentum in private equity By Alex Bajdechi, VP of Sales, Deal Engine   Everyone is trying to get AI into their business. Across private equity, there is growing pressure to adopt AI. Firms are continuing to experiment with LLM’s, automation tools, and agents, often with the expectation that these technologies will unlock efficiency, insight, and competitive advantage. The focus, however, is frequently on deploying the latest capability rather than addressing the conditions required for it to work effectively.In many cases, AI is being layered onto environments where data remains fragmented across CRMs, sourcing platforms, market data tools, document libraries, internal models, and bespoke systems. Without integration, consistency, and reliable data flows, AI does not clarify decision-making. It amplifies uncertainty. Models trained on incomplete, conflicting, or poorly governed data struggle to produce outputs that deal teams trust or act on. This creates a paradox. AI is intended to reduce inefficiency, yet when introduced without first consolidating systems and data, it can compound the very problems it is meant to solve. Time is still lost moving between tools, reconciling information, and validating outputs. Adoption suffers, confidence erodes, and opportunities are missed. The result is not transformation, but fatigue, at a point when firms are under more pressure than ever to move quickly and decisively. Over the last decade, private equity firms have made significant investments in technology and data. CRMs, deal sourcing platforms, market intelligence tools, portfolio analytics, expert networks, and bespoke internal systems have all become part of the modern deal team’s toolkit. Individually, many of these tools are valuable. Collectively, they have created a new problem: UI fatigue. Today’s dealmakers spend an increasing amount of time toggling between platforms. Logging in and out, cross-referencing screens, reconciling conflicting data points, and trying to remember where a particular insight came from. What was meant to increase efficiency has, in many cases, had the opposite effect. More tools, less time for actual dealmaking Every additional platform adds cognitive overhead. Instead of spending time building relationships, travelling to meet business owners, or deepening ties with intermediaries and investment banks, deal professionals are stuck stitching together fragmented views of the same opportunity. The issue is not a lack of intelligence. Firms are better informed than ever. The issue is that intelligence is scattered across too many interfaces, each with its own assumptions, data models, and definitions of truth. As more products and data sources are layered in, it becomes harder, not easier, to answer basic questions. Which data do we trust? What has already been reviewed internally? Where should attention be focused right now? Trust and alignment do not scale across dozens of platforms UI fatigue also creates misalignment inside the firm. Different teams rely on different tools. Different partners trust different datasets. Notes live in one system, scores in another, and decisions somewhere else entirely. When that happens, confidence erodes. Deal teams second-guess the data, hesitate to act, or duplicate work that has already been done. The cost is not just time. It is momentum. Why consolidation is now a strategic imperative At this stage of the market’s maturity, the answer is not adding yet another point solution. It is consolidation. Firms need a single environment where intelligence is unified, validated, and presented through operating models specific to how their firm makes decisions. That means fewer screens, fewer signals, and far less noise. The goal should be intentional reduction. By design, a dealmaker should have less to look at, not more. Less toggling. Less deciphering. Less ambiguity. How Deal Engine addresses UI fatigue by design This is where Deal Engine fits. Deal Engine is the conncective tissue, bringing together your internal data and external structured and unstructured data to create a clean single source of truth that powers your LLMs of choice. The technology applies firm-specific data validation rules and workflows so that only relevant, trusted information rises to the surface - saving the dealmaker days of research and sourcing time. Instead of forcing deal teams to reconcile conflicting signals across tools, the platform does that work upstream. The result is clarity. Dealmakers see what matters, understand why it matters, and can move forward with confidence, without drowning in interfaces. After a decade of tech proliferation, private equity does not need more dashboards. It needs fewer, better ones. Deal Engine helps firms get there, so deal teams can spend less time managing software and more time doing what they do best: finding, building, and closing great investments. Get your demo now to see how this works in practice.

 In association with RealDeals, this article was published first on the RealDeals website. The dealmaker blueprint: five ways to build decision-ready origination data As private markets mature and AI adoption accelerates, origination data must be decision-ready. These five principles show how teams are building clarity, consistency and conviction at the point of judgement. Judgement, timing, and confidence in private markets dealmaking increasingly hinge on one thing: whether origination data is decision-ready before pressure mounts. As AI becomes more prevalent, many private equity and corporate finance firms are discovering an uncomfortable truth – advanced tooling cannot compensate for weak data foundations. When data is fragmented, inconsistent, or context-poor, hesitation can begin to surface precisely when conviction matters most. This has shifted the conversation away from AI experimentation and toward a more fundamental imperative: disciplined data engineering. The most sophisticated deal teams, working closely with technology leadership, are focused on building a long-term competitive edge while retaining room for experimentation. At the same time, they are laying the data foundations that support dealmaking today and position the firm to capitalise on advances in foundational LLMs in the years ahead. Below are five principles shaping how firms are turning origination data into decision-ready intelligence, based on emerging best practices across the market and the experience of Deal Engine (formerly Filament Syfter) supporting early adopters. 1. Clarity over completeness The instinct to collect and deploy AI against every datapoint at the firm remains strong, but volume rarely translates to insight. Decision-ready data is selective by design, structured around the specific questions deal teams need answered, not the maximum amount of information that can be gathered. High-performing origination functions often focus on relevance: which deal dynamics consistently influence prioritisation and conviction. By curating data models around decision drivers rather than exhaustive detail, teams can reduce noise and accelerate judgement. Clarity also enables faster pattern recognition, ensuring that, when a deal surfaces, teams can interpret signals instead of wading through excess. 2. Context at the point of judgement Data without context risks misinterpretation for dealmakers. Origination decisions are comparative and directional, shaped by prior activity and market signals rather than single data points. Decision-ready data situates opportunities within historical performance, market movement and competitive dynamics, linking what is happening now to what has happened before across sectors, counterparties, and comparable deal types. When context is embedded into origination workflows, teams avoid over-weighting isolated signals. Judgement is anchored in a broader narrative, with information framed against historical patterns and market dynamics rather than viewed in isolation. 3. Consistency across opportunities Inconsistent data structures can undermine decision-making. When deals are assessed against shifting baselines, with different definitions, levels of depth, or ad hoc metrics, comparisons stand to lose reliability. Firms investing in consistent origination frameworks are able to confidently establish common data standards across deals, sectors, and regions. This preserves nuance while ensuring that differences reflect real performance rather than structural inconsistencies. Consistency allows teams to trust comparisons, align internally, and build conviction without having to question the integrity of the data itself. 4. Reliability under pressure The true test of origination data comes late in the deal lifecycle. As competition intensifies and timelines compress, weak foundations surface through last-minute reconciliation, data gaps and uncertainty. By contrast, decision-ready data is built to hold up under pressure, remaining dependable as scrutiny increases rather than only during early exploration. This helps to reduce redundant work, prevent stalled momentum, and preserve confidence when the stakes are highest. Increasingly, reliability is about predictability across teams. Each function within a private equity or corporate finance firm should understand what the data can and cannot support, and make decisions accordingly. 5. AI as an enhancer, not a substitute AI delivers value only once foundations are stable. Without structured, contextualised data, AI amplifies inconsistency rather than insight. Leading firms treat AI as a layer applied after core data disciplines are in place – to surface patterns, accelerate synthesis, and support judgement, not replace it. When applied to decision-ready data, AI enhances speed and depth without undermining trust. When applied prematurely, it introduces opacity and risk. Building for judgement first The firms extracting real value from AI are those that first invested in disciplined origination data. As deal teams reassess how data supports judgement at the point of decision, the focus is shifting to building foundations that hold up under pressure, well before AI enters the equation. Deal Engine works with origination teams to design these data foundations, informed by how early adopters are structuring decision-ready workflows. Get your demo now to see how this works in practice. Originally published in collaboration with RealDeals - see the original article on the RealDeals website.

Leading pan-European mid-market private equity firm with $12bn+ in assets under management begins onboarding Deal Engine to strengthen early-stage sourcing, prioritization and AI readiness.