sifars-wordpress

Category: Finance & Growth

  • Measuring People Is Easy. Designing Work Is Hard.

    Measuring People Is Easy. Designing Work Is Hard.

    Reading Time: 4 minutes

    Most organizations are fantastic at measuring people. They define metrics, create dashboards, schedule reviews and doggedly track targets. Labour time, outcomes, utilisation rates and KPIs may all represent productivity. As an outsider looking in, it seems like performance is a tightly-scripted process.

    However in spite of all this measurement, many organisations wrestle with the same enduring issues: work feels transacted not deep; teams are ripped, outcomes fall shy and high performers burn out. That raises an uncomfortable question: if you’re so good at measuring, why does productivity still fail?

    The answer is simple, if not easy: it’s far easier to measure people than to design work.

    The Comfort of Measurement

    Measurement feels reassuring. Numbers give the illusion of control. When leaderships can look at charts, scores and ranks then there is this air of objectivity to how performance are being managed.

    Most organisations invest heavily in:

    • Individual performance metrics
    • Time and activity tracking
    • Output-based targets
    • Review and appraisal frameworks

    These are well-known systems, scalable and easy to standardise. They also shift responsibility downward. When things don’t work out, the temptation is to assume that the problem is one of effort rather than that of how work itself is organized.

    Why Measurement Rarely Fixes Productivity

    The issue with measurement is that it’s not bad but it’s insufficient. Deciding what to do with them doesn’t magically make work flow better through an organisation.

    People who never work on bad design suffer too. Responsibilities are fragmented, dependencies are muddy, priorities change frequently and decisions lag. There, quantity often serves as a catalyst of symptoms rather than causes.

    People are rated, coached and pushed harder, yet the underlying friction that was holding you back is allowed to fester.

    Work Design: The Secret to Productivity

    Designing work is deciding how jobs are arranged, how tasks are allotted and how decisions course through the organisation. “An ideology of effort dispensates or multiplies,” he said.

    Badly performed work often rears its ugly head as:

    • Constant context switching
    • Excessive coordination and handoffs
    • Unclear ownership and accountability
    • Work pending approvals and no Progress.

    None of these problems is addressed by better measurement. They require intentional design.

    Why It’s So Much Easier to Make Decisions About Someone Else’s Work

    Unlike measurement, work design makes organisations uncomfortable in the face of inconvenient truths. It forces leaders to question structures, practices and decision rights that have been part of the company for years.

    The design of work at its best raises other questions that are harder to answer:

    • Who truly owns this outcome?
    • Where’s work slowing? And why?
    • Which ones are adding value, and which are just there because of repetition?
    • Which decisions should get made closer to the execution?

    These three questions challenge hierarchy, routine and control. As a result, many organizations tend to measure the people instead.

    When Measurement Becomes a Distraction

    Over-measurement can actively harm productivity. When people are judged based on narrow measures like these, they will optimize for the metric and not for the goal we actually want to accomplish. Partnerships are hurt, risks are shunned, and short-term results trump long term value.

    Work in those places… work becomes performance. The activity picks up, but the influence does not. Teams cross fingers to prove they are productive, instead of simply being productive.

    Measurement is then distracting from the real work of improvement.

    The Human Toll of Poor Work Design

    When work is poorly designed, people absorb the waste. They work late, patch over gaps and bend around broken processes. Initially, this looks like commitment. It eventually demoralizes and alienates people.

    It is the high performers who start feeling this pressure first. They are given more work, with more complexity and more ambiguity. Eventually, they crash or break down or leave — not because they cannot handle the job but because it’s impossible to keep at that pace.

    Moving Its Gaze from People to Work

    Productivity increasing organizations are those that stop looking at individuals and start focusing on a better system of work.

    This means paying attention to:

    • How work flows across teams
    • Where decisions get delayed
    • How priorities get made (and remade)

    Whether the functions are such that roles can be designated or muddied

    Good design naturally leads to better performance. This creates a mentality where measurement is supportive, not punitive.

    A Model of Better Work Design

    Good work Places have some things in common.

    • Clear ownership of outcomes
    • Fewer handoffs and dependencies
    • Decision-making authority aligned with responsibility
    • Procedures that create, rather than minimize friction

    People are not needed to keep an eye on such systems. Productivity does not manifest in hours, productivity shows up in results.

    How Sifars Approaches Productivity Differently

    We believe at Sifars that problems of productivity are rarely problems with people. They are design problems. 

    Shaping work: an examination of the ways in which we divide up and structure work, make decisions and design systems that do – or don’t – support performance.

    We’re dedicated to helping leaders go beyond just measurement to intentional work design that drives clarity, pace and sustainability.

    Conclusion

    It will always be easier to measure people than it is to design work. It’s quicker, it memorizes and it disrupts less. But it is also less powerful.

    After all, real productivity gains accrue from deliberately shaping environments in which it’s easy to do good work and hard to do bad work.

    Work designIf organisations can get the work design right, then individuals don’t have to be pushed.

    They perform.

    If your company monitors performance closely but still finds productivity lagging, the problem may not be effort — it may be how work is constructed.

    Sifars enables organisations to reimagine the design of work, flow of decisions, and execution models so that effort translates into real impact.

    👉 Chat to us about how stronger work design can reboot sustainable performance.

  • When Faster Payments Create Slower Organisations

    When Faster Payments Create Slower Organisations

    Reading Time: 4 minutes

    Faster payments have remade how we do banking over the past decade. Real-time settlement, instant payments and 24/7 payment rails have changed the game on both customer expectations and competitive conditions. Boasting about your speed is no longer a point of distinction, it’s table stakes. The ability to move money instantly has become associated with progress for FinTechs, banks and payment platforms.

    But inside a lot of organisations, there is something almost paradoxical going on. Payments speed ahead rather more quickly than the organisations that support them. Decisions come late, controls can’t keep up and the operational complexity goes up. Something that should make business run faster can, if not handled well, slow the organisation down.

    A Speed Angle in Payments

    High-speed payment systems were supposed to banish that friction. They cut down on settlement times, enhance management of liquidity and provide customers more immediate value. To an outsider - they’re all about “efficiency” and “innovation.”

    Behind the scenes, though, speedier payments require much more than better technology. They demand that organizations work with real-time insight, instantaneous decisions and durable controls. Without such capabilities, transaction-level speed puts pressure on an organization.

    Real-Time Transactions, Real-Time Pressure

    The traditional payment systems had buffers. Settlement delays allowed time to have data reconciled, to look out for exceptions and to step in when there were problems. By making payments faster, these buffers vanish completely.

    Operational team under pressure As transactions complete on-line there is continuous pressure to detect, evaluate, respond in real time. When it is not clear who owns what, and how calls are escalated if necessary, that urgency isn’t channeled into action; it just turns into indecision and chaos. The organization responds more slowly even as transactions become faster.

    Risk and Compliance 

    Faster payments amplify risk exposure. Let’s face it — even when most of your tasks are automated, attempting to defraud a business no longer involves being met in opposition by the stern glare of an office auditor; potential mistakes suddenly don’t take weeks or months to be caught and rectified. While automation helps you manage volume, it’s not an excuse to externally distribute judgment and governance.

    Many organizations find that their risk and compliance programs were built for slower systems. What was once a good-enough infrastructure of controls now seems unable to maintain control. Reviews increase, approvals become more hesitant and interventions more complex — the organisation is becoming less slippery.

    Operational Complexity Grows Quietly

    Faster payments can often depend on interconnected systems, third-party providers and exchanges in real time. Each integration introduces dependency. Things do not get any easier as time goes by to navigate the operational terrain.

    Complexity of this kind doesn’t just slow transactions — it slows organisations. Teams are spending more time co-ordinating across systems and resolving exceptions and dependencies. What seems effortless to consumers is typically precarious behind the scenes.

    The Latency of Decisions in a World that is Real Time

    Decision latency is one of the biggest challenges that faster payments pose. When money can travel in an instant, the cost of slow decisions becomes much higher.

    But many organizations still have approval structures and governance models that were designed for a more glacial pace. Teams escalate only those issues that need to be addressed immediately, yet decisions are stalled. This dissonance between transaction speed and organisational speed exposes risk and diminishes trust.

    Edge speed requires core speed.

    Always-On Systems and The Human Factor

    Faster payments operate continuously. And with real-time payments, there is no room for error, as with cash-based cut-off systems in the past. This keeps constant pressure on the operations teams.

    In the absence of intelligent workforce design and process clarity, heroics instead systems are what people pin their hopes on within an organization. Burnout goes up, mistakes go up and productivity goes down. As time goes by the organisation gets slower – not because technology fails but rather people become overloaded.

    Why Faster Payments Alone Don’t Necessarily Make For Faster Organisations

    There is no reason to believe that faster technology will beget faster organisations. Speed at the Speed at the transaction level will exacerbate structural, governance and decision making weaknesses.

    Faster payments expose:

    • Unclear ownership and accountability
    • Fragile risk and compliance processes
    • Overdependence on automation without oversight
    • Models of governance that won’t work in the speed of life

    If it can’t be fixed, then speed is a disadvantage, not an advantage.

    Designing the Organizations to Fit Payment Speed

    Such organisations which are successful with faster payments match their operational design to technology. They’re investing not just in platforms but in clarity.

    This includes:

    • Real-time decision frameworks
    • Clear escalation and ownership models
    • Embedded risk and compliance controls
    • Cross-functional collaboration between operations, technology and governance

    When people move at the speed of your organization, faster payments are more strength, less stress.

    How Sifars is Ameliorating Organisations to Bridge the Speed Gap

    We are working with financial industry leaders and FinTechs at Sifars to close the chasm between payment velocity and organisational preparedness. We work with leaders to determine areas where faster payments are causing friction, rethink operating models and build governance structures that operate effectively in real time.

    We want fast without losing control, reliability or regulatory trust.

    Conclusion

    Fast payments are changing financial services but they don’t automatically change an organisation. And without the proper underpinnings to the operation, speed at the transaction level can actually impede everything else.

    It’s not transaction speed that will decide the winners; the organisations that do win out are likely to be those that can bring together technology, people and governance to operate comfortably at this pace.

    If your pay systems operate in real time but your organisation can barely keep up, here is the point to reflect on how speed should be handled internally.

    Sifars assists financial organizations create sustainable, scalable operations for fast payments — safely and clearly.

    👉 Click here to get in touch and see how local governments are making payment speed a real competitive advantage for their teams.

  • Automation Isn’t Enough: The Real Risk in FinTech Operations

    Automation Isn’t Enough: The Real Risk in FinTech Operations

    Reading Time: 4 minutes

    Within the FinTech industry today, automation is key. From instant transfer of payments and real-time prevention of fraud to automated onboarding or compliance checks, the use of technology has allowed financial services to move faster, spread more widely and run with greater efficiency those at any time in history. In many companies, automation is exciting stuff — as it should be.

    But as financial technology firms increasingly depend on computers to make their decisions, another type of threat presents itself — silently and more dangerously. Automation by itself does not ensure operational resiliency. Indeed, a heavy reliance on automation without the attendant organisational checks and balances can create vulnerabilities that are orders of magnitude more difficult and costly to uncover.

    At Sifars, we commonly observe that the actual risk in FinTech operations is not non-automation, but inadequate operational maturity around automation

    The Automation Advantage—and Its Limits

    It’s not hard to see why automation is so valuable for FinTech. It alleviates manual work, shortens turnaround times and ensures repeatable execution on scale. Processes that used to take days now occur in seconds. Customer demands have changed accordingly, adding significant strain on FinTech companies to deliver fast and easy.

    Yet automation thrives in predictable environments. Financial operations are rarely predictable. They are influenced by changes in regulations, fraud trends, system interdependencies and human judgement. If automation is applied without taking this complexity into consideration, it ends up concealing the weakness rather than solving it.

    But then efficiency is fragile.

    Operational Risk Doesn’t Go Away — It Morphs

    One of the great myths is that in FinTech, everybody believes automation removes risk. In truth, it just moves where risk resides. Human errors might decrease, but systemic risk rises when activities get closely bound up and secretive.

    Automated systems can fail silently. A single misconfiguration, discrepancy in data, or third-party outage can surge through operations before anyone observes it. Once the problem has become known, customer impact, regulatory liability and reputational harm can already be substantial.

    In automated settings, risk is more opaque and more potent.

    The Technology illusion of control

    Automation can lead to a false impression of control. Dashboards are green, workflows run as expected, and alerts are fired when they exceed the threshold. This has the potential to hypnotise organisations into thinking that they can run without a hitch.

    In fact, most FinTech companies don’t have enough insight into how their machine processes perform under stress. Exception handling is weak, escalation channels are ambiguous and manual triggers are infrequently exercised. When systems misbehave, teams run around like headless chickens – not because they are any less talented or skilled but more that no one in the organisation ever thought to plan for what happens when their failure modes actually occur.

    Real control can be had only through preparedness, not merely as a result of automation.

    More Than Speed Needed on Regulatory Complexity

    The environment in which FinTechs are doing business is one of the most regulated. Automation is a great way to manage enforcement at scale, but it should not be a substitute for judgment, accountability or governance. Regulatory requirements are constantly changing and an automated rule will soon be out of date if not scrutinized.

    Without investment in operational governance, organisations may build compliance processes which are technically effective but strategically vulnerable. Regulators are not measuring for sophistication in automation – they’re measuring outcomes and a company’s accountability and controls.

    Speed without control is dangerous in regulated environments.

    People and Processes Still Matter

    As we continue to automate much of this, a number of organizations underinvest in people and process design. Responsibilities blur, ownership becomes fuzzy and teams no longer have end-to-end visibility into how things operate. When there are problems, nobody knows who is responsible or where to step in and fix things.

    Top performing FinTech firms understand that automation should serve as an enabler of human potential, not a robot in disguise.“ Effective ownership, documented processes and trained teams are still important. Without them, automation is brittle and hard to maintain.

    Operational resilience relies on all the people who understand how that system works — not just systems that operate independently. 

    Third-Party Dependencies Multiply Risk

    External vendors, APis, cloud platforms and data providers play a significant role in modern FinTech ecosystems. The dependence on these systems has been incorporated more tightly into production processes through automation, making exposure to external failures higher.

    Automated workflows often collapse in an unpredictable manner as soon as third-party systems fall over or misbehave. For organisations without contingency planning and visibility into these dependencies, it’s a case of respond rather than react.

    Automation increases scale — but it also increases dependence.

    The Real Danger: Maximizing Efficiency Only For some reason, it never occurred to us that having this muscle cramp meant my muscles couldn’t work as well!

    The risk in FinTech is not a technical one- it’s strategic. A lot of organizations over optimize for efficiency and under optimize for resilience. Automation becomes the end rather than the means.

    This results in systems that do very well under ideal conditions, but buckle when things get tough. The real source of operational strength is our ability to adapt, recover and learn — not just to execute.”

    Building Resilient FinTech Operations

    Automation is only one element of the overall operational approach. Resilient FinTech organisations focus on:

    • Robust operational governance:  And Strong ownership of process:
    • Continuous monitoring beyond surface-level metrics
    • Regular tests of edge cases and failure modes
    • Human-in-the-loop in an automated pipeline
    • Alignment of various Technology, Compliance and Business teams

    Those who make these things work together will see automation as an enabler, not a multiplier of risk.

    How Sifars Assists FinTechs In Going Beyond Automation

    We are working with FinTech companies to build a sustainable operational models & technology backbone at Sifars. We identify the invisible risks, we improve process transparency and we create a governance framework that keep pace with automation.

    We enable businesses to transition from automation-centric efficiency to operational resilience and control – so that growth does not mean sacrificing stability.

    Conclusion

    Automation is certainly key to the success of FinTech—but it is also insufficient. Without rigorous operational design, governance and human oversight, automated systems can introduce risks that are “far easier to see than to manage.”

    Future of FinTech goes to those that combine speed with resilience and innovation with control.

    If your FinTech operations are entirely dependent upon automation without an understanding of risk, governance and resilience, then maybe it is time to assess what’s happening underneath the water.

    Sifars Sifars supports the world’s best FinTech companies to surface operational blind spots and to build systems that work securely and resiliently at scale.

    👉 Get in touch to discover how your operations can scale securely—as well as quickly.

    www.sifars.com

  • Busy Teams, Slow Organizations: Where Productivity Breaks Down

    Busy Teams, Slow Organizations: Where Productivity Breaks Down

    Reading Time: 3 minutes

    Many organisations today are rich with movement but poor in momentum. They juggle busy schedules, support various projects at the same time and are always on the phone or e-mail to satisfy their customer’s wishes. On the outside, productivity seems high. But internally, leaders feel that something is wrong. Projects are slower than you thought they would be, decisions sputter along, and strategic aims seem to take more effort to attain than they should.

    It is no accident that gap between what we see as a child’s effort and real progress. It’s illustrative of the way productivity tends to disintegrate at an organisational level even when team members are pulling out all the stops.

    The Illusion of Productivity

    Being busy is a status symbol. The perception is that work is being achieved effectively when people are always “busy. Indeed, busyness is frequently a cover for inefficiency deeper down. Teams are losing out on the flow time to work that catalyzes for lasting impact as they spend endless hours in coordinating, updating, aligning and reacting.

    Real productivity isn’t working hard, it’s whether all the work you’re doing is moving your organisation forward.

    Too Many Priorities, Too Little Attentiveness

    The lack of prioritisation is one of the biggest problems. Teams are often summoned to work on multiple initiatives simultaneously, with each presented as key. Attention gets scattered and the momentum slows.

    The result is a familiar cycle:

    • Strategic initiatives fight for resources with day-to-day operational duties
    • The context switching over and over again, no depth for a team or momentum.
    • Long-term interests are sacrificed to short-term needs.

    No amount of skills can get the job done without focus, uninspiring even for the best teams.

    Decision-Making That Slows Execution

    Speed of organisation is inextricably linked to how decisions are taken. In a lot of organizations decision-making is centralised, with teams needing approval to progress. Though it can be make you feel in control, small tasks have a way of then leading to delays and loss of momentum.

    Decision bottlenecks show up in a few common ways:

    • Teams held up while awaiting sign-offs
    • Missed opportunities with delayed responses
    • Cut ownership and interest in calibrator level

    Where there is slow decision-making, execution always lags.

    Strategy Without Clear Translation

    Another key breakdown happens when the strategy is communicated but not translated into day-to-day work. Teams may know what they are doing, but not necessarily how it relates to the goals of the institution.

    This disconnect frequently leads to:

    • High volume but low strategic impact
    • Teams head down Different paths and hard at work
    • Difficulty measuring meaningful progress

    Productivity is greatly enhanced when teams know not just what to do but why it matters.

    Process Overload and Organisational Friction

    Processes are designed to provide structure, but they can quietly pile up without scrutiny over time. What was once a facilitator of efficiency may also start slowing everything down. Too much give-the-thumbs-up, outdated tools and inflexible processes all contribute to friction that teams are working against.

    Typical consequences include:

    • Delays in execution
    • Increased rework and inefficiency
    • Frustration among high-performing teams

    Fast companies periodically audit and streamline their processes to make sure that they enhance rather than impede productivity.

    Silos That Limit Collaboration

    Clockwise, on the other hand, believes that working in silos is a productivity killer. Information moves sluggishly, feedback is slow to arrive, and coordination becomes reactive rather than proactive. There is a lot of duplication of work, and only wait until there’s a big headache to see where the problem lies.

    Siloed environments commonly experience:

    • Misalignment across departments
    • Delayed problem-solving
    • More reliance on meetings for understanding

    Timely transparent collaboration is critical for maintaining organisational velocity.

    The Hidden Impact of Burnout

    If you’re constantly busy but not supported systemically, it’s draining on people. Where teams take organisational inefficacies personally there will be burnout. Talent may get away with it for while, but productivity drops off.

    Burnout often manifests as:

    • Reduced engagement and creativity
    • Slower decision-making
    • Higher turnover and absenteeism

    Sustainable productivity goes with systems that honour the human, not just deliver outputs.

    Why Productivity Fails at The Company – Level

    The shared challenge in these cases isn’t effort; it’s design. Agencies typically try and improve individual performance without considering structural obstacles to effectiveness. But asking them to do a better job or work harder, without removing friction, only makes the problem worse.

    Productivity does not fail because people break. It falls apart because systems do not adapt.

    How Sifars organisation regains momentum Most of our Services

    We at Sifars see productivity as an organisational strength and not an individual burden. We partner with executives to surface where effort is being lost, connect strategy to execution, and map the right workflows that lead to faster decision making and a more focused business.

    Our aim isn’t to make work more stressful for teams; we hope to facilitate the creation of environments in which productivity comes naturally, and is sustainable and positively impactful.

    Conclusion

    In a busy teams are good sign of commitment, not inefficiency. The problem comes in when they do not funnel that commitment into momentum. Clarity, alignment and systems are the ingredients with which organizations can unlock productivity as they scale without burning out their people.

    If your teams never seem to have any downtime, but the progress continues to feel glacially slow, it may be time to start looking beyond individual performance.

    Sifars works with businesses to unlock bottlenecks in productivity and develop systems to transform effort into measurable value.

    👉 Start a chat with our team to see how your business can move faster — with explanations and intuitive confidence.

  • Why Leadership Dashboards Don’t Drive Better Decisions

    Why Leadership Dashboards Don’t Drive Better Decisions

    Reading Time: 3 minutes

    There are leadership dashboards all over the place. Executives use dashboards to keep an eye on performance, risks, growth measures, and operational health in places like boardrooms and quarterly reviews. These tools claim to make things clear, keep everyone on the same page, and help you make decisions based on evidence.

    Even if there are a lot of dashboards, many businesses still have trouble with sluggish decisions, priorities that don’t match, and executives that react instead of planning.

    The problem isn’t that there isn’t enough data. The thing is that dashboards don’t really affect how decisions are made.

    Seeing something doesn’t mean you understand it.

    Dashboards are great for illustrating what happened. Trends in revenue, usage rates, customer attrition, and headcount growth are all clearly shown. But just being able to see something doesn’t mean you understand it.

    Leaders don’t usually make decisions based on just one metric. They have to do with timing, ownership, trade-offs, and effects. Dashboards show numbers, but they don’t necessarily explain how they are related or what would happen if you act—or don’t act—on those signals.

    Because of this, leaders look at the data but still use their gut, experience, or stories they’ve heard to decide what to do next.

    Too much information and not enough direction

    Many modern dashboards have too many metrics. Each function wants its KPIs shown, which leads to displays full of charts, filters, and trend lines.

    Dashboards don’t always make decisions easier; they can make things worse. Instead of dealing with the real problem, leaders spend time arguing about which metric is most important. Instead of making decisions, meetings become places where people talk about data.

    When everything seems significant, nothing seems urgent.

    Dashboards Aren’t Connected to Real Workflows

    One of the worst things about leadership dashboards is that they don’t fit into the way work is done.

    Every week or month, we look over the dashboards.

    Every day, people make choices.

    Execution happens all the time.

    By the time insights get to the top, teams on the ground have already made tactical decisions. The dashboard is no longer a way to steer; it’s a way to look back.

    Dashboards give executives information, but they don’t change the results until they are built into planning, approval, and execution systems.

    At the executive level, context is lost.

    By themselves, numbers don’t always tell the whole story. A decline in production could be due to process bottlenecks, unclear ownership, or deadlines that are too tight. A sudden rise in income could hide rising operational risk or employee weariness.

    Dashboards take away subtleties in order to make things easier. This makes data easier to read, but it also takes away the context that leaders need to make smart choices.

    This gap often leads to efforts that only tackle the symptoms and not the core causes.

    Not just metrics, but also accountability are needed for decisions.

    Dashboards tell you “what is happening,” but they don’t often tell you “who owns this?”

    What choice needs to be made?

    What will happen if we wait?

    Without defined lines of responsibility, insights move between teams. Everyone knows there is a problem, yet no one does anything about it. Leaders think that teams will respond, and teams think that leaders will put things first.

    The end outcome is decision paralysis that looks like alignment.

    What Really Makes Leadership Decisions Better

    Systems that are built around decision flow, not data display, help people make better choices.

    Systems that work for leaders:

    Get insights to the surface when a decision needs to be made.

    Give background information, effects, and suggested actions

    Make it clear who is responsible and how to go up the chain of command.

    Make sure that strategy is directly linked to execution.

    Dashboards change from static reports to dynamic decision-making aids in these kinds of settings.

    From Reporting to Making Decisions

    Organizations that do well are moving away from dashboards as the main source of leadership intelligence. Instead, they focus on enabling decisions by putting insights into budgeting, hiring, product planning, and risk management processes.

    Data doesn’t simply help leaders here. It helps people take action, shows them the repercussions of their choices, and speeds up the process of getting everyone on the same page.

    Conclusion

    Leadership dashboards don’t fail because they don’t have enough data or are too complicated.

    They fail because dashboards don’t make decisions.

    Dashboards will only be able to generate improved outcomes if insights are built into how work is planned, approved, and done.

    More charts aren’t the answer to the future of leadership intelligence.

    Leaders can make decisions faster, act intelligently, and carry out their plans with confidence because of systems.

    Connect with Sifars today to schedule a consultation 

    www.sifars.com

  • Why Healthcare AI Struggles with Data Continuity, Not Accuracy

    Why Healthcare AI Struggles with Data Continuity, Not Accuracy

    Reading Time: 4 minutes

    In fact, it has been an era of fast-progress AI in health care. AI-powered systems can, for instance, carry out medical imaging and diagnosis or provide prognosis analytics clinical decision support that equals – and every now and then even surpasses-humans in results.

    Today, however, many medical AI endeavors fail to achieve consistent real outcomes.

    The problem usually lies not with model accuracy.

    More likely, it is finding the real cause of random data.

    The main problem with healthcare AI is not that it cannot analyze data well. Rather, the problem is a data environment where the data itself is broken into pieces, arrives late or not at all, or exists in separate silos across systems.

    The Real Problem Is No Longer Accuracy

    Today’s AI models in health care are trained on vast datasets, and possess the capacity to far greater degree than before. They can find patterns in images and anomalies in lab values not known by human experts, and assist doctors with risk scoring at bouquet precision levels.

    These systems work well under controlled conditions.

    However, reality for healthcare professionals is not like that. Patients’ data doesn’t arrive as a clean stream-Either it comes from different hospitals and laboratories, different departments within the same hospital; Or alternatively emerges at some time after previous events have taken place (sometimes through various channels for multiple reasons); All this is stored by insurers etc in a variety going back.

    We have to Emphasize Again That Precision Is the Key

    Thus, an accurate model is only useful when it proves itself relevant.

    Data Continuity in Healthcare: An understanding

    Data continuity is the complete, timely, and connected flow of patient information throughout its experience in health practice.

    This could involve:

    Medical history from multiple providers

    Diagnostic reports out of four or more laboratories.

    Imaging data (e.g. x-rays and MRIs ) stored on one system Medication records revised at varying intervals

    Notes on follow up which never end up getting back into any main system With this information not moving together, AI systems work off half a picture.

    They are forced to make decisions based on snapshots instead of the full story of the patient being worked over by modern medical treatment.

    Artificial Intelligence Deepens Fragmentation in Healthcare Data

    Healthcare data fragmentation is nothing new. It had already appeared long before AI came on the scene. What has changed? Today we just think that AI could help us “fix” this problem.

    In fact, AI magnifies the existing problems further.

    For example, perhaps a predictive model will show a patient is at low risk simply because the recent test results don’t match what was put into the computer before a certain deadline on some Thursday morning or afternoon. A diagnostic AI misses crucial historical patterns because past records are all but unavailable from your hospital system. If underlying data is inconsistent, then clinical decision tools produce differing suggestions.

    These are not algorithm failures. They are discontinuity failures.

    But this in itself is neither here nor there. In their view, true interoperability is about getting systems to talk to each other rather than trying to convert incompatible pipes

    By itself, interoperability will not do the trick.The patient must find his own way through time and rain. Whether in person or by phone on a network, this is essential.

    You may encounter any of the following problems even when systems are technically connected: Data may arrive after the decision has been made and so have no influence upon it.

    The first comprehensive reinternalization of history.Then, patient (or family) trains a video camera on twelve four-channel nocturnal studies for ten minutes each channel and receives back three hours of full-on sleeping lab science. No clinician attending upon him can recall such a thing as this in any hospital that he has ever seen.

    Clinicians may not trust or act on AI outputs if data sources are unclearWithout continuity, AI outputs feel unreliable–even when they are statistically accurate.

    The Human Cost Of Missed Continuity

    When systems lack continuity, human clinicians are left to fill in the gaps by hand.

    They carry out inspections for verification, and experience is relied on rather than the computer’s recommendations.

    This increases the cognitive load and trust in AI tools drops.

    Gradually, AI becomes an “added bonus” rather than a vital component of clinical workflow. Its adoption falters not because medical staff refuse technology but because this just does not match the real world of delivering patient care.

    As healthcare AI today strides forward with ever more intricate and powerful models, it is important to address a vital point.Successful healthcare AI must take into account how care actually unfolds, not just how data is organized.This means knowing (or at least taking educated guesses about) things like: When and where in the care cycle information becomes available Who needs it and in what format How people make decisions under time pressure Where people have to hand work off from zone to another AI systems adapted to clinical workflows – and capable of handling imperfect data flows – are much more likely to work than those designed in isolation.

    From Smart Models to Reliable Systems

    Healthcare AI’s future is no longer to gain marginal increases in accuracy. Instead, it is all about building systems that work effectively and safely live up in all manner of messy real-world environments.

    This calls for:

    • Strong data governance and version control
    • Context-aware data pipeline
    • Full data provenance view
    • Design right when some or all information is missing

    If continuity improves, AI becomes reliable, powerful and not just for show.

    Conclusion

    Healthcare AI does not fail because to a deficiency in intellect. It doesn’t work because intelligence needs continuity to work.

    As healthcare systems grow more digitized and connected, the real competitive edge will not be who has the most advanced model, but who can keep a full, trustworthy picture of the patient’s path.

    AI will keep having problems, not with accuracy, but with reality, until data flows as smoothly as caring is supposed to.

    Connect with Sifars today to schedule a consultation 

    www.sifars.com

  • Why FinTech Scale Fails Without Transaction Intelligence

    Why FinTech Scale Fails Without Transaction Intelligence

    Reading Time: 3 minutes

    FinTech companies are built for rapid scaling. Today, faster payments, instantaneous lending decisions and smooth digital experiences are no longer differentiating factors – rather they are requirements. Nevertheless, many FinTech platforms find that as their transaction volume goes up, system performance, reliability, and management actually deteriorate rather than improve.

    This is not a technology shortage problem.

    It’s a lack of intellect problem.

    Instead, when transactions scale without visibility or context, growth becomes brittle. Systems start failing in ways that can’t immediately be seen, but are downright expensive over time.

    Growth without understanding is risky

    Most FinTech platforms start out simply. Volumes are modest, failure rates low and problems can be solved in a manual way. Screens tell you what you need to know.

    But as the platform grows large, the paths of transactions multiply. More banks, more payment rails, more integrations and edge cases sneak into the system. In the end this will start to slow us down not because our systems can’t handle the volume, but rather her lack of understanding what is happening in real time.

    Failures emerge from nowhere Settlements to be settled on time. Support tickets increase and teams simply react

    This is the moment when intelligence in transactions becomes necessary

    What “transaction intelligence” really means

    Transaction intelligence is not about making payments faster. It’s about knowing the entire life cycle of a transaction–where it goes, which parts slow it down, and where things don’t work.

    It tells you why. Why did this transaction fail? Was it a transient bank issue, a routing problem, or some risk signal? Which among the paths is performing best at a given moment? And where is money stuck here, for how long?

    Without these answers, teams depend on conjecture. With intelligence, they depend on data.

    The Hidden Price of Scaling Meantime

    Most people are inconspicuously inefficient at anything on a large scale. A tiny level of failure doesn’t seem worrisome until it starts touching thousands of users daily. Slightly slow settlements equal a cash-flow problem. Lapses in minor reconciliations turn into compliance risks.

    The danger is that these issues seldom come up all at once, thus slowly gathering steam by themselves–the more quietly the sooner the worse things get. They largely go unnoticed until customers complain or regulators ask questions in response.

    At that point however, to replace the system is already worth even more costly.

    Why automation by itself doesn’t fix the problem

    When FinTechs feel the need to grow, they often incorporate more automation, like automatic retries, automated reporting, and automated compliance checks. This helps in the near term, but automating things without thinking just makes them less efficient.

    When systems don’t know why something went wrong, automation makes the same mistakes more quickly. More retries mean more load. More alerts make things noisy. More rules make it harder for real users to get along.

    Smart systems act in different ways. They change. They learn. As the volume goes up, they make better choices.

    Sustainable Scale Needs Context

    FinTechs that grow successfully don’t merely handle more transactions. They can see them more clearly.

    They know which routes work best when traffic is heavy. They notice strange behaviour early on, before it becomes fraud. They fix problems faster because they can spot the reason right away. Their operational teams spend less time putting out fires and more time making systems better.

    This intelligence builds up over time. The platform gets smarter with each transaction.

    The Quiet Advantage of Transaction Intelligence

    Features are easy to imitate and price advantages don’t last in competitive FinTech industries. What really sets long-term winners apart is how well they deal with complicated situations when they’re under duress.

    Transaction intelligence gives you an edge that no one can see. Customers have fewer problems. Merchants get their money faster. Instead of guessing, internal teams move with assurance.

    The platform doesn’t simply get bigger; it also gets more stable as it does.

    Conclusion

    The number of transactions alone does not determine FinTech size. It depends on how well a system works when things go wrong.

    If you don’t have transaction intelligence, growth makes things weaker.

    It makes the scale last.

    FinTechs who get this early on don’t only move money faster; they also make systems that survive.

    Connect with Sifars today to schedule a consultation 

    www.sifars.com

  • Operational Risk in FinTech: Where Automation Still Falls Short

    Operational Risk in FinTech: Where Automation Still Falls Short

    Reading Time: 3 minutes

    Speed, size, and efficiency are what make FinTech companies work. At the heart of this promise is automation, which makes payments easier, onboarding clients easier, compliance easier, and decision-making in real time. Automation has changed the way financial services work, from KYC routines to keeping an eye on transactions.

    Even though a lot of money has been put into automation, operational risk is still one of the major problems in FinTech today.

    The problem isn’t that automation doesn’t function. It’s that automation alone doesn’t get rid of risk; in fact, it might sometimes make it worse.

    FinTech companies that want to grow safely, stay compliant, and keep their customers’ trust need to know where automation doesn’t work.

    What does operational risk mean in FinTech?

    Operational risk is the money you lose when your internal processes, people, systems, or outside events don’t work. In FinTech, this risk is bigger since there are a lot of transactions, there is a lot of pressure from regulators, and there are complicated connections across banking, payment, and data platforms.

    Some common sources of operational risk are:

    • Data that is wrong or missing
    • System downtime or lag
    • Not following the rules
    • Workarounds that need manual work in automated systems
    • Bad handling of exceptions

    Automation fixes a lot of surface-level problems, but it often has trouble with these deeper, less predictable ones.

    The Myth of ”Fully Automated” Operations

    Many FinTech participants believe that as soon as a workflow is automated, so too is control. In real life, automation tends to drive flaws out of the way you have a process set up.

    For instance:

    • Automated onboarding could continue to require manual reviews for special cases.
    • Transaction monitoring systems might send alerts more quickly, but they also generate far too many false positives for teams to respond effectively.
    • Automated compliance checks are feasible, however manual analysis and alerting is still a prerequisite.

    Poorly constructed workflows, however, result in automation that speeds only the amount of work — not its quality or how you are able to deal with it.

    “Now it feels like you’re in charge, and then the operational risk piles up quietly.

    Where Automation Still Falls Short

    1. Exception Handling and Edge Cases

    Predictable inputs The first truth is that automation likes getting predictable inputs. Financial systems, of course, are rife with exceptions — out-of-pattern transactions and incomplete data, regulatory gray areas and customer behavior that doesn’t conform to pre-set rules.

    Most automation passes these exceptions to human without context and priority. As the volumes pile up, so do the overwhelmed teams and, with them, the chances of errors, delays or overlooked red flags.

    Without smart exception management automation simply moves risk, not eliminates it.

    1. Data Quality and Context

    Automation requires data, yet most FinTech platforms fetch pieces of information from various sources such as banks, payment gateway or third-party APIs and internal systems.

    If data is inconsistent or Lagging behind:

    • Automated decisions become unreliable
    • Risk models produce false outcomes
    • Compliance reporting becomes fragile

    Automation deals with data volumes effectively yet it is unable to be skeptical about the accuracy and timeliness of information. Operational risk remains if there is no robust data governance and context- aware systems.

    1. Regulatory Interpretation

    Regulations are not fixed, bright-line rules — they change over time and necessarily entail judgments. While known compliance checks can be enforced by automation, it lacks interpretation and nuance into what is being looked for in a data set and the intent of the regulation.

    And FinTech companies will frequently pile human processes over the top of its automated systems to make up for this. This produces hybrid workflows that are difficult to track, audit and scale, thus new points of risk.

    Real risk reduction entails systems that are designed to enable regulation, not just enforce rules.

    Automation vs. Operational Resilience

    It’s more about designing resilient systems and less about automating the majority of the system!

    Resilient systems:

    • Anticipate failures and exceptions
    • Make ownership and escalation paths clear
    • Maintain transparency across workflows
    • Adjust as regulations and markets change

    Automation is a factor in resiliency — but not the basis of it.

    How the FinTech Champs Are Bridging the Divide

    FinTech high-achieving companies do automation right. They focus on:

    • Workflow design before automation
    • Clear exception management frameworks
    • Context-rich dashboards for decision-makers
    • Adaptable modular Systems that adapts seamlessly.
    • Human-in-the-loop models for critical decisions

    This method minimizes operational risk while retaining the speed and scaling that FinTech requires.

    Here’s How Sifars Aids FinTechs in Reducing Operational Risk

    At Sifars, we can assist in transforming a fintech company to automate processes beyond the surface with an eye on resilient and scalable operations.

    We work with teams to:

    • Expose obscured operational risk in your automated processes
    • Redefine process with clarity and ownership
    • Upgrade legacy systems with no impact to day-to-day operations
    • Create flexible, secure and scalable solutions

    we aren’t merely striving for faster systems — but also safer, smarter and more dependable ones.

    Conclusion

    FinTech has been revolutionized by automation—but it hasn’t removed operational risk.

    Risk exists in the seam: on exceptions, data quality, regulatory interpretation and system design. Solving these issues necessitate a careful, business-first approach to automation.

    FinTechs that acknowledge the limitations of automation—and build systems appropriately—sit in a stronger position to scale securely, remain compliant and earn long-term customer trust.

    If you’re running a FinTech company, and it is automated but still seems brittle, perhaps the solution isn’t focused at tools and that you need to consider fundamentally how risk flows through your system. Sifars assists FinTech teams in developing reliable processes that scale securely.

    Connect with Sifars today to schedule a consultation 

    www.sifars.com

  • The Silent Bottleneck: How Decision Latency Hurts Enterprise Performance

    The Silent Bottleneck: How Decision Latency Hurts Enterprise Performance

    Reading Time: 5 minutes

    Most companies blame performance problems on things that are easy to see, such as not enough resources, slow teams, old technology, or pressure from the market. To boost productivity, leaders spend a lot of money on people, tools, and infrastructure.

    Still, a lot of businesses feel that they’re moving too slowly.

    It takes longer to start projects. Chances pass you by. Teams are always busy, but it seems like development is slow instead of fast. A lot of the time, the problem isn’t effort or aptitude; it’s something much less evident and far more harmful.

    It’s the time it takes to make a decision.

    Decision latency is the period that goes by between when information is available and when a choice is really made. At first, it doesn’t look like a system breakdown or a missed deadline. Instead, it builds up gradually across teams, approvals, and levels of leadership, which slows down execution and makes the organisation less flexible.

    Decision delay becomes one of the most expensive problems for businesses over time.

    How Decision Latency Looks in Real Businesses

    Decision latency doesn’t normally show up as a single breakdown. It becomes increasingly clear as businesses become more complicated.

    You might see it when:

    • Even when they have all the information they need, teams have to wait days or weeks for approvals.
    • Different people look at the same decision without being able to hold anyone accountable.
    • We hold meetings to “align” on things we’ve already talked about.
    • Leadership requires more proof before making decisions, so they are put off.
    • Action is put off until the “perfect” information comes in.

    None of these cases seem really serious. They seem sensible, even responsible, when looked at alone. But when they work together, they always slow down execution.

    The group isn’t sitting around. People are putting in a lot of effort. But moving forward seems weighty, slow, and broken.

    Why it takes longer to make decisions when companies grow

    As businesses get bigger, it gets harder to make decisions, but the speed at which they make decisions typically goes down even more. There are a few structural reasons why this happens.

    Broken-up Information

    Businesses today have a lot of data, but it’s not really clear. Dashboards, CRMs, ERPs, spreadsheets, emails, and internal tools all save information. People who make decisions spend more time checking data than using it.

    Decisions stop when leaders aren’t sure that what they see is complete, up-to-date, or correct.

    The problem isn’t that there isn’t enough data; it’s that people don’t trust the system that gives it to them.

    Unclear Decision Ownership

    In many organizations, it’s unclear who genuinely owns a decision. There is a lack of clarity about who has authority, but responsibility is shared.

    This results in:

    • Decisions pushing upward unnecessarily
    • Teams waiting for approval instead of acting
    • Leaders are getting in the way of operational decisions.

    When ownership isn’t apparent, decisions don’t move forward—they circulate.

    Risk-Averse Processes

    Enterprises often add layers of inspection to decrease risk. Over time, these layers accumulate: legal checks, compliance assessments, executive sign-offs, cross-functional alignment sessions.

    These safety measures can make things riskier by making it harder to respond quickly to changes in the market, customer needs, and problems within the company.

    Speed and control aren’t the same thing, but bad processes can make them feel that way. 

    The Unseen Cost of Making Decisions Slowly

    Decision latency doesn’t show up on financial accounts very often, but it has a big effect that can be measured.

    It leads to:

    • Missed chances in the market
    • Launching products and features more slowly
    • Higher costs of doing business
    • Teams that are angry and not involved
    • Leadership that reacts instead of planning ahead

    Employees spend more time making updates, presentations, and justifications than doing work that matters. The momentum slows down, and it gets tougher to keep growing.

    In marketplaces where there is a lot of competition, the cost of waiting to make a decision is generally more than the cost of making a bad one.

    Why More Tools Don’t Speed Up Decision-Making

    Many companies add technology, like new analytics platforms, reporting tools, workflow software, or AI-powered dashboards, when decision-making slows down.

    But just having tools doesn’t speed up decision-making.

    When decision rights aren’t clear, approvals aren’t in line, or workflows aren’t well thought out, technology just makes the delay worse. Dashboards make the problem easier to see, but they don’t fix it.

    In some circumstances, extra tools slow things down by adding:

    • More information to look over
    • More reports to match up
    • More systems to look at before doing something

    Speed of decision-making only gets better when systems are built around how decisions are actually made, not how data is stored or tools are sold.

    Decision latency is an issue with the workflow.

    Decision latency is really a workflow problem, not a deficiency in leadership.

    There is a path for every choice:

    • Making information
    • It goes from one team or system to another.
    • Someone looks at it
    • An action is either approved or denied.

    When this path is unclear, broken up, or too full, it takes longer to make decisions.

    High-performing businesses plan out these decision flows on purpose. They want to know:

    • Who needs this data?
    • When do you need it?
    • Who has the power to make the decision?
    • What happens right after the choice?

    When you plan workflows with decisions in mind, speed naturally follows.

    How High-Performing Businesses Cut Down on Decision Latency

    Companies that want to move swiftly without losing control focus on making things clear and designing systems.

    They:

    • Make it clear who is responsible for making decisions at every level.
    • Cut down on superfluous levels of approval
    • Make sure that strategic decisions are different from operational ones.
    • Give people information that is rich in context right when they need it.
    • Get rid of reports and steps that don’t lead to action.
    • They don’t tell teams to “move faster.” Instead, they get rid of things that slow them down.

    The consequence isn’t quick choices; it’s timely, confident action.

    What UX and System Design Do

    It’s not only about reasoning when it comes to making decisions; it’s also about how easy they are to use.

    Decision-makers are hesitant when internal processes are messy, hard to understand, or don’t make sense. Bad UX makes people think more, which means leaders have to figure out what the data means instead of acting on it.

    Systems that are well-designed:

    • Only show relevant information
    • Give context, not noise
    • Make the following stages clear
    • Make it easier to make a decision in your head

    When processes are easy to use, making judgments is easier, and things go faster without stress.

    How fast you make decisions can give you an edge over your competitors.

    In today’s businesses, how quickly something gets done depends more on flow than on effort. When choices are made quickly, teams work together, things get done faster, and leaders can focus on strategy instead of dealing with problems.

    Companies don’t go out of business suddenly because of decision delay.

    It subtly stops them from reaching their full potential.

    Companies that grow successfully aren’t only well-funded or well-staffed; they are also built to make decisions.

    Conclusion

    Doing more work doesn’t always mean doing better.

    It’s about making decisions faster, without becoming confused, having to do things over, or being unsure.

    When decision systems are clear, integrated, and purposeful, getting things done is easy, not hard. Teams move forward with confidence, and growth becomes easier instead of tiring.

    Organizations don’t slow down when people stop working hard.

    They slow down because systems don’t help people make judgments the way they really do.

    If your company feels busy but slow, it might be time to look at how choices move through your processes, not just how work gets done.

    Connect with Sifars today to schedule a consultation 

    www.sifars.com

  • Why “Digital Transformation” Fails Without Fixing Internal Workflows

    Why “Digital Transformation” Fails Without Fixing Internal Workflows

    Reading Time: 3 minutes

    Businesses in all fields are making digital transformation a top priority. Companies spend a lot of money on new platforms, moving to the cloud, automation tools, analytics, and AI. All of these things are meant to help them become faster, smarter, and more competitive.

    But even with these efforts, many digital transformation projects don’t have a substantial effect on the business.

    The problem is often not the technology itself, but something far more basic: dysfunctional internal processes.

    Digital transformation becomes surface-level change—impressive on paper but useless in practice—if you don’t fix how work really moves throughout the company.

    Digital tools can’t fix broken ways of doing things.

    Most change projects are about what new technology to use, including CRMs, ERPs, dashboards, or AI technologies. But they don’t think about how teams use those systems every day.

    If your internal processes are unclear, broken up, or too manual, new tools will just bring back old problems:

    Processes are still slow, although they’re on newer software. Teams make workarounds outside the system. Approvals still slow down progress. Data is still inconsistent and hard to trust.

    In these situations, digital transformation doesn’t get rid of friction; it makes it digital.

    How Broken Internal Workflows Look

    Leadership generally doesn’t see problems with internal workflows since they don’t show up as direct failures. Instead, they silently slow down progress and efficiency.

    Some common indicators are:

    • Teams using different tools to finish the same job
    • Adding manual approvals on top of automated systems
    • Entering the same data again and over again in different departments
    • Uncertainty over who owns what and when to make decisions
    • Reports that take days to put together instead of minutes

    Every problem may appear like it’s possible to handle on its own. They work together to slow down execution and stop organisations from getting the full value of change.

    Why Digital Transformation Projects Get Stuck

    When workflows aren’t fixed initially, transformation projects tend to become stuck for the same reasons.

    Adoption is still low since the systems don’t fit how people really operate.

    Productivity doesn’t get better because the steps haven’t been made easier.

    Data is spread out and delayed, which makes it hard to make decisions quickly.

    As more workers are hired to fix problems, operational costs go up.

    Over time, executives start to doubt the return on investment (ROI) of digital efforts, even if the true problem is deeper than that.

    The basis of change is workflow design.

    Not choosing the right technology is the first step in a successful digital transformation.

    This implies knowing:

    • How work moves between systems and teams
    • Where choices are made and put off?
    • Which tasks are worth it and which aren’t? 
    • Where automation will really help?
    • What information do you need at each step?

    When workflows are based on genuine business goals, technology helps instead of getting in the way.

    From Automation to Real Operational Efficiency

    A lot of businesses try to automate first. But automating a workflow that isn’t well thought out just makes it less efficient quickly.

    The following things lead to true operational efficiency:

    Making things easier before putting them online

    Taking away permissions and handoffs that aren’t needed

    Making systems based on positions and duties

    Making sure that data moves smoothly between platforms

    Automation only makes things faster, more accurate, and bigger when it accomplishes this.

    What UX Does for Internal Systems

    Not only are internal workflows logical, but they also make sense to people.

    Teams are less likely to use corporate tools if they are hard to use, cluttered, or don’t make sense. Good UX design makes things easier to understand, helps people complete difficult activities, and makes workflows feel natural instead of forced.

    Digital transformation that doesn’t take UX into account typically fails not because the technology is powerful, but because it’s hard to use.

    How Sifars Helps Businesses Change for the Better

    We at Sifars think that digital transformation only works when the way things work inside the company is changed along with the technology.

    We help businesses with:

    • Look at and make sense of complicated workflows
    • Update old systems without stopping work
    • Make architectures that can grow and are cloud-native
    • Make the user experience easy to understand for both internal and customer-facing tools.
    • Use automation and AI only when they really help.

    Our method makes sure that transformation improves not just IT metrics, but also execution, decision-making, and long-term scalability.

    Conclusion

    When you go digital, it’s more than just a software update. People are doing their work in a very different way.

    If you don’t fix your internal workflows, even the best technological investments won’t function. But when procedures are clear, efficient, and centred on people, digital tools can help people get more done and lead to long-term success.

    Companies don’t fail at change because they don’t want to.

    When systems don’t support how people genuinely operate, they don’t work.

    👉 Want to see real results from your digital transformation?

    You can ask Sifars to help you change your systems and workflows so that they can grow with your business.