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Tag: business transformation

  • Why AI Exposes Bad Decisions Instead of Fixing Them

    Why AI Exposes Bad Decisions Instead of Fixing Them

    Reading Time: 3 minutes

    We’ll let AI sneak in on a small hope:

    that smarter ones will make up for human foolishness.

    Better models. Faster analysis. More objective recommendations.

    Surely, decisions will improve.

    But in reality, many organizations find something awkward instead.

    AI doesn’t quietly make bad decision-making go away.

    It puts it on display.

    AI Doesn’t Choose What Matters — It Amplifies It

    AI systems are good at spotting patterns, tweaking variables and scaling logic. What they cannot do is to determine what should matter.

    They function in the limit that we impose:

    • The objectives we define
    • The metrics we reward
    • The constraints we tolerate
    • The trade-offs we won’t say aloud

    When the inputs are bad, AI does not correct them — it amplifies them.

    If speed is rewarded at the expense of quality, AI just accelerates bad outcomes more quickly.

    When incentives are at odds, AI can “hack” one side and harm the system as a whole.

    Without clear accountability, AI generates insight without action.

    The technology works.

    The decisions don’t.

    Why AI Exposes Weak Judgment

    Before AI, poor decisions typically cowered behind:

    • Manual effort
    • Slow feedback loops
    • Diffused responsibility

    Smell of doughnuts “That’s the way we’ve always done it” logic

    AI removes that cover.

    When an automated system repeatedly suggests actions that feel “wrong,” it is rarely the model that’s at fault. It’s not that the organization never has aligned on:

    • Who owns the decision
    • What outcome truly matters
    • What trade-offs are acceptable

    AI surfaces these gaps instantly. You might find that visibility feels like failure — but it’s actually feedback.

    The True Issue: Decisions Not Designed

    Numerous AI projects go off the rails when companies try to automate before they ask how decisions should be made.

    Common symptoms include:

    • Insights Popping Up in dashboard with Division of Responsibility is not defined
    • Overridden recommendations “just to be safe”
    • Teams that don’t trust the output and they don’t know why
    • Escalations increasing instead of decreasing

    In the midst of those spaces, AI makes clear a much larger problem:

    decision-making was not optimally designed in the first instance.

    Human judgment was around — but it was informal, inconsistent and based on hierarchy rather than clarity.

    AI demands precision.

    It’s also usually not something that organizations are prepared to offer.

    AI Reveals Incentives, Not Intentions

    Leaders could be seeking to maximize long-term value, customer trust or quality.

    AI competes on what gets measured and rewarded.

    It becomes manifest when AI is added to the mix, that space between intent and reward.

    When teams say:

    “The AI is encouraging the wrong behavior.”

    What they often mean is:

    “The AI is doing precisely what our system asked — and we don’t like what that shows,” he says.

    That’s why AI adoption tends to meet with resistance. It is confronting cosy ambiguity and making explicit the contradictions that human beings have danced around.

    Better AI Begins With Better Decisions

    The best organizations aren’t looking at A.I. to replace judgment. They rely on it to inform judgment.

    They:

    • Decide who owns the decisions prior to model development
    • Develop based on results, not features
    • Specify the trade-offs AI can optimize
    • Think of AI output as decision input — not decision replacement

    In these systems, AI is not bombarding teams with insight.

    It focuses the mind and accelerates action.

    From Discomfort to Advantage

    AI exposure is painful because it takes away excuses.

    But that discomfort, for those organizations willing to learn, becomes leverage.

    AI shows:

    • Where accountability is unclear
    • Where incentives are misaligned
    • The point where decisions are made through habit rather than intent

    Those signals are not failures.

    They are design inputs.

    Final Thought

    AI doesn’t fix bad decisions.

    It makes organizations deal with them.

    The true source of advantage in the AI era will not be individual analytic models, but the speed at which models are improved. It will be from companies rethinking how decisions are made — and then using A.I. to carry out those decisions consistently.

    At Sifars, we work with companies to go beyond applying AI towards developing systems where AI enhances decisions not just efficiencies.

    If your A.I. projects are solid on the tech side but maddening on the operations side, that problem may not be about technology as much as it is about the decisions it happens to reveal.

    👉 Contact Sifars to create AI solutions that turn intelligent decisions into effective actions.

    🌐 www.sifars.com

  • The Myth of Alignment: Why Aligned Teams Still Don’t Execute Well

    The Myth of Alignment: Why Aligned Teams Still Don’t Execute Well

    Reading Time: 3 minutes

    “Everyone is aligned.”

    It is one of the most comforting sayings that leaders choose to hear.

    The strategy is clear. The roadmap is shared. Teams nod in agreement. Meetings end with consensus.

    And yet—

    execution still drags.

    Decisions stall.

    Outcomes disappoint.

    If we have alignment, why is performance deficient?

    Now, here’s the painful reality: alignment by itself does not lead to execution.

    For many organizations, alignment is a comforting mirage — one that obscures deeper structural problems.

    What Organizations Mean by “Alignment”

    When companies say they’re aligned, they are meaning:

    • Everyone understands the strategy
    • Goals are documented and communicated
    • Teams agree on priorities
    • KPIs are shared across functions

    On paper, this is progress.

    During reality however, that disrupts precious little of the way work actually gets done.

    Never mind when people do agree on what matters — but not how to advance their work.

    Agreement is not the same as execution

    Alignment is cognitive.

      Execution is operational.

      You can get a room full of leaders rallied around a vision in one meeting.

      But its realization is determined by hundreds of daily decisions taken under pressure, ambiguity and competing imperatives.

      Execution breaks down when:

      • Decision rights are unclear
      • Ownership is diffused across teams
      • Dependencies aren’t explicit
      • In the local incentives reward internal the in rather than success global outcome.

      None of these are addressed by alignment decks or town halls.

      Why Even Aligned Teams Stall

      1. Alignment Without Decision Authority

        Teams may agree on what to pursue — but don’t have the authority to do so.

        When:

        • Every exception requires escalation
        • Approvals stack up “for safety”
        • Decisions are revisited repeatedly

        Work grinds to a halt, even when everyone agrees where it is they want to go.

        Alignment, with out empowered decision making results in polite paralysis.

        1. Conflicting Incentives Beneath Shared Goals

        Teams often have overlapping high-level objectives but are held to different standards.

        For example:

        • One team is rewarded speed
        • Another for risk reduction
        • Another for utilization

        It’s agreed on what you’re trying to get to — but the behaviors are optimized in opposite directions.

        This leads to friction, rework and silent resistance — with no apparent confrontation.

        1. Hidden Dependencies Kill Momentum

        Alignment meetings seldom bring up actual dependencies.

        Execution depends on:

        • Who needs what, and when
        • What if one input arrives late
        • Where handoffs break down

        If dependencies aren’t meant to exist, aligned teams wait for the other—silently.

        1. Alignment Doesn’t Redesign Work

        Many change goals converge while work structures remain the same.

        The same:

        • Approval chains
        • Meeting cadences
        • Reporting rituals
        • Tool fragmentation

        remain in place.

        Teams are then expected to come up with new results using old systems.

        Alignment is an expectation on top of dysfunction.

        The Real Problem: Systems, Not Intent 

        In short, it’s not who you are or what goes on inside your head that most matters; only 2.3 percent of people who commit crime have serious mental illness like schizophrenia.

        Execution failures are most often attributed to:

        • Culture
        • Communication
        • Commitment

        But the biggest culprit is often system design.

        Systems determine:

        • How fast decisions move
        • Where accountability lives
        • How information flows
        • What behavior is rewarded

        There’s no amount of alignment that can help work get done when systems are misaligned!

        Why Leaders Overestimate Alignment

        Alignment feels measurable:

        • Slides shared
        • Messages repeated
        • OKRs documented

        Execution feels messy:

        • Trade-offs
        • Exceptions
        • Judgment calls
        • Accountability tensions

        So organizations overinvest in alignment — and underinvest in shaping how work actually happens.

        What High-Performing Organizations Do Differently

        They don’t ditch alignment — but they cease to treat it as an end in itself.

        Instead, they emphasize the clarity of an execution.

        They:

        • Define decision ownership explicitly
        • Organize workflows by results, not org charts
        • Reduce handoffs before adding tools
        • Align incentives with end-to-end results
        • Execution is not a capability, it’s a system

        In these firms, alignment is an incidental effect of system design that the best leaders do not impose as a replacement for it.

        From Alignment to Flow

        Work flows more efficiently when execution is good.

        Flow happens when:

        • Work is where decisions are made
        • Information arrives when needed
        • Accountability is unambiguous
        • No harm for judgment on teams

        This isn’t going to be solved by another series of alignment sessions.

        It requires better-designed systems.

        The Price of the Lone Pursuit of Alignment

        When companies confuse alignment with execution:

        • Meetings multiply
        • Governance thickens
        • Tools are added
        • Leaders push harder

        Pressure can’t make up for the lack of structure.

        Eventually:

        • High performers burn out
        • Progress slows
        • Confidence erodes

        And then leadership asks why the “aligned” teams still don’t deliver.

        Final Thought

        Alignment is not the problem.

        It’s the overconfidence in that alignment that is.

        Execution doesn’t break down just because they disagree.

        It fails because systems are not in the nature of action.

        The ones that win the prize are not asking,

        “Are we aligned?”

        They ask,

        “Can we rely upon this system to reach the results that we ask for?”

        That’s where real performance begins.

        Get in touch with Sifars to build systems that convert alignment into action.

        www.sifars.com

      1. Why Most Digital Transformations Fail After Go-Live

        Why Most Digital Transformations Fail After Go-Live

        Reading Time: 3 minutes

        For most companies go-live is seen as the end point of digital transformation. Systems are rolled out, dashboards light up, leadership rejoices and teams get trained. On paper, the change is total.

        But this where failure typically starts.

        Months after go-live, adoption slows. Workarounds emerge. Business outcomes remain unchanged. Something that was supposed to be a step-change quietly becomes yet another overpriced system people endure, rather than rely on.

        Few digital transformations fail because of technology.

        They don’t work because companies mistake deployment for transformation.

        The Go-Live Illusion

        Go-live feels definitive. It is quantifiable, observable and easy to embrace. But it stands for just one thing: the system now exists.

        But systems do not make transformation happen. It’s about the ways work changes because the system is there.

        For most programs, the technical readiness is where it ends:

        • The platform works
        • Data is migrated
        • Features are enabled
        • SLAs are met

        Operational readiness is seldom tested-Does the organization really know how to work differently (or more often the same) on day one post go?

        Technology Changes Faster Than Behavior

        Digital transformations take for granted that when tools are in place, behavior will follow. In fact, behavior lags software by a distance greater than the space between here and Mars.

        People return to what they already know how to do, when:

        • Releases for new workflows feel slower or more risky
        • Accountability becomes unclear
        • Exceptions aren’t handled well
        • The system is in fact introducing, rather than eliminating, friction.

        When roles, incentives and decision rights aren’t intentionally redesigned, in fact, teams just throw old habits around new tools. The transformation becomes cosmetic.

        The system changes. The organization doesn’t.

        Design of Process is as a Side Work 

        A lot of these are just turning analog processes into digital ones, without necessarily asking whether those analog processes make sense anymore.

        Instead, legacy inefficiencies are automated not eradicated. Approval layers are maintained “for security.” Workflows are drawn like org charts, not results.

        As a result:

        • Automation amplifies complexity
        • Cycle times don’t improve
        • Coordination costs increase
        • They work harder to manage the system.

        Technology only exposes what is actually a problem, when the processes aren’t working.

        Ownership Breaks After Go-Live

        During implementation, ownership is clear. There are project managers, system integrators and steering committees. Everyone knows who is responsible.

        After go-live, ownership fragments.

        • Who owns system performance?
        • Who owns data quality?
        • Who owns continuous improvement?
        • Who owns business outcomes?

        Implicit screw you there in the lack of post-launch ownership. Enhancements stall. Trust erodes. The result is that in the end it becomes “IT’s problem” rather than a business capability.

        Nobody is minding the store, so digital platforms rot.

        Success Metrics Are Backward-Looking

        Most of these transformations define success in terms of delivery metrics:

        • On-time deployment
        • Budget adherence
        • Feature completion
        • User logins

        Those are decisions metrics and they don’t do anything to tell you if this action improved decisions, decreased effort or added illimitable value.

        When leadership is monitoring activity, not impact, teams optimize for visibility. Adoption is thus coerced rather than earned. The organization is changing — just not for the better.

        Change Management Is Underestimated

        Pulling a training session or writing a user manual is not change management.

        Real change management involves:

        • Redesigning how decisions are made
        • Ensuring that new behaviors are safer than old ones
        • Cleaning out redundant and shadow IT systems
        • By strengthening use from incentives and managerial behavior

        Without it, workers regard new systems as optional. They follow them when they need to and jump over them when pushed.

        Transformation doesn’t come from resistance, but from ambiguity.

        Digital Systems Expose Organizational Weaknesses

        Go-live tends to expose problems that were prior cloaked in shadow:

        • Poor data ownership
        • Conflicting priorities
        • Unclear accountability
        • Misaligned incentives

        Instead of fixing this problems, companies blame the tech. Confidence drops, and momentum fades.

        But it’s not the system that’s the problem — it’s the mirror.

        What Successful Transformations Do Differently

        Organizations that realize success after go-live treat transformation as an ongoing muscle, not a one-and-done project.

        They:

        • How to design the workflow around outcomes instead of tools
        • Assign clear post-launch ownership
        • Govern decision quality, not just system usage
        • Iterate on programs from actually trying them out
        • Embed technology into the way work is done

        Go-live, in fact, is the start of learning, not the end of work.

        From Launch to Longevity

        Digital transformation is not a systems installation.

        It’s about changing the way an organization works at scale.

        If companies do fail post go-life, it’s almost never because of the technology. That’s because the body ceased converting prematurely.

        The work is only starting once the switch flips.

        Final Thought

        A successful go-live demonstrates that technology can function.

        A successful transformation is evidence that people are going to work differently.

        Organizations that acknowledge this difference transition from digital projects to digital capability — and that is where enduring value gets made.

        Connect with Sifars today to schedule a consultation 

        www.sifars.com