The version of SMART goals that most people use is not what George T. Doran wrote in 1981.
Doran was a Washington State University director of corporate planning who published a two-page note in the November 1981 issue of Management Review titled “There’s a S.M.A.R.T. way to write management’s goals and objectives.” His acronym stood for Specific, Measurable, Assignable, Realistic, and Time-related. His audience was managers writing performance objectives for their teams.
Over the following four decades, the acronym went through multiple rounds of reinterpretation. “Assignable” became “Achievable.” “Realistic” became “Relevant” in some versions and “Results-oriented” in others. Paul Meyer’s 2003 formulation in Attitude Is Everything standardized one variant. Paul Drucker’s management by objectives framework was frequently cited alongside SMART in ways that blurred their origins. Today, a Google search returns at least six different definitions of what the letters stand for.
Understanding what SMART actually says — and what the accumulated evidence supports — requires going back to the original and separating the framework from the folklore.
What Doran Actually Wrote
Doran’s 1981 note had a specific, narrow purpose. He was trying to help managers move away from vague, unmeasurable annual objectives — the kind that said things like “improve morale” or “increase market presence” — toward objectives that could be evaluated at year’s end.
His five criteria:
Specific: The objective should target a specific area for improvement or accomplishment, not a general improvement in a broad domain.
Measurable: There should be an indicator that tells you whether the objective is being achieved. This doesn’t require a precise numerical target, just a mechanism for assessment.
Assignable: The objective should be assigned to a particular person. Doran was writing for managers — “we” as a goal owner was a common failure mode in corporate planning, where diffuse ownership meant no one was truly accountable.
Realistic: Given available resources and constraints, the objective should be achievable. Doran explicitly noted that “realistic” depended on context — what’s realistic for one team is impossible for another.
Time-related: The objective should specify when it will be achieved or reviewed.
Notice what Doran did not say. He didn’t say goals must always have a numerical target. He didn’t say ambitious goals are wrong. He didn’t say “achievable” means “easy.” He was writing a quality checklist for manager-written performance objectives, not a universal theory of goal pursuit.
How the Framework Evolved (and Where It Drifted)
The first major drift was replacing “Assignable” with “Achievable.” This substitution made the framework portable for individual goal-setters rather than just managers — you can’t assign a goal to yourself, but you can assess whether it’s achievable. The substitution was reasonable and the change is mostly benign.
The more consequential drift was treating “Realistic” as a constraint rather than a context-check. In Doran’s original framing, “realistic” was relative — it asked whether the goal was achievable given your specific situation. In popular usage, it evolved into a signal to write goals that feel comfortable, goals that don’t risk failure, goals well within existing capability.
This reinterpretation quietly reversed one of the most consistent findings in goal-setting research.
Edwin Locke and Gary Latham, whose goal-setting theory drew on decades of experimental research, established that specific, challenging goals consistently produce higher performance than specific, easy goals. Their 2002 review in the American Psychologist synthesized over 1,000 studies. The positive relationship between goal difficulty and performance is one of the most replicated findings in organizational behavior.
A framework that evolved to encourage “realistic” (read: easy, safe, comfortable) goals is pointing in the opposite direction from where the evidence points.
What Each Component Does and Doesn’t Do
Specific
The strongest component. Specific goals focus attention, activate relevant knowledge and skills, and create a clear criterion for success that prevents motivated reasoning at review time.
The failure mode: over-specifying in ways that close off better paths. A specific goal can become a cage if the underlying situation changes and the specific formulation can’t adapt. Build review checkpoints that allow re-specification.
Measurable
The second strongest component, with an important caveat. Having a measure is better than not having one. But choosing the wrong measure — a proxy that’s easy to track rather than one that captures what you care about — can be worse than having no measure at all.
When you define a measure and begin optimizing for it, you change your behavior in response to the measure. If the measure is disconnected from the underlying goal, you get a behavior change that increases the number without improving the thing that mattered. The research on performance metrics in organizations documents this extensively — Goodhart’s Law (when a measure becomes a target, it ceases to be a good measure) applies to personal goals too.
The right test for Measurable: not “can I attach a number?” but “does this number track the thing I actually care about, and how might optimizing for it lead me astray?”
Assignable / Achievable
Useful for accountability but not structurally important once you’re writing individual goals. At the personal level, the more relevant question is whether the goal is connected to your actual priorities — a goal assigned to “you” that isn’t genuinely yours will be abandoned under pressure.
Realistic
The most contested criterion and the most frequently misapplied. Locke and Latham’s finding is clear: within the range of commitment, harder goals produce better performance. The “realistic” criterion is valuable only as a sanity check against truly unachievable goals (completing a PhD in six months, running a 4-minute mile without athletic training). It is not an argument for setting comfortable goals.
The practical recommendation: replace “is this realistic?” with “am I genuinely committed to this, and do I have a plausible path?” A goal that requires real effort and has a reasonable path is not unrealistic — it’s appropriately challenging.
Time-Related
Solid empirical grounding. Deadlines activate urgency, reduce procrastination, and create natural review points. The implementation intentions literature (Gollwitzer & Sheeran) extends this: not just when the goal will be complete, but when you’ll work on it, in what location, in response to what cue. The T in SMART is doing important work when applied at this level of specificity.
The Three Missing Components
Doran’s framework was designed for a narrow use case. Applied broadly to personal and professional goals, it’s missing three components that the research suggests are critical for sustained goal pursuit.
Process layer. SMART describes outcomes. It says nothing about the behaviors that produce outcomes. A goal of “close $200K in new business by Q4” is SMART but doesn’t specify what the salesperson should do daily. The outcome target needs to be accompanied by process commitments — the specific behaviors that make the outcome likely — or execution will remain an open question.
Motivation mechanism. SMART goals can be technically perfect and emotionally inert. If the goal doesn’t connect to something you genuinely care about — a value, a meaningful purpose, an identity you’re building — it will lose to the inevitable friction of competing demands and difficult moments. Edward Deci and Richard Ryan’s self-determination theory identifies autonomy, competence, and relatedness as the foundations of intrinsic motivation. A SMART goal that scores poorly on all three will fail regardless of its technical precision.
Adaptation protocol. Goals are set against a model of how the world will unfold. That model is usually wrong. A SMART goal that can’t adapt to changed circumstances will either be abandoned or maintained rigidly past the point of usefulness. Building in scheduled recalibration — where you explicitly review whether the goal, the measure, and the timeline still make sense — converts a static commitment into a dynamic learning system.
How AI Addresses the Gaps
The components SMART is missing are exactly where AI assistance is most valuable.
For the process layer: AI can generate implementation intentions, suggested weekly behaviors, and the if-then plans that convert outcome goals into action commitments. A prompt that asks “what would I need to do each week to make this goal likely?” produces the process layer that SMART doesn’t include.
For the motivation check: AI can ask the questions that reveal whether a goal is genuinely yours or an imported obligation. “Why does this matter to you? What would it mean to achieve this? What have you tried before and what happened?” These reflective questions surface motivation or the absence of it before you’ve invested significant effort.
For adaptation: AI can run structured mid-point reviews that assess whether the original goal was calibrated correctly. The model has no sunk-cost investment in the original framing — it will point out that your six-month target should probably have been twelve months without the social discomfort that makes human reviewers hesitant to say it.
Beyond Time’s planning layer connects SMART goals to the day-to-day time allocation that makes them real — so the goal you’ve defined isn’t abstract good intention but a set of scheduled behaviors with a live track record.
The Framework in Its Proper Scope
SMART is a goal-clarity tool. In that role, it remains genuinely useful. Writing a goal that meets the Specific, Measurable, and Time-related criteria forces a level of commitment that vague intentions avoid.
It is not a motivation system, an execution system, or a framework for setting ambitious goals. Treating it as any of those things — which is how it’s commonly deployed — produces the failures that give SMART its mixed reputation.
The framework works best when used in combination: SMART for clarity, process commitments for execution, a meaningful “why” for motivation, and a review cadence for adaptation. Used in that combination, it’s part of a complete system. Used alone, it’s a well-formatted placeholder.
Related:
- The Complete Guide to SMART Goals vs AI
- Why SMART Goals Are Often Criticized
- SMART Goals vs Other Goal Frameworks
Tags: SMART goals, goal-setting theory, Locke and Latham, goal-setting frameworks, productivity research
Frequently Asked Questions
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Who invented SMART goals?
George T. Doran, a management consultant and corporate planner, published the original SMART framework in the November 1981 issue of Management Review. His version stood for Specific, Measurable, Assignable, Realistic, and Time-related. Subsequent adaptations by Paul Meyer, Peter Drucker's interpreters, and others replaced several letters, producing the multiple variants in circulation today.
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What does each letter in SMART stand for?
In Doran's original version: Specific (focused on a particular area), Measurable (with a quantifiable indicator of progress), Assignable (to a person), Realistic (achievable given available resources), and Time-related (with a completion date). Modern versions typically replace Assignable with Achievable and Realistic with Relevant or Results-oriented.
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Is there scientific evidence that SMART goals work?
The Specific and Measurable components have strong empirical support from Locke and Latham's goal-setting theory research (reviewed in their 2002 American Psychologist paper). The Realistic criterion is more contested — the same research tradition shows that difficult goals outperform easy goals, which is in partial tension with 'realistic.' The Time-related component is supported by deadline research and Gollwitzer's implementation intentions work. The overall package has less controlled experimental evidence than its individual components.