Implementing an AI system properly usually isn’t a quick or solo effort. It often takes a small, dedicated team working over several months. Data engineers handle the heavy lifting around cleaning and preparing datasets, while developers focus on building the integrations that connect the system to existing tools.
Alongside them, business analysts translate real business needs into clear technical requirements, and project coordinators keep stakeholders aligned as the work moves forward. Quality assurance comes in to test, break, and retest the system before it goes live.
But you have two people or maybe three if you count someone who can dedicate half their time. Executives still expect delivery on the original schedule because the business case was approved based on those timelines.
This math doesn’t work, however, the project is already underway, budgets are committed, and stakeholders are waiting for results.
Pretending two people can do six people’s work through sheer determination just guarantees failure and burnout. You will need to choose a strategy that acknowledges reality and makes deliberate trade-offs about scope, timeline, or investment.
The First Reality
Before exploring options, let’s establish what you cannot do. You cannot manufacture time. There are still only 24 hours in a day, and your people still need to sleep, eat, and maintain some semblance of life outside work.
The two people you have cannot magically become six no matter how talented they are. You cannot make two people as productive as six through efficiency improvements or better tools. A 50% productivity gain doesn’t bridge a 200% resource shortage.
When organizations ignore this reality. Projects limp along chronically understaffed as deliverables slip from their original dates. Quality suffers because there’s no time for proper testing or refinement. Your two people burn out trying heroic efforts that cannot possibly bridge a threefold gap.
You need to choose a real strategy now, before this predictable pattern plays out and destroys both the project and the people working on it.
Also read, AI Implementation Without Your Best People Quitting
Reduce Scope
The first option is cutting the project down to what your actual resources can deliver. This means identifying core functionality that solves the primary business problem and making everything else Phase 2 for a future implementation.
You’re not abandoning the full vision but being realistic about what two people can accomplish in six months versus what six people could accomplish.
The key is identifying what must stay and what can wait. Ask yourself what solves the most urgent business problem that justified this AI project in the first place. That functionality stays in Phase 1. Everything that’s “nice to have” or “eventually useful” but not essential to addressing the core problem gets deferred.
This requires difficult conversations with stakeholders who want everything, but it’s better to have those conversations now than to promise everything and deliver nothing functional.
Selling scope reduction to stakeholders requires framing it as responsible project management rather than failure.
A scope that goes like this; “We can deliver the core functionality in six months with current resources and create immediate business value, or we can attempt to build everything simultaneously and deliver nothing functional by the deadline.”
Which option actually keeps the business moving forward? Most rational stakeholders choose functional core capabilities over comprehensive vapourware, but you need to articulate this choice explicitly rather than assuming they understand the trade-offs.
Extend Timeline
The second option is accepting that the timeline needs to change based on actual available resources. Six people for six months equals 36 person-months of work. You have two people, which means the same work requires 18 months, not six.
We are assuming everyone is equally productive, but that’s an optimistic view. Small teams often deal with more interruptions and need more time to stay in sync. We’ll use these figures as a basic starting point for our strategy.
Managing stakeholder expectations around timeline extensions requires reframing the conversation. This is responsible project management that acknowledges resource constraints. Understaffed projects that claim they’ll hit original timelines are lying, not ambitious.
They’re setting everyone up for disappointment when reality inevitably asserts itself. Presenting an honest timeline based on actual capacity demonstrates maturity and protects the organization from making commitments it cannot keep.
This option works best when deadlines are preferences rather than hard requirements. If there are regulatory deadlines, contractual obligations, or genuine business crises that demand specific timing, extended timelines might not be viable.
But if the urgency is about executive impatience rather than external constraints, delayed success beats on-time disaster. A working system delivered late provides value. A broken system delivered on schedule provides nothing but cleanup work.
Hire or Contract Strategically
The third option is bringing in external help, but doing so strategically rather than just throwing bodies at the problem.
This is about acquiring specific expertise for specific gaps in your current team’s capabilities.
While the price for these six months is high, the alternative is worse. It is better to invest now than to face a failed project or a team that is completely burnt out.
The key decision is what to outsource versus what to keep internal. Hiring contractors works best for three things: handling short-term busy streaks, providing skills your team lacks, and finishing projects with a clear end date. This approach ensures that everyone understands the goals and knows when the partnership ends.
What you want to keep internal is anything involving core business logic, ongoing system maintenance, daily operations, or institutional knowledge that contractors won’t stick around to preserve.
Bringing in contractors represents budget overrun compared to the original plan that assumed sufficient internal resources. But is it better than burning your two permanent staff members while the project still fails?
The contractor costs are temporary and bounded. The cost of losing burned-out permanent staff and then having to hire, onboard, and train replacements while starting the project over is much higher. Sometimes the expensive option is actually the economical one when you account for all costs.
Borrow Resources Temporarily
Another alternative is borrowing staff from other departments for defined periods, then returning them to their original roles. This approach spreads the burden across the organization rather than concentrating it on two people.
You might borrow someone from operations for three months to handle business requirements, someone from IT for two months to manage infrastructure, and rotate different people through the project rather than burning the same small team continuously.
Making this work requires discipline about scope and duration. The borrowed staff need clear expectations about what they’ll work on and how long they’re committed.
Knowledge transfer must be documented so these people aren’t trapped in the AI project permanently because they’re the only ones who understand certain components. Set actual calendar dates for when people return to their home departments, and honor those commitments even if it’s inconvenient.
The risk with borrowed resources is that “temporary” becomes permanent unless you actively prevent it. Organizations develop dependencies on whoever is doing the work, and it becomes easier to keep them assigned than to properly transition their responsibilities.
Set calendar reminders to actually return people as promised. Treat the return dates as seriously as you treated the original project deadlines. Otherwise, you haven’t solved the resource problem. You’ve just redistributed it and upset multiple departments instead of one.
The Honest “No”
This option is often the hardest to choose, is admitting the project isn’t viable with current resources and pausing until proper staffing is available.
This feels like failure, especially after initial investment and stakeholder excitement. But sometimes continuing guarantees worse failure.
Sometimes your two people are already breaking under the strain. Sometimes stakeholder expectations are so impossibly misaligned with reality that no amount of effort can bridge the gap.
The key to this option is pausing rather than canceling. Document everything learned during the initial phase. Preserve pilot work and prototypes that demonstrate feasibility.
Create a proper resource plan showing exactly what staffing the project needs for success. Frame this as responsible stewardship of organizational resources rather than abandonment of AI ambitions.
The project can restart when conditions allow for success rather than limping forward in conditions that guarantee failure.
This option is right when continuing means certain failure, when your two people are already showing signs of serious burnout, or when the gap between stakeholder expectations and resource reality is too large to manage.
It takes courage to say “we need to stop” after investment has been made. It takes more courage than continuing doomed projects and hoping something changes.
Pausing preserves the possibility of future success but continuing until catastrophic failure often kills projects permanently.
The Decision Matrix
Your choice depends on what you can change. If you can launch just the basics now and add extra features later, then cutting the project size is the best path. This gets the work done fast with the resources you have.
However, if your deadline isn’t a strict rule, it makes more sense to push the date back. This works best when the business is willing to wait for a perfect, finished product.
If you have budget availability and can secure additional funding, hiring strategically brings needed expertise without burning internal staff. This trades money for time and preserves your permanent team for long-term operations.
If internal capacity exists elsewhere in the organization, borrowing resources temporarily spreads the work while maintaining overall staffing levels.
If none of these conditions exist, if you have no scope flexibility, no timeline flexibility, no budget availability, and no borrowable resources, the honest “no” is your only viable option.
Pausing prevents the worse outcome of burning people and budget on doomed implementation attempts.
What definitely doesn’t work is pretending two people can do six people’s work through “efficiency” or “hustle” or “smart execution.”
What Most Organizations Do (And Shouldn’t)
Here’s the pattern that plays out repeatedly across industries and organizations. Leadership asks the two people to “do their best” with subtle or explicit implications that heroic effort will somehow close the gap.
The two people, being competent and committed, attempt the impossible. They work nights to catch up on tasks that couldn’t fit into normal hours. They work weekends to maintain momentum. They skip vacations because the project can’t afford their absence.
Months pass and yet the project delivers subpar results late because two people cannot do six people’s work no matter how hard they try.
Quality suffers because there was no time for proper testing. Documentation is incomplete because there was no time to write it.
Technical debt accumulates because there was no time to do things properly. Then the two people who tried so hard either quit from exhaustion or become so burned out they’re ineffective in their roles.
Why does this fail? Because you cannot close a threefold resource gap with effort alone. Human endurance definitely doesn’t work that way. A person working 80 hours weekly isn’t twice as productive as someone working 40 hours.
They’re probably less productive per hour due to fatigue, stress, and declining decision quality. And they can’t maintain that pace for months. Eventually, they break.
Smart Resourcing Beats Heroic Effort
The two people you have are doing their best. The problem isn’t their effort or capability but the math. Two people cannot do six people’s work regardless of how talented, dedicated, or hardworking they are.
It’s reality-based planning that creates conditions for actual success rather than guaranteed failure disguised as ambition.
- Choose scope reduction if you can deliver core value with available resources.
- Choose timeline extension if you can wait for quality results.
- Choose strategic hiring if you can invest in success.
- Choose temporary borrowing if you can coordinate resources across departments.
- Choose honest pausing if none of those options are available.
The secret to successful AI implementation is being honest about your team’s limits. You need to plan based on the resources you actually have and make smart choices to fill the gap between where you are and where you want to be.
