Category: Deliver

Three forces working against your next engagement

Being brilliant at your work is one of the most reliable ways to destroy your pipeline.

In the opening chapters of The E-Myth Revisited, Michael Gerber introduces a character named Sarah. Sarah is an exceptional baker. She loves the craft, she is brilliant at it, and one day she decides to open her own bakery. The logic seems sound: if you are good at the work, you should be able to build a business around it.

Within a year, Sarah is exhausted. She works longer hours than she ever did as an employee. The quality of her baking is as good as ever. Her customers are happy. But the business is barely surviving, and she has no idea how to fix it. The problem, Gerber argues, is that Sarah assumed that understanding the technical work meant understanding the business of doing it. It doesn’t. The skills that make you exceptional at delivery are not the skills that build a practice. They are, in fact, often the skills that consume it.

Most consultants who read that passage recognise something in Sarah. They also quietly assume it applies to someone else.

Being good at your work is not enough to sustain a consulting practice. If you want a business that lasts beyond your current engagement, you need to understand the three forces that turn full utilisation into a slow decline.

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Force #1: The Absorption Force

Delivery expands to fill all available time.

This is not a time-management problem. It is a structural one.

When you are in an active engagement, every available hour has a natural claimant. There are client emails that require a considered response. There are deliverables that need one more review before they go out. There are scope items that weren’t in the original brief but feel too small to push back on. There are stakeholder conversations that weren’t planned but need to happen before the week ends.

None of these feels like a problem in isolation. Collectively, they occupy the entirety of your working week, and then some.

David Maister observed this pattern across dozens of professional service firms over two decades of research. The consultants with the highest utilisation rates consistently allocated the least time to business development, not because they were unaware of the need, but because billable work exerted a gravitational pull that no non-billable activity could match. In a practice built on one person’s expertise, that pull is even stronger. There is no colleague to pick up the slack. There is no business development function to keep the pipeline moving. There is only you, and you are in a client meeting.1

The result is not a schedule that is too full for pipeline work. It is a schedule that never made room for it in the first place.

The fix is structural, not motivational. Time for business development needs to be allocated before the engagement fills the week, not after. A protected, recurring block, treated with the same seriousness as a client deadline, is the only mechanism that consistently survives contact with a demanding workload.

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Force #2: The Avoidance Force

Business development feels wrong when you’re already busy.

This is a psychological force that operates below the level of conscious decision-making.

When you are fully engaged with a client, the income feels certain. The work is in front of you. The relationship is active. The value you are delivering is visible and immediate. Reaching out to a prospective client, by contrast, involves none of those certainties. The outcome is unclear. The response is unpredictable. And the return, even in the best case, is weeks or months away.

Daniel Kahneman’s research on loss aversion established that people weigh the psychological pain of a potential loss roughly twice as heavily as the pleasure of an equivalent gain. Applied to consulting, this means the discomfort of uncertain, unrewarded business development activity is felt more acutely than the anticipated value of future work, even when the rational case for doing the outreach is obvious.2

There is also a social dimension that reinforces the avoidance. Reaching out to a prospect when you have nothing new to offer, no news, no completed project to reference, no specific reason to be in contact, feels transactional in a way that many technically-oriented consultants find genuinely uncomfortable. So they wait. They tell themselves they will reach out when the current engagement has produced something worth sharing. That moment either never arrives or arrives too late.

Recognising the avoidance force for what it is, a predictable psychological response, not a reasonable business decision, is the beginning of working around it. The goal is not to feel motivated to prospect when you are fully occupied. The goal is to have a contact rhythm and a system that runs regardless of how you feel.

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Force #3: The Attrition Force

The pipeline erodes quietly while the work looks healthy.

This is the most dangerous of the three forces, because it is the least visible until it is expensive to fix.

A consulting pipeline is not a real-time indicator of business health. It is a lagging one. The engagement you’re working on today was sold three to six months ago. The relationships that produced it were being maintained before that. If you stopped doing pipeline work at the start of your current engagement, the effect does not appear in your revenue until the engagement ends, at which point the gap is already open, and you are filling it from a standing start.

Research on professional services sales cycles shows that the average time from initial contact to signed engagement ranges from eight to sixteen weeks, depending on scope and organisational complexity. For a solo consultant working without a sales function, the timeline is often longer.3 This means the work you need in six months requires pipeline activity that begins now, not when the current project wraps.

The consultant who waits until they are available to look for work will always be behind. By the time the urgency is felt, the lag has already accumulated. What follows is the feast-and-famine pattern that most experienced consultants recognise, but few have structurally resolved: a period of high utilisation, followed by an unplanned gap, then a reactive scramble to secure the next engagement, which in turn produces the next gap.

The discipline required here is counterintuitive. When you are busiest, that is precisely when pipeline activity matters most. Not because you need work now, but because you will need it in the window that follows.

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Putting It Together

The three forces operate in sequence, and they reinforce each other.

The Absorption Force fills your time with delivery. The Avoidance Force provides the psychological justification for failing to protect any of it. The Attrition Force ensures that by the time the damage is visible, it is already expensive to repair.

None of this is inevitable. But reversing it requires first understanding the mechanism, then deliberately building against it.

The practical response requires three things, held simultaneously: a protected weekly block for business development that runs regardless of your current workload; a contact rhythm with past clients and prospects that does not depend on having something to sell; and an honest accounting of the pipeline lag, knowing that the work you need in the next quarter requires investment in relationships that begins this week.

Sarah, the baker, was not failing because she was bad at baking. She was failing because she had no system for anything other than baking. The same trap, with different terminology, is available to every consultant who mistakes a full schedule for a healthy practice.

Imagine what becomes possible when your pipeline is managed with the same rigour as your delivery, when the end of one engagement is a transition, not a gap. That kind of practice does not happen by accident. It is built deliberately, in the margins of a busy week, by consultants who understood the forces working against them and built systems anyway.

What would your practice look like if your most productive delivery months were also your most consistent business development months?

Footnotes:

1 Maister, David H. Managing the Professional Service Firm. Free Press, 1993. Chapter 9, “The Anatomy of a Consulting Firm.”

2 Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011. Chapter 26, “Prospect Theory.”

3 Hinge Research Institute. Inside the Buyer’s Brain, Third Edition. Hinge Marketing, 2018. See also Blair Enns, The Win Without Pitching Manifesto, RockBench Publishing, 2011, on professional services sales cycles and relationship lead time.

The Agony of the Expert

You have spent weeks, maybe months, on the proposal. The technical specification is flawless, the methodology robust. You’ve double-checked every calculation and proofread every line. You hit “send,” confident that your expertise will be recognized and rewarded.

Then, silence. Or worse, a polite rejection. The frustration is immense. The problem, however, isn’t the quality of your technical solution. It’s a fundamental misunderstanding of the audience’s psychology. You believe you’re writing for experts who appreciate technical elegance, but the reality is far different.

They don’t want a drill; they want the hole. Better yet, they want the peace of mind that comes from knowing the hole will be drilled on time and on budget, without hitting a water pipe.

1. You’re Writing for the Wrong Audience

The single biggest mistake technical professionals make is writing for their peers. You feel comfortable with the details, specifications, and jargon of your field. But in any high-stakes decision, your peer is rarely the person with the final authority. Your proposal must successfully navigate a hierarchy of three very different readers.

• The Technical Gatekeeper: This is your peer, the subject matter expert.
  • Their Goal: Compliance and feasibility.
  • Their Power: They can say “No,” but rarely have the authority to say “Yes” to a large expense.
  • What they need: Proof that your solution works and meets technical requirements.
• The Financial Guardian: The CFO or procurement officer. They view your proposal as a financial instrument.
  • Their Goal: Value and risk control.
  • Their Power: They can kill a project if the numbers don’t align with the budget.
  • What they need: Total cost of ownership, payment terms, and return on investment.
• The Strategic Decider: The CEO, board member, or council member who signs the contract. They see the world through a wide-angle lens.
  • Their Goal: Outcomes and optics.
  • Their Power: Absolute authority.
  • What they need: Political wins, speed to market, and reputation management.

The trap is focusing 90% of your effort on the Gatekeeper. They are a crucial first step, but they can only say “No.” If your proposal doesn’t speak directly to the goals of the Financial Guardian and Strategic Decider, it will die on their desk.

2. They Don’t Want the ‘Best’ Solution—They Want the ‘Safest.’

There is a fundamental psychological disconnect between how an expert and an executive view a proposal. The technical professional seeks the “best” solution, the most innovative, efficient, and durable. The decision-maker, however, is looking for the “safest” solution.

The reason is a powerful cognitive bias called Loss Aversion. Psychologists have proven that the pain of losing is about twice as powerful as the pleasure of gaining. A City Manager who approves a revolutionary bridge design that fails gets fired. If they approve a standard, more expensive design that works perfectly, nobody notices. This is why the adage exists: Nobody ever got fired for hiring IBM.

From the Decision Maker’s perspective, “Innovation = Risk.” When you highlight your cutting-edge methodology, you think you’re selling excitement. The person signing the check hears: “Unproven. Experimental. Dangerous.”

They want certainty, not novelty. Your task is to reframe your features in terms of risk reduction. For example:

• Don’t say: “We use a novel, experimental polymer.”
• Do say: “We use a material proven to reduce maintenance calls by 40%, ensuring you meet your operational uptime targets.”

Your job as a persuasive professional is not to dazzle them with your brilliance; it is to comfort them with certainty. You must translate your specs into safety.

3. Your Expertise is Your Biggest Blind Spot

It is exceptionally difficult for a subject matter expert to explain a complex concept to a non-technical stakeholder. This isn’t a failure of intelligence; it’s a consequence of being too smart. The cognitive bias at play is known as “The Curse of Knowledge.”

Once you know something, it becomes almost impossible to remember what it felt like not to know it. In your writing, you inadvertently skip logical steps, use undefined acronyms, and assume a baseline of knowledge that your reader simply doesn’t possess.

This has a dangerous emotional impact. It makes the reader feel stupid. The fix is not to simplify your technology, but to clarify its purpose: you must explain the technology’s implications, not just the technology itself.

When a Decision Maker feels stupid, they do not blame themselves. They blame you… They will reject your proposal not because it is wrong, but because it made them feel small.

4. The Simple Test to Bridge the Gap: Ask “So What?”

The practical solution to overcoming these psychological barriers is the “So What?” Test. This simple tool helps you translate your technical features into benefits that resonate with the people who hold the budget.

For every technical fact or feature you write, ask yourself, “So what?” Keep asking until you arrive at an outcome that matters to a specific decision-maker.

Consider this step-by-step example:

• Fact: “We will use pre-fabricated concrete modules.”
• So What? This allows us to install them at night.
• So What? This means we won’t shut down a busy road during rush hour, preventing voter complaints and negative press.

That final statement is what draws the Strategic Decider’s attention. It speaks directly to their world of optics and political wins, translating a technical detail into a tangible, valuable outcome.

Conclusion: From Technical Exam to Human Connection

A high-stakes proposal is not a technical exam designed to prove your intelligence. It is a risk-mitigation document designed to solve your reader’s problems and quiet their anxieties. The person on the other end is stressed, risk-averse, and almost certainly not a technical expert. They don’t care about your engineering prowess; they care about their own security and success.

The next time you write, will you be selling your solution, or will you be selling peace of mind?

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(and how to move past them)

AI is reshaping what it means to be a technical professional; and not everyone feels ready.

For many technical experts, the challenge of AI isn’t just technical, it’s psychological.

The rapid shift from deterministic systems to probabilistic AI creates hidden anxieties: “Will I still have an edge?” “Can I trust an algorithm I can’t fully explain?” “What happens when a client wants answers I don’t have?”

Here’s the good news: these barriers are not permanent. Once we see them for what they are; predictable psychological responses to change, we can equip ourselves with the tools to move past them.

In this issue, we’ll explore three psychological barriers slowing down technical professionals in adopting AI, and the concrete steps you can take to overcome each one.

1. AI Imposter Syndrome: “If I don’t master this now, I’ll be left behind.”

The rise of AI has created a pressure many technical professionals quietly carry: the sense that unless you learn everything about AI immediately, you’ll become obsolete. This is a classic case of AI imposter syndrome, the nagging doubt that you’re never knowledgeable enough about the most recent tools, models, or frameworks.

Why it happens:

• Engineering culture is built on mastery of systems. Determinism, if you know the inputs, you can predict the outputs. AI cracks that wide open. It’s messy, probabilistic, and feels unpredictable.
• When the rules of the game shift, technical professionals fear losing their advantage.

How to move past it:

• Reframe expertise: No one will ever “master AI” in its entirety. Instead, slice the problem. Choose a specific application area (e.g., automating reports, anomaly detection). Depth comes from use, not theory.
• Normalize the learning curve: Label yourself a practitioner-in-progress, not a fraud. Clients and colleagues value progress they can see, not hidden mastery.
• Pair old with new: Anchor AI knowledge against deterministic engineering strengths. For example, pair your knowledge of system behaviors with AI’s ability to surface anomalies faster than rule-based logic.

👉 Takeaway: You don’t need to know “all of AI.” You need to know how it applies to your domain.

2. The Black Box Problem: “How do I trust what I can’t explain?”

Technical professionals love transparency. If a system fails, you debug, trace, and locate the flaw. AI doesn’t always play by those rules. Models, especially deep learning ones, generate outputs without human-readable logic. Enter the black box anxiety: fear of adopting a tool you can’t fully justify.

Why it happens:

• Technical professionals historically are rewarded for 100% explainability. AI asks for comfort with probabilities (“80% confidence we’re right”). That shift feels like abandoning rigor.
• Clients often magnify the anxiety: “How can I trust results you can’t explain?”

How to move past it:

• Shift from explanation to assurance: Adopt an evidence-based trust approach. You might not explain every decision layer of the model, but you can validate its output through controlled testing, benchmark comparisons, and consistent performance.
• Practice probabilistic communication: Technical professionals who can clearly say, “Here’s what the system predicts, here’s the margin of error, here’s how we’ll monitor it” build credibility.
• Lean on frameworks of interpretability: Tools like SHAP (using game theory), LIME (explains each prediction), or simpler surrogate models often give enough transparency to bridge the trust gap without overpromising.

👉 Takeaway: Clients don’t need to see inside the algorithm; they need confidence in your process of governing it.

3. Loss of Control Fear: “What happens when the machine outpaces me?”

Perhaps the deepest anxiety is the fear of becoming secondary; that AI won’t just assist you, but replace you. This isn’t just about job security; it’s also about professional identity.

Why it happens:

• AI is framed as “autonomous,” which frames engineers as redundant rather than essential.
• Technical professionals often underestimate their human differentiators, judgment, context, and systems thinking.

How to move past it:

• Adopt the Co-Pilot Mental Model: AI doesn’t need to be a competitor. Treat it as a junior partner: fast at tactical tasks, but still requiring your strategic oversight.
• Highlight unique value: Focus conversations with clients and leaders on what humans uniquely provide: ethical reasoning, contextual awareness, and the ability to align outputs with business goals.
• Experiment in low-stakes environments: Deploy AI first in safe zones (documentation support, trend analysis), where there’s minimal reputational risk. Experience builds confidence.

👉 Takeaway: AI doesn’t erase your role. It enlarges the playing field where your judgment and creativity matter most.

Practical Steps for Psychological Flexibility

If you want a quick framework for navigating the human side of AI, try this:

1. Name the fear. When you label it (imposter syndrome, black box anxiety, control loss), you instantly reduce its power.
2. Pair AI with purpose. Don’t learn AI in the abstract; anchor it to a business problem or engineering challenge.
3. Start microscopically. Pick one workflow to augment, track its impact, and scale from there.
4. Build conversational fluency. Practice explaining AI in terms of probabilities, trade-offs, and business relevance.
5. Celebrate iterative wins. Progress in AI comes from applied learning, not overnight mastery.

Closing Thought

Resistance to AI adoption among technical professionals isn’t about laziness or lack of skill, it’s about psychology. When systems change the rules of certainty, trust, and control, it’s natural to feel unsettled.

But here’s the truth: the future doesn’t belong to the people who know AI best. It belongs to those who know how to work with it, frame it for others, and integrate it into human-driven systems of trust and value.

The barrier isn’t technical. It’s human. And once you cross that psychological frontier, you’ll be leading where others are hesitating.