A new kind of partnership: what consulting should look (and feel) like
The recently published book “The Big Con: How the Consulting Industry Weakens our Businesses, Infantilizes our Governments and Warps Our Economies” makes some pretty damning claims about the consulting industry. The authors suggest that consulting companies actually stunt the clients they purport to serve by denying them the ability to build institutional capabilities. A direct quote reads: “The more businesses outsource, the less they know how to do, causing organizations to become hollowed out, stuck in time and unable to evolve.”
It may come as a surprise that our first reaction was not to cringe, but to exclaim an emphatic “YES! This is what we have been saying all along!” Furthermore, we have been actively working as a firm to engage very differently with our clients to make sure they – and we – don’t go down that road to ruin.
The book also prompted us to put pen to paper to share our point of view and advice to all companies out there – whether they are our clients or not – on how to expect more and get more from their consultant partner. Below we share a recent conversation on this topic and what your organization can take away.
The good and the bad about consulting
Anne: Kathryn, you are a long-time consultant with a deep love of consulting. Why would you want to share with the world what’s wrong with something you care about so deeply?
Kathryn: When I “found” consulting, I was in awe that companies would pay you money to have so much fun helping organizations solve really difficult problems. But over time, I lost faith in the big consulting model. I saw it delivering too little value, creating too much dependency, while consulting firms keep making money doing the same things over and over again because their clients didn’t learn how to do it themselves.
Don’t get me wrong, I do believe that there is a place for consultants. Organizations and leaders need outside perspective, and we bring that from working across many companies and industries. They need someone to hold up an objective mirror to see what is no longer obvious to them. They sometimes need skills in the moment that they won’t need over the long term. Those are all situations where consultants make sense. But organizations need to be careful about what they outsource – they cannot outsource thinking, judgment or accountability for business decisions, leadership, and results.
Anne: You mentioned “over and over again” – isn’t that part of the consulting business model? To turn one engagement into the next engagement?
Kathryn: I love having long term relationships with clients. You learn how to complement each other’s skills and knowledge. You build a strong foundation of trust to try new approaches. You stand on the shoulders of your collective accomplishments. But I never want to solve the same business problem with a client over and over again, because that means they haven’t increased their capability and I’ve failed. If clients are not better off – more skillful, more capable, more confident – after our engagement or initiative, we haven’t earned our money. If they have to hire a consultant one to three years later to solve the same problem, was the problem solved in the first place?
Anne: I am interested in your response to this quote from the book, “The more businesses outsource, the less they know how to do, causing organizations to become hollowed out, stuck in time and unable to evolve.”
Kathryn: Unfortunately, it’s an accurate description of how the industry has evolved. The good news is that it doesn’t have to be that way. Companies hire consultants for all kinds of services, but here’s the key: Don’t hire someone to make the decision for you or do the job for you. Instead, benefit from external expertise and build internal capability at the same time. This is the best of both worlds, and it’s actually why I came to BTS in the first place.
The founders of BTS and I share a common origin story. BTS was founded by former management consultants who also got tired of making recommendations that never went anywhere in organizations. They started building high-fidelity simulations that their clients could use to help people more deeply understand the new strategic direction. Then, the portfolio of tools and approaches grew from there.
Changing the approach to consulting for the better
Anne: Explain more about the role and power of simulation and practice, and how they help change the consulting game for clients.
Kathryn: I’ve learned over time that you can’t tell anyone about change, but you can help them experience it so that they become owners and authors of the future. BTS’s history of leveraging simulation to make strategy and behavior concrete and practical with real tools, approaches, and expertise is different. I saw breakthrough possibilities in the way BTS created alignment and excitement about a future that felt real and tangible for their clients. It was compelling for me when I first saw it – and a large part of what I saw was missing in the larger consulting space.
The future is never as scary as we think it is when it only lives in our head. When you can simulate the future, when you can “work through it” with others, then it becomes concrete. Even when the future is uncertain, after experiencing it, it feels less scary, and people and organizations can move forward in a more productive way.
Anne: Another fundamental element of consulting you share is that people are at the heart of an organization’s ability to change and thrive. You have said “you have to pay more attention to the people than the things.” Tell us more about how our clients should think about this.
Kathryn: In almost all cases, strategies don’t fail because they are bad. They fail because people don’t see themselves in the strategy and in the picture of the new future for their organization. Because of the way the consulting industry has evolved, clients think there is a tradeoff between getting stuff done and engaging people. But it’s actually a false tradeoff because at the end of the day it’s people who are doing the work. The paradox is that, the more you try to exclude people from the process in service of speed, the slower you will go. As we saw in stark contrast during the pandemic, while supply chains, processes and systems were challenged and disrupted, people changed, adapted, and improvised to keep thing going. We know this can happen outside of a crisis.
Great consultants work to make sure that your people have more than just an understanding of where they’re going as an organization. They help employees discover the intrinsic motivation to actually work in a new way and make new choices by connecting behavior and strategy, values and vision to initiatives in action.
What it feels like to work with a great consultant
Wondering how to ensure you are getting the most value from your consultant partner? And more importantly setting your organization up for success long term? Consider this checklist.
✔︎ Great consultants don’t make things more complex: they simplify, and help you connect the dots. They go beyond understanding the analytics and economics of your business model, your market, and your strategic aspirations. They bring deep understanding of what it takes to create real change – which only happens through people.
✔︎ Great consultants know how to effectively help your people find meaning and purpose in your organization’s new direction because ultimately that’s what will create progress.
✔︎ Great consultants should make you feel smarter and more capable after working with them. So many consultants have made people feel bad for so long that we almost accept it as a given, which is a shame.
✔︎ Great consultants hold a mutuality mindset. They live out the perspective, “We’re in this together — you bring value and so do we.” Great consultants bring insights AND respect and rely on their client’s wisdom about their organization.
✔︎ Great consultants get to root causes. They get to the underlying limiting mindsets because they come from a place of mutuality, curiosity, and respect.
When should you NOT hire a consultant
At the same time – heeding the learnings from our own experience, and the challenges unearthed in the book – there are instances when you shouldn’t hire consultant:
- Don’t hire a consultant when you want to rubber-stamp a tough decision you know you need to make (layoffs, restructuring, strategy pivots). This is about leadership courage. While it might provide air cover in the short term, in the long term it will damage your leadership brand and organizational trust.
- Don’t hire a consultant to redo consulting work you did with them before. If that way didn’t actually solve the problem, don’t do it over again.
- Don’t hire a consulting company to do something your own employees, or lower priced resources could do – like program management or research.
Check out this podcast if you want to hear more of our conversation on this important topic.
Related content
Related content
Global spending on AI is forecast to reach $2.52 trillion by 2026, a 44% year-over-year increase, according to Gartner. At the same time, only about 10% of AI pilots scale beyond proof of concept.
What’s the disconnect?
Why aren’t most organizations seeing the ROI they hoped for, despite making such large investments?
It’s not because the technology isn’t ready. And it’s not because the use cases are unclear.
The disconnect exists because many organizations are investing in AI as a technology upgrade and expecting a business transformation in return.
The tools are advancing at breathtaking speed, and most organizations already have AI in motion. But the work itself often stays the same. AI gets layered onto existing tasks instead of being used to rethink workflows end to end. Adoption metrics go up, while decisions, operating models, and value creation remain largely untouched.
When teams first start using AI, they do what makes sense. They try to recreate today, just faster. Can it help me write this? Analyze that? Save a bit of time?
That’s a smart place to begin. But it’s not where ROI, or reinvention, actually shows up.
Getting over the hump
Real returns begin when teams experience what we often call “getting over the hump.”
This is the moment when two things click at once:
- AI can fundamentally change how work gets done.
- People don’t need deep technical expertise to make that change happen.
When teams see weeks of work compress into hours, or watch an end-to-end workflow suddenly run in a new way, something shifts. Confidence replaces hesitation. Curiosity replaces caution. The questions change, from “How do I use this tool?” to “What’s possible now?”
That shift matters, because ROI doesn’t come from using AI more often, it comes from using it to work differently.
Why ROI stalls as AI scales
As AI initiatives expand, many organizations discover that the limiting factor isn’t the technology itself. It’s the environment surrounding the work.
ROI shows up when teams are able to explore and redesign workflows, not just automate steps. That requires clarity on outcomes and guardrails, but also room to experiment, learn, and iterate. When AI is tightly controlled or narrowly deployed, pilots stay pilots. When people are trusted to rethink how work happens, value starts to compound.
Organizations that unlock ROI don’t chase perfect use cases upfront. They focus on learning faster and applying those insights where they matter most.
The early signal that ROI is coming
Long before AI shows up in financial results, there’s an earlier indicator that organizations are on the right path.
People are energized by the work.
You see it when teams start sharing experiments, when ideas move across functions, and when learning becomes visible rather than hidden. Progress feels owned, not imposed.
That energy isn’t accidental. It’s a signal that people feel trusted to rethink how work happens, and that trust is essential to turning investment into impact.
Reinvention happens closer to the work than most expect
AI reinvention rarely starts with a sweeping rollout or a multi-year roadmap. More often, it begins with one meaningful workflow, one team close to the work, and a willingness to ask a different question.
With the right support, that team gets over the hump. What they learn becomes reusable. Patterns emerge. Over time, those insights connect, creating enterprise-wide impact and sustained ROI.
That’s how organizations move from isolated pilots to real returns.
What this means for AI investment
No organization feels fully “caught up” with AI, and that’s true across industries.
The organizations that will realize ROI aren’t waiting for certainty or the next breakthrough tool. They’re reinvesting their AI spend into new ways of working that scale human potential alongside technology.
Handled thoughtfully, AI doesn’t distance people from the work. It brings them closer - to better decisions, stronger collaboration, and better outcomes.
For many organizations, that’s where the real return begins.
Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.
This article was originally publish on Rotman Management
IN OUR CONSULTING WORK with teams at all levels—especially senior leadership—my colleagues and I have noticed teams grappling with an insidious challenge: a lack of effective prioritization. When everything is labeled a priority, nothing truly is. Employees feel crushed under the weight of competing demands and the relentless urgency to deliver on multiple fronts. Requests for prioritization stem from both a lack of focused direction and the challenge of efficiently fulfilling an overwhelming volume of work. Over time, this creates a toxic cycle of burnout, inefficiency and dissatisfaction.
The instinctive response to this issue is to streamline, reduce the number of initiatives, and focus. While this is a step in the right direction, it doesn’t fully address the problem. Prioritization isn’t just about whittling down a to-do list or ranking activities by importance and urgency on an Eisenhower Decision Matrix; it also requires reshaping how we approach work more productively.
In our work, we have found that three critical factors lie at the heart of solving prioritization challenges: tasks, tracking and trust. Addressing these dimensions holistically can start to address the root causes of feeling overwhelmed and lay the foundation for sustainable productivity. Let’s take a closer look at each.