INBOX INSIGHTS, April 2, 2025: Failing Up, AI Stability vs. Capability - Trust Insights Marketing Analytics Consulting

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Inbox Insights: April 2, 2025 Failing Up and Choosing AI Tools

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Why Failing Up is a Business Liability in the AI Era

We’ve all been there. You’re sitting in yet another meeting watching a colleague present their quarterly results. On paper, the numbers were abysmal. Customer satisfaction had tanked, team turnover was at an all-time high, and their projects were consistently behind schedule.

Yet somehow, by the end of the meeting, they were being congratulated. Two weeks later? They got promoted.

Ok, maybe it wasn’t that smooth and swift, but you get the idea. Another classic case of “failing up” – where mediocre (or worse) performance somehow leads to career advancement. And in today’s business landscape, it’s not just annoying – it’s actually dangerous.

What Exactly is “Failing Up”?

“Failing up” happens when someone advances despite mediocre or poor performance, typically due to factors beyond merit, such as:

Strong networkers who “play the game” better than they do their actual job
People who match the existing company culture (aka look/think like leadership)
“Safe” choices who won’t rock the boat or challenge the status quo
Those skilled at deflecting blame or taking credit for others’ work

I once worked with a director who consistently missed targets and had alienated more than half of their team. Their response to criticism? Reframing failures as “learning opportunities” while taking credit for the few successful projects (which, coincidentally, were run by their direct reports). Six months later? VP of their department.

Why It’s Extra Problematic in the AI Era

With AI becoming more of a necessity in business, failing up isn’t just frustrating – it’s increasingly dangerous to business. Here’s why:

AI demands new competencies. The integration of AI requires leaders who can genuinely understand and navigate technological change. Not just talk about it in meetings.
Strategic thinking is more critical than ever. With AI handling more routine tasks, human value comes from creative problem-solving and strategic insight. Precisely what mediocre performers often lack.
Teams need authentic leadership. As work environments become more complex, teams need leaders who can genuinely guide them through change. Not just those who excel at office politics.
Innovation requires risk-takers. Companies that reward mediocrity and “safe” choices will fall behind those willing to promote genuine innovators and creative thinkers.
Ethical challenges require ethical leaders. The AI revolution raises important ethical issues. It needs leaders with true integrity, not those who get ahead by taking shortcuts.

The Real-World Impact

Here’s what’s happening today. Companies often choose someone to lead AI integration based on their presentation skills and connections. This happens even when teams say the person lacks technical knowledge and has poor project management skills.

Six months later? The implementation will be millions over budget, morale will collapse, and competitors will have leapfrogged them. The cost of failing up isn’t just frustration. It can cost you revenue, resources, and customers.

Breaking the Cycle

Ok, so what can we actually DO about this? Here are seven practical approaches:

1. Implement SMART Goals

Generic advice says “measure performance objectively” – but how do you actually do that?

Start by defining success metrics before projects begin, not after. Document these in shared workspaces where everyone can see them. Include both outcome metrics (what was achieved) and process metrics (how they were achieved). Consider using SMART goals. SMART stands for Specific, Measurable, Achievable, Relevant, and Timely. Using a template can help ensure your goals are consistent and easier to track.

Don’t just track “implemented new AI tool,” but also measure adoption rates, efficiency gains, team satisfaction, and whether it was delivered on time and budget.

Make reviews data-informed by requiring specific examples and evidence rather than general impressions. This makes it harder for charismatic under-performers to talk their way around actual results.

2. Gather Genuine 360-Degree Feedback

Emphasis on the word “genuine.” If you’re asking for feedback, make sure you’re going to do something with the data.

Create regular (quarterly to start) feedback mechanisms where team members can provide anonymous input on leaders. Use specific questions like “How effectively does this person remove obstacles for the team?” rather than vague rating scales.

Consider feedback from direct reports as heavily as feedback from superiors; those experiencing someone’s daily leadership often have the clearest perspective on its quality.

Most importantly, make feedback meaningful by tying it directly to business goals that everyone understands.

3. Value Demonstrated Outcomes Over Confidence

Confidence is often mistaken for competence, especially in leadership roles. You can combat this by creating opportunities for blind evaluation where decision-makers don’t know who proposed which solution. This helps eliminate the “loudest voice wins” problem.

You need to create a space where everyone’s ideas can be heard and considered.

Celebrate and reward accurate analysis and good outcomes, not just assertive presentations or bold promises. Make “being right” more valuable than “sounding right.”

4. Create Advancement Paths for Individual Contributors

Many organizations force their best resources into management. Then they wonder why they have mediocre managers. This is not the fault of the individual contributor. Many people take a management role because it is the only path to more money. Instead of this, develop parallel career tracks with equivalent compensation and status for these team members. Your best engineer shouldn’t need to become a subpar manager to earn more.

Create advisory roles that influence strategic direction without requiring management responsibilities. This keeps your technical expertise in positions of influence.

You can also acknowledge and reward specialized skills with certification programs and knowledge-sharing platforms. These options allow for growth without needing to be a manager.

5. Consciously Diversify Leadership Selection*

“Culture fit” often becomes code for “reminds me of myself.” You can start to break this pattern by using structured interview protocols that focus on demonstrated capabilities rather than personal chemistry or background similarities. Do blind reviews of resumes and look only at capabilities. This removes identifying information that could create bias.

Engage diverse selection panels that include people from various departments, backgrounds, and thinking styles. Set clear diversity goals for leadership roles. Focus not just on demographics but also on cognitive diversity and different work experiences.

*Note—this suggestion is easier said than done. Depending on your role in your organization, this one may be out of reach.

6. Reward Ethical Decision-Making

Ethics often takes a backseat to results in advancement decisions. Rebalance this by:

Creating specific recognition programs for ethical leadership moments. Make these visible across the organization.

Including ethics scenarios in leadership assessments that evaluate how candidates handle complex situations with competing pressures.

It’s important to document and celebrate times when leaders made tough but right decisions. These choices may not have been easy or instantly profitable, but they count. Make ethics part of the success stories.

7. Develop Better Selection Processes

Traditional interviews reward those who interview well, not necessarily those who perform well. You can change this by using work simulations that require candidates to demonstrate skills rather than just talk about them. For AI leadership roles, this might involve analyzing an implementation challenge or a resistant team member.

Set up probationary leadership periods for new promotions. They should have clear performance goals for the first 90 days. Advancement depends on meeting these goals.

The Bottom Line

I’ve seen firsthand how teams thrive under genuinely capable leadership and wither under those who’ve failed upward. The stakes were always high, but in today’s rapidly evolving business landscape, they’re astronomical.

The companies that will thrive aren’t those with the slickest presenters or best networkers in leadership positions – they’re the ones that can identify, develop, and promote genuine talent and ethical leadership.

Breaking the “failing up” cycle isn’t just about fairness—it’s about building organizations that can thrive in rapidly changing environments. When we promote based on genuine capability rather than presentation skills or political savvy, we create resilient teams that can navigate the complex challenges of the AI era.

What’s your experience with “failing up” in your organization? Have you found effective ways to combat it? I’d love to hear your thoughts and strategies. Reply to this email to tell me, or join the conversation in our free Slack group, Analytics for Marketers.

Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the ethical dilemmas surrounding digital twins and AI clones. You’ll discover the crucial ethical questions surrounding digital twins and AI clones in today’s rapidly evolving digital world. You’ll learn why getting consent is not just good manners but a fundamental ethical necessity when it comes to using someone’s data to create a digital representation. You’ll understand the potential economic and reputational harm that can arise from unauthorized digital cloning, even if it’s technically legal. Tune in to learn how to navigate the complex ethical landscape of digital twins and ensure your AI practices are responsible and respectful.

Watch/listen to this episode of In-Ear Insights here »

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In this week’s Data Diaries, let’s talk about AI tool choice and software. One of the most common questions I’m asked at literally every event is what AI software an organization should be using, especially given the pace of change. There are two schools of thought on this, revolving around the two fundamental needs of organizations.

Need 1: Stability

Anyone who’s ever worked in enterprise software knows that stability is mandatory. Software takes time to be certified for things like HIPAA, SOC 2, ISO 27001, and the rest of the alphabet soup of certifications. They’re a delight (I say that with some sarcasm) but they’re essential for working in high stakes situations. As a result, stable software isn’t allowed to change too much.

That’s especially challenging in AI, where the equivalent of Moore’s Law says that AI doubles its capabilities roughly every 7 months. Within the span of a calendar year, AI’s capabilities will be 2x where you started the year.

However, and this is the big however, if you have a problem you can solve with a known model – especially local models like Gemma 3, Mistral, etc. – you can download those, bake them into a system requiring certification, and then freeze it as is. It won’t get smarter. It won’t get better.

But in many situations, it might not need to, if it works predictably and reliably. This is especially useful for any kind of automated task where you don’t want much variance – if you can structure the system around a small, local model like Gemma 3 and have it give reliable outputs, you take the win and you don’t change it until there’s a need to change it.

Need 2: Capability

The second fundamental need for organizations is capability, where you want to use whatever’s the most capable tool at any given time. Use the best, leave the rest – and the best changes on a regular and frequent basis.

Chasing the best is a moving target – for a while, GPT-4 was the king of the hill. Then GPT-4o, then Gemini 1.5, then Claude Sonnet 3.5, then DeepSeek R1, then Claude Sonnet 3.7, and now Gemini 2.5. Each of these models, made by different manufacturers, advanced the field of AI considerably and changed our understanding of what they’re capable of.

When does capability outweigh stability? When you need ever-more advanced capabilities. For example, with most generative AI models, writing a blog post is not something that requires quantum leaps ahead in capability, but writing code does. The more correct, thoughtful, and intelligent code written by AI becomes, the easier it is to build, test, deploy, and maintain.

The same is true for new capabilities. Models that have vision enabled are a huge leap over models who can’t see. If you have applications of AI that would benefit from being able to see the images in a document, through the lens of your phone’s camera, or what you’re doing on screen, stability is worth less than capability and it makes sense to chase capability.

Balancing the Needs

So how do we reconcile these two needs? The answer, unsurprisingly, looks a lot like software development. In good software development, you have three environments:

Development, where everything is broken all the time
Staging, where broken stuff should mostly be fixed and is being tested for quality and bug removal
Production, where everything should work most of the time

As you set up your generative AI infrastructure in your company, it should have similar organization. Your advanced AI pilot group/team is the development environment, chasing capabilities.

As capabilities mature, that group works with your larger AI task force to codify how new capabilities should be used, and settles on a recommended tool set.

And for the broader users in your company who don’t need the cutting edge, you stabilize on known good tools and methods.

If you organize your AI efforts similar to how you manage other software – with an acknowledgement that even the production setup may change faster than traditional software – you’ll be able to balance the needs of capability and stability more easily.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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