By Wendy Lynch, PhD and Founder of Analytic Translator
AI is already rewriting job descriptions. Analyses from the World Economic Forum and others suggest that tens of millions of roles will be automated or reshaped as AI takes over routine, repeatable work. The point isn’t that “work disappears,” but that the most valuable work shifts: away from pure production and toward roles that blend technical understanding with human judgment and communication.
That is exactly where analytic translators – and now AI translators – live.
These are the people who don’t just build or run models, they make sure AI and analytics solve real problems, get adopted, and lead to better decisions. As AI adoption accelerates, that skill set becomes one of the most valuable roles.
And there’s a second, powerful truth: based on decades of research on empathy and communication, women are especially well-positioned to thrive in these translator roles.
Analytic & AI translator skills make data professionals more valued and resilient
Consulting firms like McKinsey have described analytics translators as the “must-have role” that connects business leaders with data scientists throughout the lifecycle of an AI or analytics initiative. From scoping the work to turning model output into decisions and action, translators are the bridge between algorithms and outcomes.
As AI tools get better at writing code, summarizing data, generating charts, and even drafting slide decks, the strictly technical parts of many data jobs become easier to automate. Labor trends show that data and knowledge workers are among the most vulnerable to AI but also stand to gain the most—but only if they learn key skills to optimize the AI-to-human connection.
Those skills are the heart of analytic/AI translation:
- Framing the right problem. Guiding business partners to define the decisions they need to make, instead of jumping straight to technical modeling parameters.
- Interrogating AI instead of worshipping it. Asking what data trained the model, where bias may creep in, and when output actually is “good enough” for a given decision.
- Communicating trade-offs and uncertainty. Translate confidence intervals, limitations, and risks into plain language so leaders neither blindly trust nor completely dismiss AI.
- Driving adoption. Envisioning how to embed AI into workflows, roles, and policies so insights turn into changed behavior, not abandoned dashboards.
Those tasks are much harder to assign to AI. In fact, the better AI becomes at doing the technical work, the more we need people who can:
- Speak “data” and “business,”
- Protect organizations from blind trust in algorithms, and
- Turn model outputs into confident decisions.
For an individual data professional, moving toward analytic/AI translator skills is a form of career insurance, by:
- pulling you closer to the decision-making center of the business.
- differentiating you from peers who rely narrowly on technical depth.
- Opening a wider path toward leadership roles where understanding people and context matters as much as understanding models.
In a world where AI can generate a decent analysis in seconds, the people who can ask the right questions, tell the right story, and guide the right action will be the ones who are hardest to automate, and most in demand.
Why women are especially well-positioned for translator roles
Anytime we talk about gender, it’s important to clarify:
- Skills are individual, not universal. We can’t assume men are less empathic, nor can we assume women are less technical.
- We’re describing average trends, not destiny. Both men and women can become excellent analytic translators with training.
That said, a large body of research suggests that, on average, women score higher than men on several dimensions that map directly onto analytic and AI translation.
Empathy and prosocial behavior
Experimental and survey-based studies have consistently found that women, on average, report higher levels of empathic concern and are more likely to show compassion and prosocial behaviors in naturalistic settings. Large recent studies using video-based tasks found that women tended to show higher empathy, compassion, and helping behavior, even when cognitive perspective-taking (understanding what others think) looked similar across genders.
These aren’t soft “nice-to-haves” for translator roles—they’re central to the job:
- Translators must sense how stakeholders feel about risk, change, and uncertainty.
- They need to pick up on when a leader is confused but afraid to admit it, or when an analyst feels dismissed or misunderstood.
- They often act as emotional “shock absorbers,” carrying concerns and motivations back and forth across business and technical teams.
Communication and relationship skills
Descriptions of analytics translators emphasize these abilities:
- Communication in clear, non-technical language.
- Stakeholder management and expectation setting.
- The ability to “speak both languages” and build trust.
Research on emotional intelligence and leadership behaviors often finds that women, on average, excel in relationship-oriented dimensions: active listening, collaboration, and inclusive decision-making. These are exactly the behaviors that help cross-functional AI projects succeed and avoid the typical spiral of misunderstanding and rework.
Put simply:
The very skills that women, on average, bring in greater measure—empathy, collaboration, relationship focus—are the skills analytic and AI translator roles depend on, and that AI is least likely to replace.
A strategic opportunity for women and for organizations
Studies on the labor impact of AI warn that women, especially in certain knowledge and service roles, may be disproportionately vulnerable to automation and restructuring. At the same time, surveys of AI adoption repeatedly highlight a shortage of people who can bridge business and data as one of the biggest obstacles to realizing value from AI investments.
That tension creates a strategic opportunity:
- For women in the data space (e.g. analysts, scientists, business partners, finance leaders) translator roles provide a path into AI work without requiring a PhD in machine learning.
- For organizations, intentionally cultivating women into analytic/AI translator roles can simultaneously:
- Fill a chronic skills gap in data and AI programs.
- Improve adoption and impact of AI investments.
- Advance gender diversity in analytics and technology leadership, not just in entry-level pipelines.
Translator roles don’t diminish technical excellence, they amplify it by making sure models matter in the real world. And they elevate relational and communication skills from “soft” add-ons to primary engines of business value.
Where to go from here
AI will automate tasks. Some roles will shrink or disappear. But the same technologies are dramatically increasing the premium on people who can interpret, question, and communicate what AI does in human terms.
Analytic and AI translators are those people.
Given what we know about average gender differences in empathy and relationship skills, women are especially well-suited to thrive in these roles—if organizations recognize and reward these strengths, and if women themselves see “translator” as a high-impact, high-value career path in data and AI.
For individual data professionals, the question is:
How much of your current work could be done by a model, and how much of your true value lies in connecting people, context, and decisions?
For women who have always been “the bridge,” “the explainer,” or “the one people come to when they’re confused,” that pattern is not incidental. In the age of AI, it’s a competitive advantage.
Selected references
- McKinsey & Company. Analytics Translator: The New Must-Have Role. 2018.
- Built In. What Is an Analytics Translator? (career overview and skills).
- World Economic Forum. The Future of Jobs Report (various editions).
- PwC. Global AI Jobs Barometer and related analyses of AI-exposed roles.
- McDonald, B. et al. “Gender differences in empathy, compassion, and prosocial behavior.” Scientific Reports, 2023.
- Christov-Moore, L. et al. “Empathy: Gender effects in prosocial behavior and neural responses.” Various reviews.
- Andrews, S. “Are Men and Women Equally Emotionally Intelligent?” Summary of EQ research, 2019.
- Seppälä, E. “Are Women More Compassionate Than Men?” Greater Good Magazine, UC Berkeley.
Wendy Lynch, PhD, is the founder of Analytic Translator, where she helps organizations bridge the gap between data teams and business leaders. With more than 35 years of experience, she specializes in turning complex analytics into clear, actionable insights. A respected author and educator, Dr. Lynch is known for training professionals to communicate data effectively, strengthen decision-making, and build high-impact analytic cultures.
