AI-Powered Leadership

As a leader of an organization, team or department, getting serious about AI adoption is the ultimate change management challenge. Successful AI integration isn’t just about learning to use the latest tools and training your staff; it’s about aligning your entire organization to undergo a process of intensive learning, adaptation and the hardest thing we Humans do: change. A few tips for taking the first steps.

AI Leadership – Fiona Passantino, 6 FEB 2025

Embracing Organizational AI Integration

In 2024, organizational AI adoption experienced a remarkable surge around the world. 72% of companies reported to be using AI in a structural way in at least one business function, as compared with 55% over 2023[i].

There’s a fundamental difference between AI Integration as a professional practitioner or implementing this for your organization on behalf of all your employees. Large-scale organizational change brings its usual array of leadership challenges. There are stakeholders to convince, employees to inspire, supporters to gather, customer expectations to manage (since things might not work exactly like clockwork during the transition), and the very Human challenge of finding the time and energy to do all this and still do your day job.

A successful organizational AI Integration pathway requires its leaders to understand the needs and challenges of every single employee, what each individual loves and hates about their job, and what type of AI will specifically make them not only more efficient and better, but also happier and more inspired. This means you have to know quite a bit about what AI options are out there, and the strengths and weaknesses of each.

Why do we need AI?

Even before the “what” of AI Leadership – what needs to change, what tools, what people are needed, what needs to happen to the data – is the “why” behind AI integration. We’re talking about a substantial investment of time and money, significant personnel downtime and its effect on the customer experience. Is AI uptake right for this organization, at this time, at this state of its maturity, for this team, for this set of customers?

AI is shiny and new. Just hearing yourself say it sounds future-focused, innovative and hip. But will it solve your biggest problems? If the answer is “yes”, are we ready to take it on at this moment? Do more pressing problems related to personnel, culture, cash flow, data hygiene, security have to be solved first?

Thus, the first question necessarily is: “Do we become an AI-Powered organization?” And many times, the honest answer is “not in this way”. Or “not yet”. This is the time to take a good, long look in the mirror and identify the top three “Points of Pain” in the organization.

This is done regardless of whether AI can fix any of them. And then work backwards. Are you bleeding talent, experiencing high customer churn, pouring money into an ineffective sales funnel, spending too much on expensive external freelancers, dealing with poor onboarding, invested in dying processes, experiencing limited employee mobility or disappointing training outcomes? The goal is to choose just three, and fix our sights on those for now.

The AI Integration Roadmap

The AI Leader takes on the Change Roadmap. Starting with “Strategy”, she manages the rollout of various phases, overlaying it with the company goals timeline (since there will likely be other priorities in line with the overarching vision).

What does this mean? Imagine you have a shipping business and you know that every member of the workforce will be working overtime with no days off during the peak weeks of the year: December 5-25 as holiday shopping ramps up, and December 26-31 as the return storm surges. The start of the AI Integration Roadmap for this company would likely be February 1 to give people the chance to catch their breath and gather their energies, with a fresh pot of investment thanks to the rush.

Then, some hard decisions will need to be made about “Prioritization” (read: “money”). Imagine the three pain areas were in three entirely different areas of the organization; data security (IT), sales (MAR) and talent retention (HR). For each area, very different, specific investments are required to set the stage for transformation. Two of the three may have nothing whatsoever to do with AI.

Which AI is right for you?

Which tools to choose? Off the shelf AI or do we zip to Hugging Face and download our own foundational model, and build our own? Do we go with cloud-based or local services? What external experts are needed to get through the short term, and which permanent AI-savvy internals will do the job for the long haul? What training will they require, both for now and in the future? What is the state of our data? Our infrastructure, our data architecture? The aim is to build the foundations that will enable AI to take root, and build on a culture of continuous, cyclical learning.

The AI leader has to grapple with the ethical and responsible use of AI. Not only is there an urgent need for governance – an AI “Ten Commandments” that everyone can see and understand – but clear vision statements explaining the “why”. Will customer or employee data be part of the equation? Will the AI solutions be deployed in such a way to ensure traceability, explainability, Human verification and anti-bias measures? 

Imagine your AI solution to take on recruitment will be targeting new hires to address the talent crunch issue. Let’s fast forward, nine months into your future. You’re running a proprietary, locally installed LLM behind your own firewall, that your internal data engineering teams have finetuned on internal HR data, overlaying this over its foundational structure. Results show that only potential recruits between the ages of 18-25 are being targeted, and only those indigenous to the local country.

Is the algorithm appropriately weighted to widen your talent pool, and is there an effort to be inclusive, including disabled talent for certain roles, recent arrivals for others, 50-plus, women and minorities? What’s important to your organization, and where are the boundaries? Knowing the questions to ask before you start will greatly help guide data engineering teams in the early stages of model training and integration.

The AI leader will need to tackle a silo culture head-on, since AI Integration will necessarily be a cross-functional undertaking to be effective: IT, data science, marketing, sales, HR and operations will need to work together closely to understand their business requirements, challenges and opportunities. A culture of collaboration will help ensure that AI solutions line up in the future, too.

Once the transformation process is underway, there will need to be benchmarking and evaluation to make sure the targets are being hit and the case for AI adoption remains salient. We need those baseline measurements outlining the current state of AI readiness, and then chart the key metrics that indicate success for the road ahead.

AI and Humans

The AI Leader is a master communicator, conveying the benefits its adoption to stakeholders at all levels of the organization. The truth is, some employees will eagerly leap towards their AI-Powered future and learn all they can, while others will be highly resistant to change, be fearful, skeptical and unwilling.

Each employee has her own set of strengths and weaknesses, and will need different approaches to convince, support, train and remind. It’s a particular challenge to remain focused and positive when it feels like an uphill struggle all the way. But take comfort in the fact that all companies, leaders and teams are undergoing this same process.

There is also the challenge of staying ahead of it all. Given the rapidly evolving nature of the beast, being an AI Leader means blocking time to stay informed about the latest advancements and trends in the field. You will need time to learn and the capacity to adjust your tooling and workflow; some slack as you test out new options as they hit the market literally every day. Most will be left alone, but some will be worth investing in and injecting it into the current learning stream.

Good AI integration is not just about the technology. It’s about the people working with it. It’s a holistic approach that combines technology, training, and organizational alignment for maximum impact. Ultimately, an AI Leader is a visionary and strategic thinker who combines technical expertise with strong leadership skills to drive a successful adoption and integration of AI within an organization. Staying positive, reminding everyone of the goals, the path forward, and keeping focus.

Need help with AI Integration?

Reach out to me for advice – I have a few nice tricks up my sleeve to help guide you on your way, as well as a few “insiders’ links” I can share to get you that free trial version you need to get started.

[i] Thormundsson (2024) “Adoption of artificial intelligence among organizations worldwide from 2017 to 2024, by type”, Statista. Accessed January 26, 2025. https://www.statista.com/statistics/1545783/ai-adoption-among-organizations-worldwide/