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The Future Of Leadership In The Age Of AI

The Future Of Leadership In The Age Of AI

Forbes2 days ago

Jani Hirvonen is Global Head of Channel Partnerships at Google.
Artificial intelligence is evolving fast. Every week brings new tools, new terms and new hype. It's easy to feel behind. But the truth is, we're still in the early stages. The best leaders won't wait to understand every model or master every technology. They'll lead with curiosity, focus on business outcomes and build a culture that can adapt.
AI is changing not just what we do but how we work and how we lead. Here's how I believe great leaders can position themselves—and their teams—for success.
The first step isn't choosing a model or tool. It's identifying a real problem that matters to your business. That could mean improving customer experience, boosting efficiency or supporting growth. Whatever it is, start there. Then explore how AI can help.
You don't need to be a technical expert to lead on AI. But you do need to be curious and intentional. Try using AI in your own work. Build hands-on experience, ask questions and share what you learn. Modeling that mindset helps others feel confident doing the same.
It's also important to understand how AI fits into your broader strategy and how to implement this. Some companies leverage in-house solutions, others choose third-party solutions. The right answer depends on your capabilities, business processes and priorities. Whatever path you choose, stay focused on value, not novelty.
Many senior leaders feel overwhelmed by AI's pace. That's understandable. But trying to keep up with every development will leave you ineffective and spinning. Focus instead on what drives business value and surround yourself with people who can translate complexity into impact.
Encourage small experiments. Give teams permission to test new tools and learn from mistakes. Create feedback loops to scale wins and learn from failures. Change happens incrementally. Choose momentum over perfection.
The most successful leaders will be the ones who show up with a learning mindset. They'll get comfortable not having all the answers. They'll ask better questions. And they'll focus on building teams that are agile, accountable and aligned.
AI is only as good as the data it uses. That's why data fluency for leaders is more essential than technical mastery. You don't need to become a data scientist, but you do need to understand how data quality affects AI outcomes. That includes knowing how your systems collect and store information, what biases might exist and how insights are being used to guide decisions.
For example, if your customer data is incomplete or inconsistent, no model can fix that. Before applying AI, make sure your inputs are accurate. Ask where the data is coming from. Challenge assumptions, and encourage your teams to do the same.
Leaders who embrace data fluency can guide smarter conversations, make better decisions and hold their teams to higher standards. It's about asking, not assuming, while creating a shared understanding of what good data looks like.
Ethics in AI is foundational. Every AI decision, including what data you use, how you automate and what outputs you accept, can have ethical implications. Leaders must be proactive in identifying and managing risks.
That means asking tough questions. Is the data biased? Are we over-automating at the expense of human connection? Can we explain how decisions are made? Are we protecting privacy and security? Ethics shouldn't come after strategy. It should shape it.
It's also important to be transparent with your teams. No one can offer complete job security in a disruptive environment. But you can offer honesty, guidance and upskilling opportunities. Helping people understand how their roles might evolve—and how they can prepare—builds trust.
AI is not just a technology shift. It's a transformative shift that will impact people, culture and operations. It's also a leadership shift. It's still up to us to decide how people and machines work together.
Today, leaders can start small. Focus on use cases with clear ROI. Pilot, measure and learn. Don't get sucked in by the noise. And don't start with AI—instead, start with a problem you need to solve.
Over time, build a leadership mindset that sees AI as a partner, not a threat. Know when to rely on AI, when to override it and how to shape a culture where both humans and AI can thrive.
The future won't reward perfection. It will reward adaptability. The best leaders won't be the ones who know everything. They'll be the ones who learn quickly, think clearly and lead with intention.
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Updated March 5, 2025. Accessed June 2, 2025. Oing C, Bristow RG. Systemic treatment of metastatic hormone-sensitive prostate cancer—upfront triplet versus doublet combination therapy. ESMO Open 2023l doi: 10.1016/ Sartor O, J. de Bono KN, Chi K, et al. Lutetium-177–PSMA-617 for Metastatic Castration-Resistant Prostate Cancer. NEJM 2021; doi: 10.1056/NEJMoa2107322. Morris M, Castellano D, Herrmann K, et al. 177Lu-PSMA-617 versus a change of androgen receptor pathway inhibitor therapy for taxane-naive patients with progressive metastatic castration-resistant prostate cancer (PSMAfore): a phase 3, randomised, controlled trial. The Lancet 2024; doi: 10.1016/S0140-6736(24)01653-2. University of Chicago Medicine. Lutetium-177 PSMA Therapy for Prostate Cancer (Pluvicto). Accessed June 18, 2024. Jadvar H. Targeted Radionuclide Therapy: An Evolution Toward Precision Cancer Treatment [published correction appears in AJR Am J Roentgenol. 2017 Oct;209(4):949. doi: 10.2214/AJR.17.18875]. AJR Am J Roentgenol. 2017;209(2):277-288. doi:10.2214/AJR.17.18264 Jurcic JG, Wong JYC, Knoc SJ, et al. Targeted radionuclide therapy. In: Tepper JE, Foote RE, Michalski JM, eds. Gunderson & Tepper's Clinical Radiation Oncology. 5th ed. Elsevier, Inc. 2021;71(3):209-249 # # # Novartis Media RelationsE-mail: Novartis Investor RelationsCentral investor relations line: +41 61 324 7944E-mail: in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

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