How Generative AI is Changing Search and Website Experiences
5 février 2025 • 2 Minute Read • Elizabeth Spranzani, Chef de la direction technologique
See All Our Insights
How Technical Leadership Is Evolving in the Age of AI
10 juillet 2025 • 6 Minute Read • Elizabeth Spranzani, Chef de la direction technologique
As technical leaders, our responsibilities vary widely depending on the organization and our specific role within it. Some of us focus on hardware, others on software. Some look ahead to predict and plan for innovation, while many concentrate on optimizing current systems and execution. We might manage teams, own sales solutions, and oversee data, platforms, systems, or security.
Yet, despite this broad range of responsibilities, one thing unites all technical leaders: we're facing intense pressure to do more with less—fewer people, tighter budgets, and shorter timelines.
The pressure is only increasing as global uncertainty rises, with already conservative budgets getting narrower. Meanwhile, the promise of AI as a silver bullet only adds to the challenge. AI is often broad, hyped, and vaguely defined, and it's up to us to translate it into real, practical outcomes.
The Real Cost and Complexity of AI Adoption
According to Gartner, Chief Information Officers (CIOs) are being asked to roll out new and increasingly expensive technology with limited budgets. At the same time, many software vendors are raising prices up to 30% annually as they embed AI features into tools, including Software as a Service (SaaS) solutions. In addition, generative AI (GenAI) projects are especially difficult to budget, often running 5 to 10 times over expectations and consuming up to a third of a company's annual IT budget.
The Shift Toward SaaS and the Need to Break Down Data Silos
SaaS has long enabled faster, more scalable deployments, even before GenAI took the world by storm via ChatGPT in late 2022. Since it's managed by software vendors, SaaS reduces ownership overhead and accelerates platform update cycles. Yet, fully transitioning to SaaS demands significant time and investment, and many organizations are only partway there.
Simultaneously, there's a growing need to break down data silos, allowing organizations to gain clearer customer and operational insights. This pursuit of de-siloing data has led to the adoption of solutions like customer data platforms (CDPs), data lakes, business intelligence (BI) tools, analytics platforms, and data visualizers.
There's also a growing focus on content and digital asset consistency and omnichannel delivery. Marketing and brand teams must ensure that text and visual content remain consistent and high-performing across every touchpoint.
Fueling Growth with AI: Opportunities and Challenges
Introducing AI features into platforms and processes has been like throwing fuel on a fire. It's created an explosion of opportunity along with confusion. Leaders must cut through the noise to identify where AI can genuinely add value.
How to Make AI Practical
The core questions to ask:
- How do we harness AI to make something useful?
- What can it realistically do beyond the hype?
- How do we keep customers, employees, and data safe?
Why Governance Is the Secret to Moving Fast with AI
AI innovations move fast, but they can't move blindly. There's a risk of bias, security gaps, unclear ownership, and poor data quality. That's where modern governance comes in—not as a blocker, but as a velocity enabler.
The most effective organizations treat governance as part of the product and platform lifecycle, not just a legal formality. They're building lightweight, scalable structures that answer:
- Where can AI be used, and by whom?
- How do we evaluate tools for compliance and value?
- How do we protect sensitive data while enabling exploration?
And most importantly, they're measuring whether it's working.
Strategic KPIs include things like:
- Percentage of AI initiatives that meet security and ethical review benchmarks
- Number of flagged issues for risk or bias during pre-deployment reviews
- Reduction in data access violations or compliance escalations over time
These metrics can signal operational maturity. And if governance works, teams move faster because they know where the guardrails are. If it's not measured, it's not working.
The goal of measuring governance effectiveness is to help you recalibrate or refine policies, improve adoption, or decide whether to invest in tooling or automation.
You're simply asking: Are the right policies and controls in place? Are they being followed and enforced? Are they producing the desired outcomes (security, compliance, efficiency, trust, and more)?
Nearly 9 out of 10 CEOs and senior executives say effective data, analytics, and AI governance is essential for innovation. Yet only 46% report having clear, strategic KPIs in place to track whether those governance policies are delivering value. That gap is where risk festers and where leadership must step in.
As technical leaders, it's our responsibility to ensure innovation isn't just enabled, but that it's effective, sustainable, scalable, and safe. Governance is how we deliver speed with accountability.
Ask yourself: Is your governance framework actually helping you move faster safely, or is it just performative?
Balancing Innovation Speed with Data Governance and Risk Mitigation
Flexibility is critical in times of rapid digital change, especially when riding the AI wave. Tech leaders often face a tradeoff between moving quickly and being thorough and cautious.
In moments like these, they may need to lean toward speed more than they're comfortable with to keep pace and ensure their organization doesn't fall behind. But it's a balancing act. Move too fast and you risk losing control of your data, which is far more valuable.
With AI, data is the fuel, especially with sensitive customer and employee data. That makes having structured data governance paired with strong security more important than ever.
Proving the ROI of AI and Making the Business Case
While AI offers new opportunities for automation and efficiencies, it's far from free. The cost is significant, whether you're upgrading licenses, buying new tools, or building custom AI capabilities like large language models (LLMs). Yet, one of the main reasons people are racing to adopt AI is to save time and money.
That puts pressure on tech leaders to build compelling, data-backed cases for investment. The challenge is that much of the return on investment (ROI) is still based on conjecture and hypothesis, not hard truth. Success requires vision, conviction, and a leap of faith, alongside practical KPIs and milestones to validate progress.
So if you're advocating for AI, make sure you genuinely believe in the technology you're championing.
Driving AI Adoption Through Cross-Functional Collaboration
Once the financial team signs off, long-term success depends on adoption. Leaders must find AI champions in relevant departments who can build excitement. In some cases, they'll need to reinforce the usage and value of these tools through measurement and feedback loops.
Since there won't be obvious training paths or a pool of talent already well-versed in these tools, look out for individuals internally and externally who are adaptable, curious, and fast learners. They'll be the ones to embrace change and motivate others to try new things.
How to Identify High-Impact AI Use Cases
The easiest way to gain approval from finance and cross-functionally ensure adoption is to focus on challenges and solutions, not tools. Identify use cases that improve how a team gets its work done. This means deeply collaborating with departmental leaders to understand their workflows, and asking:
- What's slowing your team down?
- What do they find tedious, repetitive, or time-consuming?
- What work adds little value but eats up hours?
Target high-impact pain points, then identify tools or platforms to address them well, and build your case around measurable outcomes.
Setting Your Business Up for Long-Term Success with AI
AI has the potential to drive real transformation. It might still feel early—or maybe not, but waiting for perfection, or overanalyzing every technology option, can leave your organization stuck while others move forward.
Success starts with a well-informed, risk-aware decision. From one tech leader to another, here's what to keep in mind:
- Find AI tools that solve real problems, have practical capabilities, and show early promise.
- Start small, experiment, and measure, then expand from there.
- Set clear KPIs from the beginning and continue to prove value to your organization's budget owners.
- Don't be afraid to pivot if the data tells you something isn't working.
When playing around with new tools, encourage experimentation with guardrails. Give teams freedom, but provide structure and oversight. And most importantly, evolve your own leadership mindset:
- Let go of what being a technical leader used to mean.
- Reframe uncertainty as a part of the innovation process.
- Accept short-term chaos as the cost of progress.
Ready to Explore What AI Could Mean for Your Organization?
The coolest part is that we're at the precipice of seeing our technological dreams come true. It may not look exactly how we imagined, but that's part of the journey. Whether you're just beginning or already experimenting, now's the time to connect with peers, share lessons, and compare notes.
Remember to lead with curiosity, clarity, and conviction, and your organization will be better positioned for what's next.
Interested in to talking it through? Our experts are here to explore ideas, challenges, and opportunities with you, wherever you are on the journey.