Let’s dig into three of the biggest gaps highlighted in the report and what to do about it.

Absence of AI Strategy and Governance

Seventy-five percent of marketing teams still lack an AI road map for the next one to two years. What’s even more telling is that companies without road maps are also missing the foundational pieces: 63% don’t have generative AI policies, 60% lack AI ethics guidelines and 67% operate without an AI council. This poses a significant barrier to effective, scalable AI adoption.

As we work with clients at Luckie, we address these gaps using a simple road-mapping exercise that starts with establishing a “foundational phase” that addresses the five key areas: initial AI governance (inclusive of a formal company AI policy), AI council establishment, data readiness audit(s), essential AI enablement, and an AI literacy and training plan.

It’s straightforward but impactful, and the findings from the report back up this approach. In fact, companies with defined AI road maps are TWICE as likely to successfully integrate training, councils and necessary policies.

Why Faster Isn’t Enough

The report confirms that saving time remains the top AI goal for marketers. Quick wins in task automation and content creation dominate early adoption strategies, aligning with broader industry findings. However, be wary of the “efficiency trap.” Simply using AI for efficiency becomes the baseline rather than a differentiator. The real competitive advantage isn’t in having AI tools — it’s in how creatively you apply them. Push yourself and your teams beyond “faster” into making your AI-driven solutions smarter and more strategic. The companies that crack this will separate themselves while others are still celebrating faster content creation.

The efficiency trap also underscores the relationship between humans and AI. It suggests that successful adoption of AI depends on augmenting, rather than just replacing, human expertise. It’s imperative to continue identifying where humans-in-the-loop are and where can we further differentiate leveraging their experience and knowledge.

Lack of AI Education and Training Persists

Since this report was first published in 2020, “lack of education and training” has been the biggest barrier to AI adoption. Sixty-two percent of respondents cite this as a primary obstacle, and 68% report getting zero AI training from their companies. What’s further concerning is the disconnect between leadership and teams. CEOs are less likely to see training as a barrier, which suggests they might not fully grasp what their people need to succeed with AI.

If your company doesn’t have an assigned “AI leader,” this is a great place for an AI council to take ownership, in partnership with HR, to map out what and how to provide learning opportunities across the employee base. At Luckie, we partner with marketing departments regularly to do lunch-and-learns or more tailored training. Additionally, all of the frontier companies have recognized this gap and are rolling out free resources in AI training. So, even if your company isn’t offering structured training opportunities, individuals who want to stay ahead of the curve and evolve their skills should be taking advantage of free resources like these:

Amazon: https://aws.amazon.com/ai/learn/new-to-ai/

Anthropic: https://www.anthropic.com/learn

Google: https://grow.google/ai/

Microsoft: https://learn.microsoft.com/en-us/ai/?tabs=developer

OpenAI: https://academy.openai.com/home

The Bottom Line

The takeaway is clear: Embracing AI isn’t optional, and neither is equipping your team to master it. This isn’t rocket science, but it can be difficult to navigate and requires a level of focus to do it right. Companies committed to a responsible, human-centered AI strategy will not only navigate disruption but emerge stronger and more agile.

Are you facing similar challenges or successes with AI adoption at your company? We’d love to hear your experience. Reach out to Mark (mark.unrein@luckie.com) if you’d like to discuss navigating AI transformation at your organization.

The post The Three Biggest Obstacles to AI Adoption, According to the 2025 State of Marketing AI Report appeared first on Luckie.

Back in the 2010s, big players like Amazon, Netflix and Starbucks paved the way, investing millions in machine learning to build AI systems that tailored experiences to individual users. Their success speaks through numbers – McKinsey reports companies excelling at personalization generate 40% more revenue than average competitors. And now, about 71% of consumers expect personalized interactions, with 76% feeling frustrated when brands miss the mark.

Why marketers should care about hyper-personalization

Unlike traditional personalization that segments customers into broad groups, hyper-personalization creates truly individualized experiences using real-time data, AI-driven analysis and dynamic content generation. This evolution means delivering messages and experiences uniquely tailored to each person’s specific context and behavior.

This level of individualization once required resources only industry giants could afford. That’s changed. AI tools have transformed what’s possible for all brands without the need for massive technology investments.

How AI transforms what’s possible

Today’s AI allows for on-brand content creation tailored to individual preferences without killing the creative team. Small client teams can now produce hundreds of personalized variations that previously would have required dozens of designers and copywriters.

Beyond content scale, these systems also adapt in real time to how customers behave, so we’re now personalizing not just the message and the creative but the entire customer experience. When someone browses a certain product, shows interest in specific topics or exhibits particular behaviors, AI can adjust what they see next and where. This dynamic approach works across websites, emails, ads, SMS and apps simultaneously.

Test & learn cycles have shortened dramatically, too. At Luckie, we’ve developed an AI-powered synthetic testing application for creative pretesting against brand personas and ideal customers’ “digital twin.” What once took months of development can happen in days or hours. Brands can quickly learn what resonates with different audience segments and refine their approach without lengthy production cycles.

Finding the sweet spot

Finding the right balance matters, though. There’s a fine line between brands surprising and delighting customers and brands being intrusive. Smart brands address this through human-centered approaches, maintaining transparency and giving users control. This ethical approach builds trust where others might trigger suspicion.

Starting your hyper-personalization journey

For brands looking to implement hyper-personalization, start with these foundational steps:

1. Unify your customer data across touchpoints.
2. Identify high-impact moments in your customer journey.
3. Begin with simple AI experiments in one channel before expanding.
4. Establish clear measurements to track impact.

Your customers already want personalized experiences. The only question left is whether your brand will give them ones that feel genuine and thoughtful. With today’s AI tools, Luckie is helping challenger brands create connections that previously seemed out of reach. And in a digital world filled with so much noise, making someone feel truly seen might be your biggest competitive advantage.

The post How AI Is Making Hyper-Personalization Accessible to Challenger Brands appeared first on Luckie.

The Speed-to-Insight Advantage

Let’s give credit where it is due. These tools can identify and process massive amounts of information in minutes, summarizing company backgrounds, financials, market trends and competitive insights. In the Nike analysis, the tools pulled anywhere from five to 35 pages of data, turning what would normally take hours or days into a nearly instant deliverable.

Research isn’t just nice to have – it’s foundational to a data-driven insights approach essential for marketers. McKinsey Global Institute recently reported what we already know: Brands driven by data are 23 times more likely to acquire customers and 19 times more profitable overall. For marketers managing deadlines and time constraints, these deep research tools deliver data at the speed we crave (sometimes, however, at the expense of accuracy).

When AI Research Tools Miss the Mark

Here’s the catch: These tools aren’t flawless. The Nike analysis revealed blind spots across every platform. Who’s Nike’s CEO? OpenAI and Perplexity correctly identified Elliott Hill (appointed October 2024), while Gemini still listed John Donahoe and Grok waffled between answers. On breaking news, only OpenAI caught the recent SKIMS partnership. Meanwhile, Gemini delivered Nike’s AI implementation analysis that no other platform mentioned.

Even more troubling: When reporting Nike’s financials, OpenAI and Gemini correctly cited $51.4 billion in revenue, while Perplexity was off by billions and Grok missed 2024 data entirely. And there were no warning flags for any of this potentially flawed data.

For marketers, overreliance on this deep research output at face value to drive strategy is a major risk. The same tool delivering brilliant insights in one area can completely miss critical developments in another.

A Strategic Approach: Human + AI Partnership

At Luckie, we’ve found that the answer to improving research efforts with tools like this involves crafting the right mindset and partnership with AI. Here’s a high-level framework we use to turn surface-level data dumps into campaign-defining intelligence:

1. Establish AI as Your Research Accelerator: Start with AI for comprehensive data collection, remembering that this is the starting point, not the conclusion. Our current preference is OpenAI’s deep research tool, which provides far more depth of analysis and information over the other platforms. However, we frequently run the same prompt in Gemini or Perplexity to augment and compare output.
2. Verify Critical Information: As Ronald Reagan once said, “Trust, but verify.” In the Nike example, cross-checking key claims against reports and other reliable sources resolved contradictions within minutes. The good news? These tools all outline their sources so you’re not guessing where to look. This preserves the time-saving advantage from step 1.
3. Apply Marketing-Specific Context: AI can assemble facts, but it doesn’t (yet) truly think like a marketer. That’s where we come in to turn those facts into a competitive strategy. While OpenAI did connect the SKIMS partnership to Nike’s women’s market expansion, translating that insight into your brand’s competitive response or market positioning still requires human strategic thinking.

The Bottom Line

Emerging AI capabilities like deep research tools should absolutely be part of your marketing toolkit. Just remember that the tools themselves don’t differentiate you (everyone has access to them). Your strategic integration and utilization of them does. The real competitive advantage isn’t in having these tools. It’s in mastering the AI + human partnership that turns raw information into marketing brilliance.

The post The AI Research Reality Check: What Marketers Need to Know About Deep Research Tools appeared first on Luckie.

AI Agents: From Chat to Action

Since the launch of ChatGPT, many have been using AI as a conversational partner to answer questions and generate content. The next iteration of that is here: it’s agents. With agents, AI is evolving into digital assistants that can take action and perform tasks on our behalf. Solutions like Salesforce Agentforce and Microsoft Copilot are moving beyond basic automation to handle more complex marketing tasks. This evolution continues with AI reasoning models and improved connectivity with data/marketing software.

Action Step: Start with automating routine campaign reporting and performance analytics.

Hyper-Personalization at Scale

While personalization isn’t a new concept, most haven’t been able to do it like Starbucks, Spotify, or Netflix…until now. Driven by advancements in AI and better data integration through Customer Data Platforms (CDPs) and Digital Experience Platforms (DXPs), brands can now deliver the right message on the right channel at the right time to the right person. We’ll see more brands moving from broad audience segmentations to a “segment of one” approach.

Action Step: Begin by auditing your customer data architecture. Focus on unifying first-party data sources (CRM data, website interactions, purchase history, etc.) to enable real-time personalization.

Voice-First Customer Experience

In 2024, OpenAI predicted the “computing interface” of the future would be voice. AI voice technologies are becoming more capable, conversational, and integrated into the hardware and software consumers use day-to-day. For marketers, this means rethinking content strategy beyond text and visuals.

Action Step: Develop a voice-search content strategy focusing on conversational keywords and test voice applications.

AI-Powered Search Evolution

The way people search online for information is changing, and brands need to adapt. AI-powered search engines like Perplexity and ChatGPT are reshaping SEO, replacing traditional keyword searches with natural language conversations. Gartner predicts that 60% of searches this year could result in zero clicks as users increasingly get answers directly from search results, including Google’s AI Overviews. This disruption will only intensify as AI agents and voice search gain traction.

Action Step: Restructure your content to optimize for featured snippets and AI-readable formats. Prioritize comprehensive, structured answers to common customer questions.

Visual AI: Beyond Image Recognition

AI vision technology is creating opportunities for richer brand experiences. Through platforms like ChatGPT, Google, and Meta, a simple mobile device scan (or AI-powered glasses) can now connect consumers to product stories, personalized recommendations, and unique loyalty rewards, creating a seamless bridge between physical and digital marketing.

Action Step: Begin with AI-powered visual guidance capabilities that enhance the customer journey. Focus on implementing virtual concierge features that turn every physical touchpoint into an opportunity for personalized assistance.

Taking Action

These fast-moving advancements aren’t just buzzwords; they’re the building blocks of the future of marketing. Your success does not depend on adopting every new AI capability, but on strategically choosing where to invest based on your brand’s unique market position and customer needs. The key is to move beyond viewing AI as just a tool for efficiency and start seeing it as a core driver of competitive advantage.

Partner in Your AI Journey

Reach out to Mark directly to learn more.

The post 5 AI Advancements Shaping Marketing in 2025 appeared first on Luckie.

Here’s a practical roadmap:

Move From Mass Marketing to Mass Personalization

AI tools have redefined what’s possible in personalization. Instead of basic demographic segmentation, AI systems now analyze cross-channel behavior patterns to customize messaging, offers, and creative for each individual customer. For challenger brands, this means delivering hyper-personalized experiences that transform casual browsers into brand advocates.

Amplify Creative Impact Without Amplifying Costs

AI has redefined creative development from a resource-intensive process into a data-informed strategic advantage. These tools analyze performance patterns, predict campaign effectiveness, and enable rapid testing at scale. AI empowers creative teams to focus on breakthrough strategies while automatically generating content variations, testing versions, and optimizing execution for maximum market impact.

Make Every Media Dollar Work Harder

AI has elevated media planning and optimization from educated guesswork to precision targeting. By processing thousands of real-time signals, these systems identify and capture high- value audience opportunities across channels. This enables challenger brands to strategically allocate resources where they’ll generate the strongest returns, effectively competing against bigger budgets through smarter spending.

Know What Your Customers Want Next

AI-powered analytics have transformed customer engagement through intelligent journey orchestration. By synthesizing data across touchpoints, these systems anticipate customer needs and recommend next best actions. This gives challenger brands the power to accelerate growth through precisely targeted experiences that stay one step ahead of competitors.

Start Small, Scale Smart

Success with AI isn’t all about budget – it’s about strategic implementation. Begin with pilot projects in areas like content optimization or predictive analytics. Test, learn, and expand based on measurable results. Challenger brands that take this focused approach today position themselves to lead tomorrow.

Partner in Your AI Journey

At Luckie, we help challenger brands harness AI’s potential through practical, results-driven implementations. Whether you’re looking to enhance customer engagement, unlock customer insights, or streamline creative production, we’re here to help you identify the right starting point. Let’s discuss how AI can strengthen your competitive advantage – reach me at mark.unrein@luckie.com.

The post How Challenger Brands Can Win with AI: Stop Playing Catch-Up, Start Innovating appeared first on Luckie.

Despite its critical role, traditional QA methods reliant on manual testing are laborious and prone to human error. Crafting test cases line by line, coupled with challenges in maintaining consistency and accuracy, creates bottlenecks in the software development life cycle. As projects expand, manual testing hinders scalability due to the volume of test cases and repetitive testing.

Automation provides repeated testing with greater efficiency. However, automation requires a significant level of technical expertise and coding proficiency, creating a barrier for many QA professionals. Maintaining and updating automated test scripts to reflect the updates requires ongoing effort and expertise. Without the necessary technical skills, automation efforts can quickly become outdated and ineffective.

Luckie Health: Championing Quality with AI-Driven Assurance

Luckie Health, recognized as one of the top 100 healthcare marketing agencies in North America by Medical Marketing + Media magazine, is at the forefront of embracing the transformative power of artificial intelligence (AI) automation in software testing and development. This has been instrumental in ensuring the quality and regulatory compliance of our healthcare websites.

Luckie Health tested several AI programs before settling on the Reflect platform, a trailblazer in the AI automation domain and part of the SmartBear company. Reflect has emerged as an exemplar of innovation, using natural language processing to empower testers to articulate test scenarios with simple, humanlike commands. This transformative approach not only expedites our testing process but also democratizes participation, allowing both our technical and non-technical team members to contribute meaningfully to the QA efforts.

In the realm of healthcare marketing, regulatory compliance is critical. The key performance indicator of success is user experience. Once marketing assets are approved, they must function exactly as intended with no deviations. This includes ensuring compliance, accessibility standards and industry-specific regulations. Luckie’s QA team navigates this intricate landscape, acting not just as marketers, but as guardians of trust and transparency.

With AI automation, testers can swiftly create end-to-end tests by recording user actions within the Reflect cloud browser, defining test steps using AI prompts in a flexible, free-form manner and effortlessly generating automated tests without coding. It accelerates the testing process by supporting the testing of multiple web browsers at the same time, enhancing scalability. This is crucial for Luckie Health to ensure that our websites are compliant with regulatory guidelines across every user experience.

Will AI Replace Manual Testing?

AI does not replace human testers; instead, it complements their cognitive skills, creativity, problem-solving abilities and emotional intelligence.

Human testers offer irreplaceable understanding and contextual decision-making, providing insights into user experience that AI cannot replicate. While AI augments our capabilities, human testers remain at the heart of our QA efforts. Their expertise, empathy and contextual insights elevate our solutions. We recognize that healthcare is first and foremost about people –  not technology – so it is critical to strike a balance between the human touch of quality assurance and artificial intelligence.

By leveraging AI-driven tools, testing teams can concentrate on strategic and creative testing tasks, enhancing overall test coverage and product quality. Luckie’s commitment to innovation and customer experience further amplifies this synergy, driving the exploration of novel testing strategies and the delivery of user-centric software solutions. The collaboration between AI, human testers and Luckie ensures that high-quality products are consistently delivered to end users, meeting and exceeding their expectations.

Luckie Health’s Commitment to Ethical AI Automation

The rise of AI testing tools leads to all types of ethical considerations. Maintaining transparency, accountability and fairness in AI testing practices is essential to uphold trust and integrity in the QA process. Luckie Health is not only pioneering the use of AI automation in software testing and development but is also deeply committed to protecting ethical standards. This includes data privacy and responsible tool deployment, as well as ensuring compliance with ethical and regulatory guidelines.

Future-Forward

Luckie Health’s strategic adoption of AI-driven quality assurance positions the agency  at the forefront of innovation in healthcare technology. By combining AI automation with our industry expertise, we’re committed to delivering exceptional digital solutions that improve patient outcomes and elevate healthcare experiences.

AI automation will continue to be a crucial driver of innovation and success in quality assurance as we look toward the future. The integration of AI into automation underscores the ongoing transformation of software testing, and this evolution represents a fundamental shift toward more effective, dependable and insightful testing, empowering both developers and testers to reach new heights in their roles.

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Imagine an ad that changes based on who’s seeing it, what they like, where they are and when they’re seeing it. That’s the kind of personalized, interactive ad that could be possible by tapping into Apple’s new AI tools.

To make the most of this, brands should look into adding AI-powered features to their iPhone and Mac apps using Apple’s updated tools for developers. Things like voice control with Siri or smart suggestions could make apps stand out and get more people using them.

Adapting to an AI-First World

Apple’s AI introduction is a wake-up call for those not thinking about a marketing future that is dominated by smart, personalized experiences. Savvy brands are already switching to an AI-friendly (if not AI-first) way of doing things. For many brands, this means ensuring content is AI-friendly so it can be indexed and served to users making AI-based searches, while more advanced brands are already exploring more advanced solutions.

No doubt Apple’s introduction of AI into its ecosystem will speed along this exploration. But, but here’s the catch: Brands must do all this while still respecting user privacy, which is a big deal for Apple. Being upfront about data sharing, maintaining transparency about data utilization, and using privacy-friendly tactics is a must for brands.

The Shifting Landscape of Paid Search and Display Advertising

As AI becomes the flagship feature of Siri, we may see a decline in traditional paid search as users begin to increasingly rely on AI assistants to answer their questions. This shift could give rise to a new advertising channel, where brands pay to manage what AI says about them.

Moreover, with user interfaces becoming more AI-driven and chat-based (like Apple Vision and Rabbit r1), traditional display advertising will face challenges in the coming decade. Advertisers will need to adapt their marketing strategies to reach consumers effectively in the rapidly evolving digital space.

Keeping Tabs on Who’s Using What

Many of Apple’s new AI tricks will only work on the latest Apple gadgets, so as Apple repositions itself in the marketplace, advertisers need to watch who’s able to see and use these features. It takes roughly 2-3 years for a given version of iOS to be adopted, meaning for the time being, we’ll be straddling the pre- and post-AI worlds simultaneously. Plus, Apple currently holds “just” under one-third of the mobile market.

For now, using a mix of AI-powered and classic ad styles is probably smart to make sure no one gets left out. How fast people flock to Apple’s AI tools will also be a clue for how quickly advertisers should make the switch. Regularly checking in on the adoption rate of this new tech will certainly serve as a meaningful trigger for advertisers looking to consolidate their strategies.

So What Are the Marketing Benefits of Apple’s AI Technology?

While we could speculate over the myriad application areas for marketers, here are some of the primary areas that we should expect to impact early and often:

Hyper-personalized marketing – Apple Intelligence will analyze user data (with user consent) to understand individual preferences and behaviors with even greater precision. Imagine ads that seamlessly integrate into your life, like suggesting a new pair of headphones based on your music listening habits or recommending a fitness tracker tailored to your activity level.

Enhanced targeting and ad placement – By understanding user behavior across Apple devices and services, AI can optimize ad placement within apps and on the App Store. Ads could appear at the most opportune moments, like suggesting a productivity app during your typical work hours.

More relevant and interactive experiences – Picture engaging ad formats that adapt to your responses. AI could personalize product demos or create interactive experiences within ads, keeping you engaged and fostering a deeper connection with the brand.

On-device intelligence – A big focus for Apple seems to be processing AI tasks directly on user devices rather than relying on remote servers (i.e., the cloud). This will make AI features faster, more secure and potentially less reliant on internet connectivity. With embedded on-device intelligence, brands will be able to deliver apps that deliver more hyper-personalized experiences.

Focus on user experience – While Apple Intelligence will be used for targeted advertising, the company might prioritize enhancing the overall user experience rather than bombarding users with intrusive ads. This could lead to a new wave of AI features that are genuinely helpful and improve our daily interactions with technology.

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