How AI Can Empower the CMO to Be the Chief Innovation Officer
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
Download the guide to:
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
Download the guide to:
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
Key Insights From Our Research
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
Get the Complete Whitepaper
How AI Can Empower the CMO to Be the Chief Innovation Officer
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
Get the Slides
How AI Can Empower the CMO to Be the Chief Innovation Officer
CMOs are moving past the “efficiency phase” of AI. We all know LLMs can churn out social copy faster or deploy predictive models to trim fat from the media mix. That’s all well and good. It’s the table stakes of 2025. But the advantage goes to the CMO who can use AI to do what efficiency alone can’t: create a new source of value.
Yes, AI can help you do more with less, and it should continue to do that. At the same time, AI is evolving in some exciting ways that unlock innovation. Here are a few of them.
Automation to Agentic Brand Ecosystems
For years, we’ve talked about omni-channel marketing as this holy grail where every touchpoint is connected. In reality, it’s often a mess of disconnected tools and manual oversight. Agentic advertising can change that.
Agentic advertising is about goal-based execution where the marketer sets the destination and AI agents navigate the terrain. Instead of managing a checklist of tasks (update the search bids, refresh the social creative, etc.), marketers give AI high-level strategic objectives, like “stabilize market share among Gen Z in the Southeast during this competitive surge.”
AI agents go beyond suggesting ideas; they execute. They can autonomously redistribute budgets between retail media and social, adjust creative elements in real time based on live performance signals, and conduct thousands of micro-tests simultaneously. Agents create a self-optimizing ecosystem that lives and breathes with the market. They allow marketers to step back from the machinery and focus on being the architect of the overall brand mission.
For instance, a retail brand might deploy an agentic ecosystem to manage a multichannel flash sale. Rather than a human team manually updating bids and creative, AI agents can monitor real-time inventory levels and competitor pricing, autonomously shifting budget from high-stock regions to low-performing social segments to ensure the campaign hits a specific sell-through goal without manual intervention.
Shortening the Distance Between Insight and Invention
When marketing moves upstream into R&D and product development, the CMO becomes more essential to the core operations of the business. Traditionally, marketing has waited for a product to be built before figuring out how to sell it. But AI is turning that sequence on its head.
CMOs can now use generative AI to drastically increase the velocity and variety of product concepts. Think about a brand trying to design a new digital service or a physical retail experience. They don’t need to start with three sketches; they can begin with thousands of AI-generated design candidates that have been scrubbed of human aesthetic bias.
CMOs don’t need to guess which one will work. Digital twins of their customer segments can test product ideas in virtual environments before a single prototype is ever manufactured. By modeling how a virtual customer might react to a new service or a major sponsorship, marketers can forecast the sales value of an innovation before they’ve even spent a dollar on a launch campaign. This is business model innovation.
Consider a consumer electronics firm that uses generative AI to brainstorm 500 variations of a new wearable device interface based on emerging ergonomics data. A marketing team could identify which features evoke the highest intent to purchase before the company invests in a single physical prototype by running those designs through digital twins of their target personas.
Multimodal Intelligence: Beyond the Text Box
Our industry has spent much of its collective energy focused on text-based AI to automate the heavy lifting of content production and SEO optimization. The next breakthrough is happening in multimodal AI (or multimodal intelligence), like computer vision and sonic branding.
Your brand doesn’t just live in an ad or a blog post. It lives in the background of a TikTok video, on a shelf in a crowded store, or in the voice of a virtual assistant. The opportunity for innovation is to deploy computer vision AI to track your brand’s visual footprint across the entire digital landscape. This goes way beyond standard tracking; it identifies how your logo is being used in user-generated content and how your competitors are positioned visually in real-world environments.
At the same time, as more consumers interact with the world through voice, sonic AI can help CMOs maintain brand consistency. These adaptive engines ensure that your brand’s voice remains recognizable and emotionally resonant, whether someone is talking to a smart speaker or an AI shopping assistant. By mastering multimodal signals, you’re building a brand that is resilient across every sense, not just every screen.
For instance, a global apparel brand can use computer vision AI to scan thousands of unbranded social media videos. By identifying that their logo is frequently appearing in “urban hiking” content (a segment they hadn’t previously targeted), the CMO can pivot the company’s strategy to include rugged, outdoors-focused sonic cues and visual assets that natively match the environments where their customers are already taking the brand.
Using Emotion AI to Bridge the Innovation Gap
One of the biggest risks with high-speed innovation is losing the human connection. We’ve all seen “innovative” campaigns that felt cold or robotic. That’s why I’m so excited about the way emotion AI can be used to evaluate creative impact before it ever hits the public.
AI can help marketers score the emotional memorability of their work in the early stages of the creative process. By evaluating narrative structure, audio cues, and visual pacing, models can predict which ideas will actually spark a human connection and which will be filtered out as noise.
For instance, a healthcare brand might use emotion AI to audit a new patient-support campaign before launch. By identifying a connection deficit in the initial draft, the team can swap a sterile musical score for more empathetic tones and extend human-centric visual shots, aiming for an increase in engagement.
This creates a high-speed feedback loop. Instead of waiting for a quarterly report to tell you a campaign missed the mark, you’re iterating in days (or even hours) to sharpen the emotional hook of your story. It allows your team to take bigger creative risks because AI acts as a safety net, ensuring the core human motivation remains intact.
Using AI to Build Beyond Optimization
When CMOs treat AI as an engine for creation rather than just a filter for costs, they escape the “efficiency trap” that keeps legacy brands stagnant. They find competitive advantage beyond savings.
Don’t get me wrong: savings are important. But turning human insight into market-shaping reality gives the CMO more agency. CMOs are more than guardians of the brand. They are brand architects.
Building the Next Era of Marketing Innovation
The CMOs who win in this next era will not just optimize what exists. They will use AI to create what did not exist before. Overdrive works with marketing leaders to operationalize that shift by connecting strategy, data, creative, and emerging AI capabilities into a practical roadmap for innovation. When you’re ready to take the next step, we’re here.
How AI Can Empower the CMO to Be the Chief Innovation Officer
