2024 Insights report
How AI is shaping product strategy
AI has seen a pivotal shift in the last 18 months—from proof of concept to core pillar of product strategy. Early adopters no longer have the upper hand, with the vast majority of businesses (84% in our survey) having already incorporated AI into their products.
We surveyed more than 200 leaders across the tech industry—61% of which were founders—to understand how they’re reshaping their roadmaps to stay competitive in the age of AI.
Learn the strategic moves these teams are making—like the shift to multimodal—and their answers to some of today’s biggest industry questions—like build or buy. Get the data on what leaders are doing to stay ahead of the AI curve.
Chapter 01
From adoption to innovation
Learn why leaders are reshaping their product roadmaps.
In the race to stay competitive, AI integration has become a strategic imperative. With time and money driving the push, businesses are moving fast to develop product strategies that capitalize on the transformative power of AI—before their competitors do.
“AI is going to be as essential as the Internet. A foundational layer of all tech moving forward.”
AI tides are moving fast
It’s sink or swim for a winning strategy.
How concerned are you that your competition’s AI strategy will outpace your own?
.… and it turns out, they’re not wrong to worry.
“Our features became outdated because AI replaced some of them. So, although we have a lot of functionality, it’s not useful—people want more.”
Why the race to put AI in products?
It comes down to time and money.
How has AI helped your business or customers?
reported time savings for business and customers
reported cost savings for business and customers
reported improved customer productivity
In my biz, AI replaces much of the human interaction with end users. It frees up resources and increases our profitability. There is no way we’re going back to the pre-AI stone age.
Speech-to-text, summarization, conversational analytics, etc. are basic features expected in all products. Vendors not providing these will be ignored.
AI allows us to accomplish in 8 hours what used to take several weeks. There simply is no argument with that kind of efficiency and speed—even with a little wobble in generative variability.
Building with AI doesn’t just save companies time and money—it builds lasting customer loyalty by delivering tangible, transformative, and profitable results.
Two examples. One qualitative data-analysis platform strategically integrated multimodal AI models to help their customers decrease time spent analyzing data by 60%—with significantly better accuracy too. Another hiring intelligence platform integrated Speech AI for its customers and effectively slashed time spent on manual tasks by 90%.
Both of these companies watched their market value skyrocket.
Outcomes like this were unachievable prior to AI—solidifying these groundbreaking capabilities as a fundamental need for end users, not just a nice to have.
chapter 02
Innovation station
AI is not a buzzword. For companies looking to get and stay ahead, it’s a requirement. Innovative teams are thoughtfully refining their product strategies to leverage a holistic AI approach that meets the full spectrum of customer needs.
AI lets us focus on the tasks that really matter instead of just busy work.
Anyone who's not leveraging AI will start to fall behind. It's about harnessing it correctly though.
We have AI working to solve issues on several fronts—creating a revenue model that is profitable for us.
Top use cases for AI integration
How are you using AI to support your products or processes?
- Product recommendations
- Predictive text
- Virtual assistants
- Automated scheduling
- Smart home devices
- Personalized news feeds
- Playlists
- Speech-to-text translation
- Sentiment analysis
- Call summarizations
- Intent recognition
- Tone and emotion detection
- Keyword & top extraction
- Chatbots
- Predictive segmentation
- Content generation
- Email campaigns
- Automated social posting
- Real-time ad bidding
- A/B testing
- Lead scoring
- Personalized web experiences
Speech intelligence is the quiet winner
AI technologies like speech-to-text and audio intelligence power an incredible number of end-user products that, in turn, improve customer productivity and experience. This could suggest that the demand for speech intelligence capabilities is even higher than reported.
What Speech AI features have you incorporated or do you plan to incorporate into your products?
The future is multimodal
“Multimodal will be the biggest disruptor in our space.”
“There’s going to be an explosion of innovation, new apps, and new companies.”
Which AI modality do you think will be the most transformative for your industry?
The release of viral tech like ChatGPT triggered a near-instant race to adopt AI and sent companies scrambling to integrate it into their products. While many of these players initially entered the space with a single-modal approach, it didn’t take long for businesses to understand that a hyper-focused, multimodal AI strategy is imperative to fully meet the vast set of customer needs.
chapter 03
To build or not to build
Implementing AI may seem straightforward, but the wrong route can come with unforeseen risks, costs, and complications. We talked to other industry leaders to learn how they approached—and answered—the foundational question of build or buy.
Do a lot of research before deciding to go with your own AI because 90% of the time it’s the wrong decision. You have to have a huge amount of data to train a model to the standards that these other providers have.
It's like trying to race a rocket. You don't compete with machines. You either build a machine or buy a machine.
Unless you're an AI company, buy it off the shelf. Companies are deep in their journey. Why not leverage what they've learned, over trying to do it yourself.
One thing’s for sure
What have been your biggest barriers to integrating AI into your products?
These barriers emphasize the importance of building a strong AI strategy prior to integration. Best practices such as considering user value, setting measurable goals, and creating action plans will help teams get to market faster.
In-house, open source, or AI provider
“They’re getting better, but do not underestimate how challenging it is to get open-source models to run predictably and produce high-quality results.”
of respondents would rather partner with an AI provider than build their own solution
An open source dilemma
“If you are using an open-source model, the learning curve is very steep. A closed API is the best top-tier quality in terms of outputs.”
Do you see drawbacks in building on open-source models vs. buying an off-the-shelf solution?
What drawbacks do you encounter in building on open-source models?
capabilities
Build in-house or out?
“Unless you work with an experienced developer that can guide you through the tech, it can be a recipe for disaster.”
Why did you decide to use an AI partner instead of building your own?
The hidden costs of DIY AI
The financial burden of in-house implementation can add up fast. It often requires significant investments in hiring top-tier AI talent, plus the additional allocation of resources for infrastructure, ongoing model maintenance, updates, privacy compliance, and more.
Open source options, while convenient in many ways, can also come with drawbacks such as the need for extensive customization, the lack of dedicated support, and the burden of handling security risks and long-term scalability.
Partnering with an AI provider can lighten the load in more ways than one. Private companies provide ready-to-use solutions, ongoing support, and the latest advancements in AI models. Unlike in-house options which require constant retraining, providers keep customers on the cutting edge with direct access to the latest models. This allows businesses to focus on innovation—without the overhead of AI maintenance—and boasts a faster time-to-market, a more predictable cost structure, and greater scalability in the long run.
“We focus on delivering customer value early, so we very often decide to buy rather than build.”
“Developing a private AI model is necessary in some niche circumstances, but, for the most part, an AI provider can provide more advanced tech faster.”
What matters most
What are the top 5 most important factors you look for in an AI vendor?
Cost
Quality and performance
Accuracy
Ease of
use and configuration
API and
developer resources
From one founder to another
Be leading edge, but not bleeding edge. Embrace it, but start slowly. Test and scale.”
Use an AI provider for as long as possible. The technology is evolving quickly—you won't be able to keep pace with your own tech.
Don't try to incorporate just because it is the current buzzword. Have a realistic feel for what AI can do to make your product better and help your customer.
In summary
The speed at which AI is moving is not just fast, it’s exponential. So, what’s different today? For starters, AI is no longer optional. While the tech’s initial swing applied mostly to internal automation, this report suggests a significant shift outward. AI has made its home in end-user products.
Despite a significant learning curve, industry leaders are no less eager to integrate. What we’re seeing as a primary workaround, is a strategic partnership with AI providers that deliver cutting-edge capabilities and handle the heavy lifting. As a result of these partnerships, businesses are thinking bigger than ever before, moving faster than ever before, and going all in on multimodal AI.
Staying competitive means staying agile, and agility in 2025 will be a whole new ball game. Based on these findings, we predict that companies building 360-degree product solutions with leading AI providers will be the ones not only creating the curve but staying ahead of it.
Our quantitative insights were gathered from survey results of 200 participants. Additionally, we interviewed 11 professionals about their current AI experiences for qualitative insights.
48% Technology (software and hardware)
13% AI development
12% Business consulting
9% Marketing
6% Finance/FinTech
5% Media
2% Manufacturing
2% Entertainment
2% Automotive
2% Logistics
2% Gaming
61% Founder or Co-founder
20% Engineer or Developer
12% Product Manager
7% GTM & Operations
27% More than 10 years
22% 6–10 years
28% 3–5 years
16% 1–2 years
7% Less than 6 months to less than a year
The average age was 43 years old
The code to meaningful voice data
Partner with the leader in Speech AI to build powerful products with breakthrough industry impact.
