17-07-2025
Taking An AI-Native Approach To Business Innovation
Dave Hengartner, co-founder/CEO of rready, a SaaS startup supporting companies to unleash their biggest asset for innovation: employees.
Many organizations are racing to embed AI into nearly every aspect of their operations. From marketing automation to customer service to supply chain optimization—new AI tools enabling more efficient workflows are emerging almost daily.
While this rapid adoption of AI often signals a forward-looking mindset, this does not always extend to corporate innovation. Corporate innovation—the ability of organizations to turn novel ideas and concepts into financial impact—remains paradoxically constrained within many organizations.
The Innovation Challenge
Although embracing technology is seen as a strategic imperative, the systems designed to drive innovation in a company often remain stuck in the past. Outdated innovation processes, top-down decision-making structures and a lack of innovation culture can breed an environment where ideas are stifled rather than scaled.
I see this misalignment between innovation methods, systems and today's demands manifesting in four recurring problems inside large enterprises: a lack of strategically relevant ideas, a lack of data-driven validation, limited time to innovate and a lack of strategic portfolio thinking.
But the solution is not to plaster AI onto these broken innovation practices like a Band-Aid. Instead, what is required is an entire reimagination of these systems and approaches through an AI-native lens.
Problem No. 1: Not Enough Strategically Relevant Ideas
Where people are given the time, space and autonomy to innovate, I've found creativity tends to flourish. Yet, when companies launch a call for ideas, they tend to face a recurring challenge: a lack of relevant ideas that are strategically aligned and timely toward the company's overarching business vision and priorities.
When such ideas are scarce, organizations risk spending both money and resources on the wrong initiatives. Even the most advanced tools—if fed by irrelevant and low-quality input—will produce misaligned output.
Traditionally, assessing an idea's relevance required manual efforts, including well-aligned ideation workshops and manual idea evaluation. AI is shifting this, and as it evolves, companies will likely rely less on traditional idea submissions to identify challenges and more on internal signals: process data as well as feedback or observations gathered through the data pools of a company.
However, adopting AI-native approaches can present challenges. Many companies struggle to adapt to a fundamentally new way of doing certain processes, and as noted earlier, legacy mindsets and a general discomfort surrounding AI can slow adoption. Additionally, it can be difficult to gain oversight of all current innovation activities since fragmented systems, silos across departments and lack of centralized oversight can make it difficult to get a clear understanding of all current innovation initiatives.
Overcoming these challenges begins by building a certain level of confidence and literacy around AI among employees. This ensures a healthy understanding of how AI can augment human creativity in the innovation process, significantly increasing the time to value. Further, when selecting tooling, it is also important to ensure that the tooling complies with robust data protection standards and seamlessly integrates with existing systems.
Problem No. 2: Lack Of Data-Driven Validation
In many companies, top-level executives still have the final say when it comes to deciding which projects are worth pursuing. These decisions are often based on gut feeling and past experience, without necessary data or feedback.
An AI-native approach bridges this by leveraging AI tools directly when validating an idea—granted that companies have access to quality data. For example, to test market demand for a product or service, AI can model customer personas or simulate responses to different value propositions. In this case, companies with extensive customer data can use this data to train AI models on specific information to yield even better and more accurate results. This accelerates validation and helps ensure that decisions are grounded in real data.
Problem No. 3: Not Enough Time To Innovate
While it would be nice for innovation to be a daily byproduct of our efforts, it requires mental bandwidth. This means that employees need to be given the headspace and the time to innovate.
For companies to achieve tangible results from their innovation efforts, it is crucial to allocate employees time to spend on creative activities. Google's famous 20% time rule saw employees being allocated one day a week to projects that were not part of their primary responsibilities. This led to some of the company's most successful products—including AdSense and Google News.
AI tools can also play an important role here by augmenting innovation processes and providing the time to focus on the most important projects and focus areas—as well as accelerate workflows. Incorporating agentic AI to evaluate, improve and even execute ideas can reduce the time usually spent on the initial phases of an idea.
Problem No. 4: Lack Of A Portfolio Approach
A common response I get from prospects is: "We already manage our innovation portfolio." Now, this might be true for some organizations, but for many, this is not the case. In reality, these portfolios are often top-heavy, lack thematic diversity and fail to adapt dynamically. Beyond this, innovation activities are often spread across systems—hindering oversight and creating redundancies.
Using an AI-native approach can help improve visibility so leaders can then effectively prune or scale ideas based on multidimensional criteria, creating an evenly weighted innovation portfolio.
To begin this transition, it is important for organizations to start by mapping out all ongoing innovation activities within the company and establish a centralized oversight to create a single source of truth. Based on this, a structured framework can be built whereby clear processes and governance are introduced—ensuring that each project moves from ideation to implementation in a systematic way.
Making this a successful transition will likely require companies to challenge long-standing processes and their levels of efficiency. Embracing an objective mindset can allow teams to eliminate inefficiencies and discover possible hindrances to establishing a portfolio-based approach to innovation.
Overall, solving the innovation illusion is not about bolting another chatbot onto the innovation process—it's about rethinking and rebuilding the entire innovation system. An AI-native approach can transform the innovation system into one that proactively adapts and responds to internal needs and external market shifts.
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