What ought to be on the agenda in 2026 for leaders in big organizations who have responsibility for driving growth, innovation, or adoption of AI?
We invited three Presidents and CEOs from InnoLead’s strategic partners to answer that question on a LinkedIn Live this week: Elliott Parker of Alloy Partners, Christian Mühlroth of ITONICS, and Vincent Atallah of Aucctus. (The video replay is available on this page, as well as on LinkedIn.) Here are 10 things they said should be on the agenda for 2026:
1. Decentralized Innovation, Anchored by a Central Innovation PMO
Mühlroth said that one dynamic he is seeing as the year begins is that innovation is increasingly “decentralizing into the business,” which he views as a positive shift as long as coordination is preserved. He points to leading companies that maintain a centralized innovation PMO that does not run the projects, but instead manages investment logic, visibility, and governance. Execution remains with business units, keeping innovation close to P&L ownership while avoiding redundancy and innovation theater.
2. Raising AI Literacy Across the Organization
Atallah emphasizes that AI literacy is not about knowing a single tool, but about cultivating habits of experimentation and learning. Teams with higher AI literacy experiment rapidly, test multiple tools, and revisit assumptions as capabilities change. Curiosity, risk-taking, and fast iteration—core innovation behaviors—are now essential to building real AI fluency. In 2026, he says, the best individuals and organizations are “finding opportunities to increase your AI literacy, wherever your baseline is.”
3. Moving From AI Experiments to Measurable Results
Parker frames 2026 as the year when boards begin asking, “Where are the results?” rather than regarding AI pilots and proofs-of-concept as worthwhile experiments. Many organizations are still seeing hype without sufficient impact, particularly when it comes to hitting big cost reduction targets, and growth. Leaders will increasingly be judged on tangible outcomes.
4. More Bespoke, AI-Enabled Software Development
AI coding assistants and “vibe coding” platforms are enabling more custom software development inside large organizations. Atallah agrees this trend is accelerating among AI-literate teams, but cautions that software reliability, security, and standardization still matter at scale. Prototype-level tools may unlock value quickly, but enterprise-grade solutions require deeper engineering discipline.
5. Rethinking Engagement With Startups
Parker argues that companies need more sophisticated ways of working with startups, especially as traditional venture capital dynamics are under strain. Rather than focusing solely on supplying capital to startups, enterprises should assess what strategic advantages — customers, data, distribution, or expertise — they can offer. In a slower M&A and IPO environment, these partnerships can unlock mutual value beyond financial returns.
6. Actively Disproving Preconceived Notions About AI
Atallah urges teams to identify one “preconceived notion” they hold about AI and actively try to disprove it. Capabilities that were impossible months ago may now be routine, and unchallenged assumptions create widening gaps within teams. This discipline accelerates AI literacy and reduces fear-based resistance to adoption.
7. Using Metrics That Matter to Allocate Resources
Several speakers stressed that innovation metrics must translate into financial language leadership understands. Parker notes that CFOs focus on EBIT (earnings before interest and taxes), and the multiple applied to it, and innovation leaders should link their work directly to both. Clear, outcome-based metrics enable smarter budget allocation and reduce reliance on activity-based reporting. “There has to be one crux metric that we are ultimately looking at, that we know undoubtedly is what we’re trying to achieve as a team,” Atallah says.
8. Preparing for the Declining Cost of Digital Labor
Mühlroth highlights that the cost of digital labor is falling rapidly as AI agents take on repeatable tasks. As execution of “standard operating processes” becomes cheaper, the primary constraint shifts from headcount to decision speed and organizational coordination. In this environment, removing bottlenecks and enabling parallel work becomes a major source of competitive advantage.
9. Leading Through Change With Optimism
Parker emphasizes the importance of leading with optimism amid rapid technological and workforce change. Optimism does not mean denying that disruption is happening, but it reflects confidence that organizations can solve increasingly complex problems. This mindset helps leaders maintain trust while navigating uncertainty and transformation.
10. Organizing Data and Context So AI Can Deliver Value
Mühlroth stresses that AI systems are limited in their impact without access to meaningful organizational context, noting that most models are trained on public web data. Without internal data and domain-specific context, organizations risk receiving generic outputs that fail to inform decisions. Rather than building massive data warehouses, companies should focus on making the right data accessible for specific AI use cases through APIs or retrieval-based approaches.
