From AI Hype to AI-Ready 

What changed between 2024 and 2025

2024: AI in the slide deck, not in the workflow 

In 2024, AI-dominated executive conversations failed to land in everyday work.  

• 77% of workers said they felt lost about how to use AI in their jobs or careers.¹ 
• Only about one in four felt properly educated on AI, while most employers believed training was “good enough.”¹ 
• Leaders expected AI to support research, workflow management, and data analysis; in practice, nearly two-thirds of employees mainly used it to double-check their own work.¹ 

The tools were there, but the context was not. Employees were also contending with app overload, vague priorities, and constant digital noise, which made it hard for AI to stand out as anything more than one more screen.¹ 

2025: AI has arrived and gone underground 

Fast forward a year, and AI is no longer an experiment sitting on the side of the business. Adoption has surged, largely driven by employee choice rather than mandates.² 

• 87% of employees report using AI by choice.² 
• Only 28% work in companies that formally require AI in their processes.² 
• 66% say they are curious about using AI more.² 

Yet at precisely the moment when AI becomes a real lever on productivity, visibility drops. Almost half of employees admit they hide at least some AI use, while a majority of employers believe there is nothing to hide.² The AI story shifts from “we can’t get people to use it” to “we can’t see how they are using it.” 

For a CEO, that is more than a subtle change. It signals that the primary constraint has moved from adoption to culture, governance, and data.² 

Why employees hide AI and why leaders misread it

A trust gap, not just a tech gap 

Executives often assume hidden AI usage reflects fear of job displacement.² The numbers tell a different story. 

• 47% of employers say they believe hidden AI use stems from fear about job security.²​ 
• Only 24% of employees name job security as the main reason they do not disclose their AI use.² 

The dominant reasons are more cultural:  

• The largest share—45%—do not feel they are required to mention that they used AI.² 
• 34% worry that using AI will look like cutting corners, and 27% fear judgment from colleagues or managers.² 

Younger workers feel this tension most acutely. Among Gen Z employees, nearly half (47%) say they hide their AI use primarily because they fear being judged, and 44% worry that their AI use will be seen as taking shortcuts.² For Millennials, Gen X, and Boomers, the main reason is more pragmatic: most do not see any formal obligation to talk about AI when they use it.² 

In other words, the workforce is not rejecting AI. It is absorbing it and then trying to avoid sending the wrong signal about what “real work” looks like.² 

Why this lands in the CEO’s lap 

Hidden AI is not just an HR issue. It distorts some of the CEO’s most important levers: 

• Risk: When leaders do not know which AI tools are in use or what data flows through them, they cannot realistically assess security or compliance exposure.² 
• Measurement: If AI-enabled gains are buried inside individual workflows, organizations cannot replicate or scale what works.² 
• Culture: A strategy that praises AI in public, but produces employees who feel they must use it in private, quietly undermines trust.² 

That is why hidden AI use belongs on a board agenda. It simultaneously affects risk posture, productivity, and the credibility of leadership narratives. 

How AI is already changing work and revenue

On the ground: what people are actually doing with AI 

The research is clear: AI is no longer confined to a handful of power users.² 

• Individual contributors mainly use AI to check and refine their work (54%) and to draft emails, reports, and other content (52%).² 

• Managers and executives are using it for higher leverage activities: analyzing team and business data (56%), running research (52%), and managing priorities (47%).² ³ 

In 2024, the headline benefit was reclaimed time: 79% said AI saved them at least 1 to 2 hours a day, and more than a third reported saving 3 to 4 hours.¹ In 2025, the story moves up a level: nearly seven in ten companies say AI has already cut at least a week from their go-to-market cycle.² 

This is the kind of movement shareholders notice. It is the difference between AI as an internal productivity play and AI as a direct contributor to revenue, speed, and share. 

Beyond the office: AI skills built at home

The way employees learn AI also matters.² 

• 33% say they use AI more at work than at home.² 
• 31% use it equally in both settings, and 22% lean more heavily on AI in their personal lives.² 
• Gen Z is more likely to experiment with personal tools than in the office (36%), while 46% of Boomers report heavier use at work.² 

Younger workers, in particular, develop AI habits in consumer apps and then bring them into corporate environments.² When those behaviors run ahead of policy and training, shadow usage is the inevitable result. 

Five pillars for an AI-ready enterprise

Across industries, organizations that move beyond hidden AI adoption share five traits.¹ ² 

1. Transparency by design 

AI-ready companies do not leave disclosure to chance. They spell out what responsible AI use looks like, when employees are expected to disclose it, and how to ask for help when situations are ambiguous.² Leaders echo that guidance in performance reviews, team meetings, and town halls so people stop seeing AI as a shortcut and start seeing it as part of how the company works.² 

2. Education as a living capability 

The 2024 research made one thing painfully clear: most employees did not feel supported in learning how to use AI. Only about a quarter felt properly educated.¹ AI-ready organizations treat skills as a moving target. They invest in role-specific, scenario-based learning that shows people when to lean on AI, when to push back, and how to combine AI with judgment.² 

3. Governance that supports experimentation 

Many companies treat AI risk as something to be slowed, not shaped. The more effective pattern is different. Legal, security, and HR teams work together to define guardrails that protect the business while still allowing responsible experimentation.² New tools and use cases move through a clear, repeatable approval process, rather than a series of exceptions.²​ 

4. Data readiness is the real constraint 

The quality and accessibility of the data underneath it still limits AI performance. Fragmented systems, inconsistent definitions, and poor data hygiene all cap the upside, no matter how capable the model.² AI-ready organizations invest in integration and data quality so teams can consult a single, trustworthy source of truth when they bring AI into decision making.¹ ² 

5. Integrated platforms instead of scattered tools 

Slingshot’s own product philosophy is that modern work requires AI, data, collaboration, and execution to live in one place.² AI-ready enterprises move away from a tangle of point solutions and lean toward platforms that: 

• Bring projects, content, conversations, and goals into a single environment.² 
• Make AI use observable in context, rather than hiding it across personal apps and browser tabs.² 

That shift does two things: it reduces friction for employees and gives leadership a clear view into how AI actually shows up in the work. 

From CEO to CEO

In 2024, Dean Guida, Slingshot CEO, emphasized that employers were “introducing AI” while most workers were still lost.¹ Training and alignment were the missing pieces. In 2025, his focus shifts to readiness: you can no longer assume that AI sits at the edge of the business. It is now baked into how teams research, write, analyze, and plan, even when it does not show up in your dashboards.² 

His argument is not about technology at all. It is about leadership. If AI is treated as a procurement project, it will fragment. If it is treated as a system change policy, culture, data, and platforms become a durable source of advantage. ¹ 

Questions to take into your next offsite 

The research offers good fodder for an executive retreat or board discussion.² A few starting points: 

Transparency and culture 

• Do employees believe they are expected to talk about how they use AI or that they are better off keeping quiet? ² 
• How explicitly do leaders signal that AI-enabled productivity is valued as long as it is transparent and responsible? ² 

Governance and risk 

• Where, realistically, is shadow AI use happening in your organization right now?²  
• Are your risk and compliance teams structured to enable safe experimentation, or mainly to say “no”?² 

Data and platforms 

• Is data quality limiting how far AI can go in your decision-making?² 
• How many different tools do teams use to manage work, and what would it take to consolidate onto an integrated platform?² 

Talent and capability 

• Which roles need to be genuinely affluent over the next 12–24 months, and how will you recognize that in hiring and promotion decisions?¹ ² 
• Are AI skills and outcomes reflected in how leaders are evaluated today?¹ ²

The next phase: becoming truly AI-ready

Taken together, the 2024 and 2025 reports sketch a three-phase journey.¹ ² 

• Phase 1 – AI confusion (2024): AI is introduced, but employees feel undertrained and overwhelmed; digital noise is high; productivity gains are uneven.¹ 
• Phase 2 – Hidden maturity (2025): AI becomes part of daily work; employees and managers adopt it widely and voluntarily; usage is often hidden; transparency and governance become the bottlenecks.² 
• Phase 3 – AI-ready: AI use is visible, governed, and linked to clear goals; data sits in an integrated platform; teams are continuously trained; AI is embedded in go-to-market and decision workflows, not just isolated tasks.² 

Most organizations are now somewhere between the second and third stages. The critical question is not whether your people are using AI. The data confirms that they are.² The question is whether that use remains scattered and invisible or whether you decide to make it the backbone of a more transparent, data-driven, AI-ready enterprise.  

Slingshot’s multi-year research is meant to give CEOs a clearer map.¹ Acting on that map through transparency, education, governance, data readiness, and integrated platforms is what will separate AI talk from AI advantage in the years ahead. 

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Footnotes:

1. Slingshot, 2024 Digital Work Trends Report and related coverage. 
2. Slingshot, 2025 Digital Work Trends Report – Part 1: AI Transparency in the Workplace. 
3. Slingshot, The Future of GTM: How AI Is Rewriting Execution Strategy and related GTM materials