Investor Pulse: Expectations for AI in the Enterprise

Survey Structure and Participant Demographics

West Park Advisory conducted an institutional investor pulse survey over the past month to understand market expectations surrounding how companies communicate their AI strategy, its financial implications, and the potential impact on valuation. The survey

included in depth, one on one conversations with over 20 portfolio managers and analysts from WPA’s professional network. Importantly, the focus was on enterprises implementing AI within their operations and excluded companies whose core business model directly depends on AI (e.g., semiconductors, LLMs, component manufacturers, data center operators). All responses are aggregated and anonymized.

Participants’ Current View of the Stock Market

Half of the investors we spoke with are bullish heading into 2026, citing a constructive rate environment and expectations of a pro-growth fiscal policy leading into the midterm

elections. Bullish respondents anticipate a rotation away from direct AI beneficiaries toward the broader market, which underperformed in 2025. The bearish minority pointed to political volatility, election driven instability, and concerns on the circular financing driving specific AI investments. Neutral respondents expressed long term optimism for AI but highlighted near term risks tied to 2026 margin guidance and overheating in AI exposed industries that have pursued creative circular financing such as semiconductors and LLMs.

Are we in a Bubble? … It’s Complicated

Interview responses were split on this oft-discussed topic with most acknowledging bubble-like traits in certain portions of the market. Most saw the benefits of AI and its eventual positive impact on productivity and revenues but were unsure of how these positive traits

would impact stocks. Pure growth investors were more positive than generalists or non-growth sector analysts, acknowledging the near-term risk of a correction balanced with the long-term benefit to revenues, margins and productivity. Generalists or more GARP-style investors were more likely to be bearish on AI specific stocks (semiconductors, etc.) and more bullish on the impact of AI on the broader market and saw an opportunity for rotation away from the early AI momentum stocks. For both groups, the majority believed there would be some type of pull back (especially amongst the direct AI suppliers) but that any correction would not likely take down the rest of the market.

Most Companies Have Done Poorly Articulating Their AI Strategy

Most investors believe that to date, companies have done a poor job articulating their AI strategies. Smaller cap companies have significantly lagged large cap peers in articulating

an effective strategy. Respondents cited an excess of AI “fluff,” with companies providing limited supporting data, unclear strategic rationale, and insufficient evidence of enterprise integration. Companies viewed positively were those that have already embedded AI deeply across their operations and were able to show early signs (even in limited examples) of how AI has positively impacted their operations. Investors who offered a more mixed view were skeptical of management teams who seemed to embrace the AI word but offered little tangible context to indicate that AI was being implemented effectively. They also noted that expectations may simply be too high at this early stage of AI adoption, highlighting the difficult challenge management teams face in navigating this dynamic environment.

Current AI Communications Efforts Viewed as Largely Defensive

Only 8% of investors viewed 2025 AI initiatives as offensive in nature. Companies that have integrated AI across the full enterprise—predominantly in the technology, logistics and

energy sectors—earned the highest marks. The vast majority, however, characterized AI communication as defensive and overly reliant on jargon, often lacking cohesive strategy or substance. “Fluff” and “FOMO” were recurring descriptors used to explain why historical AI narratives have struggled to resonate with investors.

AI Initially Seen as Productivity Tool but Revenue Needs to Follow

More than 2/3 of investors surveyed believe the earliest and most tangible benefits of AI will come from productivity gains and operating expense efficiencies. While substantial

investment and operational changes will be required to unlock deeper enterprise-wide improvements, participants expect revenue driven benefits to become the real differentiator over time. Investors were clear they are willing to be patient for tangible signs of AI-driven revenue contributions, but only if companies credibly explain why that patience is warranted, with a clearly defined strategy and initial milestones. Those able to demonstrate credible benefits are widely viewed as the long-term winners.

Metrics Investors Really Want and What They View as Realistic

Investors have a clear wish list of metrics they would like companies to disclose, while still acknowledging the practical challenges of measuring AI’s impact in its early stages. ROI was widely cited as the “holy grail,” with revenue contributions—either absolute or as a percentage uplift in conversion—also high on the desired list. Productivity and cost savings metrics were viewed as the most realistically attainable in the near term.

Investors are looking for evidence in metrics such as:

• Operating expense reductions or slower opex growth

• Revenue per employee or revenue per functional headcount improvement

• Specific examples or use-cases, even if small or isolated, demonstrating measurable AI driven impact.

• Companies cited by participants that provided helpful examples include: Robin Hood (tracking revenue per employee), Shopify (flat headcount), Loreal (marketing optimization), XPO (improved pricing) and C.H. Robinson (reduced costs and share gains).

These illustrative proof points are seen as meaningful indicators of real progress.

AI Payback Expectations Show Measured Patience

few investors expect quantifiable AI payback within a year. Those who do, tend to focus on industries with clear, near term efficiency opportunities. The majority expect a meaningful

payback beginning in 2027, assuming AI deployment scales more quickly than historical enterprise transformations such as ERP modernization. The 3–5 year cohort views AI as transformational, similar to the internet or industrial revolution, and expects meaningful differentiation to emerge only after sustained investment. During the next 12 months, investors primarily expect to see thoughtful strategies, credible execution plans, and increased management confidence as opposed to a quantified impact on the financial model.