VCs Forecast Robust Enterprise AI Adoption for Next Year — Once More

AI in Enterprise: Where Do We Stand After Three Years?

Three years post the launch of ChatGPT, the AI landscape has experienced a remarkable shift. While optimism around AI’s role in enterprise software has fueled a surge of investment in new startups, many companies are still grappling with effective integration of AI tools.

Enterprises Struggle to Reap AI Benefits

Despite considerable investment in AI, enterprises haven’t effectively realized its potential. A recent survey from MIT revealed that a staggering 95% of organizations reported not receiving a meaningful return on their AI investments.

The AI Adoption Timeline: What to Expect by 2026

So, when can businesses anticipate real value from AI integration? Insights from a TechCrunch survey of 24 enterprise-focused VCs suggest that 2026 is poised to be a pivotal year for meaningful AI adoption and budget increases for this technology.

Industry Opinions on AI’s Future in Enterprise

Here’s what industry leaders are saying:

Kirby Winfield, Founding General Partner at Ascend

“Enterprises are learning that LLMs aren’t a catch-all solution. The focus will shift to custom models and improved data management.”

Molly Alter, Partner at Northzone

“Some AI companies may transition from product-based to consulting models, utilizing their expertise to create tailored solutions.”

Marcie Vu, Partner at Greycroft

“We are excited about voice AI, which represents a fundamental shift in how humans and machines interact.”

Alexa von Tobel, Founder at Inspired Capital

“AI will reshape industries like infrastructure and manufacturing by enabling predictive capabilities.”

Lonne Jaffe, Managing Director at Insight Partners

“We’re observing frontier labs focusing more on turnkey applications in sectors like healthcare and education.”

Tom Henriksson, General Partner at OpenOcean

“In 2026, we expect momentum in quantum technologies, but major software breakthroughs may still be a way off.”

Investment Trends in AI

Key investment areas include:

Emily Zhao, Principal at Salesforce Ventures

“We’re focusing on the intersection of AI and physical environments, as well as advancing model research.”

Michael Stewart, Managing Partner at M12

“Our interests lie in future datacenter technology, emphasizing efficiency and sustainability.”

Jonathan Lehr, Co-founder at Work-Bench

“We’re drawn to vertical enterprise software, particularly in regulated sectors.”

Aaron Jacobson, Partner at NEA

“We’re investing in software and hardware that enhance performance while reducing energy consumption.”

Evaluating AI Startups: Key Metrics for Success

According to experts, a strong “moat” in AI isn’t solely defined by advanced models; it encompasses economic integration and proprietary data access.

Kirby Winfield on AI Moats

“It’s all about being embedded in enterprise workflows and providing unique, defensible outcomes.”

Anticipating 2026: Will Enterprises Begin Seeing Returns on AI Investments?

Industry leaders provide mixed insights on whether 2026 will truly be the turning point for enterprises in realizing value from their AI investments, highlighting the journey ahead.

Shifting Budgets: A New Era for AI Investments

As companies navigate AI vendor sprawl, many are expected to consolidate their spending, directing funds toward proven tools and solutions.

What Will It Take to Raise Series A Funding in 2026?

Startups will need compelling narratives and strong customer adoption metrics to secure funding in an increasingly competitive landscape.

The Rising Role of AI Agents in Enterprises by 2026

Insights indicate that AI agents will evolve from their initial adoption phase, potentially becoming integral to organizational workflows.

Fastest-Growing Companies: Identifying Trends

Companies that adapt to security and workflow gaps created by AI are witnessing rapid growth, underscoring the need for innovative solutions.

Strong Retention: What Makes a Company Stick?

Successful companies are those that continuously solve evolving problems as AI becomes more integrated into their clients’ operations.

Here are five FAQs related to the topic of strong enterprise AI adoption predicted for the upcoming year:

FAQ 1: What is driving the predicted adoption of AI in enterprises next year?

Answer: The anticipated surge in enterprise AI adoption is largely driven by advancements in technology, increased investment from venture capitalists, and the growing need for businesses to enhance efficiency, automate processes, and leverage data for decision-making.

FAQ 2: How are businesses planning to implement AI technologies?

Answer: Businesses are planning to implement AI technologies through various strategies, including integrating AI into existing workflows, investing in AI infrastructure, and collaborating with AI-focused startups to develop tailored solutions that meet their specific needs.

FAQ 3: What challenges might enterprises face when adopting AI?

Answer: While the adoption of AI presents significant opportunities, enterprises may face challenges such as data privacy concerns, integration issues with legacy systems, a lack of skilled personnel, and resistance to change from employees accustomed to traditional processes.

FAQ 4: Which industries are expected to see the strongest AI adoption?

Answer: Industries such as healthcare, finance, retail, and manufacturing are expected to see the strongest AI adoption, as they seek to leverage AI for improved customer experiences, predictive analytics, and operational efficiencies.

FAQ 5: How can companies ensure a successful AI adoption strategy?

Answer: Companies can ensure a successful AI adoption strategy by conducting thorough research on AI solutions, investing in employee training, establishing clear objectives for AI initiatives, and continuously monitoring performance and outcomes to make necessary adjustments.

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Equity’s 2026 Forecast: AI Agents, Major IPOs, and the Evolution of Venture Capital

<div>
    <h2>TechCrunch’s Equity Podcast: Annual Predictions for 2026</h2>

    <p>
        <iframe class="tcembed-iframe tcembed--megaphone wp-block-tc23-podcast-player__embed" height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://playlist.megaphone.fm?e=TCML6939230889"></iframe>
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    <h3>Reflecting on 2025: Major Tech Developments</h3>
    <p>In the latest episode of <a target="_blank" href="https://techcrunch.com/podcasts/equity/" rel="noreferrer noopener">TechCrunch's Equity</a>, hosts Kirsten Korosec, Anthony Ha, and Rebecca Bellan, alongside Build Mode's Isabelle Johannessen, analyze the pivotal tech trends of 2025. From unexpected AI fundraising successes to the emergence of “physical AI,” they outline their predictions for the upcoming year.</p>

    <h3>AI Trends & Challenges: What to Expect</h3>
    <p>The discussion spans essential topics, including why AI agents fell short in 2025 yet hold promise for 2026, Hollywood's response to AI-generated content, and the current liquidity challenges facing venture capitalists.</p>

    <h3>Key Insights from the Episode</h3>
    <p>Don’t miss the full episode where you’ll discover:</p>

    <ul class="wp-block-list">
        <li class="wp-block-list-item">The significance of world models in AI and their distinction from large language models.</li>
        <li class="wp-block-list-item">The decline of “stealth mode” in AI startups and the rise of new funding avenues.</li>
        <li class="wp-block-list-item">Predictions on the turbulent regulatory landscape regarding AI policy, including implications of Trump’s recent executive order for startups.</li>
        <li class="wp-block-list-item">Perspectives on upcoming IPOs: Are OpenAI and Anthropic gearing up for a 2026 public offering?</li>
        <li class="wp-block-list-item">Rapid-fire predictions, from Johnny Ive and Sam Altman's anticipated split to the resurgence of basic cell phones and the rise of “AI native” identities.</li>
        <li class="wp-block-list-item">A sneak peek into Build Mode Season 2, focusing on team building, hiring practices, and co-founder dynamics.</li>
    </ul>

    <h3>Stay Connected with Equity</h3>
    <p>Subscribe to the Equity podcast on <a target="_blank" href="https://www.youtube.com/@TechCrunch" rel="noreferrer noopener nofollow">YouTube</a>, <a target="_blank" href="https://itunes.apple.com/us/podcast/id1215439780" rel="noreferrer noopener nofollow">Apple Podcasts</a>, <a target="_blank" href="https://overcast.fm/itunes1215439780/equity" rel="noreferrer noopener nofollow">Overcast</a>, <a target="_blank" href="https://open.spotify.com/show/5IEYLip3eDppcOmy5DmphC?si=rZDFHv2sQUul_g94iCRgpQ" rel="noreferrer noopener nofollow">Spotify</a>, and various other platforms. Follow us on <a target="_blank" href="https://twitter.com/EquityPod" rel="noreferrer noopener nofollow">X</a> and <a target="_blank" href="https://www.threads.net/@equitypod" rel="noreferrer noopener nofollow">Threads</a> at @EquityPod.</p>
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Here are five FAQs based on Equity’s 2026 Predictions regarding AI agents, blockbuster IPOs, and the future of venture capital (VC):

FAQ 1: What are AI agents, and how are they expected to impact businesses by 2026?

Answer: AI agents are advanced software systems designed to perform tasks autonomously, utilizing machine learning and data analytics. By 2026, they are expected to significantly improve efficiency in various sectors by automating complex tasks, enhancing customer interactions, and enabling data-driven decision-making, ultimately transforming workplace dynamics and productivity.

FAQ 2: What trends are anticipated for IPOs in 2026?

Answer: The predictions suggest that 2026 will witness a surge in blockbuster IPOs, particularly from technology and biotech companies. This influx is expected to be driven by a stable economic environment and investor appetite for innovation. Companies that successfully leverage emerging technologies are likely to attract significant public and institutional investment, making their IPOs highly anticipated events.

FAQ 3: How will venture capital evolve by 2026?

Answer: By 2026, venture capital is expected to become more focused on companies utilizing AI and sustainable technologies. Investors will likely prioritize startups that demonstrate scalability and adaptability in fast-evolving markets. Additionally, there may be a heightened emphasis on diversity in funding, addressing gaps in representation within the startup ecosystem.

FAQ 4: What role will data privacy play in the future of AI agents?

Answer: As AI agents become more integrated into business operations, data privacy will emerge as a critical concern. Companies will need to prioritize robust data protection and compliance with regulations to maintain consumer trust. By 2026, businesses that successfully navigate these challenges will set themselves apart and foster stronger customer relationships.

FAQ 5: What should investors look for in potential IPO candidates?

Answer: Investors should look for companies with strong growth potential, innovative technology, and a solid business model. Additionally, understanding a company’s market position, management team, and commitment to sustainability will be essential. As the landscape evolves, investors may also gauge a company’s adaptability to AI technologies as a key indicator of its future success.

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Leveraging AI to Forecast Box Office Hits

Harnessing Machine Learning to Predict Success in Film and Television

While the film and television industries are known for their creativity, they remain inherently risk-averse. With rising production costs and a fragmented production landscape, independent companies struggle to absorb substantial losses.

In recent years, there’s been a growing interest in utilizing machine learning (ML) to identify trends and patterns in audience reactions to new projects in these industries.

The primary data sources for this analysis are the Nielsen system, which, despite its roots in TV and advertising, offers valuable scale, and sample-based methods like focus groups that provide curated demographics, albeit at a reduced scale. Scorecard feedback from free movie previews also falls under this category, though substantial budget allocation has already occurred by that point.

Exploring the ‘Big Hit’ Theories

ML systems initially relied on traditional analysis techniques such as linear regression, K-Nearest Neighbors, and Decision Trees. For example, a 2019 initiative from the University of Central Florida sought to forecast successful TV shows based on combinations of actors, writers, and other key factors.

A 2018 study ranked episode performance by character and/or writer combination

A 2018 study rated episode performance based on character and writer combinations.

Meanwhile, existing models in recommender systems often analyze projects already deemed successful. This begs the question: how do we establish valid predictions for new films or series when public taste and data sources are in flux?

This challenge relates to the cold start problem, where recommendation systems must operate without prior interaction data, complicating predictions based on user behavior.

Comcast’s Innovative Approach

A recent study by Comcast Technology AI, in collaboration with George Washington University, tackles this cold start issue by employing a language model that uses structured metadata from unreleased movies.

This metadata includes key elements such as cast, genre, synopsis, content rating, mood, and awards, which generate a ranked list of likely future hits, allowing for early assessments of audience interest.

The study, titled Predicting Movie Hits Before They Happen with LLMs, highlights how leveraging such metadata allows LLMs to greatly enhance prediction accuracy, moving the industry away from a dependence on post-release metrics.

Video recommendation pipeline illustrating indexing and ranking processes

A typical video recommendation pipeline illustrating video indexing and ranking based on user profiles.

By making early predictions, editorial teams can better allocate attention to new titles, diversifying exposure beyond just well-known projects.

Methodology and Data Insights

The authors detail a four-stage workflow for their study, which includes creating a dataset from unreleased movie metadata, establishing a baseline for comparison, evaluating various LLMs, and optimizing output through prompt engineering techniques using Meta’s Llama models.

Due to a lack of public datasets aligning with their hypothesis, they constructed a benchmark dataset from Comcast’s entertainment platform, focusing on how new movie releases became popular as defined by user interactions.

Labels were affixed based on time taken for a film to achieve popularity, and LLMs were prompted with various metadata to predict future success.

Testing and Evaluation of Results

The experimentation proceeded in two main stages: first, establishing a baseline performance level, and then comparing LLM outputs to a more refined baseline that accurately predicts popularity based on earlier data.

Advantages of Controlled Ignorance

Crucially, the researchers ensured that their LLMs operated on data gathered before actual movie releases, eliminating biases introduced from audience responses. This allowed predictions to be purely based on metadata.

Baseline and LLM Performance Assessment

The authors established baselines through semantic evaluations involving models like BERT V4 and Linq-Embed-Mistral. These models generated embeddings for candidate films, predicting popularity based on their similarity to top titles.

Performance of Popular Embedding models compared to random baseline

Performance comparison of embedding models against random baselines shows the importance of rich metadata inputs.

The study revealed that BERT V4 and Linq-Embed-Mistral excelled at identifying popular titles. As a result, BERT served as the primary baseline for LLM comparisons.

Final Thoughts on LLM Application in Entertainment

Deploying LLMs within predictive frameworks represents a promising shift for the film and television industry. Despite challenges such as rapidly changing viewer preferences and the variability of delivery methods today compared to historical norms, these models could illuminate the potential successes of new titles.

As the industry evolves, leveraging LLMs thoughtfully could help bolster recommendation systems during cold-start phases, paving the way for innovative predictive methods and ultimately reshaping how content is assessed and marketed.

First published Tuesday, May 6, 2025

Here are five FAQs on the topic of using AI to predict a blockbuster movie:

FAQ 1: How does AI predict the success of a movie?

Answer: AI analyzes vast amounts of data, including historical box office performance, audience demographics, script analysis, marketing strategies, and social media trends. By employing machine learning algorithms, AI identifies patterns and trends that indicate the potential success of a film.

FAQ 2: What types of data are used in these predictions?

Answer: AI systems use various data sources, such as past box office revenues, audience reviews, trailers, genre trends, cast and crew resumes, social media mentions, and even detailed film scripts. This comprehensive data helps create a predictive model for potential box office performance.

FAQ 3: Can AI predict the success of non-blockbuster films?

Answer: Yes, while AI excels in predicting blockbuster success due to the larger datasets available, it can also analyze independent and smaller films. However, the reliability may decrease with less data, making predictions for non-blockbusters less accurate.

FAQ 4: How accurate are AI predictions for movie success?

Answer: The accuracy of AI predictions varies based on the quality of the data and the algorithms used. While AI can provide insightful forecasts and identify potential hits with reasonable reliability, it cannot account for all variables, such as last-minute marketing changes or unexpected audience reactions.

FAQ 5: How is the film industry using these AI predictions?

Answer: Film studios use AI predictions to inform project decisions, including budgeting, marketing strategies, and release scheduling. By assessing potential box office performance, studios can identify which films to greenlight and how to tailor their marketing campaigns for maximum impact.

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Utilizing Machine Learning to Forecast Market Trends in Real Estate through Advanced Analytics

The Revolution of Machine Learning in Real Estate Forecasting

Traditionally, real estate evaluations relied on local economic indicators and historical data, but machine learning has transformed the industry.

The Power of Predictive Analytics in Real Estate

Advanced algorithms analyze diverse data, from social media sentiment to infrastructure plans, revolutionizing market analysis.

Data Integration and Challenges

Machine learning requires a robust data infrastructure and effective integration methods for accurate insights.

Advanced Analytical Techniques

Discover how machine learning uncovers intricate relationships and predicts market fluctuations with precision.

Practical Applications of Machine Learning in Real Estate

Explore the transformative impact of machine learning in predicting trends, increasing property value, and optimizing portfolios.

Ethical Considerations and Challenges

Learn about the ethical implications of machine learning in real estate and how they can be addressed.

Conclusion

Machine learning in real estate offers endless possibilities for predictive accuracy and strategic decision-making, shaping the future of the industry.

  1. What is advanced analytics in real estate?
    Advanced analytics in real estate involves using sophisticated techniques, such as machine learning, to analyze large amounts of data in order to make predictions and optimize decision-making processes within the industry.

  2. How can machine learning be used to predict market shifts in real estate?
    Machine learning algorithms can analyze historical data on real estate sales, market trends, economic indicators, and other factors to identify patterns and make predictions about future market shifts. This can help real estate professionals anticipate changes in property values, demand, and other key factors.

  3. What are some common applications of advanced analytics in real estate?
    Some common applications of advanced analytics in real estate include predicting property values, identifying potential investment opportunities, optimizing pricing strategies, and forecasting market trends.

  4. How can real estate professionals benefit from implementing advanced analytics?
    By implementing advanced analytics in real estate, professionals can gain a deeper understanding of market dynamics, make more informed decisions, and stay ahead of competitors. This can lead to improved profitability, reduced risks, and better overall performance in the industry.

  5. What are some challenges to implementing advanced analytics in real estate?
    Some challenges to implementing advanced analytics in real estate include data quality issues, the need for specialized skills and expertise, and concerns about data privacy and security. Overcoming these challenges typically requires investment in technology, training, and collaboration with data scientists and other experts.

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