AI Hype vs. Reality: A Guide to Smarter Tech Investments
In today’s tech investment landscape, artificial intelligence has emerged as both a potent force for innovation and a widely used marketing term. As investors explore potential opportunities, they frequently encounter companies touting AI capabilities. However, not all AI claims are equally valid. The emergence of ‘AI washing,’ where companies exaggerate or misrepresent their AI capabilities, has become a serious concern. This underscores the critical need for rigorous technical due diligence.
Understanding AI Washing
AI washing occurs when companies exaggerate their AI capabilities to appear more innovative or technically advanced. Common examples include:
Labeling basic automation or rule-based systems as "AI-powered": For instance, an e-commerce site's "You May Also Like" feature that simply shows products from the same category being marketed as an "AI-powered personalization engine."
Claiming proprietary AI technology while using off-the-shelf solutions: Companies may use pre-trained models or APIs from providers like Google or Amazon without acknowledging their reliance on external technology.
Overstating the sophistication or capabilities of their AI systems: A company might claim to have "deep learning" capabilities when their system primarily relies on simpler machine learning techniques.
Marketing AI features that are still in conceptual stages as current capabilities: This creates unrealistic expectations and can mislead potential investors.
Why Do Companies Engage in AI Washing?
The motivations behind AI washing can vary. Some companies may genuinely believe they are developing cutting-edge AI, while others may intentionally mislead investors to attract funding or inflate their market valuation. Regardless of the motive, AI washing can have significant consequences for investors and the broader market, eroding trust and hindering the responsible development of AI.
Maturity Expectations Across Investment Stages
When evaluating AI capabilities, it's crucial to calibrate expectations based on a company's funding stage:
Seed Stage: At this early stage, expect to see a proof of concept or minimum viable product (MVP) demonstrating basic AI functionality. A clear technical roadmap for AI implementation, a core technical team with relevant AI/ML expertise, and initial data collection and basic processing capabilities are essential.
Series A: Companies at this stage should have working AI models in production, early-stage data pipelines with basic ETL (Extract, Transform, Load) processes, and a defined data architecture.
Series B: Expect to see scalable AI infrastructure, sophisticated data pipelines with automated workflows, a robust data governance framework, and advanced analytics capabilities.
Series C and Beyond: At this mature stage, companies should have enterprise-grade AI systems, production-grade data pipelines with real-time capabilities, advanced MLOps practices (machine learning operations), and a proven track record of AI-driven business impact.
NorthBound Advisory's Technical Due Diligence Framework
At NorthBound Advisory, we conduct comprehensive technical due diligence across ten critical pillars, ranging from Product and Technology to People and Management Practices. When evaluating AI claims, we pay particular attention to our AI, Data and Analytics pillar.
Deep Dive: AI, Data and Analytics Assessment
Our AI, Data and Analytics pillar encompasses five key areas that help investors validate a company's true capabilities:
Data Architecture and Management: We evaluate the foundation of any AI initiative by examining data strategy, architectural design, storage solutions, data modeling, metadata management, and data integration approaches.
Data Pipelines, ETL, and Data Warehousing: We assess the company's ability to process and transform data through ETL processes, real-time data streaming capabilities, pipeline orchestration, and data warehouse architecture.
Analytics Capabilities and Usage: We examine the practical application of data through business intelligence platforms, reporting and dashboard capabilities, self-service analytics infrastructure, and user adoption metrics.
Data Governance and Quality Management: We verify the company's data management practices, including governance policies, quality assessment, data lineage, security controls, and regulatory compliance.
Machine Learning and Data Science Capabilities: For companies claiming AI capabilities, we specifically evaluate machine learning platforms, data science workflows, model development and deployment processes, feature engineering practices, and model monitoring and maintenance.
Emerging Role of AI in Business Logic
In addition to these core areas, we assess how companies are preparing for the future of AI-driven business systems. A key trend involves the migration of traditional business logic from SaaS applications to AI layers, where intelligent agents act as orchestrators across multiple systems.
For instance, AI agents could replace manual workflows in CRM, HR, or ERP systems by updating multiple databases simultaneously and making data-driven decisions without human intervention. Companies that embrace this shift can unlock significant operational efficiencies and create innovative, AI-first products.
This evolution challenges the traditional value proposition of standalone SaaS tools, making it crucial for investors to evaluate a company’s readiness to adapt to this new paradigm. Our due diligence process examines whether companies have the foundational AI architecture and vision to thrive in this agent-driven era.
The Value of Stage-Appropriate Assessment
Our structured approach helps investors:
Evaluate AI capabilities within the context of company maturity.
Identify red flags regardless of company stage.
Assess technical debt and scalability challenges.
Validate the credibility of AI development roadmaps.
Understand how future trends, such as AI-driven business logic, might impact a company’s growth trajectory.
Investor Takeaways
To help investors act decisively, we recommend asking the following questions during due diligence:
How does the company’s AI approach align with its business objectives?
What is the real-world impact of their AI capabilities today?
How prepared is the company to adopt emerging trends, such as AI-driven orchestration?
Are there any technical debts or limitations that might hinder scalability?
Conclusion
In today’s AI-driven investment landscape, separating genuine technological capabilities from marketing hype is crucial for investor success. While expectations for AI capabilities should align with a company’s maturity, investors need expert guidance to navigate the complex landscape of AI claims and capabilities.
NorthBound Advisory’s comprehensive assessment methodology helps investors cut through the AI washing phenomenon by providing clear, stage-appropriate evaluation of a company’s actual AI capabilities. Our technical due diligence process demystifies AI implementations, translating complex technical realities into clear, actionable insights for investors.
Whether evaluating a seed-stage startup’s AI potential or validating a Series C company’s sophisticated data pipelines, our structured approach helps separate fact from fiction, reducing investment risk while identifying genuine technological value.
Contact NorthBound Advisory today to learn how our expertise can help you understand the implications of AI-first business logic and make informed investment decisions in the age of AI.
Checkout a 8 minute Podcast from Rick and Amanda on Northbound’s approach for cutting through the AI Hype and how it is critical to Scaling a Startup.
