What If [insert] Was a Software?

Uber. Airbnb. Wolt. Revolut. The usual suspects.

The services on demand that have become pretty much verbs in themselves just as Google and Googling. And it would be pretty naive to think that the trajectory of development would stop with them.

I find myself coming back to this topic frequently for two reasons: 1) the amount of opportunities there are for improvement and creating new businesses is just pretty exciting, 2) the more we ”platformalize” the services and organizations, the more this is going to impact how our daily lives and, more importantly, our jobs are going to look like in the not-so-distant future.

A recent Tweet by ex-McKinsey consultant turned VC, Aravind Srinivas, highlighted a thought-provoking idea: the transformation of high-value knowledge work into software. His list – McKinsey Consultant as a Software, VC as a Software, FP&A Analyst as a Software, and Legal/Compliance as a Software – touches on something both inevitable and incredibly disruptive.

But this shift goes beyond just making consulting digital. It’s part of a broader trend where specialized expertise is being systematically codified, automated, and made accessible in ways that were previously unimaginable.

The implications?

A redefinition of how we work, what skills remain valuable, and which industries are most ripe for disruption.

“Expertise as a Software” – an Inevitable Evolution?

For decades, industries like management consulting, venture capital, financial analysis, and compliance have relied on a combination of proprietary knowledge, human judgment, and network effects. But today, three major forces are accelerating the shift towards software-first solutions:

• The Standardization of Expertise – Many industries thrive on structured methodologies, frameworks, and playbooks that are increasingly easy to distill into AI models. What was once the secret sauce of elite firms is now openly discussed in books, online courses, and shared knowledge bases.
• Advancements in AI and Data Processing – The ability to process vast amounts of data, recognize patterns, and generate insights at scale has reached a level where AI can replicate much of what human analysts do – only faster and with fewer biases.
• The Cost Efficiency of Automation – Businesses are realizing that AI-driven tools can significantly reduce costs while maintaining, and sometimes even improving, decision-making quality. This makes the shift from human-led services to AI-powered platforms not just a possibility but an economic necessity.

Beyond Consulting: The Larger Impact on Knowledge Work

Venture Capitalist as a Software

VC firms already leverage data for deal sourcing, but AI can push this further by:

• Identifying high-potential startups through traction and market analysis.
• Evaluating founder credibility based on digital footprints and historical success patterns.
• Automating aspects of due diligence through document and financial analysis.

FP&A Analyst as a Software

Financial Planning & Analysis involves forecasting, budgeting, and identifying financial risks. AI can now:

• Automate financial modeling with real-time data inputs.
• Provide predictive insights with greater accuracy.
• Flag anomalies before they become costly problems.

Legal & Compliance as a Software

Regulatory compliance and contract analysis are becoming prime targets for automation. AI can:

• Instantly scan contracts for risks and discrepancies.
• Automate ongoing compliance monitoring and regulatory updates.
• Reduce human errors in high-stakes legal documentation.

The Business Model Behind “Expertise as a Software”

The shift towards automation in knowledge industries isn’t just about replacing human tasks – it’s about creating scalable, high-margin businesses that operate on entirely new models. And it’s the following what makes these ventures financially compelling:

1. Subscription-Based SaaS – Most expertise-driven software businesses will follow the SaaS (Software-as-a-Service) model, offering tiered pricing based on usage and features. This ensures predictable, recurring revenue streams.
2. AI-Augmented Marketplaces – Some platforms will monetize by acting as intermediaries, where AI-powered matching and insights enhance traditional service marketplaces (e.g., legal tech platforms pairing clients with lawyers, but with AI streamlining the process).
3. Outcome-Based Pricing – Certain expertise automation platforms may adopt a performance-based pricing model, charging fees based on the tangible business value delivered (e.g., cost savings, revenue growth, risk mitigation).
4. APIs and Enterprise Integrations – Many of these platforms will monetize by offering APIs and integrations into enterprise ecosystems, allowing businesses to embed AI-driven expertise directly into their workflows.

But..

Of course, while the shift toward “Expertise as a Software” brings significant potential, it doesn’t come without challenges.

The speed of change is already leading to job displacement in some areas, forcing a rethinking of workforce skills. Routine tasks are being automated, but new roles are emerging that require expertise in managing AI systems. This shift is prompting companies and education systems to prioritize reskilling and rethink how we prepare the workforce for the future.

At the same time, automating high-stakes tasks like legal or financial work is encountering resistance. Regulators, especially in the EU, are stepping in to ensure safeguards, with heavy regulation already taking shape. Concerns about the risks of AI decisions -such as biases in models and the potential for unfair outcomes – are at the forefront of these discussions. This regulatory push reflects the need to strike a balance between innovation and responsibility in the AI space.

But in any case, SaaS is increasingly becoming the future, and it’s something for both organizations and individuals to consider and prepare for – today.

Conclusion

The trend of turning expertise into software is accelerating. The real challenge now is execution – who can effectively integrate AI into these domains and create the next wave of industry-defining platforms?

For those who do, the upside is massive. These businesses scale in ways that human-led firms never could, offering near-zero marginal costs and global accessibility. As Aravind Srinivas pointed out, investors are already watching. If you’re working on this, the market isn’t just ready – it’s demanding it.