The End of Software: Why Generative AI Will Replace Salesforce, SAP, ServiceNow, and More

When AI started generating code, it felt like a game-changer. Productivity soared, and developers thought they had discovered a new golden age of efficiency. According to a study by GitHub, developers using GitHub Copilot reported a 55% increase in coding speed. Data from McKinsey further showed that AI adoption in software development has improved efficiency by 20-30% across projects. But what if this is merely a short-term illusion masking a more profound shift? What if this boost is just the tip of the iceberg?

Let’s talk about the death of legacy software as we know it and why generative AI is poised to transform not only how we code but also how we interact with business systems entirely.

AI Is Transforming Developers Into AI Developers

AI is fundamentally altering the role of developers and system integrators. Rather than hand-crafting lines of code, developers are evolving into AI developers—prompt engineers and orchestrators of AI tools with strong business knowledge.

Think of it like the shift from horses to cars. As Henry Ford famously (and incorrectly) said, “If I had asked people what they wanted, they would have said faster horses.” AI is the car in this analogy. Instead of incremental productivity gains (“faster horses”), generative AI offers a revolutionary leap in how software is built and maintained.

With LLMs (Large Language Models), Retrieval-Augmented Generation (RAG), and continuous fine-tuning, we’re creating a new paradigm for software development. This approach allows each company to leverage the data they generate, importing “quality and relevant data” into their own LLMs. The result? AI systems that become smarter, more contextual, and deeply aligned with the organisation’s specific needs over time.

Why Screens and Interfaces Will Disappear

For decades, business software has followed a predictable formula: input data through forms on a screen, process that data, and then display a deterministic output. This is true whether you’re using Salesforce, ServiceNow, SAP, or any ERP system.

But generative AI is changing that. Imagine a future where employees no longer interact with clunky interfaces, dropdown menus, or static dashboards. Instead, they will converse with AI systems using natural language.

Take CRM interactions as an example: Today, after a sales call, a rep must manually input details into Salesforce. It’s a tedious, time-consuming process that reduces time spent on actual selling. With generative AI, that workflow changes entirely. A rep can simply describe the conversation to the AI in natural language or add the AI generated , and the AI updates the CRM, drafts follow-up emails, and analyses customer sentiment—all without a single click.

This level of automation will make traditional screens and interfaces obsolete, whether it’s entering sales data in Salesforce, logging a service request in ServiceNow, or configuring workflows in SAP.

LLMs + Deterministic Algorithms: A Powerful Combination

While generative AI offers unprecedented capabilities in understanding natural language and automating workflows, some processes still require strong deterministic algorithms for accuracy and precision.

Take investment banking as an example: calculating risk variables like Value at Risk (VaR) demands complex mathematical models that must be precise and consistent. Generative AI can handle summarising reports or automating data entry, but risk calculations should be handled by robust, deterministic algorithms.

Similarly, in sales and marketing, AI might handle conversational interactions and summarise meetings, while deterministic algorithms evaluate and score leads based on KPIs. This powerful combination of LLMs and deterministic logic will be essential for businesses to balance AI’s adaptability with the precision required for critical calculations.

Data Is the New Competitive Advantage

In a world driven by generative AI, data becomes the core competitive advantage. Without high-quality, structured data, even the most advanced AI models are useless. Companies that invest in curating and maintaining their data will thrive, while those that rely on traditional software interfaces without a data strategy will fall behind.

Consider organisations like Google and OpenAI. Their success isn’t solely due to better models—it’s driven by access to high-quality data. The same will be true for CRM LLMs, HR LLMs, and other generative AI solutions.

The End of Software as We Know It

We’re standing on the precipice of a seismic shift. Legacy software, as we know it today, is on the verge of extinction. The future won’t be about better interfaces or incremental improvements—it will be about eliminating interfaces altogether.

We’ll rely on AI-driven conversational systems that handle inputs, outputs, and processes through natural language. Business systems will become invisible, embedded within AI workflows that adapt to the user’s needs. We will interact with AI Agents who will run deterministic algorithms when they need to.

How to Adapt: Becoming Data-First in an AI-Driven Future

To thrive in this new AI-driven paradigm, businesses and professionals must take proactive steps to adapt:

1. Embrace a Data-First Approach: Invest in data curation, cleaning, and maintenance. High-quality, structured data will be the foundation for any successful AI implementation.

2. Upskill in AI and Data Analysis: Teams should learn how to work with AI tools, interpret AI-generated insights, and leverage data analysis for strategic decision-making.

3. Become AI Trainers: Professionals will need to learn how to “teach” AI systems by fine-tuning models, creating effective prompts, and continuously improving the AI’s performance based on feedback.

4. Focus on AI-Orchestrated Workflows: Look for opportunities to automate repetitive tasks using generative AI and deterministic algorithms, freeing up employees for more strategic, creative work.

5. Champion Continuous Learning: AI systems should be continuously fine-tuned to adapt to new data, industry changes, and evolving business needs. Encourage a culture of continuous improvement and learning.

Are you ready for it? Is this the future, or just my wild prediction?

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