PwC Spells Out How AI is Transforming Software

AI has transformed the software industry like never before, according to a recent study by global accounting and consulting firm PwC. From how software is built to how it is priced and sold, every stage of the business is being reshaped. Moreover, as it goes on performing jobs traditionally performed by humans, software vendors are set to control the entire $50 trillion labor market.

Here are seven key takeaways from the report:

1. Software is now targeting the $50 trillion global “labour” market

Until now, software companies largely depended on corporate IT budgets. Enterprise software — tools that manage records and automate workflows — is already a massive $1.3 trillion market. But AI is changing the game.

New AI-powered “digital workers” can handle tasks once done by humans — in law firms, accounting offices, customer support centres, sales teams and research desks. Instead of simply helping employees work faster, software is beginning to replace certain services altogether.

This shift turns labour costs into recurring software revenue. Tasks once performed by people may now be handled by software.

As a result, the potential market for software could double or even triple, as it chases a slice of the $50 trillion global labour market.

2. Software is automating jobs traditionally performed by people

For years, business software acted like a digital filing cabinet. Systems such as ERP, CRM and HR platforms stored data and managed workflows. Their value lay in capturing years of business rules and industry know-how — especially in sectors like healthcare and insurance, where accuracy and history matter.

Now AI is turning those record-keeping tools into active workers.

Instead of simply storing information, AI-powered systems can analyse data, make decisions and carry out tasks on their own. An ERP platform can forecast cash flow. A CRM can identify high-value sales leads. A service system can automatically route — and sometimes resolve — customer complaints. Work that once required human attention is increasingly handled by software.

AI also smooths the shift to modern systems by cleaning and mapping old data, making upgrades faster and less disruptive.

For software companies, this marks a profound change. They are no longer just selling licences to manage information. They are selling automation and outcomes.

3. You no longer need large engineering teams to build software

In the past, every new feature required more engineers. Code was written line by line. Bugs were fixed manually. Old systems demanded constant maintenance. Product growth meant headcount growth.

Now AI coding assistants can generate routine code, detect errors, modernise legacy systems and even translate old programming languages into newer ones. Engineers still oversee the work, but they are no longer doing everything themselves.

The result: smaller teams can deliver more.

Companies no longer need to expand engineering teams every time complexity rises. The key question has shifted from “Do we have enough engineers?” to “What should we build next?” AI speeds up development and testing, while product teams focus more on solving customer problems.

Team structures are also changing. Instead of large hierarchies, firms are building compact, cross-functional teams that blend domain experts with AI-savvy developers. Engineers are becoming architects of intelligent systems rather than writing every line of code.

4. The full form of SaaS is now Software-as-a-Solution

In the AI era, Software-as-a-Service is evolving into something bigger: Software-as-a-Solution.

Companies are no longer just selling tools. They are selling outcomes. AI allows software to complete entire tasks from start to finish. An accounting platform can function as an automated bookkeeping service. Payroll software can manage payroll end-to-end. A CRM can generate leads, not just track them. Helpdesk systems can resolve routine complaints automatically and escalate only complex cases.

To stay competitive, vendors must combine software, data and AI into integrated packages that deliver clear business results. That opens access to larger budgets beyond IT, including operations and outsourcing.

This shift also changes the economics. Traditional SaaS relied on predictable, per-seat subscriptions. AI introduces new computing costs and variable usage patterns. Customers are cautious about paying fixed fees for unproven AI capabilities.

As a result, pricing is evolving. Some vendors charge for AI add-ons through tiered or usage-based plans. Others go further, charging for measurable results — per meeting booked or per ticket resolved.

SaaS is no longer just about access. It is about solutions.

5. Vendors are no longer selling software the old way

AI software is not sold as a massive, company-wide installation. It enters through a side door.

Instead of replacing entire systems at once, vendors begin with one small but high-impact task. An AI voice agent may handle after-hours customer calls and feed them into a CRM. A legal tool may automate contract review. These targeted uses deliver quick, visible results — often funded by individual departments rather than central IT.

Once adopted, the AI system learns and improves. As trust grows, its role expands. A ticket-sorting tool can evolve into full case management. An invoice assistant can grow into a fully automated accounts-payable system.

What starts as a small feature can become a core platform.

6. The way software is sold has also changed

In the early days of cloud computing, vendors had to persuade companies to move online. Today, businesses are actively seeking AI solutions. This demand shortens sales cycles and increases inbound interest.

However, real transformation still requires process redesign, data cleanup and operational change.

Two sales paths are emerging.

The first is product-led growth. AI tools that enhance everyday tasks — such as search or summarization — spread quickly within organizations. Adoption starts at the user level and moves upward.

The second is sales-led transformation. AI that reshapes core operations — such as payroll or claims processing — requires pilots, structured rollouts and close collaboration between vendors and clients.

Together, these approaches create a hybrid model: rapid user-level adoption combined with long-term enterprise transformation.

7. M&A activity may slow down

The AI boom could cool the tech sector’s long reliance on mergers and acquisitions.

For years, software companies grew by acquiring smaller rivals. But AI transformation cannot simply be bought and bolted on. It must be deeply integrated into products and workflows.

AI startups are expensive, and their top engineers often leave after acquisitions, reducing the long-term value of deals.

Meanwhile, AI is making in-house innovation cheaper and faster. Coding and testing now require fewer resources. That makes internal R&D more attractive than paying high premiums for acquisitions.

M&A will continue, but it is likely to become more selective. Innovation — not acquisition — may become the primary engine of growth.