- 7th December, 2025
- By Rob Lawson
AI Tools and the Importance of Data Quality
The software industry is standing at a pivotal crossroads. On one side lies the immense promise of AI, a transformative force capable of reshaping operations, accelerating efficiency, and unlocking new levels of creativity. On the other side, however, is a much more sobering reality: many software-as-a-service (SaaS) companies are reporting declining profitability even after investing heavily in AI.
McKinsey’s latest analysis sheds light on this growing dilemma. Despite AI’s potential to redefine how businesses operate, a significant number of AI initiatives still fail to deliver the financial returns companies anticipate. Yes, standout players like GitHub and Salesforce have demonstrated meaningful gains, but they remain the exception rather than the rule.
What this contrast makes clear is that AI’s widespread success may depend on more than innovation alone; it may require a shift in traditional SaaS business models. Without evolving the way value is created, packaged, and delivered, AI’s transformative power could remain underutilised.
The Current AI Challenge
Cost Implications:
The idea of AI continues to inspire visions of efficiency, automation, and breakthrough innovation. Yet, in reality, many organisations are running into significant roadblocks. High costs, resistance to change, and a lack of immediate, measurable outcomes are slowing down AI adoption and diminishing enthusiasm.
Lack of Profitable Projects:
Despite the hype surrounding AI, many projects fail to deliver the level of profitability companies expect. A major contributor to this gap is ineffective change management. Without proper training, cultural readiness, or alignment with business objectives, AI systems become underutilised, misunderstood, or poorly integrated. The result? AI initiatives that consume resources but generate little value.
Role of Change Management
AI might be a powerful catalyst for transformation, but it represents just one part of a much larger equation. For organisations to truly unlock the benefits of AI, they must cultivate a culture that welcomes change and prepare their workforce to adopt new technologies with confidence.
Training and Adoption:
Even the most advanced AI tools fall short without proper user adoption. Employees need structured training, guidance, and continuous support to use AI effectively in their daily workflows. According to McKinsey, organisations should invest three times more in change management than in the AI tool itself, highlighting just how essential training and adoption are to achieving meaningful results.
Change Management as a Necessity:
Resistance to change remains one of the biggest barriers to AI success. Without clear strategies to educate, motivate, and incentivise teams, AI initiatives can struggle to take off. Building awareness, communicating value, and providing hands-on experience are crucial steps in helping teams embrace new technologies and leverage AI tools to their full potential.
Lessons from the Dot-Com Era
The hesitation we see today around adopting new AI-driven business models is not new. History has shown similar patterns before, most notably during the dot-com boom. Many industries were slow to adapt to the rapid shift toward digital solutions, and the consequences were significant.
In Australia, traditional business models, especially newspapers, held tightly to their long-standing revenue systems. They underestimated the impact that digital transformation would have and hesitated to evolve. As a result, emerging digital-first competitors quickly gained ground and reshaped entire markets.
Newspapers, once the primary source for job listings and classifieds, resisted the transition to digital platforms. This reluctance opened the door for disruptive players like Seek, which embraced an online-only model and rapidly outperformed traditional providers by offering greater convenience, reach, and relevance.
The same pattern played out in the property and automotive sectors. Platforms such as realestate.com.au and Carsales.com.au surged ahead because they provided tailored, digitally focused services that aligned far better with evolving customer expectations. Their willingness to innovate enabled them to surpass their newspaper-based competitors, who struggled to keep pace with the digital shift.
The Path Forward with AI
For SaaS companies, the next big challenge is not simply adopting AI; it’s reimagining their business models to align with an AI-driven future. To fully capture the value AI promises, organisations must be willing to rethink how they deliver, price, and scale their solutions. Several promising approaches are beginning to emerge:
Value-Based Payments: Instead of traditional per-seat or per-license pricing, AI-first companies could move toward compensation models based on measurable productivity gains. In this model, revenue is tied directly to the value delivered, such as time saved, improved accuracy, or enhanced output, creating a stronger alignment between customer success and business growth.
Innovative Deployment Models: Another possibility is offering free or low-cost deployment and earning revenue only after proven efficiency improvements. This performance-driven model lowers the barrier to adoption while incentivising companies to build tools that genuinely deliver impact. Though still a hypothesis, it has the potential to rewrite how SaaS companies compete and differentiate themselves.
Ultimately, the goal is to cultivate a workplace culture that embraces experimentation. Companies must encourage continuous innovation, giving employees the freedom to create, test, and share new AI-powered ideas and tools. This mindset not only sparks creativity but also accelerates the discovery of breakthrough solutions that can push the entire organisation forward.
Encouraging Continuous Experimentation
In a rapidly evolving technological landscape, continuous experimentation isn’t just beneficial; it's essential. To stay competitive and fully harness the power of AI, organisations must actively promote a culture of curiosity, learning, and exploration.
One key approach is participating in industry conferences and staying informed about advancements in critical areas such as marketing, operations, and logistics. These environments expose teams to fresh ideas, emerging tools, and real-world success stories that can inspire innovative thinking within the organisation.
Equally important is maintaining high-quality, reliable data. AI systems are only as effective as the data they’re trained on. Ensuring data accuracy, consistency, and integrity enables companies to leverage the latest AI capabilities with confidence and achieve more meaningful outcomes.
Future-Proofing Through Data
Quality data collection is no longer just a best practice; it is the foundation for long-term competitiveness in an AI-driven world. As AI tools grow more sophisticated, the organisations that have invested in clean, reliable, and well-organised data will be the ones able to fully harness their capabilities.
Strong data systems enable companies to train models more accurately, personalise customer experiences, and generate actionable insights with far greater precision. Businesses that prioritise data integrity today are essentially building the infrastructure required to seamlessly adopt future AI technologies. They can pivot faster, innovate more confidently, and respond to market changes with agility.
In essence, robust data practices are the key to future-proofing. Companies that maintain disciplined data collection and management will not only keep pace with AI advancements but will also continually stay ahead of those that neglect this critical foundation.
Key Takeaway
In an industry being reshaped by rapid AI evolution, the software world is reaching a defining moment. The companies that plan proactively, adapt early, and embrace smarter business models will be the ones that rise above market noise and unlock AI’s true potential. AI alone isn’t the solution; success comes from pairing innovation with strategic change management, strong data foundations, and business models built for the future.
Instead of struggling with low ROI or fragmented adoption, organisations that approach AI with intention can operate with clarity, confidence, and long-term vision. With the right structures in place, businesses can improve profitability, accelerate efficiency, and create meaningful value for their customers, turning AI from a buzzword into a genuine competitive advantage.
At Digital Assassin, we help businesses shift from reactive adoption to strategic transformation. By combining deep industry insight, modern AI-driven frameworks, and execution that prioritises performance, we ensure companies don’t just adopt AI-they leverage it to evolve, scale, and lead.
If your organisation is ready to modernise its model, strengthen its technical capability, and build an AI strategy that works not just today but for the future, connect with an assassin who can help you rethink, redesign, and revolutionise the way you operate. Together, we’ll position your business to innovate faster, adapt smarter, and succeed bigger in the AI-first era.
Frequently Asked Questions
1. Why are SaaS companies struggling to profit from AI adoption?
SaaS companies struggle with AI profitability due to high implementation costs, limited short-term returns, and weak adoption strategies. Without strong change management, clear use cases, and aligned business models, AI tools often fail to deliver measurable value, preventing companies from achieving meaningful financial gains.
2. What is the biggest challenge for organisations implementing AI?
The biggest challenge is organisational readiness. Companies often face resistance to change, poor-quality data, and insufficient training. These issues limit adoption and prevent teams from using AI effectively, leading to reduced impact and slower returns on investment.
3. Why is change management important in AI adoption?
Change management ensures employees understand, accept, and effectively use AI tools. Without proper training and communication, teams may resist new technologies, reducing adoption and limiting the tool’s success. Strong change management drives smoother transitions and maximises AI’s real world value.
4. How much should companies invest in AI change management?
Companies should invest significantly in change management—ideally up to three times the cost of the AI tool. This ensures proper training, cultural alignment, and strong user adoption, helping organisations unlock meaningful, long-term value from their AI investment.
5. How can SaaS companies redesign business models for AI success?
SaaS companies can shift from traditional per-seat pricing to value-based models tied to productivity or efficiency gains. This approach aligns cost with real impact, encourages adoption, and ensures customers only pay when the AI tool delivers measurable improvements.
6. What are some innovative AI-driven pricing models?
Innovative pricing models include value-based payments, performance-linked pricing, and free or low-cost deployment with revenue earned through proven efficiency improvements. These models reduce upfront barriers and motivate SaaS companies to deliver meaningful, measurable AI-driven results.
7. What lessons can tech companies learn from the dot-com era?
The dot-com era showed that companies slow to adopt digital models fall behind. Platforms like Seek and realestate.com.au succeeded by embracing online-first strategies, while traditional businesses stagnated. The lesson: adapt early, innovate continuously, and align offerings with evolving customer needs.
8. Why is data quality essential for AI success?
AI systems rely on clean, accurate, and well-organised data. High-quality data ensures better predictions, insights, and performance. Companies with strong data foundations can integrate advanced AI tools more effectively, adapt faster, and stay competitive as AI technologies evolve.