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Successful AI depends on quality data, realistic goals and strong culture, with experts showing how ethics and impact drive real-world adoption
It’s nothing new that AI is reshaping industries and promising transformative efficiencies and new capabilities.
Yet, as AI systems are embedded deeper into business processes, it brings up a critical conversation around ethical AI: how do companies innovate boldly while maintaining integrity, avoiding bias and ensuring positive impact?
This is not just a theoretical concern but a practical challenge facing organisations deploying AI in real-world settings.
“About 95% of all AI projects are not successful or don’t go past proof of concept.”
“The key way we’ve got across this is by ensuring that there is understanding of where it's going to be impactful.”
“Understanding the business problem that you're trying to solve – it needs to be clearly defined and it needs to be realistic.”
Contrary to the hype of AI as a silver bullet, success lies in focused problem-solving. Ausenco integrates AI into day-to-day workflows through tools like Microsoft Teams and agentic AI assistants to boost employee productivity.
Anuj cautions against “jumping into AI for the sake of AI”. Effectiveness depends on understanding which business problems AI can solve and setting measurable targets.
The importance of quality data
“Anybody and everybody will boast they've got AI. That's really not impressive.”
“What's impressive to boast is that you've got quality data – quality data that is unique to your business and you're leveraging it.”
Ausenco has focused on improving data quality through rigorous data cleansing and integration to avoid the notorious “garbage in, garbage out” problem.
Data governance and quality control are crucial to avoid biased or flawed AI outcomes. Without strong data integrity, algorithms risk perpetuating historical inequities or skewed decisions.
Weighing up measuring impact and ethical risks
To ensure AI remains effective and ethical, Anuj’s team employs incremental implementation with rigorous metrics. He compares adoption to “eating an elephant – the best way is in small bites”.
They set “golden gates” or milestones to measure impact continuously, allowing course correction or halting initiatives that do not create value.
Bias detection is addressed through diverse testing teams that surface algorithmic blind spots. Organisational diversity is crucial to challenge assumptions embedded in AI models.
But technology is only half the story. Using AI as a human amplifier introduces a separate set of cultural and ethical considerations.
“Designing work so that technology amplifies and not replaces human potential.”
Rebecca highlights inclusivity and skills recognition as essential in creating equitable opportunities through AI.
“Using AI to map capabilities and redeploy talent internally was the most efficient way for us to do that. It isn’t just about efficiency, but about saving lives.”
“AI adoption isn’t anchored in just the technology, but in solving real human problems.”
AI enabled BMS to move employees into roles they were not traditionally considered for by identifying transferable skills, unlocking career mobility and growth.
Ethical AI and the need for cultural transformation
Ethical AI is inseparable from cultural transformation. It requires redesigning work around people, not merely attaching AI to existing processes.
This includes shifting from job-based roles to skills-based ecosystems, demanding openness, transparency and continuous learning.
“We’re moving from this job-based mindset to a skills-based ecosystem. Reverse mentoring and multigenerational collaboration are key.”
“The goal is to have humans amplify what AI puts out. It’s really about people.”
Leaders must balance digital acumen with emotional intelligence. At BMS, leadership is trained to interpret AI data while keeping human judgment central.
AI’s role in amplifying humans
“I don't think we can deny the power that it's going to have across every facet of business.”
“It’s going to continue to evolve and provide a lot of power for organisations.”
Anuj believes AI will remain an evolving force, with agentic AI and large language models driving transformation, but without a final endpoint.
“It’s evolution. We can’t deny the power that it’s going to have.”