How Leaders Can Achieve Long-Term Enterprise AI Success

As AI’s capabilities continue to accelerate worldwide, enterprise leaders are left trying to optimise, keep up with and regulate it.

However, despite a positive initial rush of AI tools used for business, the consequences of rapid implementation are now coming back to haunt leaders trying to keep ahead.

Now, leaders are faced with the seemingly impossible task of making AI work for them, their employees and customers in a way that is safe, ethical and sustainable.

This is where Global AI Value advisors like Edosa Odaro come in – and AI Magazine asked him some questions to help guide leaders to long-term AI success.

How should leaders reshape how people think about AI’s value?

First, leaders need to stop asking “What can AI do?” and start demanding “What should AI do?”

The industry has become obsessed with technical achievement while we all ultimately pay the price for our collective failure to think about purpose.

If there is one thing I have learned after working with over 35 leading organisations globally, it’s that the most dangerous AI isn’t the one that fails, it is the one that succeeds brilliantly at optimising the wrong things. 

We measure model accuracy while human trust erodes. We celebrate computational breakthroughs while communities are increasingly fractured.

True AI leadership means recognising that value isn’t singular; it is multidimensional. 

Customer value and societal value aren’t competing priorities; they are interconnected imperatives. 

The most transformative AI implementations create value for customers while strengthening the communities those customers live in. 

Economic returns matter, but so does employee wellbeing, community resilience and societal trust.

The leaders creating lasting transformation understand that AI’s greatest power isn’t computational. Instead, it is the collective human wisdom that guides it.

They are building systems that amplify human agency rather than replacing human judgment in decisions that literally shape lives.

What are leaders’ biggest challenges in preparing their organisations for AI transformation?

The biggest challenge isn’t technical - it is translation. 

Most organisations struggle with what I call the “value fog”: brilliant AI models that can’t connect to business impact. 

Leaders need to become fluent in three languages simultaneously: technical capability, business strategy and human experience.

From my work with top Fortune and FTSE companies, the organisations that succeed are those that build cross-functional integration from day one. 

They don’t wait for AI projects to be “ready” before involving business stakeholders. Instead, they collaborate from the beginning.

Another critical challenge is managing the workforce transition. 

People resist what threatens them and support what benefits them. Leaders who address job displacement fears head-on, with concrete plans for skills evolution rather than job elimination, experience far lower change friction and see significantly higher adoption rates.

How can leaders challenge the current AI status quo?

Leaders can start by rejecting the industry’s obsession with what’s technically possible and by demanding what's actually necessary. 

Too many boardrooms are mesmerised by impressive demos while ignoring whether these systems serve human needs.

The status quo assumes that AI development should outpace human adaptability – that’s not inevitable – it is a choice. 

Bold leaders are demanding that technology serve society’s timeline, not the other way around.

Leaders can challenge the assumption that AI success requires replacing human judgment with machine precision. 

Some of the most transformative implementations I’ve seen augment human decision-making rather than attempt to automate it away.

Most importantly, leaders can stop accepting that ethics and profits are competing interests. 

The organisations building sustainable competitive advantage are those proving that responsible AI isn’t just morally right but actually strategically essential. 

They understand that trust, once lost, becomes the most expensive business problem to solve.

What kind of AI future should leaders be building?

Leaders should build an AI future where technology serves humanity’s deepest values, not just immediate business metrics. 

This means designing systems that enhance human connection rather than replacing it.

I’ve seen healthcare organisations use Gen AI to help doctors explain complex diagnoses in ways patients truly understand. 

That’s the future we should pursue, AI that makes us more human, not less.

The future belongs to leaders who understand that AI’s greatest power isn’t in its algorithms, but in the collective human wisdom that guides them. 

Whether you’re optimising supply chains or serving citizens, the same principle applies: AI amplifies not just our capabilities, but our choices about what matters.

How do leaders ensure AI serves humanity, not just business?

AI serving humanity starts with expanding the definition of value beyond financial metrics. 

In my work, I’ve seen organisations transform when they measure employee satisfaction, community impact and ethical outcomes alongside revenue and efficiency.

The practical approach is building societal value into your AI frameworks from the beginning. 

Don’t make ethics an afterthought – make it a design principle. This means asking hard questions: Does this AI system increase fairness or perpetuate bias? Does it strengthen communities or extract value from them?

Leaders need the courage to make trade-offs that prioritise long-term human flourishing over short-term optimisation.

The organisations that thrive long-term are those that recognise AI’s success should be measured by the positive impact on all stakeholders, not just shareholders.

What misconceptions do tech companies still hold about AI’s purpose and potential?

The AI industry suffers from a fundamental delusion: believing that technical sophistication automatically translates to societal value. 

I’ve unfortunately witnessed organisations spend millions building elegant solutions to problems that don’t exist while ignoring challenges that affect millions of lives.

There’s also this dangerous assumption that human behaviour will adapt to AI, rather than AI adapting to human behaviour. 

Companies design systems based on how they think people should make decisions, not how people actually make decisions. 

Then they’re surprised when the true adoption of even the most sophisticated of systems fails.

Perhaps most concerning is the industry’s treatment of AI deployment as a technical challenge rather than a social responsibility. 

Every algorithm becomes part of how society functions, yet most companies design them as if they exist in isolation from human consequences.

The companies that will define the next decade understand that AI’s value isn’t measured by what it can do, but by what it helps humans become. 

Until the industry grasps this distinction, we will continue building impressive systems that miss the point entirely.

How can leaders build ethical frameworks that evolve as fast as technology does?

I recommend a stop to building ethical frameworks as if they’re compliance checklists. 

The organisations succeeding at this understand that ethics isn’t a constraint on AI. The answer lies in embedding behavioural understanding into every design decision. 

Instead of asking “Is this technically possible?” start with “How will people actually respond to this?” Most AI failures aren’t technical – they are actually behavioural. We build systems based on idealised user behaviour rather than real human psychology.

We must create governance that learns and adapts, not just monitors and restricts. 

Build cross-functional teams that include people who understand human decision-making, community impact and long-term consequences – and not just technologists debating in isolation.

The companies leading this space have stopped treating ethics as something you add to AI after it’s built. They’ve made it fundamental to how they think about AI from the beginning.

They understand that trust, once broken, becomes their most expensive business problem to solve.

You’ve said the biggest risk isn’t flawed code, but flawed imagination. What does bold imagination look like in AI leadership?

The biggest risk is our collective failure to imagine AI’s potential beyond efficiency and optimisation. 

We are designing the future of human decision-making, yet most conversations remain trapped in discussions about computational power and model accuracy.

Bold imagination means envisioning AI that creates entirely new possibilities for human flourishing, not just faster versions of existing processes. 

I’ve worked with leaders who stopped asking “How can AI make this cheaper?” and started asking “How can AI make this more meaningful?”

It's having the courage to say no to AI applications that serve immediate profits over long-term human welfare. 

Some of the most transformative work I’ve seen comes from leaders who understand that AI’s greatest power isn't in replacing human judgment, but in enhancing human wisdom.

Real imagination means designing systems where artificial intelligence amplifies human agency rather than diminishing it. 

It’s building AI that helps people make better decisions, not removing their ability to make decisions altogether.

If you could rewrite the global AI agenda in one sentence, what would it be?

‘Build AI systems that amplify human wisdom, protect human dignity and create shared prosperity – because the future belongs to those who understand that AI’s greatest power lies not in what it can do, but in what problems we choose to solve with it.’