The AI transformation is having a profound impact on organisations, industries and the business landscape. Leaders are facing new challenges, from shifting skillsets and decision-making at pace to identifying the right technological investments and use cases. But AI is no longer just a competitive tool; it’s a mindset. To thrive in the era of intelligent systems, businesses must move well beyond pilots and proofs of concept, embedding AI into the very fabric of how they operate and think.
In June we hosted a forward-looking exclusive event, ‘AI by Night’, with a select group of customers and partners to explore the impacts of AI on the landscape. Following a keynote from our CEO John Cotterell, we held a dynamic panel welcoming senior leaders and AI experts to explore crucial questions around how to become AI-native, navigating the pace of change and how leadership is evolving in the AI era.
Our experts spent the evening unpacking the realities of decision-making and adaptability in a fast-moving AI landscape, led by our Global VP of Operations, Maria Gomez, in conversation with:
- Jon Woodward – Microsoft
- Matt Weaver – OpenAI
- Nir Evron – ServiceNow
- Paul Kelly – AlixPartners
- Richard Pugh – Endava
Keeping in step with the pace of change
“We’re in a similar phase as the dot.com boom today. The tech works, but integrating it into enterprise environments is harder than expected.” John Cotterell, CEO at Endava.
Today’s technology landscape is evolving faster than organisations can absorb. This was the general consensus among our panellists. From hallucinations and security to cost, regulatory uncertainty and data access, concerns and uncertainty surrounding AI’s longevity and competence have been prolific, but as we shift from hype to tangible enterprise-grade AI, it’s become clear that the value of AI is unquestionable. Its foundations are strengthening, processing costs are falling, regulation is catching up and AI tools continually improve at pace.
Making informed decisions amid exponential improvement, experimental pressure and regulatory flux remains daunting. Don’t wait for perfect clarity. Start small, learn fast and build a foundation for bolder initiatives.
Adapting at pace: what sets leading organisations apart
But even with starting small, AI adoption should not be lead blindly. Leadership should be strategic, with effective goals and frameworks put in place to ensure the right use case is selected, ideally decided based on data with KPIs to ensure AI implementation creates a return on investment (ROI), not a false start. With that said, leadership requires flexibility and a culture of experimentation.
What differentiates leaders from laggards? There are three critical success factors:
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1. Top-down vision and bottom-up enablement
The best use cases often come from experimentation with the technology, rather than through planned initiatives.
“Often there’s a lot of excitement in the boardroom… but what we’ve seen consistently is when you put these AI tools in the hands of employees... the killer use cases don’t often come from the top down — they bubble up.” — Matt Weaver, OpenAI
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2. Strategic clarity and bold goals
Bold innovation and incremental wins go hand in hand, and both require the ability to work towards a goal strategically and finding that North Star.
“You've got to go after the low-hanging fruit… but you've also got to look for the big bold ideas — because someone’s going to come into your industry and disrupt it if it’s not you.” — Paul Kelly, AlixPartners
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3. Agility over perfection
Organisations succeeding today don’t have it all figured out. But they’ve created the conditions to move and learn as they go. This means more than just providing teams with tools, but encouraging teams to experiment with new ways to achieve goals and perform tasks using AI. This cultivates a constantly evolving learning environment for testing new ideas with the agility to pivot, speeding up the journey to becoming AI-native.
“Informed action beats passive observation. You can’t sit this one out.” — Richard Pugh, Endava
This approach will also help people and teams naturally understand AI’s evolving capabilities, especially as agentic AI unlocks new possibilities beyond prompt-based interaction.
Agentic AI: from co-pilot to autonomous decision-making
“AI is no longer just answering questions. It's performing tasks, collaborating with other agents, and triggering business action.” — Richard Pugh, Endava
Agentic AI has the ability to function like an assisting team, managing multiple tasks at a time with relevance. They can act autonomously, make decisions, and carry out multi-step tasks with minimal human intervention. These AI agents are designed to operate within defined parameters and goals, often coordinating with other agents to achieve outcomes. Examples of use cases can include:
- AI agents that monitor systems and automatically suggest or take corrective action (e.g. in IT operations).
- Supply chain impact analysis where agents research external events, assess internal exposure and recommend mitigation steps.
- Legal or compliance reviews, where agents analyse documents and highlight issues based on predefined policies.
- Enterprise applications like Compass used by Endava to analyse legacy codebases, plan technical changes and even generate production-ready updates through autonomous collaboration between agents.
This shift creates new governance requirements, but also unprecedented scale and speed. For developers, this means the “what to build” decision remains human, while the execution becomes increasingly automated.
The human factor: leadership in an AI world
The human aspect of AI transformation, however, does not stop at decision-making. It’s a much greater challenge; one that is or will have psychological impact on people, their identities and their abilities to fulfil or evolve in their roles as they are. Becoming AI-native therefore isn’t just a tech strategy, it becomes a leadership challenge. To navigate this transition authentically, keeping people at the centre of the story, organisations should:
- Build AI literacy at every level.
- Encourage curiosity, risk-taking and rapid iteration.
- Frame AI as augmentation, not threat.
“We can’t just give people a tool and call them AI enabled. We have to give them permission to use it.” — Matt Weaver, OpenAI
What sets successful AI organisations apart
The need for effective leadership will become crucial as organisations and leaders are faced with a complex and increasingly uncertain future. The specific traits that distinguish AI leaders from laggards can be summarised as:
- Empowerment: broad access to tools like GPT builds literacy and sparks grassroots innovation.
- Leadership: a tech-savvy, committed C-suite is essential to drive change.
- Mindset: agile, growth-oriented cultures experiment boldly and adapt swiftly.
- Clarity: the most successful companies begin with clear purpose – whether that’s market leadership, cost reduction or product reinvention.
Reimagine, don’t just automate
The resounding thought was that building resilience to change will be essential. AI’s true potential lies not in making today’s tasks faster but in reimagining what’s possible and adapting to change with curiosity. Disruption is not going away, and leaders that differentiate themselves will be those that navigate this uncertainty successfully.
Watch the highlights in this video