Solving the productivity puzzle: four ways to become AI-first

Winning with AI isn’t about the tech alone. It requires a rethink of how your organisation operates. Leaders need to refocus on a top-to-bottom workflow review -- redesigning decision-making, and value creation around AI. In competitive markets, data and AI maturity now define who moves first and who falls behind.

 

In Box’s The State of AI in the Enterprise report, results show that companies on the leading edge of AI adoption saw a 37% lift in productivity on average – a competitive advantage against their peers. The report also found that 60% of companies expect to achieve AI transformation within two years. These are substantial markers and we’re now seeing the pace of change, and the benefits that come with it. With this in mind, here are four practical ways organisations can build a true AI-first advantage.

 

 

1. Build on secure, scalable AI foundations

AI is a capability expander. It can do more than speed up old tasks, opening doors to things you couldn’t do before. Imagine writing software with a single prompt or saving hours from new hire onboarding processes. But in order to solve problems creatively and make smart decisions proactively, scaling that benefit, you need to get to a point where you have successfully bridged the human-machine divide, and have leaders advocating for scalable AI infrastructure.

 

AI penetration is climbing, as highlighted by McKinsey, with eight in ten (78%) saying their organisations use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier. We’re in the stage of AI immersion, which is where leaders start to define the perimeters of AI deployment. What specific tools would benefit teams – is it about AI to help with data review, content creation, or automating workflows through agents? Fine-tune what the challenges are, and map tools accordingly, providing teams secure, interoperable AI tools supporting multiple models and platforms, giving them space to test these out in their day-to-day roles, ensuring integration across the tech stack. Interoperability is critical for adapting to rapid AI innovation and maintaining future flexibility.

 

 

2. Focus on ethics and governance frameworks early on

If you’ve got leaders and tools in play, you need to ringfence that with policy, and ensure that you are operating in legal guidelines. Look at what regional regulation covers, and how this affects your organisation. Do you work with companies in the EU? The EU AI Act requires organisations to ensure basic AI literacy and put controls in place for high-risk AI tools. But this is just the beginning. AI regulation is still evolving – not only in Europe, but globally – and success depends on working with partners that are prepared to adapt as regulations change. This means having policies around training and transparency, and for high-risk systems in particular, keeping documentation that explains how and why AI is being used, its intended purpose, and the safeguards in place to manage risks.

 

Over half (53%) of organisations feel overwhelmed by AI regulations, according to Vanta, with top challenges including a lack of internal expertise, shifting rules, and the rapid pace of AI tool development outpacing policy. Six in ten (62%) leaders are very concerned about AI compliance, especially around evolving laws and vendor accountability. This is why you need to embed ethics, privacy, explainability, and bias mitigation from the start. Recognise policies will evolve as AI advances, requiring regular review to align governance with new technology while ensuring responsible deployment. On agentic developments, iterate carefully to progress securely – leaders need to understand where AI can slot into workflows, before rushing to implement it. Create AI-native workflows from scratch, not on legacy systems, for real-time agent collaboration

 

 

3. Train and upskill your workforce on key AI skills

GenAI learning is picking up significant pace. As enterprises explore ways to stay ahead of the AI innovation curve, IBM predicts over 450 million workers globally will need upskilling by 2030. Creating those opportunities for learning and development are vital. With AI, you need to keep humans in the loop. There are pitfalls around GenAI with hallucination and bias, leading to incorrect responses – possessing skills around AI literacy, prompt engineering and data science can help unpack these issues, and navigate the right path.

 

This is why it’s critical for leaders to establish training initiatives. If you train all employees on AI tools, prompting, and workflows to encourage broad adoption, then you are setting up your organisation for a successful future with emerging technology. Use “AI Days” and prompt libraries to share best practices, fostering experimentation while aligning with governance and guardrails. It can also be beneficial to set up AI town halls, and conduct pulse surveys, to gauge where skills gaps are emerging, and then create ways to respond to those AI skill needs. Create that culture from the moment of onboarding – by leveraging AI, new starters can accelerate their understanding across departments, grasp complex data quickly, and focus on creativity and problem solving from day one. But businesses will need structured training and mentoring to prepare young professionals for the emerging role of “AI manager”, guiding agents, as well as people.

 

 

4. Engineer workflows around AI use cases that solve problems

AI agents are renewing focus on how AI can automate workflows. According to PwC, 88% say their team or business function plans to increase AI-related budgets in the next 12 months due to agentic AI - which can perform at a higher level than simple prompt-and-response. Agentic AI can perceive its environment, reason through options, act autonomously, and learn from outcomes, embodying a “sense‑think‑act” model. Unlike traditional AI or generative models, which are reactive, agentic AI actively pursues goals and adapts its actions, coordinating across tasks and systems.

 

But we’re at a crossroads. Choosing the right agent for a specific use case is key – we’re at a moment where two employees using AI for the same task can get wildly different results. Small choices, like which model they use, how they prompt it, or what tools they plug in, can mean the difference between a 25% productivity bump and a 250% leap. AI success isn’t one-size-fits-all: different models shine in coding, research, translation, or summarisation, and their strengths evolve constantly. Choosing the right model has become one of the biggest levers for real ROI. That’s where smart partnerships come in. By working with providers that offer multiple models through secure, enterprise-ready platforms, businesses gain flexibility without juggling contracts or tools. Those that combine these partnerships with internal experimentation will not only close the productivity gap, they’ll free their people to focus on higher-value, creative, and collaborative work.

 

 

Why ‘AI-first’ is the new market advantage

Becoming AI-first is more than simply adopting new tools – it’s a strategic shift that sets market leaders apart. Companies that embed AI at the core of their operations don’t just gain incremental efficiencies; they reimagine products, workflows, and customer experiences with intelligence at the centre. This positions them to move faster, personalise at scale, and unlock entirely new sources of value. In a competitive landscape where speed, adaptability, and innovation define winners, being AI-first is the differentiator that turns technology into lasting advantage.

 

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Samantha Wessels is President EMEA at Box

 

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