Artificial intelligence is here, it’s transforming industries and redefining the way businesses operate. But while AI’s potential is vast, unlocking its full value requires more than just plugging in the latest technology. It demands a thoughtful, strategic approach.
With 65% of organisations now using generative AI – a dramatic jump from last year – the message is clear: businesses aren’t just experimenting with AI; they’re committing to it. But how can they scale AI effectively while ensuring responsible governance and seamless adoption? Let’s break it down.
Where to start and how to ‘futurify’
Bringing AI into an organisation isn’t like flipping a switch. Unlike a flashlight, where the purpose is immediately clear, AI’s vast range of applications requires a more deliberate approach. The key is to identify high impact use cases and measure AI’s return on investment (ROI) effectively.
Take internal research, for example. AI can streamline this process, but many organisations struggle to quantify the time and resources currently spent on it. Without these baseline insights, it’s difficult to measure AI’s real impact. The first step is accurately assessing operational costs before rolling out AI-driven efficiencies.
Tailoring AI to your business
Once organisations identify where AI can drive value, they must decide how to implement it. Should companies buy off-the-shelf AI tools, build custom solutions, or partner with experts? The answer depends on their goals and capabilities.
Pre-built solutions offer quick deployment but may lack customisation. Building AI in-house provides full control but requires significant investment. A hybrid approach, mixing proprietary AI development with strategic partnerships, often delivers the best balance.
By carefully weighing these options, businesses can ensure their AI strategy aligns with long-term objectives and delivers lasting value.
Leveraging internal data for AI
With an AI strategy in place, the next step is ensuring AI has the right data to learn from. Data is the fuel that powers AI, but not all data is created equal. The amount and type of training data required depend on various factors like domain specificity, quality, and diversity.
Consider AI-powered sales proposals. For AI to generate compelling proposals, it needs access to historical sales data, customer insights, and competitive analysis. Some businesses may choose to train AI on every past proposal, while others may focus only on the most successful ones.
Compliance with privacy regulations like GDPR and CCPA is another key consideration, requiring careful handling of sensitive information. Ensuring data is cleaned and formatted correctly before AI processing is also essential for accuracy and reliability.
By addressing these factors, organisations can train AI models to produce high-quality, consistent proposals – ultimately saving time and increasing efficiency. But AI alone isn’t enough. Employees must be upskilled to work alongside it, ensuring smooth adoption rather than resistance.
Addressing human resistance to AI integration
Even with a strong data strategy, AI adoption faces another major hurdle: people. The biggest AI challenges aren’t always technical. 59% of AI adoption roadblocks stem from managerial and cultural resistance rather than issues like data security or legacy technology.
Employees need to see AI as an enabler, not a replacement. Businesses that invest in training and align AI with existing workflows will find smoother adoption, higher engagement, and ultimately, greater returns. AI’s success ultimately lies in the hands of people, it’s all about how well it integrates with the people using it.
Pilot, perfect, and scale
With great power comes great responsibility. Ethical AI use isn’t a regulatory checkbox – it’s a trust-building exercise. Without clear guidelines and oversight, risks can quickly spiral, threatening both credibility and compliance.
A structured rollout helps mitigate these risks. Starting with small, high-impact pilot projects allows organisations to test AI’s effectiveness, refine its application, and build confidence before expanding its use. Early wins create momentum, helping secure buy-in from leadership and employees alike.
Ensuring AI works for the future
AI has indeed become a core part of how businesses operate and compete. But making it work takes more than just great technology. Success comes from finding the right use cases, choosing between building or buying, making sure your data is in top shape, and, most importantly, bringing people along for the journey. AI should empower teams, not replace them. And for AI to truly deliver value, it needs to be governed responsibly, with clear guidelines and ethical oversight.
Get these elements right, and AI won’t just be another tool – it’ll be a game-changer for the future of work.
