AI has rapidly evolved from a futuristic concept to a critical component in modern business strategy.
The journey towards AI adoption, however, is not without its challenges. Balancing transformation with pragmatism is crucial for organisations aiming to harness AI’s full potential while maintaining operational stability and efficiency. This article outlines 12 practical insights to help businesses get started with AI and adopt it successfully by blending visionary goals with practical steps.
Employees want AI at work—and they won’t wait for companies to catch up
The AI Imperative
AI is no longer a futuristic concept; it’s an integral part of the modern workplace. According to Microsoft’s Work Trend Index, AI is poised to revolutionise how we work, augmenting human capabilities and driving productivity. McKinsey is conjecturing that by 2030, activities that account for up to 30 per cent of hours currently worked across the US economy could be automated—a trend accelerated by generative AI. Some could even call this heading into a chaotic future!
The key is not just to adopt AI but to do so in a way that is sustainable and aligned with organisational goals. The very first step is to have the end in mind. What are your drivers for AI adoption? Do you have clear business drivers? How does AI support your corporate strategy?
Without guidance or clearance from the top, employees are taking things into their own hands and keeping AI use under wraps:
- 78% of AI users are bringing their own AI tools to work (BYOAI)—it’s even more common at small and medium-sized companies (80%).
- 52% of people who use AI at work are reluctant to admit to using it for their most important tasks.
- 53% of people who use AI at work worry that using it on important work tasks makes them look replaceable.
This approach means missing out on the benefits that come from strategic AI use at scale. It also puts company data at risk in an environment where leaders’ #1 concern for the year ahead is cybersecurity and data privacy.
Microsoft Work Trend Index 2024
1. Consider your organisation’s maturity in value management
Before embarking on any major initiative, consider your organisation’s readiness to effectively manage and realise the value of corporate strategy and investments. Give some thought to your organisation’s maturity in:
- Realising business value and assessing the progress of your corporate strategy and individual investments.
- Realising and demonstrating business value for each of the stakeholders.
- Defining and managing the business value from AI and Automation.
Your AI pilot can only be successful if you can clearly articulate the value, benefits & return on investment realised by your organisation.
2. Understanding the AI Landscape
Before diving into AI adoption, it’s essential to first understand the landscape. Understand what the technology can and can’t do right now – that way you can be pragmatic about what can be achieved today. AI encompasses a range of technologies, including machine learning, natural language processing, decision intelligence, digital and robotic process automation. The term “Intelligent Automation” is used to describe them collectively. Each of these technologies offers different capabilities and can be applied to various aspects of business operations.
Machine learning, for instance, excels at analysing large datasets to uncover patterns and make predictions. Natural language processing enables machines to understand and respond to human language, making it invaluable for customer service (chat and voice bots) and for content analysis (Communications Mining). Robotic and digital process automation (RPA and DPA) can automate repetitive tasks & even end-to-end processes, freeing up human workers for more complex activities.
Understanding these technologies is the first step towards strategic AI adoption.
3. Understand Your Current State and Set Clear Objectives
The next step in adopting AI is to understand what your organisation is doing with it today, before setting clear, achievable objectives that align with your strategic goals and ensure a focused and effective implementation. The use of a maturity model can provide a structured framework to assess your current capabilities, identify strengths and gaps, and guide the development of a comprehensive AI strategy.
What is your organisation’s posture on AI adoption and use? How is AI being used within your organisation today? What projects are already in the pipeline? What specific problems do you want AI to solve? Once you have clarity on where you’re at, the next step is to build a roadmap to get you to where you want to be and to ensure your AI initiatives deliver measurable value.
Making sure that your AI initiatives are strategically aligned with your organisational priorities is crucial for driving impactful results, maximising return on investment, and ensuring that technological advancements support and enhance your core business objectives.
4. Your first use case should be a simple one
Consider starting with pilot projects that address well-defined use cases. For example, your HR department might implement an GenAI-powered chatbot trained on your organisation’s specific content to respond to questions on company policy, leave and opportunities for learning and development, while a finance team might use Generative AI-enhanced computer vision (rather than old-school OCR) to digitise invoices for automatic entry into an ERP system and statements for automated reconciliation. These initial projects can provide valuable insights and build momentum for broader AI adoption.
Importantly, you really want your first few projects to be internal, rather than customer facing. This is because things will inevitably go wrong with the first one, and it’s much easier to clean up after any mistakes with your staff rather than it is with your customers!
5. Building the Right Team
AI adoption requires a multidisciplinary approach. As your ambitions grow, you will need AI specialists, business analysts, domain experts, data scientists and IT professionals. This diverse team can ensure that AI solutions are not only technically sound but also aligned with business needs and objectives.
Investing in training and development is also crucial. Upskilling your workforce to work effectively with AI tools and technologies can drive greater engagement and adoption. Moreover, fostering a culture of continuous learning and innovation can help your organisation stay ahead in a landscape that is evolving faster than just about anything before it.
While leaders recognise the value of bringing on new employees with AI aptitude, they’re missing the value of developing their own people:
- 45% of US executives are not currently investing in AI tools or products for employees.
- Only 39% of people globally who use AI at work have gotten AI training from their company.
- Only 25% of companies are planning to offer training on generative AI this year, further cementing this training deficit.
More and more people aren’t waiting for official guidance or training—they’re skilling up themselves.
Microsoft Work Trend Index 2024
6. Data: The Lifeblood of AI
Data is at the heart of AI. The quality and quantity of data available to your AI systems directly impact their performance. Therefore, establishing robust data management practices is essential. This includes ensuring data accuracy, completeness, and security.
Organisations should also focus on data integration. AI systems often need to pull data from multiple sources, both internal and external. Creating a unified data platform can streamline this process and provide a single source of truth for AI applications.
7. Ethical Considerations
As AI becomes more prevalent, ethical considerations must be worked through.
Ensure AI systems operate transparently and fairly by addressing biases, protecting user privacy, and providing explainable AI decisions.
Implementing ethical AI guidelines and governance frameworks can help organisations navigate these challenges. Regular audits and reviews can also ensure that AI systems remain aligned with ethical standards and societal expectations.
8. Integration with Existing Systems
One of the significant challenges in AI adoption is integrating new AI tools with existing systems. This requires a clear understanding of your current IT infrastructure and the potential impact of AI on various processes.
Adopting a phased approach can mitigate risks. Start with non-critical systems or processes, then gradually expand AI integration as confidence and experience grow. Collaboration between IT and business units is essential to ensure that AI solutions are practical and deliver the intended benefits.
9. Measuring Success
Expanding on the first point about Value Management, to gauge the effectiveness of AI initiatives, it’s crucial to establish clear metrics and KPIs. These should be aligned with your initial objectives and provide a comprehensive view of AI’s impact on the organisation.
Metrics might include efficiency gains, cost savings, customer satisfaction scores, and revenue growth. Regular monitoring and analysis can help identify areas for improvement and guide future AI investments.
10. Overcoming Challenges
Despite the potential benefits, AI adoption is not without challenges. Resistance to change, lack of expertise, and concerns about regulatory compliance, data security and job displacement are common hurdles. Education is key; most organisations have a fear of moving forward – the very opposite problem to FOMO – as the leadership feel that they don’t know enough to move on AI initiatives. Addressing these issues requires strong leadership, clear communication, stakeholder engagement, and a focus on building trust and confidence in AI technologies.
Providing transparency about AI’s role and impact can alleviate fears and highlight the positive aspects of AI adoption. Moreover, emphasising AI’s role in augmenting rather than replacing human capabilities can foster a more positive outlook among employees.
The opportunity ahead for leaders is to channel employee enthusiasm for AI into business transformation. This will look different for every organisation, but here’s how to get started.
- Identify a business problem, then apply AI: There are efficiency gains to be had across every function—the key is to pick a process and apply AI. For example, start with customer service and focus on improving call-handling time.
- Take a top-down, bottom-up approach: Going from experimentation to transformation requires engagement at every level of the organisation, from the CEO to the entry-level employee. Business gains will come when you enlist your business line leaders to activate teams around AI.
- Prioritise training: AI power users aren’t doing it on their own—they receive ongoing training, both on universal tasks and uses more tailored to their role and function. LinkedIn Learning is a great place to start to skill up.
We’ve arrived at a pivotal moment for AI; over time, it will change every aspect of work.
Microsoft Work Trend Index 2024
11. The Role of Leadership
Leadership plays a critical role in successful AI adoption. Senior leaders must champion AI initiatives and drive a culture of innovation. Remember, fear thrives in a vacuum. By setting a clear vision and providing the necessary resources and support, leaders can empower teams to experiment with AI and develop innovative solutions.
Leaders should also prioritise continuous learning and development. As AI technologies evolve, staying up-to-date with the latest advancements and best practices is essential. This ongoing commitment to learning can ensure that the organisation remains competitive and capable of leveraging AI’s full potential.
Waiting for the pace of AI development to slow down is a poor strategy…
12. Future-Proofing Your Organisation
AI is not a one-time investment but an ongoing journey. To future-proof your organisation, it’s essential to stay agile and adaptable. This includes regularly reviewing and updating your AI strategy, investing in emerging technologies, and fostering a culture of continuous improvement.
Collaborating with external partners, such as AI vendors and research institutions, can also provide access to cutting-edge technologies and expertise. These partnerships can help your organisation stay at the forefront of AI innovation and maintain a competitive edge.
In Summary
Balancing transformation with pragmatism is the key to successful AI adoption. By assessing the current state of AI in your organisation, setting clear objectives, building the right team, and focusing on data management and ethical considerations, organisations can navigate the complexities of AI implementation.
One stakeholder in a government agency we were speaking with recently said, “I think we’ll just wait until everything in the AI space starts to slow down.” It’s as if they’re expecting AI to suddenly hit the brakes, pour a cup of tea, and ask if now is a good time to chat about the future.
This sentiment reflects a common hesitation among some organisations to fully embrace AI, as they grapple with the rapid pace of technological advancements and the uncertainty it brings. Rather than jumping into the fray, they prefer to stand on the sidelines, hoping for a moment of calm to better understand and manage the complexities of AI integration.
However, waiting for technology to slow down is like waiting for a glacier to start a sprint – it’s not going to happen, and in the meantime, opportunities for innovation and efficiency – not to mention the competition – will pass them by.
Don’t try to “boil the ocean” and go too big too fast. Adopting a phased approach and measuring success through clear metrics can ensure that AI initiatives deliver tangible value. Overcoming challenges requires strong leadership, clear communication, and a commitment to continuous learning and development.
As AI continues to evolve, staying agile and future-proofing your organisation will be essential.
Get in touch with us below if you’d like to chat about how your organisation can make better use of the latest advances in AI and automation.
Balancing Transformation with Pragmatism: Getting Started and Adopting AI
