Beyond AI washing: Unlocking AI’s true potential with strategic implementation

You know it’s powerful, you know it has a high-end engine, but without the skills and understanding to control it, the powerboat remains moored in the marina—full of potential but ultimately unused. This is where many business leaders find themselves today with artificial intelligence (AI).

AI has evolved rapidly from being a niche topic in technology circles to becoming a buzzword that echoes through boardrooms and C-suite meetings across industries. In a way, the challenge is encapsulated by “Leaders recognise the importance of AI, but that’s about it”; ok, this may be a bit of an exaggeration, but the reality is that a significant issue facing today's businesses is a widespread recognition of AI's potential, but a limited understanding of its breadth and nuances. You’ve got the keys to the powerboat, but now what?

Fuel is the next thing to sort out. AI runs on data, and planning to take AI out for a spin is like trying to drive the boat without fuel. It’s got to be the right type, too; AI systems are only as powerful as the data they are trained on, and without proper data governance, quality control, and infrastructure, even the most advanced AI will fall short of its potential. A business strategy that includes AI must first ensure that a strong data foundation is in place. Businesses that focus solely on implementing AI without aligning it with their data strategy or broader business goals may find themselves unable to realise the full benefits of their AI investments. They’re in the powerboat, but can’t get up speed. We can help fix that for you.

As companies scramble to integrate AI into their operations, many leaders find themselves grappling with a fundamental question: What exactly is AI, and how can it be harnessed effectively? This article seeks to address these questions by exploring the misconceptions surrounding AI and offering a broader perspective on its diverse applications.

This lack of deep comprehension isn’t just a minor inconvenience—it’s a critical gap that could prevent businesses from truly capitalising on AI’s potential. Most C-suite leaders see AI as a game changer, yet they’re left feeling uncertain, even as they publicly champion its possibilities. The result? A lot of talk about AI, but not nearly enough action.

AI is more than just ChatGPT

In recent times, when most people hear "AI", their thoughts immediately gravitate towards tools such as Siri, Alexa, and Google Assistant that help with tasks like setting reminders. Or they think of ChatGPT, the conversational AI developed by OpenAI that has made headlines for its impressive language generation capabilities. However, this is just one facet of AI—a single example within a broader, more complex landscape. The misconception that AI equates to ChatGPT alone can lead to underutilisation of AI's potential in other critical business areas.

ChatGPT is part of a subset of AI known as Natural Language Processing (NLP) and Generative AI (Gen AI), which focuses on the interaction between computers and human language. While NLP applications are indeed powerful, limiting AI to this one application overlooks other vital forms of AI that can drive significant value in business contexts.

Exploring the breadth of AI technologies

To truly leverage AI, it’s essential to understand the different types of AI technologies and how they can be applied across various industries.

Let us start by addressing a common question that business leaders often ask: “What’s the difference between AI and Machine Learning (ML)?”AI refers to the broader concept of machines being able to perform tasks that would normally require human intelligence, such as decision-making or understanding natural language. ML, on the other hand, is a specific subset of AI that focuses on systems that learn from data and improve over time without being explicitly programmed. So, while all ML systems are AI, not all AI involves machine learning. This distinction is critical because it helps in understanding which AI technologies are best suited for specific business applications.

Below is an overview of some key AI types that are driving innovation today:

1. Machine Learning (ML): The backbone of many AI systems, ML enables computers to learn from data and improve their accuracy over time without being explicitly programmed. This capability is widely used in predictive analytics, fraud detection, and recommendation systems. For instance, Amazon's recommendation engine suggests products based on past purchases made by people like you, while financial institutions use ML algorithms to detect unusual transaction patterns indicative of fraud.
2. Natural Language Processing (NLP): Beyond just generating text, NLP allows computers to understand, interpret and respond to human language. This technology powers chatbots, virtual assistants and automated customer service systems. In the legal industry, NLP is used for contract analysis, enabling firms to quickly review large volumes of legal documents and identify key terms, obligations and risks.
3. Generative AI (GenAI): As mentioned above, this is one of the most talked-about applications of AI today. Unlike other AI technologies that classify or predict based on existing data, Gen AI can create entirely new content—whether that’s text, images, or even music—based on learned patterns. ChatGPT is an example of Gen AI, but the possibilities extend much further. In industries like marketing, design, and content creation, Gen AI is being used to generate advertisements, visual assets and even business reports, streamlining processes and boosting creativity.
4. Computer vision: This type of AI enables machines to interpret and make decisions based on visual data. Computer vision is used in industries like manufacturing for quality inspection, where it can detect defects in products on an assembly line. In healthcare, it assists in analysing medical images such as x-rays and MRIs, helping doctors diagnose conditions more accurately.
5. Robotic Process Automation (RPA): RPA uses AI to automate repetitive, rule-based tasks. In finance, for example, RPA can streamline processes like invoice processing and reconciliation, reducing human error and speeding up operations. Human Resources departments also benefit from RPA by automating the onboarding process for new employees, freeing up time for HR professionals to focus on strategic initiatives.
6. Reinforcement learning: A lesser-known but highly impactful area of AI, reinforcement learning involves training models to make sequences of decisions by rewarding desirable actions. This type of AI is used in complex decision-making environments such as supply chain management, where it can optimise routes and reduce costs.

The business case for a holistic AI strategy

You’ve probably heard of “greenwashing”, where companies make superficial environmental claims to appeal to business customers and consumers. Well, now there’s a new buzzword in town: “AI washing”. This occurs when businesses declare they’re embracing AI without actually integrating it into their operations in any meaningful way. It’s all show and no substance—a dangerous position for any company in today’s fast-evolving landscape.

To avoid AI washing, organisations must approach AI with a clear, strategic plan. This involves understanding the different types of AI and identifying the right technology for specific tasks. Recognising the diversity of AI technologies is the first step toward building a comprehensive AI strategy. For instance, a retail company might use NLP to enhance customer interactions through personalised marketing, while employing computer vision to optimize inventory management.

Moreover, AI should not be seen as a plug-and-play solution. Its integration requires a thorough understanding of data management, ethical considerations, and the potential need for re-skilling employees. For example, as AI takes over mundane tasks, employees can be trained to focus on higher-value activities, such as decision-making, particularly for edge cases, and strategic planning.

A strategic approach to AI implementation

Imagine walking into a boardroom, not just talking about AI, but demonstrating how it’s already driving measurable results in your business. Imagine being the leader who doesn’t just understand AI but is leveraging it to outpace the competition. Sounds good, right? To get to this point, simply knowing about the technologies isn’t enough. The real magic happens when you have a clear strategy, the right data infrastructure, and experts on hand who can guide you through the integration process. That’s where you avoid the pitfall of AI washing and start reaping real, tangible benefits.

For businesses to succeed in implementing AI, they must move beyond the hype and focus on practical, value-driven applications. This requires a shift in mindset, from viewing AI as a futuristic, all-encompassing solution to seeing it as a set of tools that can solve specific problems.

Here are some steps businesses can take to develop a robust AI strategy:

1. Identify business objectives: AI must be deployed with a clear purpose in mind, aligned with the organisation's overall business goals. Whether it's improving customer experience, increasing operational efficiency, or driving innovation, AI initiatives need to be tied to measurable outcomes.
2. Invest in data infrastructure: AI systems are only as good as the data they are trained on. Ensuring that data is accurate, relevant, and well-managed is crucial for successful AI implementation. This may involve investing in data cleaning, data governance, and data analytics capabilities.
3. Build cross-functional teams: AI implementation is not just a technical endeavour—it requires collaboration across various departments, including IT, operations, finance, and HR. By building cross-functional teams, businesses can ensure that AI initiatives are aligned with broader organisational goals and that the necessary expertise is in place, in the right place.
4. Prioritise ethical AI: As AI becomes more integrated into business processes, ethical considerations become increasingly important. Companies need to consider how AI decisions will impact customers, employees and society at large. Transparency, fairness and accountability should be at the core of any AI strategy.
5. Focus on change management: Implementing AI typically requires changes to existing workflows and processes. Effective change management is essential to ensure that employees are on board with the changes and that they have the necessary skills and training to work alongside AI systems.

Avoiding the hype: asking the right questions

Given the hype surrounding AI, it's easy to be swayed bygrandiose promises. Business leaders should ask critical questions whenconsidering AI investments:

• What specific business problem are we trying to solve? AI should be deployed with a clear purpose in mind, rather than as a catch-all solution. For instance, if the goal is to improve customer service, a company might deploy an NLP-based chatbot, but if the goal is to optimise logistics, a reinforcement-learning model might be more appropriate.
• Do we have the necessary data infrastructure? AI systems rely on high-quality data. Companies need to ensure that their data is clean, well-organised, and easily accessible. This might involve investing in data management platforms or hiring data scientists to oversee data quality.
• How will AI integrate with our existing systems? AI should complement and enhance current workflows, not disrupt them without clear benefits. For example, in a manufacturing setting, computer vision systems should integrate seamlessly with existing quality control processes.
• What are the ethical implications? As AI decisions can have significant impacts on people, it is essential to consider the ethical implications of AI applications. This includes ensuring that AI systems are free from bias, that they are transparent in their decision-making processes, and that they comply with relevant regulations.

Embracing AI’s full potential

The future of AI in business is bright, but only for those who take the time to understand and leverage its full potential. By moving beyond the ChatGPT stereotype and embracing a diverse range of AI technologies, companies can unlock new efficiencies, enhance customer experiences, and gain a competitive edge.

At a time when AI is poised to reshape industries, staying at the forefront requires not just awareness but deep understanding and strategic action. Business leaders who equip themselves with this knowledge will be well-positioned to turn AI from a buzzword into a game-changing reality for their organisations.

As the AI landscape continues to evolve, the most successful companies will be those that approach AI with a clear, strategic vision—one that goes beyond the hype and focuses on tangible, value-driven outcomes. By doing so, they can ensure that AI becomes a powerful tool for innovation and growth, rather than just another trend that comes and goes.

When AI is implemented strategically, the rewards are substantial. You become the visionary leader who isn’t just following trends but is ahead of the curve. Your company doesn’t just survive the AI revolution—it thrives. Let’s talk about how we can turn your AI ambitions into reality. Because with the right partner, you can not only keep up with the AI revolution—you can lead it.

We can help you gain a sustainable competitive advantage in the deployment of AI capabilities. We have experts who specialise in building AI strategies, delivery roadmaps, data governance, MLOps frameworks, infrastructure build, improving data literacy and much more. Do get in touch.

‍

About the author

Dr J Rogel-Salazar is Data Science & Machine Learning Lead at The Data Practice. He is a renowned consultant, leader and author and has worked with AKQA, IBM Data Science Studio, Dow Jones, Barclays, Volvo, Nike and H&M, as well as several startups. He has a deep understanding of complex systems and data-driven methodologies that supports his scientific and technological innovation.

‍

Photo credit: Herb Aust