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How can businesses prepare for the future of AI?

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This blog has been expertly reviewed by Jason Vigus, Head of Portfolio Strategy, Commercialisation, and Governance at Nasstar. 

Artificial intelligence (AI) has the potential to transform the way we work. According to an IBM report, 59% of firms increased their AI investments and rollout efforts in 2023, demonstrating a strong shift towards embracing AI-driven efficiency and insights. 

But joining the AI revolution involves much more than just plug-and-play.  Before even starting to implement AI tools, your data must be cleaned, prepared, and secured. This begs the question: When is an organisation prepared — both technically and culturally — for AI adoption? 

Jason Vigus, Head of Portfolio Strategy, Commercialisation, and Governance at Nasstar said: “To make the most of this impactful technology, it's vital to focus on preparing your business. This involves safeguarding your data, building a culture and strategy that embraces technological advancements and understanding both the risks and the immense possibilities AI brings. Then, your business will be prepared to not only adapt to AI — but to thrive.” 

What is Artificial Intelligence? 

Artificial Intelligence, or AI, has gained huge momentum in recent times. At its core, AI is about using technology to perform both skilled and repetitive tasks that usually require human intelligence. 

Over the past few years, advancements in this field have been monumental. AI has gone from being a concept to a legitimate competitive advantage. Tools like ChatGPT have transformed how we perceive and interact with technology in the workplace while showing almost all sectors how AI could impact their roles and the customer experience. 

How does AI work? 

The magic behind AI is its ability to learn from data. AI uses large datasets and compute-intensive algorithms to sift through data, discover patterns, and make future decisions based on those patterns. The beauty of AI is that, in theory, the more quality data it has access to, the smarter it becomes. We can use it to tackle increasingly complex tasks. 

While all AI systems rely on data to work, different models may learn from or process their information differently. Broadly, the most common business AI systems use the following approaches with their models: 

  • Machine learning: These systems learn from previous examples to predict outcomes and make future decisions based on their experience. An example is facial recognition software. Many of these algorithms incorporate continuous learning and improve as they encounter more data. 
  • Deep learning: Deep learning takes machine learning a step further by using multiple layers of decisions in its problem-solving methods. Most commonly, it builds these layers into neural networks that mimic the human brain's decision-making processes, letting it handle complex tasks.  
  • Natural language processing and large language models: AI systems like ChatGPT and Gemini analyse enormous amounts of text to process written language at an almost human level. It can then use that training to understand new inputs or generate responses. 

The types of AI businesses can use 

Among its potential uses, AI presents opportunities for businesses to improve operations, enhance customer interactions and gain strategic insights that help make data-informed decisions. AI has the potential to provide enormous value across varying business domains.  

Generative AI 

Generative AI hit the headlines over the past year and has become a household name. This form of AI can rapidly produce content, including text and images, simulating human creativity and innovation – at a highly accelerated pace. It opens up new possibilities for content creation, product design, and customer engagement, offering a new way to spark creativity and problem-solving within your business. The text outputs produced by ChatGPT are perhaps the most widely known example of generative AI. 

According to a McKinsey report, a remarkable 79% of professionals reported some level of engagement with this technology in 2023. Around 22% are incorporating it into their daily work tasks already.  

Jason commented: “If 79% of professionals have tried the tech, and 22% are already incorporating it – knowing how much of that is sanctioned vs shadow IT is difficult. This is why it’s important for businesses to get in front of this and provide their employees with proper guidance and safe access to these tools. Then, it can become a differentiator and an accelerator, rather than a risk.” 

AI-powered insights 

AI-powered insights involve analysing vast amounts of data to unearth trends and predictions that were previously invisible. This is possible simply because AI can analyse data in such huge volumes compared to humans.  

AI insights revolutionise the way decisions are made, providing a competitive edge with a deeper understanding of market trends, the customer experience and financial forecasting. This leads to data-driven decision-making, where every choice is backed by solid metrics rather than gut feelings. 


AI-enabled automation allows businesses to streamline repetitive tasks. This frees up valuable human resources for more complex and creative tasks. Automation not only improves efficiency but also employee satisfaction by reducing monotonous and error-prone workloads. 

Customer-facing AI applications 

Customer-facing AI applications enhance the customer experience through personalised recommendations, virtual assistants and chatbots. These tools can boost business by providing real-time, 24/7 customer service and support. Likewise, personalisation and instant responses can help companies set a new customer satisfaction standard. 

Overall, the use of AI can provide a substantial competitive advantage. It sets you apart in the market, allowing you to offer unique services and respond quickly to changes. Fundamentally, this helps you meet customer needs more effectively. However, to implement AI properly, businesses need to ensure they are ready for new technology. 

The potential risks when implementing AI systems 

Adopting AI comes with challenges that businesses need to navigate carefully. Rushed, poor quality or unreliable AI models can perform poorly at best — and may even cause more damage than they’re worth. 

A key hurdle, for example, is the gap in AI skills and expertise. IBM report that 33% of businesses see a skills gap as a major barrier to their AI deployment. Likewise, a quarter struggle with the complexity of managing vast amounts of data, and 23% are concerned about the ethical implications. 

To overcome these obstacles, it's important for your business to collect high-quality data. AI datasets should not only be large but relevant. They need to directly contribute to solving specific problems. Alongside this, safeguarding the security of your data is paramount to protecting your operations and maintaining customer trust.  

Finally, a lack of infrastructure can also impede AI implementation. Investing in training for your team, upgrading your technological framework, and good data governance are vital steps towards integrating AI effectively.  

Jason commented: “Another risk of Gen-AI is its ability to ‘hallucinate.’ This happens when a response is provided by AI that contains false or misleading information, and responses can be quite convincing. For this reason, it’s always important to make sure responses provide reference/sources that can be checked.” 

How can business leaders prepare for AI? 

With the benefits and pitfalls in mind, how can businesses prepare for the future of AI? 

  • Consider business use cases: Firstly, envision the possibilities and explore business use cases relevant to your industry, organisation and workflows. By understanding how AI works, you can identify opportunities that align with your business objectives. 
  • Ensure data quality and security: Data is the cornerstone of effective AI. Ensuring the quality and security of your data is a huge step when building a model. High-quality, well-organised data will train AI algorithms to deliver accurate insights, while strong security measures will protect it from potential breaches. 
  • Build a computer infrastructure ready for AI technologies: Another vital step is to build a computer infrastructure capable of supporting AI. This involves not just powerful hardware but also the right data and software platforms to develop and deploy AI efficiently. Consider your other business systems and needs when deciding whether to base this in-house or use a third-party platform provider. 
  • Reskill or upskill staff: As technology reshapes job roles, equipping your team with new skills will enable them to work effectively alongside AI. This will help ensure your business remains innovative in the future. 
  • Partner with experienced providers: Partnering with experienced providers can help bridge gaps in expertise and technology, inject innovation, and accelerate key initiatives. A trusted partner can offer valuable guidance, from selecting the right AI tools to implementing them effectively. 

How Nasstar can help 

AI is changing business operations for good. With three-quarters of businesses investing in AI already, we are already seeing this technology reshaping digital transformation by adding intelligence and new customer experiences to products and services, providing better insights, and automating mundane tasks in all industries. 

However, the adoption of AI isn't without its challenges. If they’re not addressed, skills gaps, data governance and security, and ethical concerns each prove barriers to effective AI implementation. For businesses to implement AI to its full potential, data quality and security, an AI-ready infrastructure and staff reskilling are all crucial steps.  

Businesses should partner with an experienced AI specialist who can help guide them through the complexities of AI integration. Nasstar’s expert engineering and cloud services teams can help you prepare for the future of AI. From preparing your data estate to AI deployment, see how our secure reference architectures and expertise can give you the resources and confidence to leverage AI effectively.  

Speak to a Nasstar specialist to learn more. 

Frequently asked questions (FAQs) 

How will businesses use AI in the future? 

Businesses could use AI to enhance decision-making, automate processes and improve customer experiences. AI will let organisations forecast trends and make proactive adjustments, making it integral to most industries’ strategic planning and day-to-day operations.  

By adding intelligence to processes, workforces can turn their attention to higher-value activities, while the AI operates around the clock. AI is poised to dramatically reshape how customers interact with businesses, from hyper-personalisation and AI-enhanced self-help, to intelligent virtual agents and predictive analytics, the possibilities are endless. 

What are the most promising areas for AI to transform business operations in the future? 

The most promising areas for AI to transform business operations include inventory management, customer service and predictive analytics. AI could change product development through generative design and improve decision-making with data-driven insights. Above all, it will significantly enhance efficiency across the board. 

How do you prepare for the future of AI? 

To prepare for the future of AI, businesses should start by building a culture of digital transformation. Investing in high-quality datasets, IT infrastructure and ensuring data security are all hugely important tasks. Likewise, creating an expert workforce through reskilling and upskilling will be essential. 

Are there cybersecurity risks of using AI? 

The cybersecurity risks associated with using AI include data secrecy leaks, copyright loss and potential biases in AI algorithms. For these reasons, ensuring AI systems are designed with highly governed data platforms that include robust security measures and ethical guidelines is crucial. 

How can businesses build an AI-ready infrastructure? 

Businesses can build an AI-ready infrastructure by investing in scalable computing resources and adopting cloud services. Ensuring interoperability among data sources and AI applications is also crucial. Those lacking the expertise to set up an AI infrastructure should consider working with an experienced managed services partner like Nasstar.