// EXPERT PROFILE // involved . That said , ‘ pay as you go ’ cloudpowered AI models that offer flexible pricing options and can be tailored to specific business requirements are now much more readily available .

Other key areas to focus on include putting the right cybersecurity and compliance measures in place , cleaning and preparing data so it can be leveraged by AI tools , and training and upskilling personnel across the business . Although AI can often be seen as something that creates security issues , a lot of the time it in fact brings pre-existing gaps in cybersecurity to light , furthering the need for thorough security protocols prior to its implementation .

Getting started : Preparation is all

Identifying areas where AI is most likely to deliver the most significant impact is a critical first step . AI ’ s primary strength lies in its ability to assist with specific tasks such as automating repetitive manual processes and analysing large datasets . So solutions that address core challenges or will generate new value-add in areas such as marketing , sales or logistics and back office administration tasks will also present prime opportunities .

Setting clear strategic objectives that are focused on core business needs will be important for maximising outcomes such as using chatbots to handle customer queries , optimising inventory , enabling product personalisation or gaining deeper customer insights .

Ideally , AI projects should be focused on activities where success can be monitored and measured . For example , an online retailer that wants to alter deals dynamically to minimise losing sales could use AI to predict stock requirements and optimise offers in real-time depending on product availability . Undertaking a like-for-like comparison with previous campaigns will reveal how AI contributed to improvements in sales volumes and margins .

Security and data privacy

Data protection and regulatory compliance is a must have and SMEs will need to choose solutions that feature robust data protection mechanisms . To assure compliance with privacy and security regulations such as GDPR and NIST 2 , SMEs will need to implement comprehensive governance frameworks and controls to ensure that customer , employee , financial or IP data is not inadvertently exposed .

AI ethics and bias poses another challenge . AI systems learn from large databases and , without the right oversight , can perpetuate existing biases that lead to unfair or discriminatory outcomes . To prevent this , SMEs will need to implement checks and balances to mitigate this risk .

Finally , SMEs need to ensure that AI responses do not deviate from socially acceptable norms . Stories about rogue chatbots abound , so SMEs will need to ensure that testing and feedback loops are in place so they can refine and fine-tune the models they use .

Integration and data management

Legacy infrastructures may not be compatible with modern AI solutions and SMEs may need to overhaul their underlying IT infrastructure to accommodate new AI integrations . In addition to addressing compatibility issues , additional security measures – such as access controls and encryption – will be required to maintain the integrity of AI systems .

Data is the lifeblood of AI , but before AI can be put to work it needs high-quality and clean data . So SMEs will need to ensure their data is accurate , detailed and appropriately prepared

Sandy Kahrod , Product Manager : Modern Work , Six Degrees

FINALLY , SMES NEED TO ENSURE THAT AI RESPONSES DO NOT DEVIATE FROM SOCIALLY ACCEPTABLE NORMS .

Intelligent SME . tech

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