Is your business ready for AI?

Reece Gohil at Six Degrees shares top tips on how to de-risk your adoption strategy

 

Artificial Intelligence (AI) promises to transform business operations, improve efficiency, enhance decision-making, and elevate the customer and user experience. As such, it’s no surprise that organisations in every industry sector want to harness its potential fast.

 

Whatever the driver for introducing new AI tools and capabilities, the hype around AI means that many business stakeholders will have unrealistic expectations about AI’s current capabilities. Similarly, they won’t understand just how much data will be needed to power even the most basic AI algorithms and models.

 

To avoid disappointment and prepare for a successful AI journey, your organisation will need to undertake some key technical and business housekeeping activities before getting started.

 

AI governance and compliance

As AI gathers pace, governments around the globe are implementing AI regulations to ensure that AI systems respect the privacy and security of individuals. Organisations looking to harness AI must ensure their AI systems comply with all relevant data privacy and security regulations and standards.

 

Prior to adopting AI, organisations must first undertake detailed in-depth governance and compliance assessments. As part of this process, they should review risk management processes and establish a governance framework to ensure accountability that includes a due-diligence process for any third parties that collect, retain or utilise customer information acquired through API integrations.

 

These data governance and control frameworks should address data integrity, use rights and consent, and data security. In addition, data will need to be classified and the movement and access to that data recorded to demonstrate compliance.

 

By creating AI-specific data governance and security policies, organisations will be able to minimise the risk of any sensitive data being compromised or misused and demonstrate compliance to auditors, clients and regulators.

 

Learning from the past

Remember when cloud-based computing was the ‘Next Big Thing’? A decade ago many companies rushed to adopt cloud technologies, driven by the fear of missing out on new capabilities, or a desire to take advantage of potential cost savings and agility benefits.

 

Convinced they’d be able to figure things out ‘on the fly’, these firms jumped headfirst into the cloud. However, the problems resulting from this approach quickly became apparent. In addition to an extended, complex and costly migration process, their initial lack of preparation led to issues like platform sprawl, poor performance, difficulties leveraging data, insufficient security and access controls, unexpectedly high usage costs… and more.

 

To avoid a similarly painful experience with their AI implementations, organisations should ensure they prepare appropriately, seek out technical guidance, and undertake some considered thinking about which areas of the business are the best candidates for AI pilot projects.

 

Let’s take a look at how you need to get your company ready from both an organisational and tech standpoint.

 

Step 1: Data strategy

AI relies on huge volumes of data and the quality of this data will determine the effectiveness and ultimately the value of its output. In other words, AI is only ever as smart as the information that’s used to fuel its algorithms.

 

Regardless of what type of AI project you plan to implement, building a high-quality data foundation will be critical. This means that data assets will need to be accurate, structured, relevant and centrally managed. If data is fragmented or sitting in silos, you’ll pay the price down the road as you dedicate time and effort to the task of consolidating, cleaning, and deduplicating disparate data pools at a later date.

 

Since data is foundational to AI, organisations should evaluate their data management and governance strategies to ensure that the right data is collected, appropriately aggregated, stored securely, and readily available in a common data model.

 

Therefore, when specific AI applications and tools – like chatbots or decision-making engines – are implemented these will perform effectively, deliver true added value, and meet user expectations.

 

Step 2: Optimise existing tools

Deploying AI can prove an expensive venture if not done in the right way. This includes undertaking some key preparations to ensure that your existing IT environment is performing optimally. A robust infrastructure will provide the all-important foundation for AI platforms that need to process vast amounts of data, often in real-time.

 

In addition to reliable on-premises or cloud-based data storage, the computationally intensive nature of AI means organisations will need to ensure that their cloud-based resources are operating optimally. Meanwhile, to support highly efficient data flows for AI systems, networks should be capable of handling high-bandwidth and low-latency workload demands.

 

The novelty and promising potential of AI means it can be tempting to overlook the importance of extracting maximum value from existing tools. By optimising the current infrastructure and making the most of available resources, organisations will be able to ensure that the foundations for AI are implemented in the most cost-effective way possible.

 

Step 3: Invest in people

Domain expertise will be crucial for ensuring that the adoption and application of AI go to plan and that any solution implemented can be maintained and evolved. From a technical perspective, companies will need to invest in data scientists, software architects, integrators and deployment specialists. Added to this, internal AI specialists will be needed to act as a bridge between business stakeholders and IT teams.

 

Alongside explaining AI concepts to business leaders on how AI can be applied, these specialists will help prioritise pilot projects by identifying where AI technologies can be combined with existing infrastructure and data resources to drive productivity, innovation, or competitive advantage.

 

Finally, employees will want reassurance on how AI deployments will impact their role, together with training on how to use AI tools so they are confident to engage and can use them effectively.

 

A strategic approach to the future

With AI set to dominate the business landscape in 2024, organisations will need to take steps to ensure they are appropriately prepared before they commence their AI journeys.

 

By taking the initiative and optimising systems, reviewing data management strategies, investing in new talent, talking to trusted partners who can help navigate the challenges ahead, and preparing the workforce appropriately, companies will be able to successfully put AI to the test.

 

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Reece Gohil is Microsoft Product Owner at Six Degrees

 

Main image courtesy of iStockPhoto.com