EXTRACT | ‘African Artificial Intelligence’ by Mark Nasila

ABOUT THE BOOK

Artificial intelligence (AI) is upending life, work and play as we know it … and it’s only just getting started. The rise of AI is a milestone on par with the discovery of fire, the invention of the wheel and the creation of the internet. In short, AI is going to change everything.  For some, that’s an exciting prospect.  For others, it’s terrifying. However you feel about AI, there’s no escaping it, whether you’re in a global metropolis or a farmer in rural KwaZulu-Natal.

EXTRACT

A SUCCESSFUL AI STRATEGY STARTS WITH THE RIGHT USE CASES

‘Understanding the potential of AI (both good and bad) starts by understanding how AI works and what it can really do,’ says Bill Schmarzo, Dean of Big Data at Dell Technologies. ‘That’s probably our best chance to ensure that AI works for us versus us working for AI.’

In the previous section, we considered why businesses need an AI strategy to successfully harness the emerging technology of AI that’s set to upend every sector. This section is about how to get started, and it focuses on the importance of choosing the right use cases to ensure success, and to make it easier to get buy-in to AI-powered solutions from the whole enterprise.

Becoming an AI-driven organisation means fully digitising the business process. It starts with using AI to augment or automate tasks. But to get to that point, first it is essential to establish a culture where data is trusted. That means business-wide data literacy and appropriate data collection, sorting, and storage solutions. Because if teams can see the value they can derive from data and that the data is sound, it makes trusting that data to AI all the easier.

Managing expectations about AI’s capabilities is important at the outset of an AI adoption journey through AI literacy. This starts with identifying appropriate use cases: activities where AI can reasonably be expected to be effective and demonstrate success, which can then be adapted to other processes and encourage support from other sectors of the organisation in the process.

It also means starting with the outcomes in mind. That is, by understanding the organisation’s priorities, goals and customers, an organisation is better positioned to create a practical AI strategy that can demonstrate its value sooner, and likely be monetised earlier.

It often makes sense to start with a clear task that’s very data dependent and for which the necessary data exists in the organisation already. By bringing on experts to help find that use case it can be implemented as a proof of concept to reduce resistance in the organisation. Thereafter, the organisation can identify subsequent projects that can benefit from similar AI implementation, whether trimming costs in a particular activity, reaching consumers in new ways, or reducing repetitive work.

Use cases will differ for each company – and may well differ by business unit – but the primary objective remains to demonstrate AI in action in a use case that creates financial, operational, or customer value.

Some potential use cases include developing more intelligent products or services, automating repetitive tasks or manufacturing processes, improving data security and risk estimation, predictive analysis to identify trends, or creating personalised customer experiences via marketing efforts or support services.

Detailing an organisation’s potential use cases makes it possible to rank them by importance, because at first it’s unlikely a business will be able to do all of them, so instead it can pick some of the manageable ones to start with. It may also be sensible to pick those most likely to get buy-in from leadership, and those with short-term horizons, so that value can be demonstrated as swiftly as possible.

But what should be the process of identifying the most valuable AI use cases?
Selecting appropriate AI use cases involves a methodical approach, based on clear criteria and a shared comprehension of how AI can enhance your specific business context, and is designed to instil confidence in your decision-making process. The process involves two key elements: identification and prioritisation. It requires strategic alignment with business priorities, assessing data relevance and predictive potential, and considering industry standards and AI maturity. Identifying suitable AI applications can be a daunting task, but with structured steps, you can ensure success. It’s crucial to avoid imitation or reliance on existing templates. This phase may seem challenging, but by following a methodical approach that includes strategic alignment, data assessment and consideration of industry standards and AI readiness, you can be prepared and aware of potential challenges in AI implementation.

Assessing strategic importance:
Firstly, assess your organisation’s strategic priorities to determine the focus areas for AI implementation. Depending on whether your strategy aims to address existing challenges or explore new opportunities, different AI use cases may emerge. Look for areas with high potential return on investment (ROI) that align with your overall strategic objectives.

For instance, in the context of quality control for mechanical components, traditional methods rely on expert manual inspection using haptic feedback. However, if your company is expanding to a new manufacturing site, relying solely on manual inspection could increase costs due to higher salaries at the new location and difficulties in transferring experienced experts to train new employees.

Identify bottlenecks or inefficiencies:
Focusing on the granular challenges hindering your processes can help you identify bottlenecks in your operations. Look for slow, inefficient, or error-prone tasks, particularly those involving repetitive actions or customer interactions. Alternatively, target areas that are ripe for innovation. Once these challenges are identified, explore how AI can offer solutions. Successful implementation requires selecting suitable use cases as well as also understanding AI/ML capabilities and limitations. Define your expectations and articulate how AI can address these issues to establish realistic goals, allocate resources effectively, and gauge project success.

Typical use cases include operational improvements to drive efficiency, enhance customer engagement, manage risk, and ensure regulatory compliance. For instance, if irregular stoppages disrupt your production line, employ root-cause analysis, advanced analytics, and AI algorithms to effectively predict and mitigate these issues.

Look ahead or leverage emerging trends:
Try to identify your industry’s trajectory but be cautious of succumbing to the allure of AI success stories and hype, particularly with the current prominence of technologies like chatbots and generative AI. Despite the temptation to emulate others’ successes, successful AI projects involve numerous factors beyond initial impressions, including data quality, team capabilities, operational frameworks, and change management protocols. Each company’s circumstances are unique, making it essential to avoid blanket assumptions about AI’s applicability based on the achievements of others. Additionally, discerning genuine ROI from mere marketing gimmicks can be challenging. While acknowledging these complexities, staying abreast of industry trends and observing successful AI implementations can offer valuable insights for identifying new opportunities and potential pitfalls. It’s crucial to maintain an open-minded approach throughout this process.

African Artificial Intelligence: Discovering Africa's AI Identity by Mark Nasila is published by Tracey McDonald Publishers. Extract provided by JDoubleD Publicity.