Every business leader knows that artificial intelligence (AI) is here, it is transformative, and it can make their businesses infinitely more efficient and effective.
The question is, are they equipped with the expertise and deployment models they need to implement AI? Probably not yet.
There are many hurdles in the way of successful AI adoption − particularly when enterprises attempt to roll out AI initiatives in-house and need some velocity to satisfy their stakeholders.
Enterprise AI challenges
It is generally accepted that up to 80% of enterprise software deployments fail sometimes after 18 months of work, and we’re seeing the same trends emerging in AI deployments.
One reason for this is that business leaders are experts in their operational requirements, so it's very easy for them to identify the pain points and challenges they want to address with AI. What's harder is being able to understand exactly what AI means in their specific business context.
They need to map the challenge to the solution, get the right stakeholders involved, mitigate business risk in the deployment, and ensure it delivers measurable value and quickly.
They need to grasp what AI means for their organisation in terms of not just what we can achieve, but also who we can achieve it with, and who do we need in the room to be able to deploy AI services and make sure those services are functional and highly performant going forward?
To overcome the challenges and get AI into production and scaling up fast, organisations need expertise to accelerate.
Another hurdle in the way of rapid AI implementation in enterprises is finding the qualified, experienced resources needed to plan, implement, manage and scale the AI project/s. As a relatively new technology, few highly-skilled resources are available − and South Africa is no different.
In addition, the new large language models (LLMs) are different from the mathematical AI/machine learning ones many organisations may already be running; for example, machine learning used to identify transactional anomalies and mitigate fraud risk.
With LLMs, organisations must consider where the data goes, how it is being used, how it is being secured, and whether systems and processes are compliant.
Fast-tracking successful AI
To overcome the challenges and get AI into production and scaling up fast, organisations need expertise to accelerate.
Accelerating digital transformation processes, building and deploying AI solutions can take years, and to install them quickly and securely within a solid risk and compliance framework is a skill many organisations are in the process of developing.
Working with an expert partner not only shortens the enterprise’s time to market, but also improves adoption by achieving small, measurable successes that demonstrate the impacts AI can have within the business.
By demonstrating to shareholders, teams and customers that the enterprise takes AI seriously and has achieved success by operationalising an AI initiative, the business achieves the momentum it needs for broader AI adoption.
Off the shelf solutions, as they’ve always been, can be rigid, limited and limiting when applied to the dynamic needs of an organisation adopting a group of new technologies.
For example, we can specifically identify use cases within an organisational environment to a very granular detail that fixes the problem without the overheads, such as intelligently populating a PDF document rather than having somebody write it all out.
One company I am working with has been able to reduce resources needed for manual document processing by 80% and processing time has been advanced from 1.5 hours to just three seconds – all achieved by simply automating the population of specific PDF documents.
This frees up resources to carry out revenue-generating, higher value and more meaningful tasks for customers. While improving efficiency, it also significantly reduces errors, making data more useful, which ultimately improves the entire customer experience.
In another case, AI implemented for a contact centre quality assurance team offered a 90% improvement in the number of calls the team could assess, for a relatively nominal investment. Where the company previously had the resources to look at 10% of calls, AI enables it to intelligently review 100% of calls and identify which ones it needs to look at in terms of contact centre agent performance, data discrepancies and fraud.
It can also automate quality scoring on all the other agents through sentiment analysis, but there are more use cases for the business: it can also ask its AI assistant, in natural language, ‘what was the most complained about issue this month?’, or ‘how many calls were from new customers?’
It can ask questions on the fly and interrogate that data instantly. Having accelerated just one intelligent implementation, the company now benefits in multiple use cases, including data sanitisation, improving efficiency and enhancing the customer experience.
Another AI success accelerator is deploying a smart layer over existing technology stacks, with preconfigured AI use cases to eliminate the need for complex and expensive technology deployments.
To fast-track AI, it isn’t necessary to develop all new technologies to fit into legacy systems: instead, an intelligence layer deployed over the top of existing business processes moves time to value from months to weeks.
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