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Business adoption of AI promises to deliver new efficiencies, time savings, productivity gains, more significant outputs, better customer service, and improved decision-making, and that’s just to start. However, a carefully crafted AI deployment is essential to maximise the return of AI.
In the UK, around 40% of businesses using AI purchase external AI solutions, 40% develop AI in-house, and the remaining 20% outsource. Your route to an AI deployment will fit your organisation's size, budget, requirements, and technical capabilities. Still, no matter the path, some key considerations exist for a successful AI deployment.
AI adoption requires a strategy, and any strategy requires clear objectives aligned with organisational goals. Particularly with AI, it’s not enough to simply add new tools without a roadmap, careful implementation, monitoring, and review.
The first step to AI adoption is to review company objectives and then closely analyse team roles, data, and workflows. From there, it’s important to identify where AI can add value and the costs and implications. Are these people and processes ready for AI? Also critical is whether your business has the technological infrastructure or tool stack to embrace AI easily. Your task may be more complex if AI doesn’t integrate easily with existing systems, processes, or teams.
An AI roadmap will outline the steps to use AI and detail its deployment, performance review, and risk mitigation. It should clearly align AI's expected benefits with company objectives and have clear goals, KPIs, and an action plan to alleviate any failings.
AI relies on data and, depending on the utilisation may also generate data, such as customer information or deep insights. AI is a quality in, quality out equation. Therefore, a robust data strategy is also necessary to use and scale AI.
Data must be managed from collection, ingestion, and storage to clearing and integration. Data used to train AI systems must be comprehensive but also free of errors or biases, which can be easily compounded with AI. Furthermore, comprehensive data doesn’t necessarily mean high volume. Small but precise data sets can yield results.
For example, training a generative AI chatbot. These tools can have guardrails that prevent the AI from using any data other than that provided by a business. They can be trained in product manuals, sales brochures, website content, and blog articles to swiftly answer a customer's basic or technical enquiries.
AI won’t work if it’s added to technological infrastructure that’s already struggling to handle volumes of data or to slow and outdated systems. The same is true for people. Handing AI tools to overloaded, unmotivated, or untrained teams is a recipe for disaster. To varying degrees, employees fear AI’s impact on their jobs. So, before AI deployment, it’s necessary to provide a foundation of technology and people ready to embrace AI.
A cloud tech infrastructure can be easily scaled to provide bandwidth for AI, leaving perhaps a few hardware upgrades to perform in-house. Legacy systems may be a little more complex, meaning that AI must be deployed as part of a wider digital transformation strategy.
AI is not a perfect technology, but its advantages lead businesses to adopt it quickly, hoping to mitigate any risks along the way. With such an approach, it’s vital to understand all the risks of AI and have processes in place to mitigate them. What are the risks of AI?
These are in the broadest sense. The actual risks will be unique to your business. For example, if you’re using ChatGPT to generate marketing content, then human oversight may ensure accuracy, quality, and relevancy. But, there’s still a risk your marketing team will inadvertently provide confidential customer data to ChatGPT in the process, which can then be exposed or used by OpenAI’s model. Suddenly, you’re in breach of data privacy law.
As another example, you deploy an off-the-shelf AI-powered HR system to scan and screen potential candidates. However, this system is later revealed to have inherent bias because it’s been trained on less than diverse historical hiring information. What are the implications?
McKinsey recommends leaders consider six overarching types of AI risk: privacy, security, fairness, transparency and explainability, safety and performance, and third-party risks.
According to Goldman Sachs, AI could replace 300 million full-time jobs. This is mainstream news, and employees are fearful, although many are also interested in how AI can improve their roles. This fear and interest require careful management. A Salesforce report discovered that 28% of employees use generative AI at work, but 55% use unapproved tools, and 40% use actively banned tools. Paula Goldman, Salesforce's chief ethical and humane use officer, says:
“To realize AI’s full potential, it’s critical that we invest in the employees using the technology as much as the technology itself. With clear guidelines, employees will be able to understand and address AI’s risks while also harnessing its innovations to supercharge their careers.”
There’s a lot to consider for a successful AI deployment. This article touches on just a few elements. Your AI strategy and goals will determine your needs, but a critical component is an AI leader or team that considers and takes responsibility for AI’s implications and potential.
In the US, 37% of larger companies have established AI units, dubbed AI Centers of Excellence (AI CoE). These units combine AI and IT skills with domain experts for specific applications of AI. Their role includes standardising practices and enabling communication, two key factors for adopting such a nascent and still-evolving technology.
As with any digital transformation, adopting AI requires a strategy crafted with a roadmap, goals, process, people considerations, and continuous monitoring and review. An AI deployment requires greater strategic flexibility both because of the breadth of its business and wider economic impact and its newness and fast iteration. Be ready to iterate just as quickly as AI evolves!
Deloitte’s State of AI in the Enterprise report recommends:
“Organizations should develop dynamic ways of assessing their strategy to ensure it remains responsive to ever-changing market and technology developments. As the organization’s core business strategy and AI capabilities mature over time, leaders should continually sharpen their goals, moving beyond staying competitive to increasingly using AI and ML as competitive differentiators.”
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