4 Steps for CX Leaders to Safely Implement AI

This guide first appeared in the market study AI in CX: The Practicalities of Implementation, sponsored by CSG.

For decision-makers, it’s no longer a question of whether or not to invest in AI. It’s how to apply it.

Fifty-four percent of CX leaders have already invested in AI driven CX solutions. The technology’s promises are enticing: faster issue resolution, richer journey maps, conversational queries and accelerated insights are all expected to streamline CX operations and improve customer journeys.

Before CX leaders can begin to realise such benefits, they need to have a strategic AI implementation plan in place. With innumerable use cases and shifting regulatory standards, it’s critical to establish clear objectives ahead of time to safeguard the investment.

How can organisations safely introduce AI in a way that will get the most out of the technology? Align on four considerations before getting started to protect the programme’s long-term success.

1. Keep the customer first

CX leaders must lead with the problem to be solved rather than the tool to solve it. AI should be introduced only after assessing areas for improvement within the customer experience. The key is to address business needs strategically instead of taking a scattershot approach for a more controlled AI implementation.

Start by identifying customer needs. Leaders can pinpoint opportunities to target with AI by asking the following questions:

• What are the strategic goals that the CX team is working to accomplish? Are there opportunities to meet them in less time by introducing AI?
• In which areas of the team are resources sparse? How could AI supplement the need?
• Where does friction surface in the customer journey? What are the most common pain points, and how can AI help overcome them?

Preserving a customer-centric perspective helps focus AI use cases on improving CX so that implementation efforts yield positive business outcomes. If the organisation is grounded in its strategy before taking the first steps, there is a greater likelihood that integrating AI will be a smooth process.

2. Start with clean data

AI output is only as good as its input. Many organisations report encountering challenges in AI implementation from a lack of clarity in underlying data. Using clean data is essential for yielding usable results.

To avoid unreliable results, focus on quality over quantity. Data needs to be clean, structured, and accessible. Root out inaccuracies and biases before feeding an AI model to prevent it from producing skewed outputs.

CX leaders can prepare for a safe AI implementation by investing in better data management. Research predicts that 75% of organizations will have established a centralised data and analytics centre of excellence by 2025. Such an initiative goes hand in hand with successful AI application, as improved data practices directly support accurate outputs.

3. Prioritise compliant processes

AI is still in its infancy, and privacy standards are constantly evolving alongside the technology. CX leaders aren’t the only ones closely examining its implications – regulators are also taking a strong interest in the potential ethical and legal consequences of AI.

Consumers are generally apprehensive about how companies will use their personal data to advance AI and tend to support increased federal regulation to protect their privacy. Businesses preparing to roll out AI programs can’t treat compliance as an afterthought. Building it into the early stages of the implementation to follow existing rules helps businesses remain agile as new standards surface.

Bring compliance teams into the picture early on, and make their task easier by adhering to these best practices:

• Start with an ethically straightforward use case to simplify regulatory compliance
• Follow General Data Protection Regulation (GDPR) requirements to keep customer data private
• Collect customer consent and make opting out easy before offering services that use customer data
• Chatbots hallucinate up to 27% of the time. Reduce the incidence of erroneous results by training the AI model to indicate if it doesn’t know the answer

The sooner compliance considerations are factored into an AI implementation, the more efficiently new policies can be integrated as needed.

4. Establish a continuous monitoring strategy

No matter your organisation’s level of readiness, it’s important to assess the performance of your AI implementation at every stage of the process.

By one survey’s measure, 81% of business executives are either developing or have already developed standards around generative AI for their organisations. But setting up guardrails shouldn’t stop there. Safely introducing AI requires continuous monitoring to catch and correct issues as they arise.

Not only does continuous monitoring position organisations to course-correct quickly when plans go awry, but it also allows teams to measure incremental progress and document successes to win stakeholder support. Collect and analyse user feedback and AI training data to establish key performance indicators (KPIs) and tally wins along the way.

Employ continuous monitoring effectively by applying three techniques:

• Automate real-time data analysis by integrating cloud-based monitoring tools
• Define KPIs that align with business objectives before introducing AI
• Establish a cross-functional monitoring team to measure progress from different angles

Constant assessment promotes constant improvement. Automating continuous monitoring processes supports the health of an AI programme by catching potential issues early and often.

Getting From Could to Should

There’s no such thing as a risk-free AI implementation. But taking proactive steps to mitigate threats – like those outlined above – can pay dividends down the line. Maximise the benefits of AI and minimise room for error by strategically integrating safeguards into the process.

When organisations lead with the customer’s needs in mind and exercise caution in handling their personal data, they can move from imagining what could be done with AI to confidently deciding what should be done.