9 Problems for CTOs Adopting AI Solutions: How to Solve Them

As anyone in the contact center industry will tell you, artificial intelligence is now an inextricable aspect of the ecosystem. AI is being deployed across countless functions to increase efficiency, improve the customer experience, and enhance ROI.

Of course, new tech brings speed bumps and there’s plenty of internal friction around AI, especially on the IT side of things. In fact, a recent survey of tech leaders showed that negative sentiment around AI (47%) outweighed the positive (37%).

There are reasons for apprehension, which we’ll explore below, but plenty of cause for optimism too. This transformative technology, if implemented with care, can open up a host of fresh possibilities for call centers.

We’re going to explore the biggest challenges CTOs face when adopting AI in the contact center—and discuss how you can overcome the friction and embrace fresh possibilities.

What’s slowing AI adoption in contact centers?

While AI is being deployed across the call center industry, internal concerns among CTOs, tech leadership, and IT teams are likely to slow adoption unless properly addressed. Common issues include:

• Data quality and reliability: Weak data brings potential for inaccurate outputs and hallucinations. This makes it risky for call center operations, especially those directly involving customers or their sensitive personal information.
• Lack of clear guidelines: The absence of clear guidelines and policies around AI usage, either internally or externally, can lead to confusion, sparking resistance and ethical concerns.
• Potential for high costs: While AI brings the promise of cost-efficiency, it may involve significant upfront costs. This can be a tough sell for those organizations with limited resources, despite the promise of a healthy long-term ROI.
• Challenge of tracking AI performance: As you would evaluate the performance of your employees, you must also monitor the efficacy of your AI tools. Given the relative infancy of this technology, many lack the necessary framework to do so.
• Finding the right staff: It can be difficult to find (and subsequently retain) employees with the kind of AI skills you’re looking for, making it a real challenge to put together the team you need for effective AI implementation in the call center.
• Commitment to continuous learning: The nature of AI means that it’s changing on a near-daily basis. This requires tech leaders to commit to ongoing learning and development in the area—a big ask given their other responsibilities.

9 problems CTOs face when adopting AI (and how to solve them)

We’ve looked at the concerns that CTOs have raised, now let’s look at solutions. Below, we’ve outlined the 9 biggest challenges CTOs face when adopting AI solutions—and how you can overcome them.

1. Working with quality data

You need clean, unbiased data to successfully operate AI models. This likely means you’ll have to invest in data cleansing and management tools at some point and, of course, establish clear data governance policies in your organization. 

For example, when it comes to labeling customer data, you must do so accurately and ensure that AI models are never making biased decisions based on demographic information.

2. Maintaining data security and privacy

Data quality is one thing, protecting real-world customer data is another. Concerns around security are proving to be one of the biggest hurdles to successful AI implementation for CTOs.

This is particularly true in the contact center environment, where leaders are responsible for sensitive customer data, and mishandling of such data can lead to severe legal, financial, and reputational repercussions. So, what can we do to mitigate this risk?

• Ensure robust data encryption—both for data in transit and data stored in your systems
• Deploy strict access limits according to role, responsibility, and necessity
• Leverage QA software to monitor interactions and maintain compliance with legislation like GDPR, CCPA, and the aforementioned EU AI Act
• Implement multi-factor authentication across your organization
• Put a robust data breach response plan in place, covering mitigation, reporting, customer communications, and more
• Carry out regular audits to identify potential vulnerabilities

It’s also vital that any third-party AI tools have their own security measures. For example, at Scorebuddy, customer data is never available to other customers or external large language models, and is not used to improve any LLM or 3rd party product. Our AI model runs within our own infrastructure and customer data doesn’t leave this ecosystem.

3. Providing a test & compare environment

Before rolling anything out, you absolutely must establish a test and compare environment for your AI tools. Given the dizzying rate of change within the artificial intelligence space, your testing must be not only rigorous, but consistent and ongoing.

This will allow you the ability to experiment with different models, compare performance, and flag any potential issues, biases, or ethical concerns. Anyone who’s played around with ChatGPT, Claude, Gemini, etc., knows that the outputs can vary significantly from model to model, so it’s critical that you compare results across the board.

In doing so, you can minimize the risk of failure (and wasted resources), build internal confidence in artificial intelligence, and keep your AI usage aligned with your own organizational values.

4. Ensuring ‘human-in-the-loop’ oversight

If you’re using AI in any capacity for your customer-facing operations, it’s essential that you maintain human oversight into the process. This is a key safeguard to ensure your AI usage remains responsible and ethical—and your customers stay happy too.

In practice, ‘human-in-the-loop’ oversight involves things like:

• Establishing clear escalation protocols

• Delivering ongoing AI-related training for staff

• Ensuring transparency about how your company uses AI

For example, if you’re using AI as part of your support function, it may be tasked with generating automatic responses in customer interactions. In this scenario, you’ll want to assign agents to regularly review these AI-generated responses and monitor conversations so they can intervene (if necessary) to protect customers and your brand.

5. Establishing clear AI guidelines and policies

Comprehensive guidelines and policies around AI usage, data handling, underlying decision-making processes, and other related functions will go a long way to soothing the concerns around artificial intelligence in the contact center. 

Clear documentation is necessary to meet emerging ethical and legal requirements around AI implementation. This is especially important given the new EU AI Act which came into force in August 2024, which establishes certain obligations depending on the level of risk associated with your particular use of AI.

6. Investing in AI training and development

Education is the key to establishing a responsible culture around AI usage, both on the ground level of your organization and up through the ranks. You must make sure that every stakeholder understands:

• Ethical implications of artificial intelligence

• Potential for bias and discrimination

• Importance of maintaining fairness and transparency

By allocating adequate resources to the delivery of comprehensive, regular training around artificial intelligence, you’ll be able to significantly reduce the risk of using the technology and promote safer innovation.

7. Promoting open communication and collaboration

As with employee training, open communication and collaboration is a cornerstone of responsible AI adoption in the call center. Transparency is key to getting buy-in from everyone—from customer-facing employees to the C-suite.

Soliciting staff feedback and making room for diverse perspectives across the business brings unique benefits too. It adds fresh insights into how your AI rollout is progressing and makes it more likely that you’ll catch any ethical concerns and make sensible decisions.

8. Focusing on measurable outcomes and ROI

At the end of the day, artificial intelligence is another tool in the contact center ecosystem and, like any other, its proponents will be expected to demonstrate tangible benefits to executive leadership, board members, and any other stakeholders.

To do so, you’ll need to establish and monitor relevant metrics, key performance indicators, return on investment, and more. Of course, it’s also vital that you do not neglect ethical standards of AI deployment in pursuit of growth targets.

9. Staying informed about AI advancements

As noted earlier, the rapid rise of artificial intelligence means that the landscape is in a constant state of flux. Technology leaders, especially those in the call center space, must make it their business to stay informed about new developments, best practices, ethical concerns, and so forth.

In doing so, they will be better equipped to keep their organization on track and handle any challenges (or opportunities!) that come along.

The role of contact center QA in safe AI adoption

While AI can be deployed to power contact center quality assurance functions, your QA program can also serve as a means of monitoring and tracking AI’s performance.

As it stands, 52% of tech leaders say there is no evaluation process in place with regards to their AI output.

Done right, it’s a symbiotic relationship which strengthens both sides. QA can power safer AI adoption by:

• Evaluating AI-powered support interactions and surfacing those that require human review.
• Monitoring the accuracy and efficiency of AI performance to ensure customer satisfaction and regulatory adherence.
• Identifying potential bias in AI-powered conversations by analyzing support interactions and customer feedback.
• Supporting human-in-the-loop protocols by flagging AI-generated responses or customer interactions that need human intervention.
• Documenting AI usage and performance so you can report on it, highlight areas for improvement, and communicate findings to stakeholders.
• Analyzing QA data to optimize AI models and better align your implementation with changing business aims and customer requirements.

How we deploy AI responsibly at Scorebuddy

At Scorebuddy, we ensure responsible usage of artificial intelligence in our organization and software by following the steps we’ve discussed above. In particular, we’ve established solid foundations by focusing on the first three items we mentioned:

• We work with quality data

• We provide a test & compare environment

• We ensure ‘human-in-the-loop’ oversight

We’ve developed our own bespoke development framework in order to benchmark, test, and evaluate new foundational models. This enables us to keep on top of new features as they emerge, while guaranteeing that we have the appropriate guardrails in place for safe, secure AI deployment.

Meet AI challenges head-on for successful adoption

Many tech leaders, with good reason, are approaching call center AI with a healthy dose of skepticism. While there are clear potential upsides in terms of long-term cost savings, customer experience, and staff productivity, there’s friction to overcome first.

Tackling the problems we’ve addressed can alleviate concerns and protect your organization from the repercussions of irresponsible implementation. Not only will this help safeguard your business, it will also unlock the full potential of contact center AI:

• More efficient operations

• Expanded customer support offerings

• Accelerated quality assurance function

• And more

If you’d like to learn more about how Scorebuddy safely manages AI usage, or try out our GenAI Auto Scoring solution, contact the team today.