Solis Corp, a mid-sized tech company, was struggling to retain clients.
The product was solid; however, customers were constantly slipping away.
The team had difficulty managing customer relationships, tracking interactions, staying updated on client needs, and engaging them meaningfully.
The leadership at Solis Corp believed AI could solve these problems.
The initial pitch for AI adoption was this – automated tools to streamline processes, track and analyze customer interactions.
Hence, AI tools were rolled out with the promise of helping employees focus on higher value tasks.
The company implemented the below AI functions:
- Automated analyzation and processing of huge customer information. This ranges from purchase history to product usage patterns.
- Chatbots and virtual assistants to handle simple customer requests. The team was to focus on strategic, relationship-building activities.
- Provide data trends and report insights.
However, soon after the rollout, cracks started to show
The initial failure: More data, less insight
The introduction of AI in organizations is viewed as a key strategy to streamline workflows and improve customer service. However, this approach can backfire, leading to scenarios where employees spend more time inputting data into AI systems rather than leveraging the insights generated to address deeper issues.
For instance, in Solis Corp, the team were inputting large volumes of customer data, including purchase histories, detailed interaction logs, service requests, and customer feedback. This led to information overload. The team faced difficulties in identifying patterns amid the noise, such as identifying recurring customer issues or understanding the context of past interactions.
Despite having access to more data than ever before, the team failed to capitalize on it effectively. For example, AI was being used to automate responses and route tickets, but employees weren’t taking the next step to analyze the data and predict customer needs.
The system could provide suggestions, but without the team fully understanding how to use the information or collaborating across departments, the potential for innovation and improved customer retention was lost.
Moreover, employees were resistant to embrace AI into their workflows, which is a common challenge across industries. According to Gallup, seven in 10 U.S. workers do not use AI effectively at work, and only one in 10 reports using it weekly or more. This hesitation often stems from lack of training, guidance, or resistance to adopting new technologies.
In this team, some employees could not fully leverage AI beyond basic tasks and lacked the skills or understanding needed to integrate insights into their daily operations.
The outcome?
The same old problem persisted. The handover process between different customer service stages remained fragmented. Agents handling onboarding passed customers to another department once their initial issues were resolved. This led to longer resolution times, disconnected experiences, and a lack of comprehensive customer engagement.
The team needed to move beyond simply collecting information and automate the right processes while using AI to support higher-level tasks, such as predicting customer needs, offering personalized support, and enabling collaboration across all departments for a smoother customer experience.
Rethinking business models for AI integration
While many organizations adopt AI with the goal of automating traditional workflows, this approach tends to optimize existing inefficiencies rather than lead to meaningful transformation.
To achieve real change, companies must rethink how they operate at the core. Simply automating bad processes won’t make them better, but rather organizations must recognize the opportunity to transform traditional processes, job roles, and organizational structures – creating a business model that reflects the new nature of work.
Building human-machine partnerships is also essential. For this, organizations must invest in technology that allows employees to focus on less time-consuming, higher-value tasks.
Organizations that have reimagined their operating models to place AI at the heart of their workflows have been able to outperform their competitors by 44% in profitability, innovation, and employee retention.
To break down existing operating models, here are some actionable steps the Solis Corp took:
- Identified where tasks are being duplicated or where handoffs create inefficiencies.
- Broke silos by making insights available across teams. It ensured everyone had access to customer data, product features, and other relevant information.
- Used AI to gather feedback and data insights in real time, helping teams adjust strategies quickly based on customer behavior and needs.
- Invested in training to ensure employees understand how AI tools can help them to make better, data-driven decisions.
Leveraging the power of human-AI partnerships
The integration of AI into the workplace should not overshadow human creativity, judgment, and empathy. Instead, AI has the power to augment a human-centered workforce by amplifying the strengths of individuals and teams.
Here are some targeted approaches to build genuine human-machine partnerships:
1. Integrate AI into decision-making loops.
Consider incorporating AI tools into existing decision-making processes to provide data-based recommendations. However, avoid using AI as a standalone solution without human judgment. Ensure that teams understand AI’s recommendations and can apply them contextually. Companies must take steps to reduce hidden bias in AI tools to build equitable teams.
2. Create AI hubs for cross-department insights.
Centralized AI platforms help teams in accessing shared data and insights. It leads to improved collaboration and faster problem-solving. Avoid siloed approaches where a sole department hoards information. This limits the value AI can provide across the organization.
3. Apply AI to improve daily workflows.
Use AI to automate repetitive tasks and streamline daily operations. However, avoid using it for tasks that require human intuition and creativity.
4. Let AI track performance and give instant feedback.
Organizations can deploy performance management software, such as Keka, that uses AI to analyze performance metrics and provide real-time feedback to employees.
5. Have AI prioritize tasks by urgency and impact.
Some task management tools are equipped with AI capabilities and assess task urgency based on deadlines and project goals. AI can understand these and prioritize automatically based on predefined criteria.
6. Leverage it to spark innovation through trend analysis.
AI analytics tool analyzes market trends, customer behaviors, and competitive landscapes. Teams can share these insights with other teams to inspire new ideas and innovative solutions. Also, it’s important to encourage teams to think creatively about how to apply AI insights.
7. Monitor morale and culture shifts with AI insights.
One best way is to implement pulse surveys to measure employee sentiment and morale. It provides insights into potential cultural shifts within the organization. These trends must be addressed promptly to maintain a positive work culture
Coming to the tech company, it could achieve genuine human-AI synergy for better customer service operations in the following ways:
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A single source of truth for every customer
AI centralized customer data and provided teams with an all-in-one view of customer interactions. No more handoffs or scattered information; every team member could step in at any stage with complete context, reducing friction in the customer experience.
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Breaking down departmental walls with data bridges
AI became the bridge between silos. Customer insights weren’t just confided to the customer service team, they flowed smoothly to product developers and marketing teams.
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Shifting from mundane to meaningful work
AI took over repetitive tasks like ticket categorization and data entry. It freed up human agents to focus on more meaningful customer interactions where they can leverage their emotional intelligence and problem-solving skills.
See how Keka’s AI simplifies your daily work
Balancing technology and human insight
While many organizations implement systems that generate detailed reports and insights to improve daily operations, the true power of these insights comes from how they are used. Human judgment must be combined with data to ensure that decisions align with organizational goals and culture. A few innovative HR platforms, such as Keka, assist in this integration by offering solutions that ensure human intervention remains central to decision-making. With Keka, organizations can track performance and gain feedback on employee engagement and productivity. It also provides a centralized space where departments can share insights and work together.
