How to Develop an Internal AI Change Management Culture

Understanding the Importance of AI Change Management Culture

What is AI culture, exactly? We’ll define it as a company culture that supports AI deployments and allows the business to mitigate risks and extract value from AI tools.

On the ground level, being an AI-first company means that your employees:

Have the right skills to use AI efficiently
Are mindful of the associated risks
Show willingness to develop their AI skills further and experiment with tools
Embrace iterative changes in processes and tools

4 Benefits of Becoming an AI-First Company

Leveraging AI in a company can pay off in a variety of ways:

Enhanced productivity: AI tools can boost employee productivity by 66%, according to Nielsen Norman Group. A Stanford University study, in turn, demonstrated that GenAI assistants can boost call center agents’ productivity by 13.8%.
Improved customer experience: 61% of customers are willing to spend more if companies personalize their approach to serving them. AI is the driving force behind meaningful personalization at scale, from tailored recommendations to real-time segmentation.
Cost reduction: 39% of GenAI adopters already report cost savings across business functions, with the highest cost savings observed in HR and risk, legal and compliance. In turn, analytical AI adoption has already brought cost savings to 35% of adopters.
Revenue increases: 58% of analytical AI adopters report revenue increases. GenAI tools have boosted revenue for 44% of adopters. Companies that add AI to their data capabilities in particular see a 30% revenue increase, on average.

Why Developing AI Culture Is Crucial

Organizations don’t benefit from AI adoption equally. An organization’s AI maturity and AI culture play a key role in the outcomes of the AI transformation.For example, the total shareholder return CAGR of leaders in digital and AI capabilities can be 2.3x to 6.1x times higher than that of laggards.

Following best AI change management practices also correlates with higher returns on investment in technology. Just take a look at McKinsey’s survey of GenAI adopters. It revealed that organizations that receive the highest returns are more likely to follow these practices than other respondents:

Senior leadership understanding how GenAI can generate value for the business (64% vs 39%)
Having curated learning journeys tailored to every role to build GenAI skills for technical talent (43% vs 18%)
Nontechnical personnel understanding the potential value and risks of using GenAI in their daily work (52% vs 21%)

Oleksandr Trofimov, CTO at unicrew, agrees that company culture is a key ingredient in successful AI adoption. “AI transformation is essential for success, but it cannot happen without the right company culture. We learned this firsthand when introducing new AI initiatives in our engineering team. The key lesson is to address the operational challenges of acquiring new skills and knowledge.”

The Role of Leadership in AI Change Management

It’s the leadership’s responsibility to:

Establish the vision for AI initiatives
Set clear guidelines on how to use AI for business purposes
Create a robust AI governance strategy to mitigate risks
Ensure enterprise-wide alignment
Select the right use cases for AI adoption based on business impact and feasibility

As Oleksandr Trofimov puts it, “In software development companies, engineers are often overwhelmed with tasks. However, it’s the leader’s responsibility to provide a clear vision and demonstrate the benefits of venturing into new territory.”

A clear AI framework does more than just bring your teams on board and protect your business against risks. It also has a positive impact on how employees interact with AI tools. For example, employees at companies with clear AI guidelines are six times more likely to have experimented with AI tools.

7 Pillars of Championing AI Adoption

To ensure a successful AI adoption, senior leaders should focus on:

Vision: Identify the strategic opportunities for AI-driven transformations, such as revenue increases, improved customer engagement, reduced costs, or improved efficiency and productivity. Ensure the company’s strategic alignment with the AI vision.
Metrics: Decide how to measure the success of your AI initiatives. Focus on business metrics rather than financial ones, use benchmarks, and monitor metrics consistently and continuously.
Gap and barrier analysis: Pinpoint resources needed for AI transformation and potential barriers to AI adoption. Those barriers can include lacking skills, subpar data quality, outdated data security protocols, etc.
Risk management: Consider potential regulatory and reputational losses stemming from AI use and how to prevent them. Take into account inherent risks of AI: false, biased, or inaccurate outputs, data privacy and security, and lack of transparency and explainability.
Enterprise-wide framework: Establish guardrails to ensure ethical, safe, and secure AI use. Be clear about what is allowed and prohibited when it comes to AI use.
Governance: Conduct impact assessments of AI systems before deployment, establish an independent review board, and include responsible AI use in policies. Conduct regular audits of AI systems and enable continuous improvement.
Stakeholder support: Foster the culture of cross-functional collaboration. Seek out and act on feedback from workers from the ground level to the senior management.

Case Study

Snaplore is a knowledge management tool that leverages cutting-edge AI capabilities to turn video calls into structured briefs without human involvement. It also comes with a Snaplore Bot, a custom AI assistant powered by GenAI, and intelligent content analysis.

unicrew’s CEO, Tural Mamedov, was behind the push to implement these AI capabilities in Snaplore. Thanks to his initiative, Snaplore became a powerful tool for aggregating, sharing, and developing the team’s knowledge. As a result, the team could implement AI in commercial projects and deliver value to clients faster.

Embracing Learning and Innovation

Fostering the culture of innovation and continuous learning is deemed the most important AI initiative success factor, according to a Bain & Company survey. 68% of respondents cited it as crucial, ahead of even strategic vision and strong leadership (49%).

Enabling this culture means embracing learning from failures and experimenting with AI to power continuous innovation.

What separates AI leaders from laggards isn’t how well they map out the AI transformation. It’s how they recognize failures and learn from them.

So, instead of trying to get everything right from the get-go, embrace an iterative approach to your AI initiatives. The agile methodology is a suitable way to manage and improve upon them.

In practice, it means:

Quickly testing hypotheses as you experiment with technology
Learning from both successes and failures
Conducting regular reviews
Improving the solution in iterations

When it comes to AI, following agile principles is a solid foundation for continuous research and development – which should be a priority for your organization.

Building and Managing Knowledge Resources

IBM’s Global AI Adoption Index puts limited AI skills and expertise into the number one spot among barriers to AI adoption. However, only 34% of organizations are actively reskilling employees to update their skills for new AI tools.

As for the skills in question, Microsoft’s Work Trend Index surveyed business leaders on the AI skills they’re looking for in their employees. The results were:

Analytical judgment (30%)
Flexibility (29%)
Emotional intelligence (27%)
Creative evaluation (24%)
Intellectual curiosity (23%)
Bias detection and handling (22%)
AI delegation (prompts) (21%)

While these soft skills are integral to making the most out of the technology, AI users also have to be well aware of the potential and risks of their tools. Developing a centralized knowledge base enables the transfer of this information, serving as a single source of truth available anytime, anywhere.

However, knowledge sharing is only the first step in leveraging AI for businesses striving to become leaders. As Oleksandr Trofimov, CTO at unicrew, puts it, “Another important lesson is to put the acquired knowledge into practice. Whether it’s for an internal product or R&D, theoretical knowledge alone is not enough. Becoming an AI-driven company requires not only certifications but also the practical application of what we’ve learned.”

6 Tips for Developing an Effective Knowledge Base

An internal knowledge base is a centralized library of FAQs, standards, guidelines, video tutorials, how-to guides, documentation, and other resources. It allows employees to find answers to their questions without seeking IT support.

Here’s how to make your knowledge base effective:

Keep it structured and organized
Enable comprehensive search and seamless navigation
Ensure all common questions are covered in the base
Make the content easy to scan and understand
Keep the content up-to-date by regularly auditing the base
Make the knowledge base a single source of truth across the organization

3 Best Practices for Knowledge Management

Knowledge management doesn’t end with creating a centralized knowledge base. Here’s what else you can do to enable knowledge sharing:

Develop AI mentorship. Tacit, implicit knowledge gained with experience can be difficult to put into writing. Introduce a mentoring program to enable its transfer between more and less AI-savvy employees.
Capture unstructured knowledge. With the help of GenAI, you can transform unstructured knowledge – like emails or forum threads – into knowledge-based articles.
Ensure organization-wide adoption. A knowledge management system can be effective only when people use it. So, provide adequate training to enable adoption – and proactively maintain it.

Ensuring Data Security in an AI-Driven World

73% of GenAI-using employees believe it introduces new security risks. Data security and privacy are also on customers’ minds:

57% of consumers worldwide agree that AI threatens their privacy.
63% of U.S. consumers are concerned about the cybersecurity risks of AI solutions.

In addition to employee and consumer concerns, regulators are also worried about data protection. In the EU, for example, ensuring data security and privacy is part of the AI Act, and personal data protections are already in place in the GDPR.

10 Policy Changes to Consider

To ensure compliance and avoid reputational losses, consider:

Preventing shadow AI, i.e., employees using AI tools without the IT team’s knowledge, with clear guidelines and/or firewalls and VPNs
Outlining what information can and can’t be shared with AI tools, especially if they’re third-party solutions
Setting up appropriate data controls to prevent unauthorized access, switching to a “need-to-know” approach to data access
Anonymizing personal data and obtaining consent if you’re using it for training or fine-tuning custom AI tools
Using synthetic data instead of real personal data for training AI models if possible
Following encryption and data masking best practices to protect data, including encryption at rest and in transit
Setting up data backup, loss prevention, and disaster recovery procedures and ensuring they’re followed enterprise-wide
Securing access with phishing-resistant multi-factor authentication and authorization
Regularly testing AI tools (adversarial, penetration testing) and conducting security audits
Conducting impact assessments on data protection for AI initiatives

Don’t Overlook the Training

Apart from putting these principles into a documented framework, you need to bring your employees up to speed on how to use AI for business tasks securely. So, invest your resources into appropriate training on data literacy, cybersecurity, access controls, etc.

Make sure any changes in policies are communicated well to avoid a transparency gap. While 31% of executives say they have AI guidelines in place, only 18% of workers say the same about their workplace.

Breaking Organizational Silos for AI Success

AI projects can’t be the prerogative of the IT team if you want it to deliver a meaningful impact across business functions. As unicrew CEO Tural Mamedov explains: “It’s crucial to involve the entire company in AI transformation, not just the engineering department. This way, we can see how AI impacts all aspects of the business.”

Collaborating across functions enables:

A holistic approach to AI initiatives that take into account feedback across the organization
Fresh perspectives and ideas that drive innovation
Continuous learning among participants
A deeper understanding of AI’s impact across functions
Greater engagement and level of commitment to the AI initiative across departments

On top of that, an AI culture that encourages cross-departmental collaboration leads to improved job satisfaction and employee retention.

10 Steps to Fostering Cross-Functional Collaboration

Here’s how you can build a culture of cross-functional collaboration:

Bring everyone on board from the very beginning. Don’t wait to connect members from other departments with AI experts. Otherwise, you risk losing time on bringing newcomers up to speed – and missing out on their valuable input.
Take the lead. Identify activities that can benefit from cross-functional collaboration and help team members build connections and kick off collaboration.
Use the right tools. To keep everyone in the loop, make sure you use the same project management and communication toolkit across functions.
Keep apps to a minimum. Juggling 16+ apps makes knowledge workers three times more likely to miss messages and actions compared to using 1-5 apps.
Break data silos to improve transparency. Much like organizational silos, data silos hinder productivity. Ensure data flows seamlessly between departments (while enforcing data controls, of course).
Encourage feedback. Establish a feedback system for team members to share their opinions anonymously and/or confidently. Keep an open mind and act on the provided feedback.
Recognize collaboration. Adapt the way you track employee and team performance to incentivize cross-functional cooperation. Consider developing a reward system; celebrate both small and big accomplishments.
Communicate on the vision, strategy, and expectations. Be clear about what’s expected of every team member and how their work fits in with the enterprise-wide AI vision and strategy. Make sure the AI policies are well-understood, too.
Keep meetings meaningful. Having inefficient meetings is the number one obstacle to productivity. So, meet when necessary, create and stick to the agenda, and keep discussions structured. Replace meetings with asynchronous or synchronous collaboration tools whenever possible.
Provide regular updates on your AI initiatives. Communicate where the company is headed, what its AI roadmap is, and what future investments are in technology. Consider hosting AI forums to help employees learn about AI initiatives.

How do you encourage knowledge hoarders to share their resources with others? One word: recognition. They have to feel that their contributions are valued.

To achieve this effect:

Start a formal recognition program for contributors
Encourage peer-to-peer acknowledgements
Offer small incentives and rewards for sharing knowledge

Knowledge sharing can happen in many forms, from workshops and AMAs to mentorship programs. You can also extend knowledge sharing beyond with a digital version of your organization’s town hall – a knowledge-sharing platform.

However, don’t confuse this platform with a knowledge base. A knowledge base exists as a somewhat static central repository of information with one-way communication. A knowledge-sharing platform is akin to an internal social network-slash-project management tool. It allows just about anyone to:

Ask questions and start discussions
Share insights and helpful resources
Celebrate accomplishments
Provide updates on the company’s policies, milestones, or activities
Centralize the company’s files, such as templates, documents, and presentations

Popular off-the-shelf knowledge-sharing platforms include Snaplore, Notion, Confluence, Microsoft Sharepoint, and Google Workplace.

Enabling Customer-Centric AI Development

Mature AI organizations are almost twice as likely to use customer success metrics in measuring AI initiatives’ impact than others. At the same time, they’re less likely to focus on metrics related to business growth, financial requirements, and estimated value.

In their turn, customers want to feel personally catered to. 82% admit personalized experience impacts their brand choice at least half the time. Ergo, better customer experience translates into increased loyalty and lifetime value and reduced churn.

Customer-centric AI strategies typically involve leveraging AI to:

Provide hyper-personalized customer experiences
Improve operational efficiency with AI-powered automation to reduce waiting times
Gain a real-time 360-degree customer view
Offer self-service customer support options for common queries
Enhance customer support agents’ productivity with GenAI assistants
Streamline customer journey with automation

How to Nail Customer-Centric AI Adoption

To adopt a customer-centric approach to AI initiatives:

Identify key sources of friction in customer experiences to map out potential AI solutions (e.g., long ticket resolution times can be dealt with an intelligent triaging tool)
Set clear objectives and customer-centric KPIs (e.g., retention rates, satisfaction scores, share of customer wallet)
Build trust by identifying potential concerns around AI use and addressing them head-on (e.g., data privacy, biases, inaccurate responses)
Test AI initiatives and improve them in iterations based on measured metrics and qualitative feedback

A data-driven understanding of your customers is key to finding the right AI use cases. Ergo, if your customer data is still residing in silos, you’ll first need to centralize it and turn it into insights.

Ready to Build a Sustainable AI Culture?

Let’s recap the key strategies for building an internal AI change management culture that supports your transformation:

Ensure the founders and managers lead by example and set the enterprise-wide framework and vision for AI use
Foster a culture of continuous learning and innovation and encourage learning from mistakes
Build and manage internal knowledge resources to ramp up AI skills across the organization
Keep your data secure by following cybersecurity best practices, preventing shadow AI use, and training your team members
Break organizational silos and foster a culture of cross-functional collaboration
Recognize and incentivize internal knowledge-sharing
Keep customer needs in mind when designing and rolling out AI initiatives

The right AI culture equals improved AI skills across the organization, higher innovation capacity, and more effective risk mitigation. In the long run, this is what separates AI leaders from laggards.

Need a hand in identifying gaps in your company’s culture to make it AI-ready? Our AI expertise is at your service. We can help you identify the AI potential for your company, ensure alignment with business goals, introduce robust change management, and provide ethical AI guidance.

Discover our AI consulting services, or go ahead and drop us a line to discuss how we can help you ramp up your AI maturity with a cultural shift.