Tag: AI

An AI-native development team is one where AI agents are embedded into every stage of the software development lifecycle, not bolted on as an afterthought. These teams pair experienced engineers with AI-driven workflows for code generation, testing, review, and deployment, producing higher-quality software faster while keeping humans accountable for architecture, security, and product decisions. The distinction between “using AI tools” and “being AI-native” is now the defining factor in engineering team performance.

That gap is widening quickly. McKinsey’s research across 600+ software organizations found that companies with 80% to 100% developer AI adoption saw productivity gains exceeding 110%, while teams with lower adoption reported only incremental improvements (McKinsey, 2025). The difference is not the tools. It is how deeply the team’s workflows, hiring, code review processes, and quality standards have been rebuilt around AI.

This guide breaks down what AI-native teams actually look like, the practices that separate high performers from the rest, and the three paths organizations take to get there.

AI-Native vs. AI-Assisted: Why the Distinction Matters

Most development teams today use AI in some form. According to the JetBrains Developer Ecosystem Survey, 85% of developers regularly use AI tools for coding (JetBrains, 2025). But using GitHub Copilot for autocomplete is not the same as restructuring your engineering organization around AI capabilities.

An AI-assisted team takes existing processes and adds AI at specific points: code suggestions, automated test generation, documentation drafts. The team’s structure, roles, and review processes stay the same.

An AI-native team rethinks the process from the ground up. Code review protocols change because the volume and nature of AI-generated code demand different review criteria. Sprint planning changes because task decomposition needs to account for what AI handles well and what still requires human judgment. Hiring criteria change because the team needs engineers who can orchestrate AI agents, not just write code from scratch.

Gartner predicts that by 2030, 80% of organizations will evolve large software engineering teams into smaller, more agile units augmented by AI (Gartner, 2025). That is a structural transformation, not a tool upgrade.

What an AI-Native Development Team Looks Like

The Roles That Stay and the Roles That Shift

The core engineering roles do not disappear in an AI-native team. Architects, senior backend and frontend engineers, QA leads, and DevOps specialists remain essential. What changes is what they spend their time on.

Senior engineers shift from writing boilerplate code to reviewing AI-generated output, defining system constraints, and making architectural decisions that AI models cannot reliably make on their own. According to a 2026 industry survey, AI now generates approximately 41% of all production code, but every line still passes through human review in high-performing teams (Index.dev, 2026).

QA engineers move from writing test cases manually to designing test strategies that account for AI-generated code patterns. The testing surface area grows because AI-generated code can introduce subtle logic errors that traditional review catches less reliably.

Engineers as Orchestrators of AI Agents

The most significant role shift is from “engineer who writes code” to “engineer who orchestrates AI agents.” In an AI-native workflow, a developer might define a task specification, assign it to an AI coding agent, review the output against acceptance criteria, iterate with the agent on refinements, and then integrate the result into the broader system.

This requires a different skill set. Prompt engineering, context management (knowing what context to give an AI agent for optimal output), and evaluation skills (quickly assessing whether AI-generated code meets quality and security standards) become core competencies.

New roles are also emerging: AI workflow engineers who design and maintain the AI toolchains the team relies on, and integration specialists who ensure AI-generated components work within existing system architectures.

Five Practices That Separate AI-Native Teams from the Rest

1. Pair AI with Experienced Engineers, Not Instead of Them

The highest-performing AI-native teams use AI to amplify senior engineers, not to replace them with cheaper junior staff. McKinsey’s data shows that the top performers saw 31% to 45% improvements in software quality, specifically because experienced engineers caught and corrected AI-generated errors before they reached production (McKinsey, 2025).

The pattern that fails: reducing team size, hiring juniors to “supervise AI,” and assuming the tools will compensate for missing expertise. AI models generate plausible code, but plausible is not the same as correct, secure, or maintainable.

2. Governance Frameworks for AI-Generated Code

AI-native teams need explicit rules about how AI-generated code enters the codebase. This includes policies on which tasks AI can handle autonomously, which require human co-authoring, and which should remain fully human-written (security-critical components, for example).

Documentation standards also shift. When a function is AI-generated, reviewers need to know: what prompt produced it, what context was provided, and what modifications were made post-generation. This metadata matters for debugging, for compliance, and for training the next generation of engineers on the team.

3. Spec-Driven Development Over Vibe Coding

“Vibe coding,” the practice of letting AI generate code from vague descriptions and iterating until it works, gained traction in 2025. But data from CodeRabbit’s research shows that productivity gains from unstructured AI coding are consistently offset by downstream bugs and security issues (IT Pro, 2026).

AI-native teams that perform well invest heavily in upfront specification. The better the spec (clear acceptance criteria, edge cases documented, input/output contracts defined), the better the AI-generated output. This is a cultural shift: writing a detailed spec takes discipline, but it pays off in fewer review cycles and fewer production issues.

4. Security as a First-Class Concern

AI-generated code introduces specific security risks. Models can produce code with known vulnerability patterns, include dependencies with security issues, or generate authentication logic that looks correct but has subtle flaws.

High-performing teams run automated security scans on all AI-generated code before review, maintain an approved dependency list that AI agents must draw from, and flag any AI-generated code that touches authentication, authorization, or data handling for mandatory senior review.

5. Continuous Upskilling as a Default

Gartner projects that 80% of the engineering workforce will need to upskill through 2027 to work effectively with generative AI (Gartner, 2024). AI-native teams treat this as an ongoing practice, not a one-time training event.

This means dedicated time for engineers to experiment with new AI tools, internal knowledge-sharing sessions on effective prompting techniques, and regular retrospectives on where AI helped and where it created problems. The teams that skip this step see diminishing returns as their AI toolchain evolves faster than their team’s ability to use it well.

The Hiring Question: Build, Upskill, or Partner?

Organizations building AI-native teams generally take one of three paths, and the right choice depends on timeline, existing talent, and how central software development is to the business.

Path 1: Hire AI-native talent. Recruit engineers who already have experience working in AI-augmented workflows. This is the fastest path if you can find the talent, but supply is limited. Candidates who can both write production-grade code and effectively orchestrate AI agents are in high demand and command premium compensation.

Path 2: Upskill the existing team. Invest in training your current engineers to adopt AI-native practices. This preserves institutional knowledge and domain expertise, which are significant advantages. The timeline is longer (typically 3 to 6 months for meaningful adoption), and success depends on engineering leadership actively modeling the new workflows, not just mandating tool adoption.

Path 3: Partner with an external AI-native team. Bring in a managed team that already operates with AI-native workflows. This is particularly effective for organizations that need to move quickly, want to see AI-native practices in action before rebuilding internally, or need to scale capacity without a lengthy hiring cycle. The external team can also serve as a training ground: your internal engineers work alongside AI-native practitioners and absorb the practices organically.

Many organizations combine these approaches. They upskill their core team while partnering with an external group for capacity and knowledge transfer, and selectively hire AI-native specialists for critical roles.

Where Teams Get Stuck (and How to Move Past It)

The Quality Trap

Projects with heavy AI code generation but weak review processes experienced a 41% increase in bugs (Index.dev, 2026). The speed of AI generation creates a bottleneck at the review stage. Teams that do not scale their review capacity alongside their generation capacity end up shipping more code with more defects.

The fix: invest in automated quality gates (linting, static analysis, automated security scanning) that filter AI-generated code before human review. This lets reviewers focus on logic, architecture, and edge cases instead of catching formatting issues and basic errors.

The Trust Gap

Forty-six percent of developers do not fully trust AI-generated code, even as they use it daily (Index.dev, 2026). This creates friction. Engineers spend time re-verifying code they have already reviewed, or they avoid using AI for anything beyond trivial tasks.

Building trust requires transparency. Teams that log AI-generated code performance metrics (defect rates compared to human-written code, review pass rates, production incident attribution) give engineers data to calibrate their trust levels. Over time, this data usually shows that AI-generated code, when properly reviewed, performs comparably to human-written code in most categories.

The Organizational Change Challenge

The most common failure mode is treating AI-native transformation as a technology project rather than an organizational change. New tools without new processes, updated review standards, and adjusted team structures produce marginal gains at best.

Leadership needs to commit to the structural changes: smaller team units, new code review workflows, updated hiring criteria, and time allocated for upskilling. Without executive sponsorship, AI-native adoption stalls at the individual contributor level, never reaching the team-wide integration where the real productivity gains live.

Frequently Asked Questions

What is an AI-native development team?

An AI-native development team is a software engineering group where AI agents and tools are built into every workflow from the start, not added on top of existing processes. Engineers work alongside AI for code generation, testing, documentation, and code review, while maintaining human oversight for architecture, security, and product decisions.

How does an AI-native team differ from a team that uses AI tools?

A team that uses AI tools adds capabilities like code autocomplete or test generation to existing workflows. An AI-native team restructures its roles, processes, sprint planning, and code review practices around AI capabilities. The difference is structural, not just technological.

What skills should you hire for in an AI-native team?

Beyond traditional software engineering skills, look for experience with AI-assisted development workflows, strong code review and evaluation abilities, prompt engineering proficiency, and the judgment to know when AI output needs human intervention. Systems thinking and architectural skills become more valuable as routine coding is increasingly AI-handled.

Can existing development teams become AI-native?

Yes, but it takes deliberate effort over 3 to 6 months. Success depends on engineering leadership actively modeling new workflows, dedicated upskilling time, updated code review standards, and willingness to restructure team processes. Teams that simply add AI tools without changing how they work see only incremental gains.

How do AI-native teams maintain code quality?

Through layered quality controls: automated linting and static analysis on all AI-generated code, mandatory human review with AI-specific review criteria, security scanning before merge, and performance metrics that track defect rates by code origin (AI-generated vs. human-written). The best teams treat AI output as a first draft that must pass the same quality bar as any other code.

Key Takeaways

The shift to AI-native development is not optional for organizations that want to stay competitive in software delivery. But the path matters as much as the destination. Teams that succeed invest in experienced engineers who can evaluate and direct AI output, establish governance frameworks before scaling AI code generation, and treat the transition as an organizational change rather than a tool rollout.

Whether you build AI-native capability in-house, upskill your existing team, or partner with an experienced engineering group, the critical factor is treating AI as a multiplier for human expertise, not a replacement for it.

For organizations exploring how to structure their AI strategy, unicrew’s AI consulting services and Chief AI Officer as a Service can help define the right approach for your team size, technical maturity, and business goals.

Sources:

• McKinsey: Measuring AI in Software Development
• McKinsey: Unlocking the Value of AI in Software Development
• Gartner: Top Strategic Technology Trends for 2026
• Gartner: Generative AI Will Require 80% of Engineering Workforce to Upskill Through 2027
• JetBrains: The Best AI Models for Coding, 2026
• Index.dev: Top 100 Developer Productivity Statistics with AI Tools 2026
• IT Pro: AI Could Transform Software Development in 2026

AI adoption is surging: 72% of organizations are using AI in 2024, up from 55% in 2023. More enterprises are also entrusting AI with multiple business functions than the year before: around a fifth more for 2+ business functions and 10% more for 3+ business functions.

Adopting AI, however, doesn’t guarantee harnessing its full potential. So, what separates top performers from laggards? According to McKinsey’s report, the key lies in a wide range of best practices – many of which constitute AI change management.

Therefore, fostering AI culture has to be an integral component of AI implementation and a continuous process that continues long after the tools are up and running.
Here’s your guide to building and maintaining an AI 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.

Fostering AI change management with Knowledge Sharing

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.

What do engineers from Unicrew, a software development company, need to develop the best-in-class solutions for their clients? Productive PC, updated knowledge, a cup of good coffee, and… AI.

Since AI advancement in the last years, many biases about its impacts on many areas have been circulating. Software development isn’t an exception. Many controversial assumptions, from “AI dismisses software engineers” to “AI creates new opportunities for talents,” arise occasionally and stir up the human imagination. 

Meanwhile, software development companies worldwide can’t ignore AI advancement and spread. Can they transform the AI potential threats into opportunities?

“AI has potential in software development, primarily in planning and research, automating mundane tasks, and writing boilerplate code expeditiously.” – says Oleksandr Trofimov, CTO at Unicrew. – “In software development are a lot of routine tasks that don’t require specific knowledge. For example, checking API calls or managing data exchange between the system’s modules could be delegated to AI, freeing the experts to perform the strategic tasks or devising the high-level solutions.”

The technology leaders at Unicrew, a software development house, have valuable experience that provides an optimistic answer to this question.

“Hello, I’m an AI bot, your new junior developer…”

Andrii Burda, a Senior Engineering Manager at Unicrew, has over ten years of experience in management in web and mobile development teams. He keeps his and his team’s knowledge up-to-day, so they organically started the use of AI tools once it became affordable.

“We usually add 30% to the task’s estimate for refactoring, writing tests, etc. Now this time can be decreased to 10% or even less thanks to AI, – says Andrii Burda. – I checked our code using AI, and it was able to detect a simple performance issue that was missed by engineers and fixed it. So, our client will receive better functionality by spending fewer funds”.

Unicrew’s engineers apply AI tools like ChatGPT and Github Copilot for the following tasks:

• Ask AI than Google. AI provides more precise answers that allow engineers to solve an issue quickly.
• Optimizing code. Engineers usually write code the way they used to do it. AI quickly analyzes code and proposes its own changes.
• Writing unit tests. Many engineers don’t like to write tests, and AI generates tests with pretty good quality and speed.
• Code autocomplete. AI helps to write code quicker. It just suggests part of codes that can complete comment, method, or even class.
• Finding bugs. Many IDE tools have simple code validators but cannot detect logical issues. AI seeks possible bugs better, and also it can find logical bugs.

Quality Assurance hero

Another area of software development where AI can contribute is Quality Assurance. AI-driven analytics can help identify the root cause of bugs and issues more quickly, allowing software teams to develop fixes faster. This can result in better overall quality of the software product, while also reducing time spent on bug-fixing activities. Furthermore, AI can be trained to recognize patterns in existing bug reports and flag similar ones that have yet to be reported, helping to identify software issues before they can lead to customer complaints.

Ihor Prudyvus, Engineering Director and Head of the Quality Management Office at Unicrew, noticed: “The QA team usually uses it for test data generation. It is a standard practice that during the test execution, a lot of test data is used. Sometimes this data has to be very specific and accurate, with appropriate patterns, and in order to create it, we spend a lot of time; however, AI tools allow us to do that in seconds”.

QA engineers also can use AI for test documentation creation, especially test cases.

“Of course, there are cases that require human interaction & understanding the business context that cannot be provided by AI yet, however for simple cases like login form validation – it is pretty helpful,” – summarized Ihor Prudyvus.

Challenges of AI for the software development company

Although AI tools significantly boost software development workflow, all engineering managers agreed that experts should verify AI outcomes.

“AI just started its way in the engineering world. So, I don’t recommend fully trusting the result generated by AI. A few times, I found issues in the code optimized by AI. Also, some tests developed using AI don’t cover all outlined cases,” – said Andrii Burda.

Oleksandr Trofimov, CTO at Unicrew, mentioned that “the value for clients isn’t the fact of usage AI, but skills and knowledge of the experts who know how and in which cases apply AI, and how to supervise it.”

While AI can greatly enhance software development, it also presents certain risks and concerns that must be addressed. As OpenAI’s large language model, ChatGPT, becomes increasingly popular, concerns surrounding using AI tools are transforming into government action. National authorities are now tasked with the challenge of establishing regulations and policies for this transformative technology.

Earlier, the Italian government banned ChatGPT over data privacy concerns. The government has relaxed the ban temporarily, provided OpenAI meets a set of demands. Currently, the French government is examining the tool as well. The European Data Protection Board has also created a task force focused on ChatGPT and AI privacy regulations.

Large language models pose several concerns. One is the amount of data they collect and use, which may include sensitive information like copyrighted material. 

“The AI platforms as a self-learning model have to consume constantly a ton of data; otherwise, it couldn’t learn and improve itself.” – says Andriy Burda. – “For that reason, any trustworthy software development company should implement a transparent policy of using AI tools in software development and testing. Our clients have to be sure that no piece of sensitive data is leaked”. 

The trick of AI is that nobody can isolate the AI tools by deploying them on the local server without external access – such measures make AI totally witless and useless, as it needs to update its knowledge with terabytes of information distributed on the internet. Or to collect this data and store them locally with constant updates – but it sounds weird for the average software development company.

We usually add 30% to the task’s estimate for refactoring, writing tests, etc. Now this time can be decreased to 10% or even less thanks to AI.

Andriy Burda, Senior Engineering Manager

“In the software industry, we also can come to a situation when software buyers would require the software development company to secure the accountability and traceability of the AI tools’ actions and logic used in development or testing. Since deep-learning algorithms could hardly be exposed to reverse engineering, the clear accountability of AI is questionable. As such, AI can simplify mundane tasks, but the architecture, logic, and high-level solutions must all rest on human expertise. It’s a complex and constantly evolving industry, but one whose progress is too important to ignore.” – mentioned Oleksandr Trofimov.

Looking forward to “black swan”

AI-powered software development companies can bring many benefits to their clients if the use of AI is ethical and transparent. First, IT vendors should explicitly declare the use of AI in the software development process. To be totally transparent, it is a good practice to define at the start of the collaboration which AI tools, for which tasks, and in which scope engineers plan to use. The ethical use of AI implies the complete restriction of exposing sensitive data for building models for training. 

But on the other hand, AI tools can assist developers in seeking a better solution that, in turn, can make the application more reliable and ensure higher customer satisfaction. Also, AI frees up engineers from redundant routine tasks, and they can commit more effort to the more essential tasks. 

“We are only in the early stage of this breath-catching rally,” – says Oleksandr Trofimov. – “The future prepares a lot of surprises for us.”