After a year of layoffs and outages, leaders are under intense pressure to increase efficiency and reliability despite having fewer resources.
According to this survey, 70% of software teams are adopting AI.
That means that capitalising on AI is becoming an industry standard.
Of the teams who have adopted AI, on average, they’ve reported a 250% increase in software development speed. To put that another way, they’re able to achieve in 5 hours what they’d otherwise achieve in 8.
Those same teams expect their efficiency to increase to 350% within a year.
Successfully adopting AI can, and regularly does, lead to engineering teams unlocking a substantial competitive advantage.
On the flip side, teams not actively adopting (or actively avoiding) AI are signing up for an enormous opportunity cost that leaves vast amounts of resources and money on the table.
AI is capable of drastically improving efficiency in several areas.
CTOs and engineering leaders commonly evaluate AI for multiple purposes, which include:
- AI for operations: streamlining internal processes, automating tasks, improving decision-making
- AI for data analytics: advanced data processing, analytics and insight generation
- AI for product development: enhancing product features, UX and scalability
- AI for customer service: chatbots, automated support, customer insights
Different AI adoption types have unique requirements but need a robust foundational infrastructure.
Step 1: Robust, scalable data and tech infrastructure
Your AI adoption's success hinges on a robust data and tech infrastructure. This applies across multiple AI adoption scenarios – whether it's for enhancing products, optimising operations, refining data analytics, elevating customer engagement, or something else.
Data availability, quality and integrity
Low-quality data disrupts AI adoption (not to mention your operations). Set data integrity standards early. Protocols vary: For customer engagement, data should be current and relevant, while operational AI needs historical and real-time data.
When using operational tools like Jira or Slack, teams should adopt communication best practices to ensure data availability, ensuring meaningful discussions are in public channels, not DMs.
Strategic technology selection and scalability
Legacy systems can be impediments or even liabilities, depending on the type of AI adoption you're considering. For product-focused AI, sluggish data access due to legacy systems can be a bottleneck, whereas, for operational AI, an outdated system or tools will impede real-time decision-making.
Don't forget to plan for data backup and recovery strategies. These measures are necessary not just for operational continuity but also to ensure the long-term viability of your AI systems.
Step 2: Building an AI-ready team and culture
Our endeavour of injecting AI into your operations is as much about people as it is about algorithms. AI adoption isn't just the task of a lone AI specialist or an isolated task force – it's an organisational shift. Here's your playbook:
Getting the right skills
Effective AI adoption often involves a designated team or individual. Depending on company size, some roles might be part-time.
I often see these kinds of people on AI task forces:
- Data security leaders (e.g. CISO)
- Product and project management leaders
- Engineering leaders
- Data and machine learning leaders
- UX professionals
- Domain experts, depending on your industry (e.g. healthcare, finance)
- Stakeholder representatives from different departments (like HR, sales, marketing)
- Ethics officers and legal counsel, if appropriate
Your task force needs to develop familiarity with AI tools, how AI works, and AI ethics if they don’t already have this. If they don’t, form partnerships or upskilling programmes to fill the gaps. Your task force needs to understand the language of engineering operations, and the language of AI.
AI-positive culture
AI adoption is a company-wide undertaking that needs the backing of the C-Suite.
If you’re the CTO, or another top-ranked engineering leader, you’ll know that the active support and continuous engagement with projects directly correlate with successful outcomes. Take an active leadership role in your AI adoption project. If that isn’t you, you’ll want to make the case for this endorsem*nt and activity from your decision-makers.
Transparency and good operational hygiene are prerequisites for operational adoption of AI. Getting this right may look like:
- Universal high standards for project management software use. Project management software (like Jira or Linear) is kept up-to-date consistently. Tickets are described completely and accurately.
- Process-driven, verbose version control and CI/CD. Pull requests are appropriately described, commit messages are uniform and follow best practices.
- Teams communicate openly and transparently. Conversations happen in public channels unless there is a specific reason (such as data sensitivity or operational and strategic irrelevance). This is particularly important for distributed or remote teams.
Your task force needs more than just a passing familiarity with AI. They need operational know-how, executive backing, and a transparent, accountable culture.
Step 3: Strategic planning and project prioritisation
Here's how to ensure your AI adoption isn't just robust, relevant, and value-generating.
Be agile and experiment
When we get it right, innovation is a competitive advantage.
The most successful AI-adopting businesses move fast and flexibly with their AI strategy. They conduct experiments to assess the effectiveness of operational AI solutions and make budget available to make them happen.
Place decision-making power for experiments with your AI task force.
It’s a good idea to select pilot projects. Always define what success will look like and be prepared to learn lessons from failed pilots.
Align AI Initiatives with Business Strategy
Define clear objectives for AI adoption, ensuring AI efforts align with desired business outcomes.
AI can offer quick wins, but it also promises long-term transformation. Start small but focus on projects that deliver immediate ROI. Use these quick wins as proof of concept for larger, more complex projects.
Assess every AI project's potential value and feasibility. Avoid just following AI trends; emphasise projects that significantly improve operations. Strategic planning and prioritisation are foundational for a fruitful AI strategy.
Agile approaches to AI
AI adoption is most successful when AI taskforces have open minds about the kinds of solutions which can unlock efficiency gains. After all, AI is one of the fastest-evolving technologies in our space.
Be open to reassessing, pivoting and/or expanding your AI strategy to adapt to market evolutions, shifts and emerging opportunities.
Continually reassess and pivot your AI strategy. This isn't a luxury; it's a necessity for adapting to market shifts and emerging opportunities.
Stakeholder Alignment
Don't make AI decisions in a vacuum. Involve key stakeholders in the process. Ensure everyone is aligned and invested in the AI journey and the tools you’re adopting.
Step 4: AI ethics, governance and compliance
Businesses are often held back from AI adoption because of a lack of grasp of the nuances around AI ethics.
Responsibility and accountability
Don't treat AI ethics as an afterthought. Make it central to your AI adoption strategy.
A dedicated AI ethics committee – or an AI ethics lead at smaller businesses – is a great way to establish awareness, education and accountability around AI ethics.
Your AI ethics function should have knowledge and authority on a range of topics, including:
- Fairness and bias
- Transparency and explainability
- Data security and privacy
- Accountability and responsibility
- Ethical data sourcing
- Social and environmental impact
- Regulatory compliance
Your AI ethics function isn’t there to roll out the red tape, but rather to help others feel confident in their AI adoption approach and position your organisation for sustainable, responsible AI-driven growth.
Assessing high and low-risk projects
Some AI implementations are inherently riskier than others. For example, you might be using AI to help your team understand the code in your projects. This doesn’t carry the same risk as, say, using AI to segment user data.
Develop thresholds of risk to help fast-track low-risk projects and reduce risk for high-risk projects.
For example, giving an AI model read-only access to infrastructure code that does not involve user data might constitute a low risk.
Conversely, an AI project involving user-facing actions based on AI analysis of user data would carry numerous risks, from data security and privacy risks to bias and discrimination risks.
Data privacy and regulatory awareness
Understanding AI regulations is vital to avoid legal risks. Embed data privacy from the project's start to respect user data from the start.
High-risk projects may benefit from – or require – third-party audits to help identify vulnerabilities and blind spots.
Ethical AI practices are both the right thing to do, and crucial for building and maintaining consumer trust. Lose that trust and you risk all kinds of things from bad PR to legal issues.
Chasing operational efficiency in your product engineering team?
You’re probably already sitting on a goldmine of data – your product engineering team’s operational data.
We’re talking about the flow of tickets through Jira or Linear, the commits and pull request activity in GitHub, and even your team’s conversations in Slack.
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