10 Tips for Successfully Implementing AI in Your Business

Artificial intelligence (AI) is rapidly transforming digital environments and leading us towards a future of increased workplace automation. Machine learning solutions are now handling many repetitive jobs and powering autonomous devices and robotics that are infiltrating different aspects of life. The scientific community relies heavily on AI to drive research and innovation forward.

As a result, businesses across industries are investing heavily in AI capabilities. Accenture forecasts that by 2035, AI adoption will boost profitability by close to 38%. IDC predicts spending on AI systems will reach $79.2 billion in 2022 with continued annual growth.

The cooperation between AI and human employees has the potential to give organizations a major boost in reaching new milestones. Many companies today are evaluating how to best implement AI within their operations. However, the key questions remain, where can AI make the biggest impact given your industry, and how do you budget for the implementations? 

Read on to learn the top strategies and best practices for implementing AI in your business. 

Foster an AI-Ready Culture

Getting employees across the organization excited about AI is key to driving adoption. But AI also brings fear of the unknown for many workers.

Proactively address concerns about AI taking away jobs. Focus on augmented intelligence, where AI enhances what humans can do. Find new roles for displaced workers.

Encourage an agile, experimental mindset when trying and testing AI innovations. Allow failure and provide the support to try again.

To further advance AI literacy, consider encouraging employees to obtain certifications that encompass broader digital strategies, such as a Digital Transformation certification. Such qualifications provide a foundational understanding of how AI fits into the wider digital landscape.

Promote AI literacy and understanding at all levels of the company. Employees who get AI will get onboard faster.

Enable Continuous Model Improvement

AI models need ongoing training and retraining to maintain their edge. Data drifts over time, businesses change, and users find ways to beat the system.

Enable continuous improvement by connecting models to fresh data sources, retraining on new data, and quickly updating models.

DevOps practices like continuous integration and delivery pipelines streamline deploying updated models into production regularly.

Automated machine learning (AutoML) also makes iteratively improving models easier for data scientists.

Staying on the cutting edge takes dedicated resources and infrastructure to continuously train, evaluate, and enhance AI systems.

Develop Collaborative Human and AI Teams

Find the right balance in responsibilities between humans and artificial intelligence. Assign each to the tasks they are best suited for.

Leverage AI for what it’s good at – consuming data, running analyses, automating manual tasks. Let humans handle strategy, creative thinking, relationship building, and complex problem solving.

Build integrated teams with complementary human and AI skills. Humans empower AI, AI empowers humans.

Cohesive human and AI teams that play to the strengths of each outperform either working in isolation.

Adopt an Iterative Approach with Agile Methodologies

AI projects have many unknowns and moving parts. Traditional linear project management approaches quickly fall apart.

Instead, take an agile, iterative approach when implementing AI. Start with a minimum viable product, get feedback from users, and continually enhance.

Use agile sprints to demonstrate incremental progress. Adapt requirements and solutions based on continuous user input.

Embrace fail-fast mentalities, allowing yourself to try ideas quickly, discard what doesn’t work, and build on what does.

Agile AI deployments deliver faster time-to-value. They provide the flexibility needed for such a rapidly changing technology.

Develop AI Ethics Principles and Policies

With great AI capabilities comes great responsibility. Developing ethical AI principles and policies is key.

Form an ethics review board with diverse perspectives to assess AI risks and alignment with values. Audit algorithms for fairness, transparency, and accountability.

Create AI guidelines that forbid uses counter to ethics and human rights. Implement training on responsible AI practices for teams.

Continuously monitor AI systems for harmful biases, errors, and unintended consequences. Have a way to disengage problematic AI if necessary.

Ethical AI earns public trust. It also helps sustain long-term success versus short-term gains absent of values.

Maintain Human Control and Oversight

A common AI fear is the technology spiraling out of control. Avoid by always keeping humans ultimately in charge.

Give users transparent oversight into AI via explainability tools. Have an intuitive way for humans to disengage or override AI if needed.

Set clear boundaries on what decisions can be automated versus requiring human review. Limit autonomous AI to low-risk scenarios.

Audit and monitor AI in operation to catch issues early. Have humans continually sample outputs and performance.

Responsible AI deployments will ensure the technology serves humanity rather than the other way around.

Prioritize AI Security and Safeguards

Like any technology, AI comes with new cyber risks and vulnerabilities. Make AI security a priority from the start.

Protect against data poisoning, model theft, and adversarial attacks looking to manipulate AI algorithms. Use encryption, access controls, and code obfuscation techniques.

Perform extensive testing to identify AI weaknesses. Check for potential unintended model behaviours before launch.

Monitor for abnormal AI activity that could signal attacks or malfunctions. Have manual overrides and emergency disengagement plans.

Leverage AI Cloud Services Over Building From Scratch

Many companies make the mistake of wanting to build their AI solutions entirely in-house. However, a cloud-first strategy is often more practical and faster.

Leverage pre-built AI services from providers like AWS, Google Cloud, and Microsoft Azure. Take advantage of their elastic compute resources and pre-trained models.

Focus your team on unique capabilities only your business needs. Use cloud AI for more commoditized functions like speech recognition, natural language processing, and computer vision.

Cloud AI services make cutting-edge AI accessible to organizations of any size. The benefits of scale and continuous platform enhancements outweigh custom-building.

Start Planning an AI Talent Strategy

AI success requires the right mix of multidisciplinary talent. However, there is a major AI skills shortage. Begin planning your talent strategy early.

Determine what core AI skills like data science and machine learning engineering you need in-house versus outsourced. Partner with AI consultancies to fill gaps.

Grow junior talent through internal AI residencies and training programs. Offer tuition reimbursement and incentives to learn AI-related skills.

Hire non-technical AI translators to bridge communication gaps between business leaders and technical teams.

Foster an AI-forward culture that attracts and retains top AI talent looking to innovate.

Democratize AI with No-Code Tools

Empowering every employee to use AI unlocks its full potential. No-code AI tools allow non-technical users to benefit from AI.

Deploy interactive natural language interfaces for easily querying data and models. Automate repetitive tasks via process automation tools staff can configure.

Let non-data scientists build, test, and deploy models with intuitive model-building UIs like autoML platforms.

Democratized AI multiplies return on investment when AI becomes accessible beyond data teams. Enable self-service access to scale AI usage company-wide.


Successfully implementing AI to drive real business value requires careful strategy and planning. Companies that follow these best practices will be poised to unlock innovation and realize the many benefits AI has to offer. Approaching AI as a journey versus a one-time project is key. 

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