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Seven Steps to Effective AI Adoption for Your Enterprise

Today many executives realize that the future success of their business may depend on the ability to effectively implement an AI strategy, to keep pace with rapid AI-driven digitalization across almost all sectors and industries. 

Businesses want to leverage the immense benefits of AI, including the ability to deliver data-driven insights, improve product-to-market fit, reduce operational costs, and increase customer satisfaction. However, many do not realize what it truly takes for an organization to be AI-ready. The cultural and technological shifts can be challenging, but the rewards can mean a windfall for your company.

The question is how to implement AI in the best possible way. The following steps can help your organization overcome the barriers to successful AI adoption.

#1 Identify the Business Challenges You Want to Solve with AI 

AI can do many things, and the team responsible for implementing a specific AI solution needs to first and foremost look at the drivers for the business in terms of both challenges and opportunities, and how AI can apply to them. This approach enables the team to measure the time and investment required to make AI work against the value of the business case.

At this point, your team should also closely look at the data available in your organization, and the ease of data retrieval. If your business has a solid AI use case to implement but does not have the necessary datasets to support the initiative, you will not be able to move forward with AI implementation. 

Bear in mind that legacy and fragmented IT systems make it difficult to access information, and your AI dev team may need to identify gaps in data collection for the company. Assessment of your data strategy will involve the company’s legal council, IT department, and any other departments that will need to prime and offer up data for analysis. 

#2 Prioritize AI Use Cases to Execute a Pilot

Once the team has a list of challenges and opportunities for AI adoption, it makes sense to prioritize the top AI use cases and develop a detailed adoption roadmap for each. This process includes assessing their technical feasibility and business ROI. At this point, the team will start to explore the complexity and cost of the cases in more detail.

The AI implementation team should begin with a meaningful but simple-to-complete use case to execute a pilot. This will allow the business to see early wins from AI from day one, giving the team an easier buy-in from leadership to expand and scale their AI efforts across the organization in the future.

#3 Build a Foundation to Enable AI Adoption Enterprise-Wide

AI is not just about having the right data or choosing the right algorithms to move forward, but also about a robust machine learning infrastructure that can host everything the business needs to design, test and build new AI applications, and effectively support existing AI solutions in production. 

Technology-wise, such foundations are built in the cloud (e.g. Amazon Web Services, Microsoft Azure, Google Cloud Platform, etc.), or as hybrid systems with specific parts of the infrastructure hosted both on-premises and in the cloud. They should feature all the technologies required to fill any gaps in existing data and machine learning pipelines, a selected setup of ML tools, and configuration templates, to enable the business to unlock the full potential of ML engineering.

Building a robust AI foundation also involves investing in an AI culture. Businesses should be ready to proactively create AI user groups in various departments, to facilitate the process of knowledge sharing and AI evangelism, enterprise-wide.

#4 Scale AI Initiatives Beyond the Pilot Use Case

Once you have successfully run a smaller pilot and have established a solid AI infrastructure, you can start scaling AI by introducing more AI use cases in selected departments. 

At this point, both your AI dev team and your leadership should have some idea of the scope, measurable outcomes, and metrics of an “average” AI solution. With that information, it should be easier to encourage different business units to start identifying ongoing issues that could be resolved by AI, to come up with ways of implementing an AI strategy and executing AI initiatives independently.

The AI foundation team should be ready to support business units in their own iterations, from ideation to production, and to invest resources in improving the engineering and experimentation culture organization-wide, based on identified gaps and niche expertise.

#5 Establish Processes to Facilitate AI adoption Across the Organization

Scaling up AI implementations across your organization requires not only a technology foundation, but also a culture and processes that align with a predefined north star.

Establishing internal AI/ML user groups is key to facilitating knowledge sharing and evangelism. The groups should be able to reach out to various business units to explain the benefits of AI, and as next steps, to establish the processes for researching AI opportunities, collecting and analyzing data, building a technology foundation, and working together with other departments to realize a common AI vision.

Keep in mind that any AI solution also involves human intelligence and learning. Schedule retros at major milestones or on a periodic basis to look at what’s working and what can be improved in the AI process. Successful AI projects should be saving your company money, increasing sales and improving your products and services. Regular assessments of lessons learned will help your organization to implement additional AI projects and improve outcomes throughout your organization.

#6 Outline Your Organization Structure to Fuel AI Transformation

Along with establishing processes around AI user groups in specific departments, businesses should work to align their organizational structure to facilitate AI transformation.

Your approach may vary depending on your business size and goals, but it is recommended that your organization aligns itself around a centralized AI hub, responsible for developing AI adoption frameworks in cooperation with selected business units. The hub will come up with policies to manage AI projects within the business, such as policies for data collection, data access, establishing and tracking KPIs, nurturing engineering talent internally or hiring external consultants, cultivating product management expertise, supporting long-term AI innovation programs, and more.

The major goal of the AI hub is to ensure that AI projects are not hampered by bureaucracy and cross-department red tape, but enabled through smooth processes, efficient operational pipelines, and supportive managers. 

#7 Keep Building on Your AI Success

AI is first and foremost a tool for generating value. Once your organization has successfully implemented several AI pilots, it is essential to keep building on that success. Work with individual departments to zero in on ongoing issues that can be resolved by AI; support them in their AI strategy, ideation, talent acquisition, and project execution; improve your organization-wide data and ML infrastructure; nurture AI/ML-aware engineering teams and improve their engineering culture. Eventually, your business will be able to execute new strategic AI use cases, to create new revenue streams and invent new business models.

Making AI work for a specific use case can be difficult. Scaling and adopting AI across your organization is a major challenge, but it is well worth the investment. You can make the AI transformation process easier for your business by basing your AI strategy on the steps listed in this article.

About the Author

Rinat Gareev is a Solution Architect and ML Practice Lead at Provectus with focus on MLOps and NLP. Rinat has 10+ years of experience in machine learning, in both business applications and academia research. He was a fellow at Kazan Federal University and worked for several AI/ML startups. Rinat’s expertise enables him to cover the whole ML process, from problem framing to model deployment and monitoring. At Provectus, he applies his vast experience to design, develop, and operationalize ML solutions for the customers.

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