The roadmap to AI adoption in 6 steps

Artificial intelligence is more accessible than ever, but successful AI adoption requires a systematic and pragmatic approach. For success, you need a game plan to adopt AI that results in a steady acquisition and maturing of knowledge, technical skills, data, and processes, all geared to solve a specific set of business problems.

To help guide you along the way and accelerate your progress towards AI adoption, we’ve outlined 6 key steps to follow in our Roadmap to AI Adoption.

1) Start with business problems to solve

Projects defined with internal business partners

Create a portfolio of Proof of Concept (POC) projects

Make rapid prototypes: 4-6 weeks turn
Use a scorecard to evaluate AI use cases

2) Use projects to guide AI development process

Design processes and set requirements through iteration

Initial projects inform decisions about data, tools and technology

Data usability assessments
Business Requirements and Technical Requirements

3) Place early emphasis on culture and mindset

AI requires different ways of thinking about business & data

Need to embrace data-driven optimization

AI has many potential uses
But no ROI without deployments

4) Scale projects as you gain experience

Increase datasets, reporting frequency, and automation

Scale POCs and push data science thinking deeper

Focus on projects with clear path to deployment
Build toward real-time automated decisions

5) Increase the scope and sophistication of analysis

Start with quick wins and measurable results

Dig deep into root causes and triggers

Expand requirements with each new project
Solicit cross-functional partners

6) Focus on business and profit drivers

Look for opportunities to embed AI across the company

Maintain focus on strategic business objectives

How can you use AI to generate incremental revenue, reduce costs?
Successful companies incorporate AI throughout the business

Roadmap to AI Adoption

While there are several roadblocks to AI, these 6 steps can accelerate the path to AI adoption

Addressing the Roadblocks to AI Adoption

The Importance of Executive Sponsorship

I recently had the opportunity to present a talk at the Machine Learning Week conference about overcoming roadblocks to adopt AI. My key takeaway was that while technical factors like data and algorithms are relevant for AI/ML projects, managing organizational challenges is what ultimately determines success or failure. According to Gartner, up to 85% of AI projects fall short of objectives. There are many reasons for this, and several have nothing to do with data, tools, or technology.

Executive sponsorship is a key success factor. Project leaders need to build a broad base of support. Relying too heavily on a single executive sponsor means progress can stall if they leave. Instead, multiple stakeholders across the organization help to ensure ongoing support and alignment to business goals. This ensures momentum continues even with leadership transitions.

Maintaining Focus as Organizational Priorities Shift

I also shared a cautionary story that illustrated how AI/ML initiatives can lose momentum, even after significant progress has been made. After a year of work with an external consultant, the project stalled and entered limbo as internal priorities shifted within the organization.

To successfully adopt AI, organizations need processes to maintain focus on the anticipated benefits and integration with operations, even as leaders juggle strategic priorities. Internal stakeholders can come from just about any department including supply chain, HR, sales, marketing, and customer service.

Overcoming the Barriers to Adoption and Capturing AI’s Potential With Internal Expertise

To ensure AI/ML initiatives remain aligned to evolving business needs, organizations should invest in developing internal AI skills and expertise. By cultivating effective AI competencies, teams can more nimbly adapt solutions to new priorities. Expert data scientists and ML engineers who understand both the technical components and business goals are essential. They can communicate the ongoing value of AI/ML projects to leadership in terms relevant to shifting objectives. With both executive sponsorship and skilled AI experts effectively applying know-how, organizations can drive priorities and progress in concert, rather than allowing misalignment to derail promising efforts.

Plotting Your AI Strategy Roadmap

Here are some ways organizations can maintain focus on AI solutions and drive AI/ML projects forward amid shifting priorities:

Establish robust governance processes with executive leadership and stakeholder input to align projects to business goals and regularly revalidate priorities.
Build cross-functional teams with shared ownership over AI/ML initiatives rather than siloed efforts.
Institute regular reviews of progress-to-date, projected benefits, and integration plans to maintain visibility.
Develop mechanisms to track benefits realization post-deployment and feed insights back to leadership.

Create transparency into models, metrics, and monitoring to sustain trust in AI/ML applications.
Communicate and celebrate incremental wins and milestones to maintain enthusiasm and momentum.
Embed pilots and prototypes into operations to demonstrate value and make discontinuation difficult.
Plan for leadership transitions and changes by cultivating broad organizational support beyond individual sponsors.

The key is instituting governance, stakeholder engagement, and benefit tracking mechanisms as part of a strategic plan to to keep AI/ML projects aligned to business priorities as they evolve.

Article updated 8/1/2023

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How Beyond the Arc can help — AI Roadmap

Our team includes experts in data science, natural language processing, and Artificial Intelligence (AI). We are passionate about making decisions and taking action based on AI technologies. We specialize in using AI and machine learning to solve business problems and deliver insights.

Interested in learning more about how to develop and deploy AI? Let’s start a conversation.

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The analytic models that are the foundation of AI are built from patterns determined by an interplay of dimensions that are measured throughout the customer journey, including which steps were skipped by a customer, which steps were repeated, how much time it took from start to finish of a process, and how much was spent.

There are plenty of opportunities to add value by adding customer journey analytics to gain insight into why customers behave the way they do. Then you can determine key actions your company should take to achieve better outcomes for your customers.