This article was originally published by the MIT Sloan Management Review.

The rapid advancement of generative AI has sparked a “fear of missing out” mentality, fueling a flurry of initiatives to envision and experiment with integrating AI into business processes to improve customer service, streamline operations, and drive innovation. No doubt gains have been made, but given AI’s rapid, consumer-driven momentum, organizations now face a critical challenge: how to scale AI initiatives while delivering tangible business outcomes.

Why is scaling AI the latest business trend?

This shift marks a new era in which the focus has moved beyond simply dipping toes in the proverbial AI waters to diving headfirst into opportunities to engineer and expand initiatives to optimize processes and scale AI technologies while maintaining a laser focus on business outcomes. The ability to scale presents the next wave of progress in gaining business value from AI and ensuring its benefits are leveraged across an organization.

CGI's approach to scaling AI

However, scaling isn’t just about expanding the use of AI. Through decades of partnering with government and business, we’ve developed a strategic framework to assess, prioritize, and operationalize use cases that align with key business goals.

In CGI’s latest Voice of Our Clients research, AI tops innovation investment priorities over the next three years. Having already made these investments, executives can’t afford to tread water while waiting for the technology to mature. Organizations face pressure to focus on the long-term scalability of their AI investments or risk future pitfalls — diminished customer expectations, loss of competitive advantage, and competition for top talent.

What are the main challenges to scaling AI?

Yet organizations often get stuck moving to the AI implementation phase. They may be either fearful — moving too slowly or tackling small, safe projects that don’t add value — or reckless, plunging ahead on too many initiatives or going all-in without fully considering risks. To avoid these scenarios, organizations must understand the building blocks necessary to lay a foundation for their AI strategy and technology.

Common roadblocks to a successful AI strategy:

  • Data quality: poor data, inaccessibility and data silos are major barriers to initial efforts of scaling AI 
  • Skills gap: a lack of in-house or hired expertise hinders the development and deployment of an AI strategy
  • Cultural resistance: employee apprehension about job displacement can lead to decreased initiative to install AI tools and processes
  • Unclear ROI: unexpected costs and difficulty measuring ROI make it hard to justify the investment to stakeholders 
  • Legacy systems: existing, outdated infrastructure is not compatible with essential AI ecosystem tools 
  • Governance and security concerns: a lack of clear policies on privacy and ethical AI use creates risk and distrust between employees, administration and clientele

Successfully scaling AI is not as simple as applying a new layer of technology on top of outdated infrastructure, inadequate data, or leveraging AI with a lack of transparency to the source data used to train AI models. Organizations must address the culture and change management as well. Scaling and delivering business value requires a foundation centered on five imperatives:

  1. Future-fit operating model: Adopt a product-centric development approach with scalable, agile cross-functional teams to standardize processes and establish metrics and KPIs to measure the quality and value of the outcomes. It is also important to achieve a balance between standardization and agility that drives innovation while ensuring responsible, secure, and reliable solutions. The operating model should enable efficient resource allocation and provide insight into the total cost of ownership.
  2. IT modernization: Update legacy systems and accelerate cloud adoption to support the speed of innovation required to successfully scale and future-proof AI use cases.
  3. Data strategy and interoperability: Focus on interoperability of data platforms across cloud, SaaS, and on-premises sources; use AI solutions to enhance data quality and break down silos to create effective models and a reliable data set that ensures AI model accuracy and performance.
  4. Agentic AI and automation: Reduce manual tasks to allow teams to perform more effectively and focus on strategic initiatives.
  5. Culture and talent strategy: Invest in upskilling employees and foster an “integrate AI everywhere” mentality to challenge them. Build a culture of innovation that reduces the risk of attrition and retains and attracts top talent. This demonstrates how AI will not replace their jobs, but how AI enablement will enhance their work environment and leverage their skills for more creative tasks.

Why scaling AI requires a strong foundation

With a strong foundation, commercial organizations that can quickly deploy AI across the enterprise are better positioned to respond to market changes, optimize operations, and innovate quickly. For public-sector entities, deploying AI solutions at scale can speed response times to citizen service requests and improve cross-agency collaboration.

Do you need a partner to create an AI roadmap?

Organizations require partnerships with trusted technology providers dedicated to scaling their AI capabilities to achieve trusted outcomes. Partners must share the organization’s strategic vision and be contractually involved in service delivery and long-term support. An experienced IT and business partner with industry expertise can help align people, processes, and technology to achieve desired returns on investment.

Questions to ask a potential AI strategy consultant:

  • What is your past experience with scaling AI in my industry?
  • What will have changed in my operations six months from now?
  • What small-scale use case do you think we should start with?
  • How do you handle compliance, data privacy and security concerns?
  • What do you suggest we implement to assist our workplace in this change?
  • How do you define and measure success in scaling AI?
  • What is your pricing model, and are there any hidden fees? 
  • When do we stop needing your aid and continue handling our AI strategy on our own? 

The future of AI in business lies in aligning responsible AI initiatives with clear governance and business goals, building scalable infrastructure, and maintaining a relentless focus on outcomes. Organizations can transform AI from a promising technology into a powerful engine of growth and innovation to optimize business value. When done right, we can turn AI’s promise into tangible and trusted outcomes — not just in labs or workplaces but in homes and communities for greater societal impact.

Let CGI bring your AI strategy to life

Want to experience the transformative powers of an AI ecosystem? Don’t do it alone. Connect with a CGI consultant today.