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Four steps to building a successful enterprise AI strategy

Published Date

Happy customers seeing tangible results through their AI initiatives

By Kelly Gonzalez, Director AI Strategy, Bell Canada

With artificial intelligence (AI) on the rise and constantly in the news, there’s a good chance your business is already making use of it – and you’re not alone. The global AI market is expected to see a compound annual growth rate of 37.3% from 2023 to 2030, reaching well over $1.8 trillion by 2030.1 Generative AI alone could reach a market size of $1.3 trillion by 2032.2

Taking advantage of this growth, however, calls for more than just adding ad hoc AI functions to your operations. To really benefit, you need an AI strategy that will give your entire organization a clear roadmap and framework for how, why and when to integrate AI. While every business will have unique needs that will inform its own unique strategy, here are some of the broad steps to developing a successful AI strategy, along with key considerations at each stage.


Step 1. Set your vision

Start by defining your company’s AI-related ambitions so all AI initiatives can build toward a clear goal. That goal should be bold but achievable. Express it clearly and briefly to help key stakeholders internalize it.

Key considerations for your vision:

1. Where does AI fit into your company? Which areas of your business would AI best support (e.g., communications, logistics, HR)?

2. What is the desired outcome for your business? AI can be used for a variety of purposes, including:

  • Enhancing the customer experience; introducing intelligent tools to analyze customer sentiment to provide high quality support. 
  • Streamlining operations through workflow automation.
  • Improving the employee experience and productivity with Gen AI capabilities.
  • Reducing costs with predictive analytics to avoid issues like equipment failure or customer churn.


Step 2. Define pillars and drivers

Describe what you need to do to achieve the vision set out in Step 1. This should include tangible, actionable and KPI-driven plans, designs and architectures.

Key considerations for your pillars and drivers:

1. What use cases should you deploy?

Identify the specific business problems you will solve with AI and the kind of changes you want to make. Will you fully automate a function or augment a manual function with AI support? Be sure to clearly define how AI solutions will work together to advance your overall corporate strategy, what benefits and value the solutions will deliver, and how you will measure those benefits.

2. What quick wins can demonstrate early success?

Consider tackling simple, everyday problems where AI can deliver immediate, tangible benefits. For example, at Bell, our HR department deployed AI to automate responses to common HR questions. It enabled employees to quickly find answers to common questions about benefits, time off and a range of other policies, helping HR save time and focus on more strategic initiatives. It’s a quick and easy win that can build excitement and help support bigger AI projects in the same department or others down the road. 

3. What operating model should you use?

Identify what you will need to realize your AI vision from a process, governance and human resource (including both tech and non-tech talent) perspective. For example, decide whether you would benefit from:

  • A centralized (powered and controlled by a server or cloud-based infrastructure) or decentralized (computing and analysis is distributed across a network of resources) deployment.
  • An AI centre of excellence to guide and oversee your organization’s AI projects, aligning them with your business vision and strategy.
  • Upskilling, training and hiring new talent in areas such as AI engineering, data science, cloud, and governance.
  • The latest and greatest models, or basic, no-code AI solutions. Explore off-the-shelf solutions to see if they will meet your needs or if you would be better served by building a custom solution.

Don’t be afraid to experiment and look for ways to scale up winning solutions.

4. What is the current status of your data strategy?

Make sure your AI strategy is aligned with your data strategy. Assess the data and technology resources already at your disposal, including compute resources, access to shared tools and models, and cloud infrastructure. If you don’t have the necessary resources to implement AI properly, you may need to look into AI infrastructure as a service or machine learning platforms.

5. How will you ensure AI is deployed responsibly?

As AI continues to evolve, new governance frameworks and regulations are emerging worldwide, compelling us to ensure that AI systems are developed and used responsibly and ethically. These frameworks help enforce principles covering key areas such as privacy, security, inclusiveness and transparency. The Government of Canada took its first stab at regulating AI in June 2022 with the release of the Artificial Intelligence and Data Act (AIDA)3 as part of Bill C-27.4 A companion document was also introduced in March 2023 with clarification on AIDA’s scope and definitions.  AIDA is expected to be introduced at the earliest, in July 2025. 

To comply, you’ll need to put in place guardrails to protect against common risks associated with AI, including data privacy breaches, hallucinations, cyberattacks, malicious output, threats to intellectual property (IP) and copyright infringement. 

At Bell, we’ve published a summary of the guiding principles we follow when designing and building AI systems.


Step 3. Ensure stakeholder alignment

To secure buy-in from executives and other key stakeholders, keep your strategy concise. A single-page document will be easier to socialize and internalize. Communicate widely and often about the strategy through roadshows, newsletters, town halls and other channels within your organization.

Key considerations for stakeholder alignment:

1. Who are the key stakeholders?

Initiatives like this typically require executive sponsorship, so be sure C-suite and executive-level team members understand the strategy and have opportunities to provide input. Identify the team members who will be responsible for executing the AI strategy, both from a technical and business perspective.

2. How much involvement should stakeholders have?

Create a RACI chart to outline which stakeholders are responsible for, accountable for, consulted on and informed about the various elements of the strategy.

3. How will you keep stakeholders engaged throughout the AI deployment journey?

Build communication and feedback loops into every stage of your AI strategy to ensure key stakeholders are aware of what’s happening, understand how their concerns are being addressed and can provide input along the way.


Step 4. Develop an execution roadmap

Define a timeline supported by a well-structured action plan. Assign specific ownership and accountability for the plan.

Key considerations for your roadmap:

1. What are the key phases and steps within each?

Divide your execution plan into distinct phases: 

  • Phase 1: Set data and AI foundations
  • Phase 2: Start implementing quick wins
  • Phase 3. Establish a governance structure, identifying accountabilities to manage and track progress
  • Phase 4: Scale by embedding AI into all company processes


Your strategy should be a living document

Although it is important to have a strategy to guide your AI implementation, you don’t have to wait until your strategy is complete to start integrating AI into your operations. In fact, it can be beneficial to start your AI deployment while your strategy is in development, because some of your most valuable insights will come from using AI.

For this reason, your strategy will serve you best if you treat it as a living, iterative document that can evolve along with your goals as you learn and understand more about how AI works within your business. Start developing your strategy while also looking for “quick wins” that AI can deliver to improve your current operations by removing manual steps or speeding up processes. Incorporate these early results and lessons learned into your strategy and use them to inform the implications and opportunities of your broader AI strategy.


Build your AI strategy with support from Bell 

The right partner can simplify the development of your AI strategy, helping you align vision, talent and technology to achieve tangible business outcomes. The Bell AI Strategy Accelerator program leverages our expertise in building, scaling and maintaining enterprise-grade AI systems to tightly couple your AI strategy with your core business objectives, optimizing ROI (return on investment) and driving measurable impact. After learning about your company and your vision for AI, we conduct a comprehensive discovery process to identify the use cases that will best serve your needs, then deliver a clear and actionable roadmap and operational plan to set you on the path to AI success. 

Connect with one of our AI specialists to learn more about how we can help you develop an AI strategy for your business.

Sources:
1. Forbes: Top AI statistics and trends
2. Bloomberg: Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds
3. Government of Canada: The Artificial Intelligence and Data Act (AIDA) – Companion document
4. Government of Canada: Bill C-27 summary: Digital Charter Implementation Act, 2022