AI Deployment Strategy

Bringing AI into a business depends on confidence and purpose.

Used well, AI transforms efficiency, creativity, and decision-making though few have a clear plan for introducing it. Managers need to understand the different strategy options and any trade-offs.

Adoption Choices

• Wait and See
  Often justified by uncertainty or risk aversion.
  Risk: Nowhere to learn sector-specific knowledge; competitors will be jealously guarding their own in-house AI learnings as they build their AI literacy and functionalities.
• 'Bundled' AI Tools
  Use existing integrations such as Microsoft Copilot or Google Workspace AI to give teams low-risk exposure.
  Risk: Restricted to limited capabilities without an extra licence and other AI platforms are often superior for specific capabilities.
• Adopt Third-Party Solutions
  Deploy off-the-shelf platforms that sit atop models like ChatGPT or Claude.
  Risk: Vendor lock-in, unclear data privacy, and possible reliance on unregulated tools. Many third-party AI offers will be time-limited options as the big platforms copy their functionalities and offer them at no extra cost.
• Develop In-House Bespoke Tools and Solutions
  Build proprietary systems using internal know-how and in-house data. Provides total control and long-term advantage.
  Risk: None (with organisation-wide buy-in and curiousity about where using AI will deliver efficiencies).
• Outsource to AI Providers
  Work with external specialists to create custom AI tools or integrations.
  Risk: Costly, slower to implement and a long-term dependency.
• Hybrid Strategy: Controlled Experimentation
  Mix several approaches and encourage learning by doing. Risk: None (with organisation-wide buy-in and curiousity about where using AI will deliver efficiencies).

Discussion

A good AI strategy is less about technology and more about organisational learning. The most successful adopters will:

• Start with low-risk, high-value pilot projects that demonstrate measurable benefits.
• Involve all staff to build confidence.
• Treat in-house AI capability as a continuing business development need and not a one-off input.

In practice the crucial ingredient is leadership that recognises AI not simply as automation, but as a once-in-a-lifetime opportunity to do things better.