The siren song of AI continues attracting enterprises seeking to transform operations. However, most AI initiatives fail to migrate beyond isolated trials. A “dual-track” adoption approach balances short-term needs with long-term strategy. Maintaining two parallel streams nurtures AI success over time.

The Pilot Trap

Many enterprises pursue AI in fits and starts. They run isolated pilots that never integrate into workflows. When these pilots plateau, they restart the process on new use cases.

This pilot purgatory emerges from misaligned incentives across teams:

  • Developers want to experiment with cutting-edge models. They push bespoke solutions unfit for production systems.
  • Business sponsors need quick returns from AI investment. They resist allocating resources for platform building.
  • Employees find new tools disruptive. They cling to familiar processes unless forced to adapt.
  • Startups promise out-of-the-box AI. But customized packages require long configuration and don’t integrate well.

The resultant piecemeal approach leaves enterprises stuck in an endless cycle of one-off demos.

Establishing Dual Tracks

Balancing short and long-term priorities via a dual-track model fosters tangible returns:

Track 1 focuses on rapid pilots using packaged AI solutions or tiny custom models. Sponsors see near-term ROI while developers play with new tools.

However, ensure these projects align with the overall roadmap. Requiring integration plans prevents pilot purgatory.

Track 2 constructs the enterprise AI platform in parallel. This develops institutional expertise, core models, data pipelines, labeling processes and governance.

With the platform in place, bespoke and prebuilt models integrate smoothly to accelerate value realization.

Coordination between the tracks aligns immediate needs with sustainable capabilities. Transferring pilot learnings to the platform makes institutional knowledge cumulative.

Plot the Transformation Journey

A transformation roadmap helps navigate the two tracks:

  1. Identity quick win AI applications through a capability assessment across the business. Target sunk-cost processes where basic automation delivers ROI.
  2. Stand up a multi-disciplinary AI Center of Excellence (COE). They provide governance, coordinate data and skills development, liaise across IT and business units.
  3. Run agile pilots using prebuilt AI packages on the high ROI use cases. Require integration plans with the target platforms.
  4. Construct production data infrastructure, model reuse libraries and deployment architectures. Focus on flexibility and ease of integration.
  5. As pilots wrap, reconstitute their models on the new platforms. Enrich with additional data and ensemble techniques.
  6. Support business units with model integration services via the COE. Encourage BYOA (Bring-Your-Own-AI) on the platforms.
  7. Expand scope into further business functions, training more employee AI talent. Set integration metrics ensuring models deploy to applications.

Incremental steps prevent resource dilution while constructing long-term assets. Quick wins maintain stakeholder support amid platform building.

Common Roadblocks

The dual track journey sees frequent roadblocks:

  • Pilots hoarded in siloes rather than integrated on platforms.
  • Platform teams focus excessively on “perfect” infrastructure rather than rapid integration.
  • Business teams trade robust platform adoption for passively piloting vendor packages.
  • Companies install generalized commercial platforms lacking customization for their needs.

Address issues transparently rather than allowing quiet deviation from the roadmap. Foster a culture oriented toward reasonable custom platforms over one-size-fits-all buys.

With sustained balancing of short and long arc priorities, companies reorient from AI tourists to transformation leaders.