We don’t have AI experts on staff, can we still adopt AI?

Many organizations, especially those in the early stages of their AI journey, may find it challenging to hire professionals who possess deep AI knowledge coupled with specific business and cybersecurity expertise. The demand for such talent far outstrips supply, making these roles expensive and difficult to fill. This is where external resources become invaluable, allowing companies to overcome skill gaps and accelerate their AI adoption.

Here’s how organizations can embrace AI without building a large, specialized in-house team:

Leveraging Off-the-Shelf Solutions and AI as a Service

Companies can jump-start their AI initiatives by acquiring AI as a product or service, rather than attempting to build every solution from scratch. This approach is particularly effective for organizations with fewer years of AI experience. Ready-to-use AI services, prebuilt APIs, and low-code/no-code platforms significantly lower the barrier to entry. These tools allow developers, even those with limited machine learning expertise, to build high-quality custom models for specific business needs, such as using prebuilt computer vision services or creating customer segmentation models with descriptive analytics. By subscribing to AI-as-a-service offerings, organizations can access advanced AI capabilities without the overhead of internal development and maintenance.

Strategic Partnerships and Outsourcing

Working with strategic partners, such as consultants and specialized technology providers, can provide the necessary advisory, consultation, and program management expertise. These partners offer specialized knowledge in various AI use cases, like chatbots or conversational applications, and bring best practices that accelerate adoption. For common AI use cases that do not offer a competitive differentiation, outsourcing to suppliers with specialized AI software is a sensible strategy. This can include:

Co-development engagements

Partnering with vendors to create customized AI systems and processes is a no-brainer as they have the expertise to build enterprise solutions, and to customise them to your needs. This requires no upfront technology development, skills development, or large commitment to infrastructure and resources.

Business Process Outsourcing (BPO) providers

These providers can automate processes not central to a company’s competitive advantage, leveraging their AI expertise to deliver efficiencies in areas like HR training, sourcing, sales support, and general accounting transactions. This allows companies to deploy advanced AI and automation technologies at pace, augmenting internal capabilities.

Cloud Infrastructure and Managed Services

Cloud service providers offer scalable resources and managed machine learning services that are indispensable for AI application development and deployment. They efficiently handle the intricate processes inherent to ML system engineering, from data storage to compute power, reducing the need for extensive in-house infrastructure management. This allows AI teams to reallocate valuable time to more strategic initiatives, freeing them from the heavy lifting of managing specialized underlying AI infrastructure.

Augmenting and Upskilling the Existing Workforce

AI is primarily about augmenting human labor, not replacing it. It enriches, supplements, and enhances human capabilities, allowing employees to focus on more complex, meaningful, and creative tasks. Organizations can transform their workforce by:

Transitioning existing talent

Many companies find it beneficial to transition some of their current employees into AI-related roles. This builds internal capability and leverages existing domain knowledge.

Fostering an "AI-first" culture

Empowering employees to experiment with AI systems and providing structured learning paths, from beginner to advanced, helps build AI literacy across all functions. This practical training, tied to business goals, ensures employees understand how AI can enhance their work.

Empowering "DIY developers"

Low-code and no-code platforms allow non-technical employees to create or modernize web and mobile applications with AI, streamlining processes and fostering innovation without needing deep coding expertise.

The Role of Internal Leaders as Facilitators

While deep technical expertise might be outsourced or acquired through services, it is crucial to have internal leaders who can bridge the gap between technology and business objectives. CIOs, product managers, and other executives are responsible for defining the AI vision, identifying high-value use cases, and ensuring that AI initiatives align with strategic business outcomes. They must foster cross-functional collaboration, manage executive expectations, and define governance to mitigate risks. They don't need to be AI scientists, but they need to understand the "art of the possible" with AI and identify its main risks.

By taking a pragmatic, business-first approach that prioritizes clear objectives, strong governance, and a strategic mix of internal enablement and external partnerships, companies can unlock the transformative power of AI and gain a competitive edge. This model allows for flexibility, cost control, and rapid innovation, ensuring that AI delivers measurable value across the organization.

Talk to us to explore your options.