Is AI too expensive for small businesses?

The landscape of AI has evolved dramatically, placing powerful and affordable solutions within reach for organizations of all sizes. Advances in tooling, algorithms, and significant reductions in the cost of data storage, compute power, and cloud computing have democratized AI, making it more versatile and indispensable than ever.

Today, the central question for SMBs isn't whether to adopt AI, but how to swiftly integrate it to stay competitive and drive value.

SMBs can leverage several flexible and cost-effective avenues to bring AI into their operations

Off-the-Shelf and Subscription-Based Software as a Service (SaaS) Solutions

Many AI capabilities are now available as ready-to-use products or services, significantly reducing the need for extensive in-house development. This approach allows businesses to subscribe to AI services, often through a low-cost entry-level subscription, rather than building solutions from scratch. Examples include off-the-shelf GenAI tools like Microsoft Copilot, which can streamline daily business activities and cut down production time for key deliverables. These commercial solutions provide robust support, seamless integration, and advanced features, suitable for a wide range of business functions from customer service to marketing. Leveraging existing foundation models from suppliers, potentially with fine-tuning for specific needs, can also offer significant positive cost impact as initial training costs are absorbed by the vendor.

Trial-Based and Freemium Models

A practical way for SMBs to explore AI's potential without significant upfront investment is through pilot projects or freemium models. These initiatives allow businesses to test the effectiveness of AI in their specific environment and demonstrate incremental business value. Starting small with proofs of concept can be relatively low-cost in terms of compute resources, providing a safe space to learn and adjust the approach before scaling to full production. This experimental phase helps to build confidence and informs future AI strategy based on tangible results.

Outsourcing to Boutique Agencies and Strategic Partners

For SMBs lacking internal AI talent or expertise, outsourcing to specialized agencies or partnering with technology providers can bridge critical skill gaps. Many companies with less AI experience rely on external partners for advisory, consultation, and program management, gaining access to specialized knowledge and best practices for specific AI use cases like chatbots or conversational applications. Strategic partners, including technology providers and global systems integrators, can contribute the necessary expertise to design and execute an AI strategy, augmenting internal capabilities and accelerating digital transformation. These partnerships can also facilitate co-development, where vendors team up with organizations to create customized solutions.

Leveraging Cloud-Native AI Services

Cloud platforms are the engine that fuels AI, offering scalable computing resources and flexibility essential for effective AI rollouts. Cloud service providers offer scalable resources that help control and minimize the cost of AI development and deployment. This means SMBs can access massive amounts of computing power on demand, with the cost to process AI applications being a fraction of what it once was. Managed machine learning services handle intricate and often prolonged processes inherent in ML system engineering, freeing up internal teams to focus on strategic initiatives.

Focusing on High-Impact Use Cases

A critical strategy for managing costs and demonstrating value is to start with specific, high-impact AI use cases. Instead of attempting a broad, organization-wide overhaul, SMBs should identify pressing problems where AI can deliver significant value quickly, such as automating repetitive customer service queries or optimizing resource allocation. Focusing on use cases that yield faster or higher returns on investment can create momentum and internal buy-in for further AI adoption.

The Critical Need for Proving ROI

Regardless of the implementation approach, it is paramount for SMBs to define and demonstrate the business value and Return on Investment (ROI) of their AI initiatives. Many organizations struggle to quantify AI's impact, with unexpected costs making it difficult to prove ROI. This challenge is compounded by the fact that AI projects can have less predictable costs, timing, and results than traditional software projects, often resembling research and development more closely.

To overcome this, SMBs should:

• Align AI initiatives with clear business goals and Key Performance Indicators (KPIs), translating AI metrics into measurable business outcomes like reduced risks, increased revenue, or improved operational efficiency.

• Establish a cost governance framework and continually monitor data, training, and inference costs over time to avoid unexpected charges.

• Prioritize projects based on business impact, feasibility, and quantifiable financial projections, focusing on short-term wins to build faith and demonstrate value across the organization.

• By strategically selecting AI solutions and meticulously measuring their impact, SMBs can confidently navigate the AI landscape, transforming challenges into opportunities for growth and innovation.

Talk to us to explore your options.