Why Your AI Deployment Strategy Could Make or Break Your Company

In today’s fast-evolving landscape, AI integration is already a necessity, even if you do not recognise it yet. Companies that successfully implement AI will see productivity gains previously unattainable. However, choosing the right AI deployment model is crucial for achieving those benefits. Whether you’re building AI capabilities internally, adopting third-party solutions, or taking a hybrid approach, understanding the pros and cons of each model is critical to long-term success.

Moreover, your choice of AI deployment model should depend heavily on the complexity of the use cases you are addressing. Large SaaS vendors and LLM functionalities are increasingly aggregating simpler AI automation use cases. Spending large sums on these simpler AI tools may backfire when companies like OpenAI, Salesforce, or LinkedIn eventually roll out similar capabilities at a fraction of the cost. Chat GPT recently launched Canvas and thousands of SaaS businesses became obsolete. With that in mind, let’s explore several AI deployment models and assess their benefits, challenges, and the considerations you need to make for different use cases.

Let’s review the different deployment models I discuss with clients every week and the pros and cons of each one.

1. Recruit In-House AI Expertise: Building from the Inside Out

For organisations looking to invest in long-term, highly specialised AI capabilities, building an in-house AI team may be the best option. By recruiting AI specialists such as data scientists, machine learning engineers, and AI architects, companies can design customised AI solutions tailored to their specific needs.

Pros:

• Customisation: In-house teams allow for AI solutions that integrate directly into your organisation’s existing processes and workflows.
• Control: You own the AI development, making it easier to adapt and evolve as your organisation’s goals shift.
• Scalability: In-house AI can grow with your organisation, expanding to new departments and use cases over time.

Cons:

• High Cost: Recruiting top-tier AI talent and building the necessary infrastructure is expensive, both in terms of upfront and ongoing investment.
• Talent Shortage: The global shortage of AI professionals makes it difficult to hire the right people. What happens if the technical expert building your AI solution leaves for a competitor and how do you replace them without loss of momentum? This is a really high risk for the technical C-suite.
• Time-Consuming: Developing custom AI solutions from scratch can take years, during which competitors using off-the-shelf solutions may gain a market advantage.

2. Block All AI Usage: The Risk of Falling Behind

Some organisations, wary of the risks associated with AI, may choose to block AI usage altogether. While this might address short-term security concerns or regulatory compliance, it opens the door to significant risks of being left behind, both competitively and technologically.

Pros:

• Security: Limiting AI usage can reduce the potential for data breaches or misuse of sensitive information.
• Compliance: In highly regulated industries, this approach may ensure adherence to stringent data protection laws.

Cons:

• Shadow IT Issues: When AI tools are blocked, employees often adopt unauthorised solutions, creating an unprecedented level of shadow IT systems that increase security risks.
• Lack of Competitiveness: Without AI, your organisation will be outpaced by competitors that are leveraging AI to drive efficiency and innovation. I know of CTOs taking this route and it beggars belief.
• Innovation Stagnation: Blocking AI use stalls progress in key areas, such as data-driven decision-making and process automation.

3. Buy Department-Specific AI Apps: Addressing Immediate Needs

Many organisations opt to deploy AI incrementally by purchasing AI tools that cater to specific department needs, such as sales, marketing, or HR. These apps, often built on APIs like ChatGPT, allow for quick deployment and address immediate pain points.

Pros:

• Speed to Market: Department-specific AI tools can be deployed quickly, offering immediate benefits.
• Specialisation: Tools like Gong for sales or Jasper for marketing provide targeted solutions that align closely with department goals.
• Ease of Use: These plug-and-play solutions require minimal setup and training, making them accessible to non-technical users.

Cons:

• Fragmentation: Each department using its own AI tool can lead to disconnected systems and siloed data, reducing overall efficiency.
• Cost Inefficiencies: With many of these apps sitting on the same AI infrastructure (such as OpenAI’s API), the cumulative cost of department-specific tools can become excessive over time.
• Risk of Overpaying for Simpler Use Cases: As large vendors like OpenAI, Salesforce, or LinkedIn begin to aggregate these simpler AI use cases into affordable SaaS offerings, companies that spent heavily on multiple small apps could find themselves paying much more than necessary.

4. The "SAP" Model: AI Platforms That Do It All

For organisations seeking a unified approach, the "SAP" model offers a comprehensive platform that aggregates multiple AI functionalities into a single solution. In the sales world, Gong is an example of a platform that centralises RevOps, conversation analysis, and deal tracking in one tool. Although no dominant platform has yet emerged across all industries, this model offers significant promise.

Pros:

• Comprehensive Capabilities: These platforms offer a range of AI-driven tools that cater to various functions within a business, creating a unified, streamlined experience.
• Consistency: Integrated AI platforms ensure data harmonisation across departments, enabling better decision-making and insight generation.
• Scalability: Such platforms can scale alongside your organisation, adding features and capabilities as your needs evolve.

Cons:

• High Upfront Costs: Comprehensive AI platforms often come with significant implementation costs and require time before the ROI materialises.
• Vendor Lock-In: Relying on a single platform can limit your flexibility and create dependencies that may be difficult to break.
• Overreach for Simpler Use Cases: These platforms are often overkill for simpler tasks, which could be handled by more cost-effective, department-specific solutions.

5. Hybrid Model: Combining In-House and Third-Party AI Solutions

Some organisations find that the best approach is a hybrid model, combining in-house AI development for critical use cases with third-party tools for less complex tasks. This allows for a balance of control, customization, and cost savings.

Pros:

• Flexibility: This model offers the ability to choose the best of both worlds—custom-built solutions for core areas and third-party tools for more general needs.
• Cost Efficiency: Focusing internal resources on high-value AI functions while outsourcing non-core tasks can reduce overall costs.
• Scalability: Third-party AI tools can be easily scaled as your business grows or as demands change.
• Less Risk: By tapping into a wider AI talent pool outside of the organisation, the risk of your AI strategy being brought to a standstill by a single person leaving the organisation is greatly reduced.

Cons:

• Integration Issues: Merging in-house systems with third-party tools can lead to integration challenges, particularly when the systems aren’t designed to work together.
• Data Silos: Managing multiple AI systems can create siloed data, limiting the organisation’s ability to get a unified view of business performance.
• Resource Demands: Managing both internal AI development and third-party vendors can place a heavy burden on IT and operations teams.

6. Managed AI Services: Outsource Your AI Deployment

For organisations that don’t have the internal resources or expertise to manage AI development, outsourcing AI to managed services providers is an option. This allows businesses to leverage cutting-edge AI without the need for deep internal capabilities.

Pros:

• Quick Deployment: Managed AI services allow you to launch AI projects rapidly, using the expertise and infrastructure of the service provider.
• Cost Predictability: Managed services operate on subscription models, offering predictable, ongoing costs.
• Focus on Core Business: Outsourcing AI lets your organisation concentrate on its primary goals while benefiting from AI-driven insights and automation.

Cons:

• Lack of Control: Outsourcing AI means giving up control over your AI data, processes, and models, which can lead to challenges down the line.
• Customisation Limits: Managed services providers may not be able to tailor AI solutions to your specific needs.
• Vendor Dependency: Long-term reliance on external vendors can create significant challenges if your AI needs to grow or change over time.

7. Cloud-Based AI Solutions: Leveraging AI-as-a-Service (AIaaS)

Cloud-based AI solutions, or AI-as-a-Service (AIaaS), offer access to powerful AI tools without the need for heavy infrastructure investment. Companies like AWS, Google Cloud AI, and Microsoft Azure provide scalable AI models and APIs that can be integrated into workflows across the organisation.

Pros:

• Scalability: AIaaS platforms can scale according to your needs, making them ideal for growing companies.
• Lower Upfront Costs: The pay-as-you-go pricing model reduces the need for large upfront investments in hardware or software.
• Advanced Tools: These platforms offer access to the latest AI tools and models, which would be difficult and costly to build internally.

Cons:

• Security and Privacy Concerns: Relying on third-party cloud platforms can expose your business to data privacy and security risks.
• Customisation: Cloud-based AI solutions often don’t offer the same level of customization as in-house or platform-based solutions.
• Ongoing Costs: While AIaaS models can reduce upfront costs, the pay-as-you-go model can result in high costs over time, especially as usage scales.

Conclusion: Matching the AI Deployment Model to the Use Case

Your choice of AI deployment model should be driven by the complexity of the use cases you're trying to address. For simpler tasks like automating emails, analysing customer sentiment, or creating reports, it may not make sense to spend heavily on department-specific tools. Large vendors like OpenAI, Salesforce, or LinkedIn are already moving toward aggregating many of these functionalities into low-cost, SaaS offerings. Spending too much on these easy wins could backfire when these services become widely available for a fraction of the price.

However, for complex, high-value use cases—such as custom sales forecasting models, advanced customer segmentation, or personalised marketing strategies—investing in in-house or hybrid AI development may offer more substantial ROI.

By carefully aligning your deployment strategy with the complexity and importance of your AI use cases, you can maximise value while avoiding the pitfalls of over-investing in areas where commoditisation is rapidly approaching.

Which deployment model best suits your AI strategy?