AI Commercialisation
Turning AI into commercial value
A practical guide to turning AI capabilities, products and opportunities into revenue.
Many businesses are experimenting with AI. Far fewer have worked out how to turn those capabilities into a commercially valuable proposition.
AI commercialisation is about closing that gap.
It involves identifying where AI can create value, understanding who will pay for it, developing the right proposition and creating a route to market.
That might mean developing a new AI product, adding AI to an existing service, creating a new revenue stream or using AI to strengthen an existing commercial proposition.
The technology matters, but commercialisation starts with the customer and the business opportunity.
What is AI commercialisation?
AI commercialisation is the process of turning AI capabilities into products, services, revenue or measurable commercial value.
It can involve:
- Developing an AI-enabled product
- Adding AI capabilities to an existing product or service
- Creating new AI-powered services
- Identifying new markets or customer segments
- Developing an AI value proposition
- Defining pricing and packaging
- Building a go-to-market strategy
- Creating sales and marketing propositions
- Developing partnerships and routes to market
- Measuring commercial performance
The objective is not simply to introduce AI.
It is to create something that customers value and that the business can commercially sustain.
Why is AI commercialisation difficult?
AI can create impressive technical possibilities, but technical capability does not automatically create a viable business.
A business may have built an AI capability without knowing:
- Who the customer is
- What problem it solves
- Why customers would pay for it
- How much they would pay
- How it differs from alternatives
- How it should be packaged
- How it should be sold
- Whether the market is large enough
- What needs to happen to scale it
This is where commercial thinking becomes as important as technology.
A technically successful AI project can still fail commercially if the proposition, market, pricing or route to market is wrong.
Where can AI create commercial value?
AI can create commercial value in several different ways.
New products
AI can enable entirely new products that were previously difficult or impossible to deliver.
This could include AI software, intelligent assistants, specialised AI applications or products built around proprietary data.
Existing products
AI can also enhance an existing product.
For example, an established software product might introduce AI-powered recommendations, automation, search, reporting or customer support.
The commercial question is whether the new capability creates enough additional value to influence customer acquisition, retention, pricing or usage.
New services
Businesses can use AI to create new professional or managed services.
This could involve AI-powered analysis, automation, content, customer support, research or industry-specific services.
Internal commercial performance
AI does not have to be sold to customers to create commercial value.
It can improve sales productivity, customer service, marketing, pricing, forecasting and other revenue-related activities.
In this case, the commercial benefit may come through increased revenue, lower costs or improved margins.
AI product vs AI-enabled product
There is an important distinction between building an AI product and adding AI to an existing product.
An AI product may be built primarily around an AI capability.
An AI-enabled product uses AI as one component of a broader customer proposition.
In both cases, the commercial question remains the same:
What customer problem does it solve, and why is it worth paying for?
What does an AI commercialisation strategy include?
There is no single approach that works for every business.
However, a commercialisation strategy will typically consider:
Market opportunity
Who has the problem and how significant is it?
This includes understanding the target market, customer segments, competitive environment and potential demand.
Customer problem
What problem is the AI capability actually solving?
The strongest propositions are usually built around a clear customer need rather than the novelty of the technology.
Value proposition
Why should a customer choose this solution?
The proposition needs to communicate the outcome rather than simply describe the underlying AI.
Product or service
What exactly is being offered?
This might be software, a managed service, consultancy, an AI-enabled product or a combination.
Pricing
How should the offering be priced?
AI can create different pricing possibilities, including subscriptions, usage-based pricing, transaction pricing, licences, managed services or outcome-based models.
Go-to-market
How will the product or service reach customers?
This might involve direct sales, partners, marketplaces, existing customer relationships or a combination of routes.
Sales proposition
How will the commercial team explain the value?
AI can be technically complex. The sales proposition needs to translate that complexity into a business outcome customers understand.
Economics
What does it cost to deliver the solution and what commercial return can it generate?
AI economics can involve model costs, infrastructure, data, people, implementation and ongoing support.
AI commercialisation vs AI strategy
AI strategy and AI commercialisation are related but have different objectives.
AI strategy asks:
Where should we use AI and how should we approach it?
AI commercialisation asks:
How can we turn an AI capability or opportunity into commercial value?
A business may therefore develop an AI strategy first and then identify specific opportunities that could be commercialised.
Alternatively, an existing technology company may already have AI capabilities and need commercial expertise to determine how to take them to market.
AI commercialisation vs AI transformation
AI transformation focuses on applying AI across an organisation and changing the way the business operates.
AI commercialisation focuses on creating commercial value from AI.
There can be considerable overlap.
For example, an organisation might use AI internally to improve productivity while simultaneously developing an AI-enabled product for customers.
The internal transformation and external commercialisation may require different expertise.
What does an AI commercialisation consultant do?
An AI commercialisation consultant helps connect AI capability with market opportunity and commercial execution.
Depending on the assignment, they may help with:
- Market and customer analysis
- Proposition development
- AI product strategy
- Product-market fit
- Pricing and packaging
- Go-to-market strategy
- Sales strategy
- Channel and partner strategy
- Commercial planning
- Revenue modelling
- Sales enablement
- Commercial team development
- AI opportunity assessment
The role sits at the intersection of technology, customers and commercial strategy.
When might you need AI commercialisation expertise?
AI commercialisation expertise can be useful when:
- You have developed an AI capability but do not know how to sell it.
- You have an AI product idea and want to test its commercial potential.
- You want to add AI to an existing product or service.
- You need to develop an AI proposition.
- Your sales team is struggling to explain the value of AI.
- You are unsure how to price an AI product.
- You want to identify new revenue opportunities from AI.
- You need a go-to-market strategy for an AI offering.
- You want to commercialise proprietary data or technology.
- You are looking for partners or routes to market.
- You need senior commercial leadership for an AI initiative.
What expertise might you need?
AI commercialisation often involves several disciplines.
Depending on the opportunity, you may need:
- AI strategy consultants
- AI commercialisation consultants
- AI product specialists
- Product managers
- Go-to-market specialists
- Sales and commercial leaders
- AI developers
- Data specialists
- AI transformation consultants
- Industry specialists
The right combination depends on how mature the opportunity is.
An early-stage idea may need market validation and proposition development.
A developed product may need pricing, positioning and go-to-market expertise.
An established AI business may need commercial leadership and sales transformation.
How to commercialise AI
A practical approach is to work backwards from the customer.
1. Identify the problem
What customer problem are you solving?
2. Establish the value
What changes for the customer if the problem is solved?
3. Define the proposition
What exactly are you offering and why is it different?
4. Test the market
Speak to potential customers and test whether the problem and proposition resonate.
5. Develop the commercial model
Consider pricing, packaging, delivery costs and potential margins.
6. Build the route to market
Determine how customers will discover, evaluate and purchase the offering.
7. Scale what works
Once there is evidence of demand, develop the sales, marketing, delivery and technology capabilities required to scale.
The technology can evolve throughout this process.
The commercial proposition should remain anchored to customer value.
Common AI commercialisation mistakes
Businesses can fall into several common traps.
Starting with the technology
Building something because it is technically possible does not establish that customers need it.
Assuming AI sells itself
Customers generally buy outcomes, not AI for its own sake.
Underestimating pricing
AI products can have very different cost structures from traditional software or services. Pricing needs to reflect both customer value and delivery economics.
Building before validating
A significant technical investment before testing customer demand can create unnecessary risk.
Treating AI as a feature rather than a proposition
Adding an AI feature does not automatically create a compelling commercial proposition.
Ignoring go-to-market
A good product still needs a route to customers.
Finding the right AI commercialisation expertise
The expertise required depends on where you are starting.
If you have an idea, you may need someone to assess the opportunity.
If you have built a product, you may need help with positioning, pricing and go-to-market.
If you already have customers, you may need commercial leadership to scale the proposition.
There is no single type of AI commercialisation consultant that fits every situation.
Not sure what expertise you need?
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Frequently asked questions
- What is AI commercialisation?
- AI commercialisation is the process of turning AI capabilities, products or opportunities into revenue or measurable commercial value.
- How do you commercialise AI?
- Start with a customer problem, establish the value of solving it, develop the proposition, test market demand and then build the pricing, sales and go-to-market model required to scale it.
- What does an AI commercialisation consultant do?
- They help businesses turn AI capabilities into commercially viable products, services or revenue opportunities. This can include proposition development, market analysis, pricing, go-to-market and commercial strategy.
- What is the difference between AI strategy and AI commercialisation?
- AI strategy determines where and how a business should use AI. AI commercialisation focuses on turning AI capabilities and opportunities into products, services, revenue or other commercial value.
- Can AI commercialisation apply to an existing business?
- Yes. Existing businesses can commercialise AI by enhancing existing products, developing new services, creating new revenue streams or using AI to improve their commercial performance.
- Do I need an AI consultant to commercialise an AI product?
- Not necessarily. The expertise required depends on the maturity of the product and the capabilities already available internally. You may need product, commercial, technical or go-to-market expertise, or a combination.
- How do I know whether an AI idea is commercially viable?
- Consider the size and urgency of the customer problem, willingness to pay, competitive alternatives, delivery economics and the ability to reach the target market.
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