AI Advantage For Small & Medium Enterprises
Turning decades' worth of data into gold
How Can Small & Medium Enterprises Benefit From Artificial Intelligence?
The rocket fuel of AI is what most established companies have aplenty: Data. The other key components of that AI rocket are market knowledge and access to customers and partners.
When you transform your business with AI, you will continue to create value with your core products or services. By injecting them with data, you will amplify this value for your customers and discover new markets and business models.
Your data history and market expertise, combined with AI, can make you become the undisputed leader in your industry.
ai value creation for sme
Customer interactions and connected products (Internet of Things) generate data about the usage of your products and how they create value for your customer.
AI and Machine Learning can identify patterns in the collected data to better understand and amaze your clients, resulting in more sales and higher margins.
AI and Machine Learning can recognize patterns in your own operational data. This allows you to:
- reduce costs – e.g. by identifying cheaper production inputs that generate the same output quality,
- reduce risks – e.g. by capturing decades of employee experience & know-how in machine learning models,
- increase productivity – e.g. with smarter sales & marketing, based on Big Data.
To maximize the return on the investment made in collecting, cleaning and organizing your data, new ways of creating value can be activated.
For example, your data can be sold to clients or other third-parties that value the insights it provides.
As its value increases, your data becomes a strategic asset with which to build partnerships with clients and suppliers.
Trading data and insights in an ecosystem strengthens your market leadership, potentially allowing you to build a digital platform.
AI Transformation - SMEs' AI Journey
Our Step-by-Step Approach to Create AI Value for SMEs
1. AI Competitive Analysis
How has AI disrupted your market so far? Are there new tech-driven entrants offering digitalized added-value solutions?
Understanding how your competitors, clients, and suppliers use data in their value chain can inform what shape your company’s future will take.
2. Early AI Strategy
How can you turn your own data into a competitive advantage? How to channel the value created by AI to your customers, and hook them to your connected products? What will this journey look like, and when will you see the first benefits?
Knowing where your AI future lays de-risks your first projects and accelerates ROI.
3. Prioritized Use Cases
Once you understand how AI can transform your business, many ideas will come up to put it to work in your company.
Choosing where to start and the right sequence is critical to overcome early setbacks and quickly build trust in AI in your organization.
4. First AI Successes
Doing a first AI Proof of Concept, turning a successful Machine Learning model into a prototype and demonstrating positive ROI is an exciting and momentous event in the history of a 21st century company.
It is also a steep learning process that is fraught with many pitfalls. Getting experienced support at this stage is highly recommended.
5. Building AI Capabilities
As your data becomes increasingly valuable, and more and more of your profits come from AI-enabled products, your company needs to adapt its processes and upgrade its infrastructure and skills.
Whether to acquire technologies and hire specialists, or build and train, is a key trade-off in your transformation journey.
6. AI Maturity
Once the power of AI starts kicking in, there is no looking back. Data, rather than instinct, informs every decision, big or small, in the company. Each employee cares for, utilizes, and innovates with data.
You create an amazing AI-driven experience for your customers, on which to build industry leadership.
How We Support SMEs On Their AI Maturity Journey
Many of your colleagues and employees are probably already busy imagining algorithms that could create tremendous value out of your company’s data assets.
How many of those ideas are actually doable, given the stage of AI maturity of your organization? Which one is the best candidate for a first AI Proof of Concept? How are you going to define and measure the business impact of your AI initiatives?
We can help you with these questions. Learn more here.
Successful first projects are crucial to demonstrate AI’s business potential and establish trust across your organization.
Our pragmatic, nimble and highly agile approach to executing early Proofs of Concepts focuses on generating quick wins while minimizing risks and costs. We also address the critical aspect of effectively communicating results to non-AI audiences, thereby making the benefits concrete and visualizable to all.
Learn more here.
Faced with many AI opportunities and competitive threats, your small Data team runs the risk to scatter their efforts, diverging from the company’s strategic roadmap. A continuous alignment process is needed, which hast to go both ways, as today’s business strategies become increasingly shaped by AI.
Learn more here about our simple and effective approach to AI innovation.
Great data assets remain buried in the ground if the necessary infrastructure, skills and processes to extract and refine them doesn’t exist. Also, your end users will need a fast, reliable, and convenient solution to access and visualize or otherwise consume the data they need, when they need it.
See here how our expertise can guide you.
Ideation & Ranking of AI Low-Hanging Fruits
The first idea may not be the best to start with. Just as the AI innovation that will bring you ahead of the competition may be one ideation workshop away from being formulated.
Our AI expertise and external perspective can help you make the right first decisions in framing and prioritizing the early opportunities:
We have extensive experience running AI Ideation workshops with teams of business and IT experts across diverse functions and industries. We surface existing ideas, generate new ones, and structure all these use cases in a framework ready to be weighted against strategic objectives.
This is a fast, light-weight process, but a critical a critical one necessary to make sure the next, more expensive steps yield maximum results.
Not every AI use case is the right candidate for a relatively inexperienced organization to start experimenting with. And an early failure can seriously set back your AI roadmap.
We can help you choose the first project that will maximize the chances of success and establish the confidence in AI on which to build the next steps.
First Successes with AI
Although an AI Proof of Concept may just look like the development of a slightly exotic software prototype, there are fundamental differences. And many potential pitfalls.
We ourselves learned a lot from our early mistakes and are happy to help you to do it right the first time.
If you want to maximize your chances of early success, it makes sense to place multiple AI bets. Our parallelized campaign approach systematically experiments with multiple use cases, and produces a winning AI prototype within weeks. You can quickly demonstrate a positive business outcome, accelerate ROI, and build trust in AI across your organization.
Among other things, we can help you with:
- Defining business metrics to measure the value and ROI of your AI Minimum Viable Product,
- Designing an A/B testing plan,
- Deciding when data scientists should stop tweaking their algorithms (trickier than it sounds!)
The results of a complex data science experiment, especially the link between algorithm outputs and business outcomes, can be difficult to explain to a larger audience.
With our expertise in Transformation and Executive Education, we can help turn dry data into organizational excitement and adoption momentum.
AI Innovation & Product Management
Treating Data & AI as products rather than pieces of technology helps accelerating business value creation with AI.
We have developed a smart, simple framework to drive AI innovation that incorporates key elements of Digital Product Management:
When designing a new AI solution, algorithms and their data output must be aligned with the needs and experience of the end customers.
This is a complex AI<>Business translation process that we are happy to help you and your team master.
As your organization gradually increases its AI Maturity, AI innovation will start to spark from all corners, especially from people executing poorly designed or antiquated operational processes.
Also, your competitors are likely to be experimenting with AI already, while Big Tech may be probing a potential disruption of your industry.
We have developed a light and effective AI innovation framework that helps ‘scanning the internal/external market’ for opportunities and threats. In turn, this focuses your AI resources on initiatives with the highest impact.
Data Service Vendors & Partnerships
With limited time and resources, it doesn’t make sense for SMEsto try and do everything by themselves. Vendors of Data & AI Software-, Platform-, and Infrastructure-as-a-Service can be brought in to quickly deploy advanced AI capabilities.
Data & AI services and products can be complex, however, and costs can rapidly spiral out of control. We can help you navigate this:
There are many companies developing great services and products to help you accelerate your journey towards Data & AI Maturity.
We have the experience and methodology to help you select the providers that offer the best value for money.
AI business services can be expensive, and extracting value from them without getting entangled is tricky.
We have extensive vendor management expertise and are happy to share our best practices.
Data Architecture & Infrastructure
While most existing companies will have accumulated large volume of valuable data over the years, there may be technical issues with utilizing it at scale if the IT infrastructure is not designed for this purpose.
We can help you define key data architecture designs to support your AI roadmap:
Even if your organization is not yet familiarized with cloud computing, creating a simple data processing and machine learning development environment is relatively straightforward.
This enables your team to quickly experiment with cloud-based data pipelines and product development technologies, without making early commitments on large-scale cloud migrations.
As your organization becomes increasingly aware of the value of the data it generates, fundamental organizational questions will start to emerge:
- who is accountable for the quality and accuracy of this critical dataset?
- who has the right to access this sensitive customer data? For which purpose?
- Are we compliant with regualtions pertaining to the collection, storage, and usage of personal information?
Our team has extensive experience with these topics and will be happy to help with designing a flexible yet effective process framework to cover these needs.
Adopting the right data grammar and vocabulary will help you talk the data language of your customers when the time comes for selling or trading your data.