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Act before the market
The retail industry is in the midst of profound transformation driven by two interconnected forces: the convergence of retail and e-commerce into a hybrid landscape, and the rapid integration of Artificial Intelligence (AI). These forces aren't just altering shopping habits - they're reshaping the entire retail value chain from manufacturing to fulfilment. McKinsey identifies these as key economic game-changers (putting aside broader geopolitical factors).
E-commerce has evolved beyond a simple digital channel into three overlapping segments:
• Manufacturers going Direct-to-Consumer (DTC): Brands like Nike, Zara, and Dyson build direct customer relationships through digital storefronts and direct shipments.
• Pure-Play digital platforms: Born-digital platforms like Zalando and ASOS innovate with personalisation and logistics, with some even branching into physical flagship stores.
• Traditional retailers adopting digital: Giants like IKEA and Walmart are integrating physical and digital experiences seamlessly.
These segments are merging into a highly competitive ecosystem where agility and market intelligence are essential. This blending doesn't simplify competition - it intensifies it, demanding constant vigilance and adaptability.
AI has moved from futuristic concept to operational necessity. Retailers leverage AI for personalised recommendations, dynamic pricing, predictive inventory management, and customer service automation, enabling smarter, faster, and more profitable operations.
Additionally, the expansion of cloud computing and the explosion of available data provide new opportunities to understand market dynamics in real-time. Historically, analysing every shelf in Europe was unthinkable; today, online data combined with AI makes large-scale, real-time market analysis entirely feasible.
A strategic framework for AI-enabled retail
Staying ahead requires a structured approach. Recently, Shish Shridhar from our partner Microsoft shared a strategic framework leveraging real-time data, AI, and automation, highlighting essential levers to drive growth.
Place – Sales channels: physical, digital, hybrid
Product – Assortment depth, availability
Value – Pricing strategy, perceived customer value
People – Customer service, store experience
Communication – Marketing, promotions, loyalty programmes
Systems – Technology infrastructure, analytics, automation
Logistics – Efficient supply chain, fulfilment methods, delivery
Suppliers – Vendor management, sourcing and COGS
At Tembi, we equip retailers and brands with large-scale market analytics derived from real-time data. We continuously track over 600,000 online retailers and 300 million products across Europe - among the largest datasets in the industry. By connecting this data with location specifics, company details, and AI-powered analytics, commercial teams gain clarity and confidence in decision-making, eliminating guesswork when it comes to understanding what drives growth.
As I see it, by unifying data across digital and physical touch points, market intelligence at scale enables smarter decisions in Product, Value, and Systems - helping businesses thrive in a hybrid, AI-first retail world.
• Based on the product categories that you excel in - which markets are then optimal, and in which geographies would it be optimal to promote and sell your products.
• If you have or plan to set up physical stores how are they threatened and compared to online retailers, and how is the specific area you think about investing in evolving. This will show whether you can expect optimal levels for sales per store and store traffic.
• Predict which product categories and brands to invest in, when you decide where to play - which product segments are you in with which brand and pricing strategies to drive market share and inventory turnover and hence sales growth.
• Understand the competition in the product categories and brands you are in and the strengths and strategies of the other players in the market.
Improve out-of stock-rate by finding out when products are sold over the year and if that differs in different geographies, and thereby make sure that availability is secured.
• Understand the current (or seasonality-defined) pricing in the market to increase gross margin, right-in-time markdowns, and possibility for price skimming but get an actual X-ray about the real price development in the market.
• When being present in the market, or especially entering a new one, you need to know how to make it successful. One of the important things to understand delivery market standards in different markets, e.g. OOH, home delivery, free shipping thresholds, delivery time etc. Some D2C try to negotiate a pan-European delivery contract without factoring ion the different market dynamics, or simply enter with the wrong expectations. This quickly becomes very expensive - e.g. if the customers are used to home deliveries and you go in the market wth parcel boxes, you would need to wait for that to be changed. Hence, very important to understand if you model fits to assure fulfilment accuracy.
Success in modern retail isn’t about choosing between physical and digital - it’s about blending them intelligently. In the era defined by AI and hybrid commerce, tools like Tembi provide retailers the crucial insights required to navigate complexity.
Whether you’re a DTC manufacturer, a digital-first retailer expanding your reach, or a traditional retailer enhancing your omnichannel strategy, winning demands strategic clarity grounded in data and real-time market intelligence.
In the early 2000s, Open Innovation emerged as a response to the Not-Invented-Here(NIH) Syndrome - a mindset particularly prevalent in engineering and IT organisations.Companies often preferred to build their own solutions rather than adopting existing ones, even when viable alternatives were readily available.
The rise of open innovation, open source, and open data has since accelerated technological progress for everyone. Instead of investing heavily in developing proprietary solutions, businesses can now leverage what already exists, saving time, money, and effort.
Despite these advancements, some businesses still choose to develop their own versions of existing solutions. The reasons often include:
However, these assumptions often lead to inefficiencies and long-term challenges.
If a solution already exists in the market, trying to replicate it internally is rarely the best approach. Here’s why:
Once a company has invested in a proprietary solution, it becomes difficult to abandon, even when it’s no longer efficient. This is how businesses end up with a giant with feet of clay, a fragile system that limits agility and innovation.
Rather than building something from the ground up, focus on what differentiates your business. If a solution already exists in the market, build on top of it rather than duplicating efforts. The key to staying competitive isn’t in owning every piece of technology, it’s in leveraging the best tools available to drive your core business forward.
n today’s business world, being data-driven is no longer a question; it is a necessity. Organisations that don’t understand how to work with data and leverage it risk falling behind or even going out of business. However, merely being data-driven is not enough anymore. The rapid growth of access to artificial intelligence (AI) and lowered computing cost has amplified the significance of data, driving a shift towards predictive (and even prescriptive) intelligence to stay ahead of the competition.
Transitioning from a data-driven to an AI-driven organisation presents immense opportunities, enabling companies to understand the competitive landscape better, and leverage both market predictions to gain an edge, as well as improving operations to lower operating expenses. This transition requires a fundamental change in how we operate and organise the company. Secondly, we need to decide where to start, and whether to build, or buy a solution.
Here we share five, simple, steps to ensure your organisations success in this transition.
Achieving success with a transition is a strategic choice and an executional leadership challenge. It is crucial for management, whether top-level executives, business unit leaders, or team managers, to clearly communicate that the goal is to capitalise on the benefits of being data-, AI-, or analytics-driven, and where these benefits will have an impact, and why the transition is imperative for the organisation’s success. Leaders should:
Clarifying responsibility is essential as well as identifying the right person to lead the operational work of the transition. Allocate funding centrally rather than locally to prevent initiatives from being perceived as competing with short-term operational needs. By centralizing funding and clarifying responsibility, organisations can ensure that the transition to an AI-driven approach is viewed as a strategic investment rather than an operational cost.
It is unfortunate when initiatives become confined to a single department or individual. The benefits of an AI-driven approach are significant and extend across the entire organisation. Therefore, it is crucial to integrate solutions into as many teams as possible where there is a business case. Engaging more teams in the adoption phase offers several benefits:
Avoid placing the burden on a single individual. Employ the innovative power of the entire organisation to achieve greater success.
For new solutions and strategies to work, they must be integrated into daily operations. Overcoming existing habits and ways of working requires repetition until the new practices become habits. Incorporate the use of data and analytics tools into the organisational rhythm, such as in weekly meetings or daily stand-ups. Measure the impact of these new practices and share the progress with the entire organisation. Highlight how the transition is improving efficiency compared to previous methods.
Fostering an adaptive mindset is crucial for the transition to an AI-driven organisation. This mindset should infiltrate the company culture, regardless of role. Here are three tips for building a stronger adaptive mindset:
It might sound simple, but actively working on lifting and promoting the right people is very often overlooked. Make sure it is part of the leaderships action plan so this practice doesn’t fall between two chairs, or is forgotten within a couple of quarters.
Building a data and AI-driven organisation is essential for maintaining competitiveness in today’s business environment. Transitioning from being merely data-driven to embracing AI and predictive intelligence offers significant advantages, including a better understanding of the competitive landscape, leveraging market predictions, and improving operational efficiencies.
To ensure success in this transition, organisations should follow five key steps. First, management must clearly articulate that becoming an AI-driven organisation is a strategic goal. This involves transparent communication about the importance and challenges of the transition, along with regular follow-ups and continuous leadership support.
Second, organising the transition is crucial. This includes clarifying responsibilities and centralizing funding to ensure that AI initiatives are viewed as strategic investments rather than operational costs.
Third, disseminating the solution broadly across the organisation is vital. Integrating AI solutions into multiple teams enhances collaboration, shares costs, and accelerates the transition, leading to a higher overall ROI.
Fourth, embedding new solutions into daily routines ensures that these practices become ingrained in the organisation’s operations. Regular use and measurement of the impact help highlight the efficiency improvements over previous methods.
Finally, fostering an adaptive mentality is essential. This involves supporting superusers, hiring individuals with an innovative mindset, and promoting a culture that celebrates successes. An adaptive mentality ensures the organisation remains agile and responsive to new opportunities.
By following these steps, organisations can effectively leverage data and AI, achieving sustained success in an increasingly AI-driven world.
