Digital
Navigating the challenges of digital transformation in traditional industries
Digital transformation is no longer a buzzword reserved for tech startups or cutting-edge companies. Today, even traditional industries — such as manufacturing, healthcare, and retail — are feeling the pressure to evolve. However, while digitalisation offers enormous potential, passing through the challenges of transforming longstanding practices is no small feat.
For traditional companies, this transition often feels like stepping into uncharted territory. Their operations, culture, and customer base may be deeply rooted in decades of established processes. Yet, the demand for digital efficiency, personalisation, and connectivity is impossible to ignore. The question is no longer whether to give way to digital transformation but how to do it in a way that honors the company’s heritage while preparing for a future defined by constant innovation.
Understanding the cultural shift
One of the most significant challenges traditional industries face is overcoming the cultural inertia that often comes with long-standing practices. Employees accustomed to manual, hierarchical systems may resist the implementation of digital tools or new ways of working. This resistance isn’t just about learning new technology — it’s often about fear of losing relevance in a rapidly changing world.
Hovering through this cultural shift requires strong leadership and clear communication. It’s essential that leaders create a sense of urgency about digital transformation while reassuring their teams that the shift is an opportunity for growth. A culture that imbibes learning, experimentation, and adaptability is key to a successful transition. Leadership must model this mindset, showing that digital transformation isn’t about replacing people — it’s about empowering them with better tools and insights.
Building the right digital infrastructure
For many traditional companies, the journey toward digitalization begins with modernizing their infrastructure. This is no small task, especially for industries like manufacturing or logistics, where outdated technology may have served them well for years. But today’s digital landscape demands agility, scalability, and connectivity, which means legacy systems often need to be restructured or replaced altogether.
A critical element of this process is investing in cloud-based technologies and data analytics. Moving to the cloud allows businesses to streamline operations, scale more easily, and increase collaboration across departments or even global locations. Furthermore, data analytics provides insights that can drive smarter decision-making, enabling companies to understand their customers, optimize processes, and reduce inefficiencies.
The importance of customer-centric transformation
Digital transformation isn’t just an internal process; it must fundamentally alter how companies interact with their customers. Traditional industries, many of which have relied on face-to-face interactions, must now adapt to a world where customers expect seamless online experiences.
This shift means more than just having a website or social media presence. It requires companies to rethink their entire customer journey, ensuring that every digital touchpoint is intuitive, personalized, and frictionless. Whether it’s enabling online purchases, providing customer support through AI chatbots, or leveraging social media for brand engagement, companies must align their digital transformation efforts with changing consumer expectations.
The use of customer data, when done ethically and transparently, becomes an invaluable tool here. Digital tools provide insights into consumer behavior, preferences, and pain points. These insights can drive tailored marketing strategies, create better products, and improve customer satisfaction.
Overcoming skills gaps and building digital talent
A common obstacle for traditional companies going digital is the skills gap within their workforce. Employees may lack the technical know-how needed to work with new systems or data-driven platforms. This can delay digital initiatives and lead to frustration across teams.
To bridge this gap, companies must invest in continuous training and development. Upskilling employees in areas such as digital marketing, data analysis, and e-commerce will help foster an internal workforce that is equipped to drive the company forward in the digital age. Additionally, partnering with tech experts or consultants can be a wise step to ensure the right digital strategy is implemented efficiently.
However, hiring new digital talent also plays a crucial role. This may mean bringing in experts from outside the industry who can introduce fresh perspectives and innovative approaches to problem-solving. The combination of retaining experienced employees and incorporating new talent creates a powerful foundation for growth.
Giving way to agile practices
Digital transformation in traditional industries also requires a shift in how projects are managed. Legacy companies often rely on rigid, top-down decision-making processes that are slow to adapt. In contrast, the digital world operates on agile methodologies — allowing for quick iterations, fast feedback, and constant improvement.
Balancing tradition with innovation
Balancing tradition with innovation is the key to digital transformation. Traditional companies have often thrived because of their deeply rooted values, customer loyalty, and proven methods. The key is not to abandon these strengths but to find ways to enhance them through digital tools.
For example, a family-owned retail business can use e-commerce platforms to reach new customers while still maintaining the personalized service that earned them a loyal base in the first place. Similarly, manufacturers can use automation to increase efficiency without sacrificing the craftsmanship that defines their products.
Digital transformation doesn’t mean losing what makes a company unique – it means finding new ways to amplify those qualities in a rapidly changing landscape.
Final thoughts: Moving forward with confidence
Hovering through the challenges of digital transformation in traditional industries is a complex, multifaceted journey. It requires a deep emphasis on cultural change, investments in the right technology, and a focus on both internal teams and customer experiences. In a world where disruption is the new normal, success will come to those who manage to blend the best of their past with the possibilities of the future.
The article has been authored by Wiredus Media founder & MD Ravish Yadav.
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
