How a transformation from reactive transactional to proactive commercial can be pivotal.
In today’s rapidly evolving technological landscape, AI is not just a future prospect—it’s a present reality. The pace of change is outstripping many organizations’ ability to keep up, creating a widening gap. At the heart of AI is data, largely proprietary data. And with AI will come disruption, acceleration of the VUCA world. Which will require even more agility.
And the pressing question is: Will you be driving the disruption pro-actively, or will you be on the receiving end, reactively ? Is your data, your organization with its processes and systems ready for AI? Ready to train and manage the AI agent efficiently and effectively? Is your data reliable ? Is qualitative data and documentation properly indexed and version managed ? What about retrofits and upgrades over the service life of your installed base ? Have you drawn in the AI agent with its input and output into your business processes? With a job description ? (You should)
Most probably it is not.
So how can you get there? Achieving the necessary data quality to efficiently adopt AI proactively – and the agility to adjust to change that comes with it – requires a transformation in organizational setup to a higher level of maturity, and an improved (lean, agile) company culture.
With over 30 years of experience in transforming sales (support), product management and customer service/service after sales in sync with operations, focusing on performance improvement through organizational change management, I am particularly concerned about the “IT-maturity” (and process maturity) of our organizations—not the IT department. What I have observed over at least the past decade or 2, is a deterioration of both process and IT maturity especially with middle management, but largely accross the board. This has negatively impacted our companies’ agility and hampered efficiency and data quality within manufacturing industries. And offers upward potential. But the race is on.
Here is why.
Reflecting on my early career with Hansen Transmissions International in the 90s and early 2000s, green screen IBM AS400 systems and custom ERP solutions were prevalent. There was a higher awareness of processes, IT and data processing among middle and top management in middle and large sized manufacturing industry. Especially with High Tech and rapidly scaling manufacturing business as was Hansen Transmissions. Back then, building and refining the ‘ERP’ code was a collaborative effort between line management and in-house IT specialists who deeply understood the business. Processes and process documentation, called the “Follow Me” were the foundation for software development and testing and day to day business management alike. And middle management was actively involved, owning the process and work instructions which were alive and at the core of the company’s culture, fostering agility, focus and engagement. Sustaining a Year on Year organic growth rate of inbetween 10 and 25% for the 13 years i worked there. That is what it takes.
With standardized ERP solutions we have outsourced and subcontracted that … and – from my perspective as interim change manager – lost on skill, expertise, culture and maturity in our organizations – under a relentless quest for cost reduction, rationalization and efficiency. However, this process awareness, IT maturity and data skill and expertise are essential for agility and data quality, thus for AI and what comes next in these geopolitical turbulent times.
As we move forward with AI, it’s crucial to revisit these foundational practices and expertise, assess where we are and restore the process and IT maturity if needed to ensure our organizations are equipped to harness the full potential of AI and thrive in the VUCA world that comes with it. The journey to yield AI ROI requires a holistic approach, integrating new tools and processes such as product and service lifecycle mgt. and with those: documentation (data) quality in combination with organizational excellence
Why does this matter ?
AI success and general efficiency (management) hinge on actionable data and documentation quality which is crucial for training mission-specific AI agents. I foresee AI unlocking efficiency gains that will be transformative and disruptive, but the bulk will be built on proprietary data. AI built on generic agents will probably be less differentiating.
Especially in the customer support, customer service and maintenance field, especially for OEM’s. But also in project engineering and contract managment pre sales for manufacturing industry in general.
I am preparing a more in depth article on which AI applications will be most disruptive and gain the highest ROI and share some experiences in this respect. And also where I see a lot of hype around AI, eg use in managing the inbox, and where I think the real benefits of technology lie elsewhere: tackle the disease, not the symptom. I have managed large transformations with multiple workgroups focusing on different processes as transformation lead and managed to keep emails in my inbox really low. Smart use of new features in teams and the broader office package (eg track changes and sending links to shared docs) gets you a long way.
Now how to go about tackling this transformation to excellence and agile?
That will largely depend on your current maturity, AI risc assessment (but that is still quite ambiguous) and quite frankly your ambition and aspiration on the long run. And your capability to support a smaller or larger transformation. Complemented with a good understanding of what change entails and requires for it to be successful and impactfull.
You need for sure specialists early involved, and a good preparation.
In manufacturing, and especially for OEM’s the topic is most pressing. And it spans all business functions. This is why we founded ThreeStones in the first place.
If I was a business owner of shareholder or CEO, I would consider the following:
A customer service transformation from reactive transactional to proactive commercial, THE transformation to reinvent your (entire) organization: build process and IT maturity, integrate product and service lifecycle mgt processes (a.o. for technical documentation quality for AI ROI) and prepare for a durable, lean and agile customer centered future which typically comes with a good return on developing your after sales business.
I have been driving this type of transformations in sales support and service after sales for the last 15 years. Not from an AI perspective but from a business (development) sense. Companies have come to realize service is an asset, not a nuisance. My latest transformation was with a medical devices company that generated over 50% of its turnover from it services business!!
But it is such a perfect fit:
– Customer service is at the end of virtually all your processes. Whatever does not work entirely smooth, sooner or later it will surface in customer service.
– embed a (lean, agile and customer centered) service culture and you move up your service potential and revenue.
– integrate operational performance and next output performance data combined with remote monitoring to unlock next level added value for your customers.
– embed service lifecycle processes and associated documentation in order to generate the right actionable data and documentation quality to support your services offering.
How does a transformation in aftersales from reactive transactional to proative commercial looks like ? In particular for an OEM ?
The link between AI and BI, and why Customer Service Transformations make sense from both AI and BI perspective for manufacturing B2B and certainly OEM to review and rebuild the business ‘foundation’.
For mission specific initiatives both from AI and BI perspective, data quality is key.
- For AI that will be largely technical and operational documentation, requiring lifecycle and version management processes. The majority of the AI disruptor categories will rely on proprietary data trained AI agents. Being mission specific.
- And for BI (Business Intelligence) the quality and availability of actionable data, not just the result data but also the effort data, problem categorisation, error and warning codes, counters, solution codes and associated SOP’s and parts lists, etc. are crucial.
What i have observed over the last 5-10 years, is that for a large part existing (or missing) data quality depends on the (IT)maturity of the organization . And IT maturity in turn is largely related to process maturity, user experience and user friendliness. And there lies the challenge: do we have the data ? Can we access and analyse the data ? Can we rely on our technical and process documentation ? Or are we stuck with forever testing the models before implementation (AI)? There is a maintenance aspect on training AI on technical documentation !!
And on more ‘actionable data’ … Can we analyze the behavior and effort triggers and data preceding the result (BI)?
Having to rigorously test the AI before roll out will then be required. And with each problem, wrong response, a manual investigation into that root cause is required, AI training has to be adjusted, launch delayed or Risc assessment made. That is still manual labor by subject matter experts. Not the most fulfilling job either for highly skilled and expensive profiles.
That is probably not sustainable. A high IT maturity, through a high process maturity will make the difference. Which in turn depends on clear direction, vision, values and leadership.
Now why does it make sense to start with your customer service processes ?
And what does it take ?
- Feedback on all your business processes.
Customer service is at the end of the value chain. It is where many company weaknesses (R&D, Supply Chain, Engineering, Manufacturing, Assembly, Sales, …) on output quality and process execution will ultimately surface. Issues in design concepts (hardware, operability, …), in component and sub assembly performance (from product quality to maintainability), in user friendliness of equipment, operator software and performance data and reports (for advanced after sales services).
2. A proactive commercial Customer Service Strategy forces a transformation in company culture and embeds agility and customer focus into the company DNA. Customer service is a people business. Result in terms of revenue and margin is tied to effort (qualitative and quantitative). But it is also the business area which is often least proactively & commercially managed and developed. Transparency and actionability of effort data in general is low, covered mostly only by CRM packages in Sales. Admittedly, customer service is a demanding and difficult environment to work in, always squeezed between customer and internal red tape (Culture!!). But it is also one of the most rewarding ones to be a part of. A clear service development strategy, from reactive transactional to proactive commercial, integrated and supported throughout the company is one of your most impact- and purposeful transformation and professionalization journeys and long term investments.
3. Potentially the highest ROI transformation in manufacturing & OEM business. Regardless whether AI potential is considered as well. Just kill 2 birds with one stone.
Customer Service and sales support, from what I have observed across a handful of B2B engineering, manufacturing and OEM businesses, are 2 of the most undervalued and underestimated departments in terms of impact on result and customer loyalty. And AI can and will boost impact and efficiency in both domains. For a variety of reasons.
An example: My very first comprehensive customer services transformation from reactive transactional to proactive commercial in 2011-2015 tripled the service revenue, from an average 1.2-1.5m€/yr (10% of total revenue over 2005-2010) to 6.1m€ ( representing 20% of total revenue – in 2015), crushing expectations while margin went from 32% to 48%. Since I handed over in 2015, the revenue has remained growing slightly to an average of 7-8m€/yr today.
The million dollar question: how to drive such a transformation, effectively ?
That is the topic for a next article !