Picture this. Your employer moves forward with layoffs or reactive reorganisation due to the fear of a recession, skyrocketing inflation, or top-heavy resourcing. Many of your colleagues leave the business, taking their knowledge with them. Yet, as part of the B2B marketing team, you rely on experience and insights built over years to inform successful marketing campaigns and the operations supporting them. Who can you turn to now for institutional memory?
This scenario happened this year, with mass redundancies in the tech industry and consulting firms, such as Google, Amazon, Microsoft and Deloitte. Losing that much expertise at once is damaging for any company, let alone those without deep pockets and agency networks. We must find solutions to prevent the disappearance of this institutional memory, and with the clamour around technological advancements, it begs the question: can AI be the keeper of institutional knowledge?
Opportunities and challenges of AI as institutional memory
The rise of desktop AI has happened so quickly that most organisations are still in a phase of figuring out how to approach the possibilities. But when we work backwards from the fundamental capability of AI, which is to digest vast quantities of varied data and identify patterns, we’re led to a key use case: to analyse corporate documents.
Before diving into AI’s capabilities, it is worth noting that these applications would happen in a scenario where companies have a ringfenced AI where institutional knowledge is captured for internal use only, rather than in an open access framework like ChatGPT.
Imagine you’re creating a plan for email testing. Or social brand management. Or any of the dozens of other arcane practices that every marketer must grapple with at some point in their career. What if you could query the people with the most relevant experience in the company’s history?
This capability would make our work faster and smarter.
Marketing works in cycles at a strategic and tactical level. New CMOs tackle the same issues, and prioritise challenges that have been faced before. In the trenches, new campaigns build on old ones and insights from similar approaches exist but are buried in emails, documents and notes.
AI solutions could provide access to all the knowledge built and lost over the years to help rediscover familiar territory, draw new insight and shorten the distance between plan and practice.
In addition, AI’s capacity to take stock and analyse data could be used to identify previously undetected points of friction. Retaining company information could help marketers better understand the cyclical nature of marketing and ecommerce campaigns, which would be particularly useful for longer cycle B2B campaigns. As a result, it could unearth specific consumer behaviours that indicate a hidden seasonality or trigger point.
However, every organisation is different, so AI cannot be a one-size-fits-all opportunity. For AI to help facilitate higher level thinking by offloading operational tasks, it needs to fit within their specific organisational framework. For instance, a smaller team might benefit most from AI’s time-saving operational aspects, while a larger organisation would want to invest in better data handling across the enterprise. Like any tech, the implementation of corporate AI must be purposeful and relevant for achieving business’ strategic goals.
From a legal perspective, incorporating AI to process institutional knowledge opens the door to data privacy issues. Companies must answer the question “How much access is too much?” to inform company policy and strategy. Then, they should build a framework within which AI can be used. While open access AI platforms pull from publicly available information, corporate AI must be limited to internal resources while protecting clients’, suppliers’ and employees’ privacy and sensitive information.
As many businesses navigate through tumultuous times and economic uncertainty, the risk of gaps in knowledge sharing and skills transfer within and across teams increases. To avoid losing business-critical knowledge and skills, let’s now explore how to incorporate AI solutions as a knowledge repository for your marketing team.
1. Reflect on existing processes
Before searching for the right AI technology, assess how knowledge is captured and shared within your organisation. Analyse the effectiveness of existing technical and knowledge transfer processes and consider the costs. If a key Product Manager or Analyst left tomorrow, for instance, measure how quickly the team could confidently and capably take on their work.
2. Assess your needs
Based on existing processes and tools, reflect on what could be improved by AI. You could use AI to overcome repetitive challenges you come across when brainstorming on new campaigns. Do you always go to the same person for questions on institutional history? If so, your team definitely needs to ensure their insights are captured in a way that can be practically accessed in the future.
3. Create a strategy
AI can be plugged in so that hard-earned knowledge doesn’t collect dust, or worst, disappear. To do so, you must first determine what you want it to ingest. To inform future campaign targeting and positioning, you could want to capture trends that affect supply chain or your clients’ responses to government regulation that affect their industry. Once this has been identified, you can then decide which channels you want to deploy or amplify. It could be based on campaign, job function, or even employee’s journey. From there, create a strategy to implement AI into your existing processes or develop new ones.
4. Fine tune training programmes
Now that you have a plan in place, it is essential to rework training programmes to support employees. Make sure your employees know the existing processes and platforms. Upon reviewing your training programmes, reflect on the format then adapt existing or create new training programmes to allow your team to share skills and knowledge where needed.
A well-rounded digital skills training programmes serves two vital purposes: equipping employees to meet current tasks effectively and enhancing their capabilities for future endeavours. Econsultancy’s research on digital skills demonstrates that blending various learning modes and offering on-demand learning for specific tasks leads to superior outcomes. Multi-modal approaches prove 35% more effective in conveying the tangible benefits of learning compared to programs heavily reliant on a single method (68% vs. 48%). Notably, executives at companies investing in multi-modal L&D are significantly more confident that their organisation has the digital skills required to meet business goals (57% vs. 27%).
Whether we are in a tumultuous environment leading to collective layoffs or in a thriving economy where people jump from one job to another, organisations constantly lose relevant knowledge and skills to churn.
If applied with careful consideration and appropriate safeguards, the effective use of corporate AI solutions can alleviate this risk, but also uncover precious insights, anticipate trends, and ultimately, dynamically change the B2B marketing scene.
In this context, AI is simply a tool to augment human memory, and marketing will always need a human touch. Rather than being displaced by AI, I think a more likely future is one where we turn to AI to inform our decisions, speed our campaigns and increase understanding of upcoming trends based on its ability to integrate past experience with new market trends. Building on our hard-won institutional knowledge, we as an industry will be well placed to create very successful B2B marketing.
Stefan is focused on Econsultancy’s learning experience and curriculum development. He manages a team of subject matter experts, designers and experience creators that turns best practice research into dynamic learning content.
Before moving to the learning side of the business, Tornquist led the company’s research arm in North America and is the primary author of over 100 studies exploring topics in marketing, technology and business transformation with partners such as Adobe, Google, IBM, Microsoft, Oracle and Salesforce.
Stefan’s work has been featured in the Wall St. Journal, New York Times, USA Today, NPR, CNN, CNBC and AdAge.
Stefan began his digital career as co-founder of ad tech pioneer Bluestreak, now part of Dentsu. He is president of The Research Wonks collective of digital business analysts and researchers.