Manufacturing sales and marketing teams: where’s your AI roadmap?

Robots and machine learning are transforming your factory floor - so why aren’t they powering the front end of your operations?  If your sales and marketing leaders haven’t got an AI roadmap in place to chart their course, here’s why they really need it.

While automation is revolutionising factory production lines, many manufacturing companies are failing to unlock AI’s transforming power for their sales, marketing, and customer-facing teams. 

The reason? Leadership lag. According to McKinsey’s 2025 research, only 1% of B2B organisations describe their teams of knowledge workers as “mature” in AI deployment - not because the technology isn’t ready, but because leadership isn’t moving fast enough.

Employees Are Ready. Leadership Isn’t.

McKinsey found that:

• 94% of employees are already using Gen AI tools in some capacity
• They’re 3x more likely to be using AI daily than leaders realise
• 47% believe more than 30% of their work will be AI-assisted within a year
• Yet nearly half report receiving no formal training or support
  

Source: McKinsey

Why is leadership lagging?

Leadership is inundated with AI choices and challenges. There’s so much noise in the market. So many applications and integrations promising to rock your world.  They’re claiming to help company’s analyse data and form marketing strategy in seconds. They say they’re going to make you ultra-agile and responsive to every twitch of your customer data. They say they’re going to make it simple for you to enrich, repurpose and personalise your content.

But building the tech stack has been hard

Strategising to harness the power of AI has been hard. The opportunity is clear, but the execution and integration process is not.  Leader’s need to select software, retrain teams and reframe process, but they don’t know what to invest in and when.  

Apart from some piecemeal experimentation sizeable investments in AI marketing tech have been rare.The will to invest is there, but the lack of co-ordinated strategy to meet specific revenue goals is showing.

Despite 92% of executives in the McKinsey research vowing to increase their AI spend, so far,  those investing in AI have not seen enough return:

Build the framework to empower your front-end teams

So how can contract manufacturers close the gap - and reap the promised rewards from AI?

Start with a structured, strategic approach that connects AI to revenue and productivity goals in measurable ways.

1. Establish an AI council

AI isn’t just an IT concern—it impacts every function. A central council ensures AI projects align with strategy and are implemented ethically, efficiently, and at scale.

• Who: Cross-functional leaders (Sales, Ops, IT, Compliance)
• What: Set vision, prioritise use cases, manage risk
• Why: Bring coherence to what is otherwise fragmented experimentation

2. Develop a strategy-led AI roadmap

According to McKinsey’s Rewired framework, a strategy-first approach is critical. Without identifying where you need help to accelerate and automate - you’ll be clutching at straws. 

Companies must define:

• Where AI will deliver ROI across knowledge work (e.g. sales forecasting, customer insights, marketing content generation)
• How to integrate short- and long-term use cases
• What resources, data, and talent are needed to scale

3. Invest in training and upskilling

Nearly 48% of employees want formal AI training, but most don’t receive it. Businesses that empower their people will be the ones who unlock real value.  The most successful teams are upping their AI games through:

• Running cross-department AI literacy sessions
• Partnering with agencies and AI specialists for tailored upskilling
• Creating internal champions to drive AI adoption

4. Start with impactful pilot projects

AI shouldn’t be experimental—it should be transformational. 

Start small, but systematically:

• Deploy pilots in content automation, market analysis, or customer segmentation
• Evaluate impact with clear KPIs
• Use success to build momentum and refine your strategy

5. Ensure data readiness

Even the best AI tools fail without high-quality data. Before scaling, build the foundations:

• Standardise and secure customer and operational data
• Maintain clean, consistent, and accessible datasets
• Foster a culture where data quality is everyone’s responsibility

6. Foster a culture of innovation

The front end of your business thrives on creativity. Don’t stifle that—amplify it.

• Encourage test-and-learn approaches to AI use
• Recognise team members who drive meaningful improvements
• Break silos—encourage cross-functional collaboration on AI projects

7. Monitor, measure, and adapt

AI transformation is not a one-off implementation—it’s a continual evolution.

• Define KPIs aligned to business outcomes (e.g., lead velocity, conversion rates, customer satisfaction)
• Review regularly and adjust based on performance and emerging trends
• Use real-time feedback loops to improve decision-making and maintain agility

From experimentation to transformation

The AI opportunity for front end teams is real.  But while most organisations are stuck in the purgatory of piecemeal implementation, the leaders of tomorrow are already moving toward systemic change.

“This is a time when you should be getting benefits from AI—and hope your competitors are still just experimenting.”

— Erik Brynjolfsson, Stanford University

AI doesn’t just automate tasks - it augments intelligence. It enables knowledge workers to make faster decisions, deliver better service, and operate at a new level of productivity.

But businesses won’t be able to do this systematically unless they have a route planner.  Manufacturing companies have tech councils specifically set up to guide investment in cutting edge equipment. They need the same for the AI powered tools that connect their sales, marketing and customer service operations.