At a tech conference in San Francisco, Dominik Asam, CFO of SAP, gave a “brutal” assessment of how AI will reshape his company and the software industry more broadly. From automating back-office work to enabling developers to work faster with AI-assisted coding tools, Asam argued that SAP can and will produce more software with fewer people. This is not a distant hypothetic; it's baked into SAP’s five-year strategy.
What struck many listeners was the directness of his message: this is not merely about augmentation or efficiency, but about structural change. Some roles will shrink. Some tasks will vanish. Some employees will have to adapt. Asam framed it clearly: AI is a catalyst and whether its impact is great or catastrophic depends on how quickly and thoughtfully it's adopted.
This article explores: what Asam said; how SAP is changing internally; what this portends for software engineers, back-office staff, customers; the risks and rewards; and what other companies (and workers) can learn from SAP’s moving fast with AI.
What Did Asam Actually Say?
Automation & AI Tools Inside SAP
Asam opened by describing himself as a heavy user of AI tools: using tools like Perplexity to pull data, help with research and presentations. He cited facts like how 28% of the MSCI World Index is now technology companies, underlining how much weight tech already holds globally. These are not just macro observations, but justification for why SAP must lean into AI at scale.
Inside SAP, Asam said, thousands of people working in back-office operations have tasks that can be automated. The company’s engineering workforce now tens of thousands is being equipped with AI-based coding tools. He posed a key competitive question: when customers begin using AI to build their own internal software, will they be less inclined to buy external products? SAP is responding not by resisting this shift, but by accelerating its productivity via AI so it retains its edge.
Producing More with Fewer People
The most attention-grabbing part: Asam openly said that with automation, SAP can deliver the same or more output with fewer employees. He says “there are certain tasks which are automated and for the same volume of output we can afford to have less people.” He doesn’t sugarcoat that this means job changes. He calls it “brutal” internally: the company intends to be ruthless about efficiency.
Competitive Urgency & Risk
Asam warned that falling behind in AI adoption isn’t just lost opportunity it could be catastrophic. He argued that the relative advantage will go to companies that implement AI tools systematically across large engineering teams. It’s not enough to “dabble”; SAP is pushing toward large scale change. Those who hesitate risk being overtaken. He frames this as a survival question, not just a profitability question.
How SAP is Structuring This Transformation
To deliver on the vision Asam described, SAP is already undertaking internal programs and making structural shifts. Here’s what is known:
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Five-Year Plan with AI Productivity Goals: SAP’s roadmap includes AI very centrally. It’s not a side project; the CFO places it among the core levers for boosting profitability and output.
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Automating Back-Office Functions: Functions that support software engineering, product management, HR, finance, operations tasks that are routine or repetitive are targets for automation. Reducing manual toil is expected to free up capacity.
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AI Coding Tools for Developers: SAP is equipping its large software engineering workforce with tools that assist coding: generating boilerplate, speeding up testing, perhaps aiding code review or debugging. This increases per-engineer productivity.
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Internal Culture & Communication: Asam emphasizes that the message is shared internally “I will be brutal” is not just a headline but a warning or directive. The idea is to align the workforce around urgency, transformation, innovation speed.
Implications for Workers
These developments raise serious questions for people working in the software industry especially at large companies like SAP.
Role Shifts and Job Losses
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Routine Task Displacement: Employees handling repetitive, less highly-skilled, or well-bounded tasks (back-office, code scaffolding, testing) are at risk of their work being at least partially automated or replaced.
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Upskilling Pressure: To stay relevant, many will have to acquire new skills: AI tool usage, prompt engineering, oversight roles, quality control, ethical/AI safety governance. Roles will shift toward those that AI can’t easily replicate: creativity, judgment, domain knowledge.
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Workforce Reduction or Reallocation: Fewer people may be needed for the same output. Some may be redeployed; others may face layoffs or voluntary redundancy.
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Geography of Risk: Regions with lower wages or where companies are slower to adopt AI may suffer more. Engineers in heavily codified, older codebases may face more restructuring.
Psychological & Cultural Effects
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Uncertainty can lead to stress: fear of job security, anxiety over needing to reskill, worries about what “efficiency” means in practice.
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Companies like SAP will need to manage change well: transparency, retraining, fairness, avoiding abrupt layoffs will likely matter to retention and morale.
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Workers may feel pressure to perform better, work faster, lean into AI usage before fully understanding the tools. There’s risk of burnout or ethical misuse.
Implications for Customers & Software Market
It’s not only SAP’s internal workforce that changes; customers, competitors, and the broader software ecosystem will feel effects.
Faster Delivery, Potentially Lower Costs
With more automation and more productive developers, SAP may deliver features faster, reduce costs of maintenance, accelerate innovation. This might benefit customers via faster updates, more frequent releases, or more features.
Shift in Feature Complexity vs Customization
As AI handles boilerplate or repetitive parts of software, companies might push toward standardization or frameworks. Highly customized, bespoke engineering projects may decline if the costs are higher relative to what AI can substitute. Customers who demand heavy customization might find it more expensive or relatively less prioritized.
Competitive Intensity
Companies that can adopt AI tools fast will have advantage. Smaller firms or legacy systems might lag. The competitive gap might widen between “AI-native” or “AI-fast” players and others.
Questions of Quality, Safety, and Bias
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Speed and automation can cut corners. Testing, audit, safety, bias controls matter more than ever.
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AI-augmented code generation may introduce bugs, vulnerabilities, or unintended behavior. Oversight will be crucial.
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Clients may become more sensitive to reliability, security, compliance especially in enterprise contexts.
Risks & Challenges for SAP’s Strategy
SAP’s vision is bold, but several challenges could complicate execution.
Implementation Difficulty
Deploying AI tools at scale is non-trivial. Integrating AI into existing codebases, ensuring data quality, ensuring tools actually improve (not hamper) efficiency; making sure developers adopt tools rather than resist them it all requires training, infrastructure, validation.
Employee Pushback
Some employees will resist: concerns about job security, sense of craftsmanship, quality expectations. Managing morale, retention, culture changes is hard. Transparent communication, reskilling programs, safety nets may be needed.
Regulatory and Ethical Concerns
Automation and workforce reduction raise labor law, union, and ethical questions. Governments may intervene to protect jobs or regulate AI usage. Also data privacy, fairness, bias, version control can become legal risk in enterprise software.
Technical Limitations & Maintenance Overhead
AI tools are not perfect. They may generate inefficient code, require correction, or be unsuitable in complex, safety-critical contexts. Tooling needs oversight. Maintenance of AI-generated code may introduce hidden costs.
Market Risks
Customers who prefer human-driven work, who value personalization or creativity may balk. Also competitors may underprice or prioritize different value propositions. SAP must balance automation with customer satisfaction.
What This Means for the Industry
SAP isn’t alone. Many large enterprises are moving similarly, but the scale and clarity from someone like Asam makes this moment a bellwether.
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Benchmarking: Other companies will watch SAP. If SAP pulls ahead in efficiency, profitability, feature delivery, others will feel pressure to follow suit or risk losing ground.
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Labor Market Changes: Salaries for certain types of roles may stagnate or decline. Demand for AI tool expertise and oversight roles likely to rise.
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Education & Reskilling: Universities, bootcamps, corporate learning programs will need to adjust: training AI tool usage, prompt engineering, software ethics, code safety, etc.
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Shift in Hiring Priorities: Instead of requiring mastery of manual scaffolding or debugging, companies may prioritize people with AI tool fluency, adaptability, system thinking.
Lessons for Workers & How to Prepare
For software developers, engineers, tech employees, and even job seekers: what can you do to stay resilient?
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Learn and Experiment with AI Tools
Don’t wait until it’s forced. Try AI coding assistants, prompt tools, learn how they integrate into workflows. Prove your fluency. -
Focus on What AI Struggles With
Edge case handling, domain expertise, architecture, design decisions that require judgment, ethics, UX nuance these are harder to automate. -
Build Hybrid Skills
Combining technical skill with business domain knowledge, project management, communication, or quality assurance. -
Advocate for Fair Change Management
If your employer is implementing automated tools, make sure there are plans for retraining, transparency, fair assessment of impact. -
Stay Agile & Open to Role Change
Be willing to shift roles: shift toward oversight, model evaluation, data governance, AI tool training, leadership in AI transformation.
Broader Societal & Ethical Considerations
This isn’t just about SAP or tech firms. Society-wide norms, policy, and expectations will need to shift.
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Job Redefinition vs Job Loss: What responsibilities do companies have to workers whose tasks are automated out? Retraining? Transition support? Severance?
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Regulatory Standards for AI in Software: Code quality, safety, security, privacy regulation will become more essential. Enterprises will need to comply.
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Union & Labor Policy: In regions with strong employee representation or labor protections, pressure may arise to limit workforce reductions or force disclosure.
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Economic Inequality: Automation could amplify wage gaps: those skilled in AI-tool work vs those in roles more easily automated.
SAP’s AI Path Is a Warning and a Model
Dominik Asam’s interview forces a stark reality: with AI, producing more with fewer people isn’t optional it’s competitive necessity, in his view. For SAP, this strategy is baked into long-range planning. The payoff could be higher profits, faster feature delivery, leadership in AI-augmented software. But for employees, customers, and the tech industry overall, it’s a transformation with trade-offs some painful, some full of opportunity.
SAP's choice is a model for what many large software firms will face. The question for others isn’t whether AI will reshape productivity but how they prepare for it how they shape culture, reskill workers, maintain quality, navigate regulation, and ensure that the move toward fewer people delivering more output doesn’t sacrifice ethics, reliability, or humanity.
Asam’s “brutal” message may be uncomfortable, but ignoring it won’t make it go away. Companies, workers, and societies that engage with this truth head-on will likely fare better in the AI-reshaped future.
