5 hard questions every process leader must answer about AI in 2026

Two years ago, process leaders debated whether artificial intelligence (AI) would work. We’re past that now.

Most leaders have experimented with tools like ChatGPT, Gemini, or Microsoft Copilot, drafting emails, summarizing documents, running pilots, and debating guardrails in AI committees. The technology is no longer hypothetical. What matters now is whether you’ve moved beyond experimentation and figured out how to deploy AI in ways that actually change how work gets done.

Today, many organizations are stuck in a low-risk, low-value phase: copilots that make existing tasks slightly easier without meaningfully reshaping workflows. These gains are real but incremental – the equivalent of shaving minutes off work that still fundamentally looks the same. Copilots do work, they just don’t rebuild processes.

The new standard for AI demands material impact – double-digit reductions in human effort across critical workflows, not marginal efficiency gains. That level of impact requires investment, integration, training, and a willingness to redesign how teams operate around constant collaboration between humans and AI agents. Your competitors are already doing this. If AI isn’t handling a meaningful share of the work in your core processes by 2026, you’re falling behind.

In short, the honeymoon phase is over, and accountability season has begun.

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Here are 5 hard AI questions you need to answer to succeed in 2026

1. Are you treating AI like magic?

AI looks like magic when it works. This appearance can deceive people into thinking it’s easy and requires no management. The reality differs sharply.

Think of AI deployment as hiring a team, not installing software. You need integration with existing systems, governance frameworks, and continuous training and iteration as your business changes and the technology improves.

Organizations that deploy AI agents at scale will have internal teams dedicated to setting them up and managing them. We can see this model in action at companies like OpenAI and Palantir with their forward deployed engineers, teams of experts who work closely with customers to set up and deploy their AI tools onsite. To justify this ongoing investment, though, you need to select use cases where AI can make a significant impact – and then commit to it. 

Magic doesn’t require effort. AI, to be effective, does.

2. Are you moving from toolkits to solutions?

Generic AI tools won’t deliver meaningful results to your company. You need mature, industry-specific products with AI fundamentally baked in. If you’re really big, you can pay OpenAI or Palantir millions of dollars to send you a team of their forward deployed engineers to build these solutions for you using their toolkit. The rest of us, however, will need to turn to specialist software vendors that have done the hard work to figure out how to use AI to transform key processes. 

In financial services, for example, this means AI agents that work within a system featuring pre-built integrations with core banking solutions, grounded in a strong compliance and audit framework. Meanwhile, in manufacturing, it means connections to MES and ERP systems with quality protocols embedded.

Toolkits shift the setup and integration burden to you. Industry-specific solutions come ready to deliver meaningful results.

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3. Have you identified high-impact use cases?

As I’ve said, applying AI is not easy. To justify the investment, you need to set it up and operate it properly, and you must identify use cases where AI can make a significant impact. It’s tempting to start small and pick incremental use cases, but these won’t transform your business.

Instead, pick big use cases – look at where your teams today spend most of their time and work to automate 50 percent or more of that effort. 

Customer service, for instance, represents roughly US$25 billion in annual labor spend across US banking alone. High agent churn, repetitive tasks, and capacity constraints make it a natural target. 

When call volume spikes 20 percent overnight, whether from M&A activity, seasonal surges, or market disruptions, AI absorbs the increase. Human hiring can’t scale that fast.

4. What will you do with the capacity AI frees up?

Here’s where many process leaders stumble. They achieve efficiency gains from their AI, then lose momentum. They pocket the savings without a clear plan. To succeed, you should have clarity for what comes after you have freed up capacity in your team with AI.

Some organizations deploy freed capacity toward growth – more proactive outreach, better training, deeper customer relationships, innovation initiatives, and so forth. Others move resources to underserved parts of the organization that desperately need them. Fraud prevention, compliance, and strategic planning are just a few examples. Others use the opportunity to consolidate operations and improve their cost structure.

All three approaches are legitimate. What undermines value is failing to choose deliberately. AI doesn’t deliver value by making people less busy, but by strategically redeploying the capacity it creates.

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5. Have you chosen the right partner?

Vendor selection matters in AI, just as it does in any important technology decision. You want partners who live and breathe it, not companies that try to keep up by bolting AI onto their legacy products.

It’s also never been more important to pick vendors who truly understand you and your industry. It’s not always possible, but to actually transform critical use cases, you need specialist help. If you’re using generic tools, you’ll have to bring that industry specialization and do that hard work yourself. If you can, find a vendor who has done some of this hard work getting AI agents to work for your industry ahead of time. 

A banking solution, for example, should come from a vendor that understands banking regulations and core integrations. A manufacturing solution should understand production systems and quality protocols. Generic solutions retrofitted from other sectors rarely deliver the results you need.

Demand measurable ROI with proof. Success stories and references with specific metrics from organizations like yours should be table stakes. Similarly, examine implementation quality. Your vendor should believe in deep partnership with your team and signal a level of commitment significantly higher than a generic dump-and-run approach.

The leadership challenge

You can’t spend 12 to 18 months evaluating options while expectations shift underneath you. Organizations that delayed previous technology waves – mobile adoption, cloud migration, digital transformation, etc. – eventually had no choice. AI is following the same path, only faster.

In 2025, AI experimentation was exciting, and process leaders could afford to tinker. In 2026, this mentality won’t be good enough. 

Process excellence in the AI era demands one thing above all: true AI transformation at the core, not just marginal efficiency at the edges.

All Access: Future of BPM 2026

You asked, and we listened. Business process management (BPM) remains the cornerstone technology for driving organizational transformation, according to the survey results featured in the latest PEX Report. As we look toward 2026 and beyond, generative AI, agentic AI, and intelligent process orchestration are redefining how processes are designed, executed, and optimized. BPM is your key to adapting swiftly and effectively in this new era.

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