The Leader’s Guide to Transforming with AI

What’s the Challenge? The Evolution of Sales.

It’s no small task for teams to adapt to the evolving B2B landscape, with its shifting buyer expectations, longer sales cycles, and heightened competition. Companies must use GenAI and predictive AI responsibly if they want to find a way through these challenges. Sales leaders can take charge of their function’s future with a transformational mindset that’s focused on data, technology, and people and process management.

What’s Possible? AI-Driven Personalization.

AI will be a big resource for B2B sales teams that want to create highly personalized customer experiences and transform operational models. Imagine AI-powered sales assistants providing real-time insights, virtual solution engineers helping you manage complex product portfolios, and autonomous agents that serve customers around the clock. This vision calls for bold leadership and a well-defined strategic roadmap to bring AI into the sales organization.

Where Do I Start? Go for High-Impact Transformation.

Sales leaders need to take several ambitious steps with AI:

• Drive outcomes. Define the North Star for your AI transformation in sales, and set clear business objectives that are linked to productivity and growth. Start with high-impact use cases, prove their value with initial tests, and scale up winners.
• Unlock data and technology. Build a tech stack that combines GenAI with predictive AI to support decision making and automation across the sales cycle.
• Rewire the operating model. Put AI into all of your everyday workflows. Compared to typical transformation, effective change management will be critical—90% of sales transformation success relies on leadership activation, sales team engagement, and robust training programs.

What’s the Challenge? Complexity in Today’s Supply Chain.

Every supply chain has snags. If you try to solve a delay in one area, chances are the problem shows up elsewhere: either downstream in manufacturing or upstream in procurement. And every stakeholder has a different level of digital maturity, so seamless efficiency is elusive. Then there’s the complexity of managing change—new partners, new employees, new suppliers.

What’s Possible? The Impact of AI.

Operations leaders can address these challenges by deploying AI across the supply chain. You can use powerful, emerging assets like digital twins and AI-enabled control towers. You make the most of data from suppliers and customers. Your teams stay in sync.

When delays occur, AI augments your team’s ability to pinpoint the problem and make tough decisions.

Where Do I Start? Transforming Supply Chains with AI.

Building a fully AI-integrated operations function calls for an end-to-end redesign. Adopting AI-based agents and deploying flexible data platforms are key starting points.

• Use a people-first lens. Consider the ways users will engage with AI and how the technology will impact your customers.
• Think big. Target high-impact transformations and ROI rather than quick wins or proofs of concept.
• Get the basics right. Align your AI strategy with your data and tech foundations—these are the elements of success. Focus on data capture and harmonization.
• Partner effectively. Get the operational teams and technology experts collaborating early to make sure that AI addresses an underlying business need.
• Manage change. Involve your people in the transformation as you rethink processes and ways of working.

What’s the Challenge? HR at Top Speed.

Chief human resources officers are facing unprecedented demands—especially when it comes to AI. There’s a lot to get right, from upskilling the workforce to fostering behavioral change. The ongoing tech-talent shortage doesn’t make things easier. But if you want to shape your HR practices for the future, you need a well-designed AI strategy.

What’s Possible? The Impact of AI.

Early adopters of GenAI in HR are starting with lower-risk opportunities that offer higher near-term productivity gains. Upskilling the organization is becoming a critical part of HR’s strategy—along with engaging AI responsibly. But it’s worth it, because your talent will shine as you achieve dynamic, agile AI deployment that’s aligned with business needs. It’s all about aiming for the North Star: AI-supported talent ecosystems and human-machine collaboration driven by behavioral science.

Where Do I Start? Transforming HR with AI.

HR teams can take steps now to bring AI into their function:

• Build an integrated GenAI roadmap toward strategic HR goals, such as better recruiting and using AI to support administrative tasks.
• Work with leadership to chart the enterprise-wide transformation journey.
• Upskill staff throughout the business and get them experimenting with everyday tools, custom GPTs, and agents.
• Develop guardrails and manage your data to ensure responsible AI and compliance.

What’s the Challenge? An Overextended Function.

CFOs face ever-increasing complexity in a world of shifting demands and uncertainty—and things are just as tough internally. Finance departments must do their best with an overworked staff, fragmented data, and Excel-heavy processes. It only gets harder when stakeholders across the business ask the finance function to unlock the full potential of the organization, essentially turning the CFO into a “chief performance officer.” With such a full agenda for finance leaders, AI can help them keep their teams focused on seamless financial management.

What’s Possible? AI for Insights and Efficiency.

CFOs and their teams can deploy GenAI-based self-serve dashboards that offer real-time insights into the factors impacting a company’s finances. Predictive AI is great for streamlining reconciliations, and the function can use AI to automate processes like supplier analysis and collections.

Powerful predictive models also help teams conduct planning, forecasting, and scenario analysis.

Where Do I Start? Key Steps for an AI-Powered Function.

Start the end-to-end transformation of your function by prioritizing high-value AI use cases. Investing in a modern tech stack is crucial, and you’ll want to rewire data hierarchies to support AI-driven workflows. Other musts:

• Use off-the-shelf AI tools when possible, and build select use cases in-house.
• Explore opportunities to better leverage the organization’s data.
• Establish safeguards against hallucinations and ensure accuracy of results with human-in-the-loop review and GenAI testing.
• Bring finance leaders and key business stakeholders into the transformation efforts—leadership buy-in will reinforce your AI strategy.

What’s the Challenge? Deploying AI the Right Way.

Keeping pace with the evolution of AI is daunting. But it’s also necessary. Your competition will be building new AI platforms, improving their data capabilities, and rewiring processes. And spending on the technology will only grow, with a CAGR of ~30% expected in the next three years. There’s so much pressure to get AI deployed across the business—and it falls to IT to make sense of it all.

What’s Possible? The Ideal Tech Stack.

The payoffs of AI transformation can be huge—including a three- to fourfold increase in ROI.

A big first step is to put in place a horizontal tech stack with a dedicated AI layer that’s supported by a platform-based operating model. The effort to integrate AI throughout the company requires robust tech capabilities—from data orchestration to performance evaluation—so train your people on these areas. And commit to using AI responsibly, with clear governance and ethics.

Where Do I Start? Engaging AI at Scale.

Prioritize high-impact AI uses that show fast results:

• Set objectives for the company’s AI deployment—say, all functions using LLMs in twelve months.
• Understand the capabilities of the current tech stack; these are the areas that IT will augment with AI.
• Invest in foundational capabilities, a strong data layer, and scalable infrastructure.
• Plan to scale—going big is the best way to capture value from your investments.

What’s the Challenge? A World of Risk.

In today’s uncertain business environment, success depends on your ability to manage multiple risks effectively. Yet companies too often focus on the immediate crisis and miss the opportunity to be proactive about risk. And while many executives rightly see AI itself as a risk, they should also see the technology as a key piece of their risk management efforts.

What’s Possible? AI, Risk Management, and a Strategic Approach.

When you bring AI and GenAI into your organization, you want to lessen the risks of these technologies while achieving maximum value from their deployment. There are several aspects of a strategic and structured approach:

• Managing AI Risks. Leaders must address challenges such as the security of AI solutions while ensuring regulatory compliance.
• Enhancing Efficiency. AI can help strengthen core risk management processes and make them up to 50% more efficient.
• Making Risk-Based Decisions. AI supports risk-based decision making that’s built on the right tradeoffs between risk and business benefit.

• Partner with functions throughout the business to get the most from AI deployment.
• Adapt scalable risk platforms.
• Train staff on new AI resources and promote an organization-wide mindset that prioritizes learning and growing with the technology.
• Adopt a comprehensive, AI-enabled strategy that aligns technology, data, and human expertise to address the full spectrum of risks.