How an AI driven economic growth strategy will reshape business in 2026

Introduction

AI driven economic growth strategy is moving from theory to boardroom reality. Therefore, leaders must translate macro headlines into actionable plans. This post connects five recent developments — from a Goldman Sachs call on AI and national debt to a $13 billion Permian merger, rising credit polarization, and consulting pivots — and shows what executives should do next. Additionally, each section highlights practical impacts and short projections, so readers can act with confidence.

## AI driven economic growth strategy: a national scale promise

Goldman Sachs’ CEO framed AI as a growth lever that could help address America’s $38 trillion debt load. He argued that small differences in long-term growth rates matter a lot. Therefore, a 3% compound growth rate versus 2% is “monstrous” in its ability to ease fiscal pressure. This is a big idea because it reframes AI from a cost-cutting tool to a macroeconomic recovery engine.

For businesses, the implication is direct. If AI can sustainably lift productivity and output, tax bases and corporate revenues could expand. However, gains will not be evenly distributed. Companies that adopt strategically and invest in reskilling will capture outsized benefits. Conversely, those that treat AI as an experiment may be left behind. Additionally, governments and large firms will need clear governance to ensure benefits are measurable and inclusive.

Looking ahead, expect three trends. First, more public-private partnerships to deploy AI at scale. Second, expanded reporting on AI-driven productivity metrics. Third, policy debates about distributional effects and workforce transition. As a result, boards should begin modeling scenarios where AI lifts revenue growth by modest but compounding amounts over a decade. That way, businesses can plan investments that align with national recovery efforts.

Source: Fortune

SPAC-like consolidation: what the Permian $13B merger signals

SM Energy’s acquisition of Civitas for roughly $13 billion is the largest oil-and-gas deal in months and underscores ongoing consolidation in the Permian Basin. Therefore, the transaction is more than a headline; it’s a signal about capital allocation in energy and resource sectors. Companies are combining scale to manage price volatility, optimize operations, and squeeze synergies from shared infrastructure.

For enterprise leaders, the lesson is clear. Consolidation changes supplier landscapes, pricing power, and counterparty risk. Energy buyers, service firms, and investors should re-evaluate contracts and exposure. Additionally, regional consolidation tends to attract different financing and regulatory scrutiny. Consequently, risk managers will need updated stress tests and scenario plans that account for fewer but larger industry players.

Moreover, mergers of near-equals typically focus on immediate cost synergies and long-term strategic alignment. Therefore, integration playbooks should emphasize governance, unified reporting, and cultural alignment to realize promised savings. For clients in adjacent industries, this deal may lead to more stable oil and gas partners but also higher bargaining power among suppliers.

In short, expect more M&A in resource-heavy sectors as companies chase operational scale. Business leaders should prepare by mapping supply chains, stress-testing counterparties, and updating procurement strategies to reflect a landscape of larger, more strategic players.

Source: Fortune

Building an AI Value Office as part of an AI driven economic growth strategy

NMS Consulting recommends setting up an AI Value Office to turn AI into measurable value. The office governs models, tracks ROI, manages cost, and runs a repeatable delivery engine. Therefore, it acts as a bridge between experimentation and scaled, accountable deployment. This idea is practical because many organizations struggle to move from pilots to measurable returns.

An AI Value Office typically covers four tasks. First, governance: it defines policies for model use, data handling, and risk controls. Second, measurement: it sets KPIs that tie AI projects to revenue, cost savings, or customer outcomes. Third, cost management: it tracks compute and tooling spend to avoid runaway bills. Fourth, delivery: it builds repeatable processes so successful pilots scale quickly.

For business leaders, this construct matters because it aligns AI activity with financial accountability. Instead of treating AI as a collection of projects, firms get a centralized capability to prioritize, fund, and measure initiatives. Additionally, by managing cost and governance centrally, firms reduce duplication and risk. Consequently, boards gain clearer visibility into AI investments and their payoffs.

Looking forward, organizations that adopt an AI Value Office will likely accelerate ROI capture and reduce failed pilots. Moreover, this office can feed into corporate strategy, helping companies present concrete AI-driven growth scenarios to investors and regulators. Therefore, consider piloting an AI Value Office focused on 3–5 high-impact use cases, with strict ROI gates and a clear scale-up playbook.

Source: NMS Consulting

Credit bifurcation and strategy: interpreting a K-shaped recovery

Recent data show both subprime and super prime loan volumes are rising, creating a K-shaped lending picture. TransUnion found the share of subprime borrowers increased to 14.4%, the highest since 2019. Therefore, the economy is diverging: credit availability and quality are improving for some households while weakening for others. This trend has direct implications for lenders, employers, and policymakers.

For corporate finance teams, a K-shaped recovery changes demand forecasting and risk management. Consumer-facing businesses may see segmented spending: affluent customers increase discretionary purchases while others retrench. Consequently, pricing strategies, credit terms, and collections playbooks will need refinement. Additionally, lenders must adapt underwriting models. Instead of broad averages, they need finer-grained segmentation that captures divergent borrower trajectories.

Moreover, this divergence raises systemic risk questions. If a larger share of borrowers is subprime, then macro shocks could trigger sharper defaults in parts of the economy. Therefore, firms with concentrated exposure to credit-sensitive consumers should hedge or diversify. At the same time, companies selling to super-prime segments may enjoy stronger growth and should plan to scale operations carefully to avoid service degradation.

In summary, a K-shaped credit landscape requires nuanced strategies. Firms should re-segment customers, update credit policies, and scenario-plan for asymmetric shocks. As a result, more businesses will build agile credit and pricing systems to navigate a two-speed recovery.

Source: Fortune

Consulting to 2026: monetizing genAI and service model shifts within an AI driven economic growth strategy

NMS Consulting’s 2026 outlook stresses genAI monetization, outcome pricing, nearshoring, consolidation, compliance, and sustainability. Therefore, consulting firms and corporate strategy teams must retool offers and go-to-market models. The report suggests the industry will evolve from time-and-materials work toward outcome-based pricing tied to measurable business results.

For clients, this means that advisors will increasingly sell measurable outcomes, not just hours. Additionally, firms will need capabilities in genAI productization — turning models into repeatable services that clients can adopt quickly. Nearshoring and consolidation will change labor markets and cost structures. Consequently, buyers should expect a mix of global delivery, local accountability, and more packaged solutions.

Compliance and sustainability will also be decisive. As firms scale AI deployments, regulatory attention and disclosure expectations will grow. Therefore, consulting teams that can embed compliance frameworks and sustainability metrics will win trust and fees. Moreover, consolidation among consultancies will create larger, integrated players that offer end-to-end transformation services.

Looking ahead, outcome pricing tied to AI-driven KPIs will spread. That requires rigorous measurement frameworks and shared risk models between clients and advisors. Therefore, organizations should shortlist partners that can commit to measurable gains and bring assembly-line delivery models — such as an AI Value Office — to accelerate adoption. In this way, consulting and enterprise demand will align around practical, measurable growth.

Source: NMS Consulting

Final Reflection: Connecting growth, risk, and delivery in a practical roadmap

These five pieces form a single story. First, AI-driven economic growth strategy is now framed as a national and corporate priority. Therefore, the debate moves from abstract promise to measurable targets. Second, capital markets and industries respond: energy firms consolidate to gain scale, while consulting firms realign toward outcome-based models. Third, credit trends remind us that gains will be uneven, which raises distributional and risk-management questions.

Taken together, the path forward is pragmatic. Businesses should build an AI Value Office to govern investments and measure ROI. Additionally, finance leaders must update models for consolidation and credit bifurcation. Meanwhile, consulting partners should offer outcome-based, compliant packages that accelerate measurable uplift. If organizations act on these signals, they can help turn AI’s macro promise into sustained, inclusive growth.

Finally, staying adaptive will matter most. Therefore, leaders should prioritize measurable pilots, clear governance, and scenario planning. In doing so, they can convert AI momentum into durable advantage while managing the risks of a two-speed economy.