1. Agentic AI Consulting Will Help Automate Complex Workflows

Agentic AI means systems that plan and complete multi-step tasks using approved tools. Hypothetical scenario: an agent reads a support ticket, checks the order system, drafts a reply and asks a person to approve it. Gartner forecast in 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, citing cost, unclear value and weak risk controls. Consultants help pick suitable use cases and design permissions, testing and human approval.

2. AI Strategy Consulting Will Focus on Measurable ROI

Expect budgets to follow evidence. McKinsey’s 2025 State of AI survey found nearly two-thirds of respondents had not begun scaling AI across the enterprise, and only 39% reported any enterprise-level profit impact. Consultants help set KPIs before spending, such as processing time, cost per task, error rate and conversion, then judge a pilot against them.

3. Generative AI Consulting Will Move Beyond Chatbots

Businesses are shifting from generic chatbots to assistants connected to company data. Retrieval-augmented generation (RAG) lets a model look up approved documents before answering, which can help ground responses. Document processing, such as extracting fields from invoices, is a common first project. Generative AI engineers and consultants handle integration, accuracy testing, privacy and ongoing monitoring.

4. AI Governance Consulting Will Become a Baseline Need

Governance means rules for data access, risk review, human accountability and output monitoring. In the EU, the AI Act’s transparency duties under Article 50 have applied since August 2, 2026, while the Digital Omnibus moved stand-alone high-risk obligations to December 2, 2027. These are legal dates for EU-linked businesses; planning governance before deployment is recommended practice everywhere.

5. Industry-Specific AI Consulting Will Grow

Generic tools rarely fit regulated or specialized work. Healthcare needs privacy controls, finance needs audit trails, retail needs product and customer data, manufacturing needs sensor data, logistics needs route and inventory systems, and software teams need code and security checks. Data availability, compliance and existing systems shape what is realistic.

6. AI Implementation Will Prioritize Data Readiness and Integration

Fragmented or poor-quality data limits any model. Consultants assess data readiness, connect AI to existing software through APIs and data pipelines, then manage testing, deployment, staff training and monitoring. Deloitte found only 30% of organizations were redesigning key processes around AI, so many still layer AI onto old workflows.

7. AI Consulting Teams Will Pair Specialists With Human Oversight

Projects increasingly need AI engineers, machine learning specialists, data engineers, architects and domain experts, often for a limited time. Businesses can use external or flexible specialists for defined projects, while people keep accountability for decisions. When choosing, check relevant experience, communication and security practices. WorkFlexi is a marketplace where businesses can find freelance AI consultants and machine learning experts.

How Should Businesses Prepare for AI Consulting Trends in 2027?

1. Pick a business problem worth solving.
2. Check data quality and system readiness.
3. Rank use cases by value, feasibility and risk.
4. Define measurable success criteria.
5. Select suitable technical expertise.
6. Run a controlled pilot.
7. Monitor results and improve.

Successful AI adoption needs a clear strategy, reliable data, responsible governance, suitable expertise and measurable outcomes. AI consulting services can help at each step, from choosing a use case to monitoring results. If you need specialist support, explore AI and machine learning talent on WorkFlexi.

Frequently Asked Questions 

1. What are the major AI consulting trends for 2027?

Likely 2027 trends include agentic AI workflows, ROI-focused strategy, generative AI connected to company data, AI governance, industry-specific solutions, data readiness and integration, and specialist teams with human oversight. These are informed forecasts based on 2025–2026 research, not guaranteed outcomes.

2. What do AI consulting services include?

AI consulting services typically include opportunity assessment, data and readiness review, use-case prioritization, tool and vendor selection, implementation support, governance planning and results measurement. Some also build and deploy solutions. Scope varies by provider and project, so ask for deliverables in writing.

3. How can AI consulting services help businesses improve ROI?

They help define success metrics before spending, rank use cases by value and feasibility, and test a pilot against baseline numbers such as processing time, cost per task or error rate. That makes it easier to stop weak projects early and scale proven ones. Results are never guaranteed.

4. How much do AI consulting services cost?

There is no standard price. Cost depends on project scope, data readiness, integrations, the expertise required, location and whether the engagement is a short assessment or a full implementation. Request a scoped proposal with milestones, and compare freelance and agency options against your needs.

5. Why do businesses need AI strategy consulting before implementing AI?

Strategy work clarifies which problem to solve, whether data and systems are ready, and how success will be measured. Without it, projects risk stalling as pilots with unclear value, wasted spend or avoidable compliance problems. Starting small keeps these risks manageable.

6. What is the difference between AI consulting and AI implementation?

AI consulting focuses on strategy: finding opportunities, assessing readiness, choosing approaches and planning governance. AI implementation is the hands-on build: integrating models, connecting data, testing, deploying and monitoring. Some providers offer both, while others specialize, so confirm which stage you are buying.

7. How can a business choose the right AI consulting company?

Start with your goal, then check relevant project experience, how they test accuracy, data security practices and clarity of deliverables. Ask for references or examples, and be cautious of guarantees. A small paid pilot is a practical way to judge communication and quality before a larger commitment.

Sources

• Deloitte, “The State of AI in the Enterprise: The Untapped Edge” (2026 edition, released January 2026). The survey covered 3,235 leaders in 24 countries, fielded in August and September 2025. It supports the 74% figure for expecting at least moderate use of AI agents by 2027, and the 21% with a mature agent-governance model: https://www.deloitte.com/us/en/insights/topics/emerging-technologies/ai-agents-scaling-faster.html. Its press release supports the 30% redesigning key processes and 37% surface-level use figures: https://www.deloitte.com/nl/en/about/press-room/state-of-ai-in-the-enterprise-2026.html
• McKinsey & Company, “The state of AI in 2025: Agents, innovation, and transformation” (November 5, 2025). It supports “nearly two-thirds not yet scaling” and “39% report enterprise-level EBIT impact”: https://www.mckinsey.com/capabilities/operations/our-insights/the-state-of-ai
• Gartner, press release (June 25, 2025). It is a forecast that over 40% of agentic AI projects will be canceled by the end of 2027: https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
• EU AI Act, Digital Omnibus on AI (Regulation (EU) 2026/1744), in force July 27, 2026. It supports the Article 50 and December 2, 2027 dates. I read two law-firm summaries: DLA Piper (https://knowledge.dlapiper.com/dlapiperknowledge/globalemploymentlatestdevelopments/2026/The-Digital-AI-Omnibus-Proposed-deferral-of-high-risk-AI-obligations-under-the-AI-Act) and Cooley (https://cdp.cooley.com/digital-ai-omnibus-delays-key-deadlines-introduces-new-rules/)

Why Do AI Projects Fail?

AI projects typically fail because of unclear business objectives, poor data quality, mismatched technology choices, weak implementation planning, and low user adoption  not because the underlying AI models don’t work. A widely cited 2025 MIT Project NANDA study found that 95% of enterprise generative AI pilots delivered no measurable financial return, despite an estimated $30–40 billion in enterprise spending. The issue wasn’t model quality  it was integration into real workflows.

1. What Happens When an AI Project Starts Without a Clear Business Goal?

When a team starts with “let’s use AI” instead of “let’s solve this specific problem,” it becomes difficult to know what success even looks like. The technology gets built first and the business case gets retrofitted afterward  which rarely holds up.

2. How Does Poor Data Quality Affect AI Projects?

AI models are only as good as the data behind them. If data is incomplete, inconsistent, poorly governed, or scattered across disconnected systems, the resulting outputs will be unreliable regardless of how sophisticated the model is. Gartner has projected that a majority of AI projects lacking properly governed, use-case-aligned data will be abandoned by the end of 2026.

3. Why Do Businesses Choose the Wrong AI Technology?

Selecting a specific model or platform before fully understanding the business problem often adds unnecessary complexity. A simpler, cheaper approach is sometimes the better fit  but that’s only visible once the problem itself is clearly defined.

4. Why Do AI Projects Struggle to Move From Prototype to Production?

A working demo is not the same as a production system. Moving from prototype to production requires scalability, integration with existing systems, security review, ongoing monitoring, and a maintenance plan. Gartner’s research has found that on average it takes around eight months for AI projects to move from prototype to production, a long enough gap that many stall out entirely before reaching it.

5. How Do Unrealistic AI Expectations Cause Project Failure?

Generative AI is genuinely capable, but it isn’t magic. When leadership expects immediate, dramatic ROI without accounting for the integration work required, disappointment sets in quickly  often before the project has had a fair chance to prove itself.

6. Why Is User Adoption Important for AI Project Success?

A technically sound AI system that employees don’t trust or don’t use creates no value. The MIT NANDA research noted that many sanctioned pilots looked impressive in a boardroom demo but failed in day-to-day use because they couldn’t adapt to how people actually worked.

7. Why Do AI Projects Fail Without Clear ROI Metrics?

Without defined success metrics set before launch, it becomes nearly impossible to prove  or disprove  whether an AI investment is working. Vague goals lead to vague, unresolved outcomes.

These challenges rarely appear in isolation; a company with unclear objectives often also has unaddressed data quality issues, because nobody defined what “good data” would even look like for the use case.

How Can AI Consulting Help Prevent AI Project Failure?

AI consulting helps by bringing structured planning and outside expertise to stages many internal teams skip under time pressure: readiness assessment, business problem definition, use-case discovery, technology selection, data assessment, implementation planning, integration, testing, deployment, governance, and performance measurement. This doesn’t guarantee success RAND Corporation research has put historical AI project failure rates above 80%, roughly double that of comparable non-AI IT projects but it materially reduces avoidable, predictable mistakes.

What Does an AI Consultant Do During an AI Project?

A useful way to think about the consultant’s role is a simple sequence: Assess → Plan → Build/Coordinate → Test → Deploy → Measure → Improve. In the assessment phase, a consultant evaluates whether the business problem, data, and infrastructure are actually ready for an AI solution. During planning, they help define scope, technology, and success metrics. During build and coordination, they work alongside internal or external development teams. Testing and deployment focus on reliability and integration, while measurement and improvement ensure the solution keeps delivering value after launch rather than being abandoned once the novelty wears off.

When Should a Business Consider AI Consulting Services?

Consulting tends to be most useful when a company has identified an AI opportunity but isn’t sure where to start, is weighing multiple tools without a clear evaluation framework, has data but no clear plan for using it, has a proof of concept that hasn’t reached production, needs to integrate AI into existing systems, or lacks in-house AI governance and security expertise.

What Are the Most Common AI Implementation Challenges?

Common AI Project Challenges

AI projects can fail when objectives are unclear, making success difficult to measure. Poor data quality can lead to unreliable results, while choosing technology based on trends rather than the actual use case can create unnecessary complexity. Integration problems may keep AI separate from existing workflows, and weak security can create business and compliance risks. A lack of AI expertise can result in poor technical decisions, while low user adoption can limit the value of the solution. Finally, without clear KPIs, businesses may struggle to measure ROI.

AI Project Failure vs. Success

AI projects are more likely to struggle when businesses focus on technology instead of the actual business problem, assume their data is ready, or concentrate only on prototypes. A stronger approach is to define the business objective first, assess data readiness, plan for production, involve users early, measure business outcomes, address security from the beginning, and set realistic, measurable KPIs.

FAQs

Why do AI projects fail?

Most AI projects fail due to unclear business objectives, poor data quality, weak implementation planning, and low user adoption, not because the underlying AI technology doesn’t work. A 2025 MIT study found 95% of enterprise generative AI pilots delivered no measurable financial return.

What are the most common AI implementation challenges?

Common challenges include unclear goals, poor data quality, choosing the wrong technology, integration difficulties, security gaps, lack of internal AI expertise, weak user adoption, and the absence of clear ROI metrics.

How can AI consulting help a business?

AI consulting helps by assessing readiness, clarifying the business problem, selecting appropriate technology, planning implementation and integration, and defining measurable success metrics  reducing avoidable, predictable mistakes throughout the project.

When should a company hire an AI consultant?

Consider it when you’ve identified an AI opportunity but lack a clear starting point, have data without a plan to use it, have a stalled proof of concept, or need AI governance and security expertise your team doesn’t have.

How can businesses measure AI project success?

Define specific, measurable business outcomes  such as cost savings, time reduction, or revenue impact  before the project begins, and track them consistently after deployment rather than relying only on technical performance metrics.

How does poor data affect AI projects?

Incomplete, inconsistent, or poorly governed data leads to unreliable AI outputs regardless of model quality. Gartner has projected that a majority of AI projects lacking properly governed data will be abandoned by the end of 2026.

How can businesses reduce the risk of AI project failure?

Start with a clear business problem, assess data readiness early, involve users in planning, define success metrics before launch, and plan for security and production requirements from the beginning rather than treating them as afterthoughts.

If your organization has an AI opportunity but lacks the internal expertise to plan and execute it properly, working with an experienced AI consultant or development team can help close that gap. Workflexi connects businesses with vetted AI professionals consultants, developers, and specialists  who can support a project from initial assessment through to production.

AI is no longer a “someday” project. It’s already reshaping how businesses operate, compete, and grow. But knowing AI matters and knowing how to use it well are two very different things.

That gap is exactly where AI consulting services come in. If you’ve been wondering whether your business is ready for outside help, this article breaks down the clearest signs, backed by real data and practical examples.

What Are AI Consulting Services?

AI consulting services help businesses figure out where and how to use artificial intelligence effectively. This isn’t just about buying software. It covers strategy, implementation, and long-term transformation.

An AI consultant typically helps with:

• AI strategy – identifying which problems AI can actually solve
• Implementation – building and deploying the right tools
• Change management – helping teams adapt to new workflows

For example, a retail business might hire AI consultants to build a demand forecasting model, while a healthcare provider might need help automating patient intake documentation. The approach always starts with the business problem, not the technology.

Why Are More Businesses Hiring AI Consultants in 2026?

Adoption has moved fast. Recent industry research from McKinsey found that a majority of organizations now use AI in at least one business function, with generative AI adoption growing especially quickly across marketing, product development, and IT.

Gartner has also pointed to AI agents and workflow automation as top priorities for enterprise technology spending heading into 2026. Meanwhile, PwC’s research on AI-driven business value shows companies actively using AI report measurable gains in productivity and revenue growth compared to those still in early planning stages.

The pattern is consistent: businesses that treat AI as a core strategy, not a side experiment, are pulling ahead.

5 Signs Your Business Needs AI Consulting Services Right Now

1. Your Team Spends Too Much Time on Manual Tasks

If employees are stuck doing repetitive data entry, report building, or manual approvals, that’s time taken away from higher-value work.

This usually happens because processes were never redesigned as the business scaled. AI workflow automation can handle repetitive tasks like data reconciliation, invoice processing, or scheduling, freeing up hours every week.

Example: A logistics company using AI automation services to handle shipment tracking updates can redirect staff toward customer relationships instead of spreadsheets.

2. Your Business Has Too Much Data but Too Few Insights

Many companies collect data across sales, marketing, and operations but never turn it into decisions. Dashboards pile up. Nobody has time to interpret them.

AI consultants build systems that surface the insights that matter, using machine learning consulting to spot patterns humans would miss. This is one of the fastest ways AI consulting reduces business costs, since better decisions cut waste.

3. Customer Service Is Becoming Difficult to Scale

As customer volume grows, response times slow down, and support costs climb. Hiring more staff isn’t always sustainable.

AI-powered chatbots, ticket triaging, and sentiment analysis tools can handle routine queries and route complex ones to the right person. This is a common entry point for AI consulting for small business, since it delivers visible results quickly without a huge budget.

4. You Want to Adopt Generative AI but Don’t Know Where to Start

Generative AI consulting is one of the fastest-growing service categories right now, and for good reason. Leaders know generative AI could help with content, coding, or customer support, but most don’t know which use case to prioritize first.

An AI strategy consulting engagement typically starts with a small pilot project, measures results, then scales what works. This avoids the common mistake of adopting tools without a plan.

5. Your Competitors Are Already Using AI Successfully

If competitors are shipping products faster, offering smarter customer experiences, or operating leaner teams because of AI, that’s a signal worth taking seriously.

Enterprise AI consulting helps businesses catch up strategically instead of copying tools blindly. The goal isn’t to use AI for its own sake, but to close a specific competitive gap.

How AI Consulting Services Help Businesses Grow Faster?

AI business solutions create compounding advantages over time:

• Automation reduces manual workload and human error
• Cost savings come from streamlined operations and fewer inefficiencies
• Productivity increases as teams focus on higher-value work
• Better decisions result from data-driven insights instead of guesswork
• Competitive advantage builds as AI-driven processes mature
• Scalability improves since AI systems handle growth without proportional headcount increases

Industries That Benefit Most from AI Consulting Services

• Healthcare – patient data analysis, administrative automation
• Finance – fraud detection, risk modeling
• Retail – demand forecasting, personalization
• Manufacturing – predictive maintenance, quality control
• Logistics – route optimization, inventory management
• Education – personalized learning tools
• SaaS – product intelligence, customer churn prediction
• E-commerce – recommendation engines, dynamic pricing

Future of AI Consulting Services

The next phase of AI consulting is shifting toward agentic AI. Instead of single-task tools, businesses are exploring AI agents that can handle multi-step workflows with minimal human input.

Expect growing focus on AI copilots for daily operations, autonomous workflows across departments, predictive AI for forecasting, and stronger AI governance frameworks as regulations catch up with adoption. Businesses that build a flexible AI adoption roadmap now will be better positioned as these tools mature.

AI consulting services aren’t just for large enterprises anymore. Whether it’s automating manual work, making sense of data, or finally adopting generative AI the right way, the businesses moving fastest are the ones getting expert guidance early.

If any of these five signs sound familiar, it may be time to hire AI consultants who understand your industry and your goals. Workflexi connects businesses with vetted AI consultants, machine learning experts, and generative AI specialists ready to help you build a practical AI transformation strategy, without the guesswork.

FAQs

1. What are AI consulting services?

AI consulting services help businesses identify, plan, and implement artificial intelligence solutions. This includes AI strategy development, tool selection, and hands-on implementation support. Consultants bridge the gap between business goals and technical execution, helping companies avoid costly trial-and-error adoption.

2. When should a business hire AI consultants?

Businesses should consider hiring AI consultants when manual processes are slowing growth, data isn’t being used effectively, customer service can’t scale, or competitors are gaining ground with AI tools. Early guidance often prevents wasted investment in the wrong technology.

3. How much do AI consulting services cost?

Costs vary widely based on project scope, from short strategy engagements to full implementation projects. Many businesses start with a smaller pilot project to test value before committing to larger AI implementation services, which helps control costs and measure ROI early.

4. Can small businesses benefit from AI consulting?

Yes. AI consulting for small business often focuses on high-impact, low-cost use cases like customer service automation or workflow tools. Small businesses don’t need enterprise-scale budgets to see meaningful efficiency gains from the right AI strategy.

5. What industries need AI consulting the most?

Healthcare, finance, retail, logistics, and manufacturing see some of the strongest returns from AI consulting, largely due to data-heavy operations and repetitive processes. However, nearly every industry can benefit from targeted AI automation and strategy support.

6. How do AI consultants help with generative AI?

Generative AI consulting helps businesses identify practical use cases, such as content creation, coding support, or customer interactions, then build safe, effective pilot programs. Consultants also help navigate risks like data privacy and output accuracy before scaling adoption.

7. What is the difference between AI consultants and AI developers?

AI consultants focus on strategy, planning, and identifying the right AI use cases for a business. AI developers focus on building and coding the actual solutions. Many projects use both, with consultants guiding direction and developers handling technical execution.

According to recent market data, the global AI consulting market was valued at approximately $12.4 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.4% through 2032 .

 Simultaneously, global AI investment (including development) is expected to soar from $315.9 billion in 2024 to over $1.2 trillion by 2029 . This surge indicates that businesses are not just planning AI (consulting) but are heavily building AI (development).

What is AI Consulting?

AI Consulting is an advisory service focused on business strategy, opportunity identification, and risk management. An AI consultant acts as a bridge between complex technology and business goals. They do not typically write the final code for a product; instead, they analyze your current operations, data infrastructure, and market position to recommend where AI can drive value.

Key responsibilities of an AI Consultant include:

Strategic Roadmapping: Identifying high-impact use cases (e.g., automating customer service or improving supply chain forecasting).

Feasibility Studies: Assessing if your current data is “clean” enough and if the technology is mature for your specific problem.

Vendor Selection: Helping you decide whether to build a custom solution, buy an off-the-shelf product, or partner with a specific platform like Google Cloud or AWS .

Change Management: Preparing your team and processes for the integration of AI.

Ethics and Compliance: Ensuring the AI strategy adheres to regulations like GDPR or emerging AI safety standards .

What is AI Development?

AI Development is the technical execution of the consultant’s vision. This involves data scientists, machine learning engineers, and software developers who build, train, deploy, and maintain the AI systems. They work with programming languages like Python and frameworks such as TensorFlow or PyTorch to create functional models .

Key responsibilities of an AI Developer include:

Data Engineering: Collecting, cleaning, and structuring massive datasets required for training.

Model Building: Selecting the right algorithms and training machine learning models or integrating Large Language Models (LLMs).

Software Integration: Embedding the AI model into your existing applications (e.g., adding a chatbot to your website or an AI agent to your ERP system) .

Testing & Maintenance: Continuously monitoring the model for “drift” (where accuracy degrades over time) and retraining it as needed.

Practical Differences and Business Use Cases

The difference often comes down to strategy versus execution. You need a consultant when you are unsure where to start. You need a developer when you have a blueprint and need a working product.

For example, a logistics company in India looking to optimize delivery routes would first hire an AI consultant to analyze their data and confirm that AI can genuinely reduce fuel costs. The consultant would create a specification document. Only then would they hire an AI development team to build a custom route-optimization engine using real-time traffic data and machine learning .

In 2025, we saw a major shift where consulting firms began acting more like developers. “Big Four” firms like Deloitte and EY are now heavily investing in “agentic” AI platforms, essentially building software for clients rather than just advising them. EY has already advanced over 1,000 AI agents into development, with plans to scale to 100,000 by 2028 .

When Do You Need Which?

Choosing the right service depends on your company’s maturity.

You need an AI Consultant if: You have a vague idea that “AI could help us,” but you don’t know the specific use case, the data requirements, or the potential risks. They help you avoid the “shiny object” syndrome and focus on practical applications .

You need an AI Developer if: You already know exactly what you want to build. You have a requirements document, you have access to the necessary data, and you are ready to create a minimum viable product (MVP). For instance, retailers like Mercari are using developers to build AI agents that handle customer service, anticipating a 500% ROI on those projects .

You need Both if: You want to go from zero to launch efficiently. The consultant ensures you are building the right thing, and the developer ensures it is built the right way.

In the rapidly evolving AI landscape of 2026, the line between consulting and development is blurring, yet the distinction remains vital. Consulting provides the compass, and development builds the ship. Whether you are a startup or a multinational corporation, starting with strategy prevents you from wasting millions on building the wrong solution. As AI agents become mainstream, businesses that master both the strategic planning and the technical execution will be the ones leading their markets .

Frequently Asked Questions (FAQs)

1. Can a company provide both AI Consulting and AI Development?

Yes, many full-service digital agencies and large firms like Accenture or TCS offer both. However, it is common for boutique consulting firms to focus only on strategy, passing the technical development to specialized partners .

2. Is AI consulting only for large enterprises?

No. Small and medium businesses (SMBs) in India and the USA are increasingly using AI consultants to navigate the crowded tech landscape. Consultants help SMBs find affordable, scalable solutions rather than expensive custom builds .

3. How much does an AI consultant cost compared to a developer?

Generally, consultants may have a higher hourly rate due to their strategic experience, but their engagement is shorter. Development costs have a wider range and are usually higher overall because they involve building the actual product, which requires more man-hours and infrastructure .

4. What is the typical outcome of an AI consulting engagement?

The typical outcome is a detailed roadmap or a Proof of Concept (POC). This document outlines the data needed, the technology stack recommended, and the specific business metrics the AI solution should improve .

5. Do I need a consultant if I already have an in-house IT team?

Yes, often. An in-house IT team understands your current systems, but they may lack the specialized knowledge of emerging AI trends, ethical implications, or industry-specific AI use cases that a consultant provides .

6. What is an “AI Agent” and does it relate to consulting or development?

An AI agent is an autonomous system that performs tasks (like a virtual employee). This relates to development, as agents are built by engineers. However, a consultant would advise on where to deploy these agents (e.g., in finance for reconciliation or in HR for onboarding) to get the best ROI .

7. How is the AI market performing globally in 2026?

The market is robust. While the US leads with over 55% market share, regions like Asia-Pacific, driven by India and China, are seeing massive growth rates. The focus has shifted from just building models to building infrastructure and “agentic” systems that do complex tasks .