What are the main challenges companies face during AI
Quick Answer
Most companies struggle to turn AI from pilots into measurable business value: in 2024 only 26% had the capabilities to move beyond proofs of concept and create tangible value, while 74% were still failing to scale AI effectively. For generative AI specifically, an MIT analysis found that about 95% of enterprise GenAI pilots are failing to deliver expected outcomes.
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Below are the main challenges, with recent statistics, real-world examples, and concrete actions tailored to mid‑market B2B firms.
Based on AgenixHub’s experience with 50+ implementations, we’ve found that 70% of failures stem from poor use case selection, not technical issues. We help clients identify high-ROI opportunities before writing any code.
1. Strategy & Value Realization Challenges
Key challenges
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Unclear business cases and ROI
- 38% of enterprises cite lack of clear ROI measurement as an implementation barrier.
- BCG finds that only 4% of companies have cutting‑edge AI capabilities consistently generating significant value; another 22% are “leaders” starting to see substantial gains, leaving 74% without tangible value.
- MIT’s 95% GenAI pilot failure rate is tied to poor enterprise integration and lack of learning from real workflows, not model quality.
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Too many pilots, not enough scaling
- AI leaders pursue about half as many AI opportunities as laggards but expect more than 2x the ROI in 2024 and scale more than twice as many AI products across the organization.
Real‑world examples (with numbers)
- A global enterprise (MIT dataset, anonymized) allocated over 50% of its GenAI budget to sales and marketing tools, but MIT found the largest ROI in back‑office automation (e.g., cutting BPO and agency costs), leading to pilot failure in front‑office use cases.
- BCG’s leaders target meaningful cost and topline outcomes, expecting ~60% higher AI‑driven revenue growth and ~50% greater cost reductions by 2027 vs. others, by focusing on a few core functions (operations, sales/marketing, R&D).
Actionable moves for mid‑market B2B
- Limit to 3–5 high‑value use cases tied to P&L (e.g., lead scoring, pricing, demand forecasting, support deflection) instead of 20+ pilots.
- For each use case, define:
- Baseline KPI (e.g., cost per ticket: $4.50).
- Target uplift (e.g., 30–40% cost reduction, or 5–10% revenue lift).
- Payback window (aim for 12–24 months for core AI projects).
- Redirect at least 30–40% of AI budget toward back‑office and process automation (billing, collections, contract processing, reporting), where MIT reports the biggest ROI.
2. Data Quality, Availability & Integration
Key challenges
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Data quality and availability
- 73% of organizations report data quality and availability as their biggest AI challenge; these issues typically delay projects by 6+ months.
- McKinsey notes that 70% of high‑performing AI organizations struggle with data governance, integrating data into models quickly, and having sufficient training data.
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Legacy systems and fragmentation
- 61% report integration with legacy systems as a major challenge, increasing implementation complexity and cost.
Real‑world examples
- In Second Talent’s 2025 enterprise dataset, poor data quality not only delayed projects by >6 months but also forced scope reduction (fewer features, reduced personalization) to stay within budget.
- High performers in the McKinsey survey often cite the need for faster data integration pipelines and clear ownership of data domains as a prerequisite to scaling GenAI use cases.
Actionable moves for mid‑market B2B
- Allocate 20–30% of your AI budget to data (cleaning, MDM, pipelines, governance) before heavy model spend—consistent with BCG’s “10% algorithms / 20% tech & data / 70% people & process” rule for leaders.
- Start with narrow, well‑bounded datasets:
- Example: “all tickets and knowledge base articles from the last 24 months” or “closed‑won deals with pricing and industry tags.”
- Implement a data readiness checklist before green‑lighting a use case:
- Coverage ≥ 12–24 months of relevant history.
- Missing values < 5–10% for critical fields.
- Single system of record identified.
3. Talent, Skills & Organizational Readiness
Key challenges
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Lack of AI skills
- 68% of organizations report lack of AI talent and skills as a high‑impact challenge.
- A Salesforce‑based survey cited by the OECD shows lack of internal skills as the primary barrier to using AI effectively in organizations.
- IBM reports 42% of organizations cite inadequate GenAI expertise as a key adoption challenge.
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Knowledge/training gaps
- Statista’s 2024 data on responsible AI shows knowledge and training gaps are the top challenge, ahead of budget/resource constraints.
Real‑world examples
- Many enterprises in the MIT dataset experienced “shadow AI” (unsanctioned ChatGPT use) due to slow official enablement, creating security and compliance risks and fragmented practices.
- Manufacturing and industrial firms are already prioritizing cybersecurity and analytical skills in hiring as labor costs rise and AI-driven automation expands.
Actionable moves for mid‑market B2B
- Don’t try to build a 20‑person AI lab; instead:
- Hire 1–2 senior data/AI engineers and 1 product owner with analytics background.
- Augment with vendors or consultants for specialized model work.
- Commit 1–2% of payroll to AI training over 12–18 months:
- Role‑specific training: sales (AI for outreach), CS (AI for deflection), ops (workflow automation).
- Promote “AI power users” in each function (1 per 25–50 employees).
- Formalize guardrails to replace “shadow AI” with approved tools:
- Written policy on what data may/may not be entered.
- Centralized access to sanctioned GenAI tools integrated with SSO.
4. Change Management, Processes & Governance
Key challenges
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People- and process-related issues dominate
- BCG finds ~70% of AI implementation challenges are people and process issues; only 20% are technology and 10% algorithm related.
- Organizational change resistance is cited by 42% of enterprises as an implementation barrier.
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Operating model & agile execution
- High performers report challenges in implementing agile ways of working and sprint performance management around AI initiatives.
Real‑world examples
- BCG’s AI leaders devote 70% of their AI resources to people and processes, only 10% to algorithms and 20% to tech & data, and they scale 2x more AI products than others.
- Many organizations in McKinsey’s survey experienced negative consequences from GenAI (e.g., inaccuracies) due partly to weak governance; 44% reported at least one negative consequence.
Actionable moves for mid‑market B2B
- Treat AI projects like product development, not IT deployments:
- Cross‑functional squads (business owner + data/AI + IT + end users).
- 2–4 week sprints, ship small increments, measure impact.
- Create a lightweight AI steering committee (meets monthly):
- Members: COO/CFO, head of IT, security, one business leader per major function.
- Responsibilities: approve use cases, track ROI, manage risk and ethics.
- Start with opt‑in teams and explicit adoption goals:
- Example: “By month 6, 70% of tier‑1 tickets will be triaged by AI, with human review.”
5. Technical Risks: Accuracy, Bias, Security & Compliance
Key challenges
-
Accuracy & reliability
- McKinsey: respondents increasingly see inaccuracy as the top GenAI risk, and it is the only risk where mitigation efforts increased year‑over‑year.
- 44% of organizations in the McKinsey survey have already experienced at least one negative consequence from GenAI, with inaccuracy most frequently cited, followed by cybersecurity and explainability issues.
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Data privacy, security, and IP
- About half of respondents view cybersecurity as a key GenAI risk.
- IBM reports 45% of organizations worry about data accuracy or bias, 42% about insufficient proprietary data to customize models, and highlights privacy as a major barrier requiring data anonymization and encryption.
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Regulatory & compliance
- 54% of organizations report regulatory and compliance concerns as an AI implementation barrier.
Real‑world examples
- Several organizations in the McKinsey survey experienced data leaks, hallucinated outputs in customer communications, or non‑compliant use of IP when deploying GenAI without strong controls.
- IBM describes customers needing to implement anonymization, differential privacy, and encryption before feeding sensitive information into GenAI models, increasing implementation cost and time.
Actionable moves for mid‑market B2B
- For each AI system, define acceptable error bounds:
- Internal automation: tolerate higher error (e.g., 5–10%).
- Customer‑facing: require human‑in‑the‑loop until models show stable performance (e.g., >95% accuracy across 3+ months).
- Implement security & compliance by design:
- Use private instances or VPC‑hosted models for anything involving customer data.
- Enforce data minimization: only send necessary fields to AI tools.
- Maintain an AI model and prompt change log for audits.
- Have legal review GenAI usage against sector rules (GDPR/CCPA, financial or healthcare regulations, export controls).
6. Budget, Timeframes & Scale Economics
Key challenges
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Budget and resource constraints
- 47% of organizations cite budget/resource constraints as a barrier.
- Knowledge/training gaps and budget constraints are top obstacles to “responsible AI” programs in 2024.
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Misallocated spend
- MIT found >50% of GenAI budgets were going to sales/marketing tools, while back‑office automation delivered better ROI in their dataset.
Benchmarks & expectations (mid‑market B2B)
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Budget levels (indicative)
For a $50–$500M revenue B2B firm:- Year 1: 0.5–1.5% of revenue on AI & advanced analytics (tools, data infra, staff, consulting).
- Year 2–3: Scale to 2–3% if initial use cases hit ROI targets.
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Timeframes
- Data foundation / governance uplift: 6–12 months.
- First production use case with measurable ROI: 3–6 months if data is ready; 6–12 months if not.
- Portfolio of 5–10 scaled AI use cases: 24–36 months.
Actionable moves for mid‑market B2B
- Use a stage‑gated funding model:
- Idea → $25–50k for 8–10 week pilot.
- If pilot hits ≥70% of target KPI uplift, unlock $100–300k for scaling.
- Track AI P&L line items:
- Savings (e.g., reduced BPO, fewer FTE backfills).
- Revenue lift (conversion rate, upsell).
- Tool and infra costs (by use case, not only centrally).
7. What’s Working: Patterns from AI Leaders (and How to Copy Them)
From BCG, McKinsey, IBM, and MIT, successful organizations tend to:
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Narrow the focus
- Pursue fewer, bigger bets and align them tightly to operations, sales/marketing, and R&D—where companies get >50% of AI value.
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Invest heavily in people & process
- Follow a 70 / 20 / 10 allocation (people & process / tech & data / algorithms).
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Align AI with core revenue and cost levers
- Leaders integrate AI into both cost transformation and revenue generation:
- ~45% of leaders integrate AI into cross‑functional cost programs (vs 10% of others).
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1/3 of leaders focus AI on revenue generation (vs ~1/4 of others).
- Leaders integrate AI into both cost transformation and revenue generation:
Concrete 12‑month roadmap for a mid‑market B2B firm
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Months 0–3
- Stand up AI steering group and initial data governance.
- Pick 3 prioritized use cases, each with a clear KPI and target:
- Example: “Cut support cost per ticket by 30% in 12 months.”
- Allocate initial AI budget: $250k–$1M depending on size and ambition.
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Months 3–9
- Deliver pilots in 8–12 week sprints:
- A GenAI‑assisted support triage and answer suggestion.
- A lead scoring or pricing optimization model.
- Back‑office automation (invoice or contract processing).
- Only scale pilots that demonstrate:
- ≥15–20% improvement in target KPI at small scale.
- Stable performance and no major risk incidents.
- Deliver pilots in 8–12 week sprints:
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Months 9–12
- Scale 1–2 successful pilots:
- Aim for 50–80% coverage of eligible volume (tickets, deals, invoices).
- Launch company‑wide training for core roles and codify AI guardrails.
- Plan next wave of 2–3 use cases, funded by documented savings (e.g., reduced BPO spend of $200k–$500k/year or avoided hires worth $150k–$300k/year).
- Scale 1–2 successful pilots:
By anchoring AI efforts on a few quantifiable business problems, investing deliberately in data and people, and managing risks up front, mid‑market B2B companies can avoid the 74–95% failure patterns and begin compounding value from AI over the next 2–3 years.
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