Chart transcript. Section heading: “Among the surveyed business leaders, optimism for AI’s impact on specific businesses and industries is high.” Three donut charts.

Chart 1: Confidence in leveraging AI for your business. Callout: 66% extremely or very confident. Breakdown: 12% extremely confident, 54% very confident, 19% confident, 9% somewhat confident, 6% not at all confident.

Chart 2: Anticipated impact of AI on your specific industry. Callout: 86% highly or somewhat transformative and positive. Breakdown: 21% highly transformative and positive, 65% somewhat transformative and positive, 9% net neutral or minimal impact, 2% somewhat negative and destructive, 4% highly negative and destructive.

Chart 3: Anticipated impact of AI on your company. Callout: 90% highly or somewhat transformative and positive. Breakdown: 27% highly transformative and positive, 63% somewhat transformative and positive, 6% net neutral or minimal impact, 1% somewhat negative and destructive, 3% highly negative and destructive.

Note: Percentages are rounded to the nearest whole number and may not sum to 100%. Combined figures are calculated from unrounded survey data.

Chart transcript. Heading: “Confidence is high across business sizes, with stronger intensity among small and medium-sized businesses.” Stacked bars for “Confidence in leveraging AI for your business” broken by business size, with the Total column and columns for Micro, Small, Medium, and Large.

Total (n=276): 66% extremely or very confident overall — 12% extremely confident, 54% very confident, 19% confident, 9% somewhat confident, 6% not at all confident.

Micro: 49% very confident share visible; approximately 51% extremely-or-very confident. Higher shares of “somewhat” and “not at all” confident than the overall.

Small: 65% very confident share; approximately 74% extremely-or-very confident.

Medium: Approximately 74% extremely-or-very confident, the highest intensity across sizes.

Large: Approximately 64% extremely-or-very confident; more “confident” and “somewhat confident” responses than smaller sizes.

Chart transcript. Heading: “Technology & Engineering, Lighthouse Industries, and Retail & Consumer Services sectors are most confident in leveraging AI.” Stacked bars for confidence in leveraging AI, broken by sector.

Total: 66% extremely or very confident.

Technology & Engineering: approximately 89% extremely-or-very confident — the highest of any sector.

Retail & Consumer Services: approximately 76% extremely-or-very confident.

Professional & Business Services: approximately 68% extremely-or-very confident.

Manufacturing & Physical Industries: approximately 54% extremely-or-very confident.

Public Services & Utilities: approximately 50% extremely-or-very confident — the lowest of any sector.

Lighthouse Industries: approximately 70% extremely-or-very confident.

Chart transcript. Heading: “Although 91% of businesses surveyed use AI, only 32% report advanced uses.” Callout: 91% using AI.

• 9% no current use.
• 58% basic use.
• 32% advanced use.

Definitions:

• Advanced use: AI integrated into core systems or processes that run automatically, or AI agents / automated workflows completing tasks for employees.
• Basic use: AI features used within business software (e.g., QuickBooks, Canva, Salesforce), or standalone AI tools / assistants (e.g., ChatGPT, Claude, Copilot, Gemini, Perplexity).
• No current use: Testing / experimenting with no current use, or not using / unsure across all categories.

Chart transcript. Heading: “The vast majority of businesses are currently using AI for writing and other creative tasks.” Types of AI technologies being used or tested, among those experimenting or using general AI:

• Writing & Creative: 87%.
• Image / Video Recognition: 47%.
• Customer Service Chatbots / Virtual Assistants: 44%.
• Transcription & Voice-to-Text: 41%.
• Autonomous Workflow Agent: 35%.
• Forecasting & Operations: 33%.
• AI Coding & Analysis: 26%.
• Advanced Machine Learning: 15%.
• Other: 3%.

A majority of medium and large companies use AI for customer service, transcription, autonomous workflows, and forecasting/operations, while smaller businesses mainly use it for writing and creative tasks. Types of AI usage vary by industry: Technology & Engineering excel at AI coding and analysis; Retail & Consumer Services focus on image/video recognition and customer service chatbots; Manufacturing & Physical Industries and Public Services & Utilities use more transcription and voice-to-text than others.

Chart transcript. Heading: “AI integration is highest for marketing departments and lowest for IT and HR.” For each business function, shares are: already integrated AI, near term (within 12 months), longer term (beyond 12 months), and no plans to integrate AI.

• Marketing / Content / Communications: 61% already integrated, 27% near term, 6% longer term, 6% no plans.
• Operations / Process Management: 54% already, 30% near term, 8% longer term, 7% no plans.
• Strategic Planning / Executive Business Development: 49% already, 28% near term, 10% longer term, 13% no plans.
• Sales / Business Development: 47% already, 35% near term, 8% longer term, 14% no plans.
• Finance / Accounting: 46% already, 32% near term, 8% longer term, 14% no plans.
• Customer Service / Support: 45% already, 32% near term, 9% longer term, 14% no plans.
• IT / Software Development: 37% already, 26% near term, 12% longer term, 25% no plans.
• Talent Management / HR: 25% already, 48% near term, 7% longer term, 20% no plans.

Note: Approximately half of respondents responded “N/A” to integration into the IT / Software Development category; those respondents were excluded from the graph.

Chart transcript. Heading: “Nearly all businesses report regular AI use has had a positive impact on employees’ productivity, yet most describe the increase as marginal.”

Total sample: 92% report a significant or slight increase — 21% significant increase, 70% slight increase, 8% no change. Very small shares report a slight or significant decrease.

By business size: Small 93% positive (7% significant, 71% slight, 22% no change); Medium 93% positive (2% significant, 51% slight, 42% no change); Large 91% positive (5% significant, 59% slight, 32% no change); Micro 89% positive (11% significant, 72% slight, 17% no change).

By sector: Professional & Business Services 93% positive (7%/75%/18%); Manufacturing & Physical Industries 92% positive (5%/59%/33%); Lighthouse Industries 92% positive (8%/54%/38%); Technology & Engineering 91% positive (7%/59%/32%); Retail & Consumer Services 91% positive (9%/77%/14%); Public Services & Utilities 90% positive (10%/76%/14%).

Chart transcript. Heading: “Two-thirds of surveyed businesses already have a dedicated AI budget.” Total: 67% significant or some budget — 37% significant budget, 31% some budget, 15% plan to allocate funds next year, 17% no plans.

By business size: Large 86% have significant or some budget (14%/41%/45%); Medium 79% (11%/47%/32%, plus 11% plan to allocate); Small 74% (8%/35%/39%, plus 18% plan to allocate); Micro 51% (34%/15%/29%, plus 22% plan to allocate).

By sector: Technology & Engineering 89% (41%/48%); Retail & Consumer Services 74% (29%/45%); Professional & Business Services 70% (38%/33%); Lighthouse Industries 68% (28%/40%); Manufacturing & Physical Industries 67% (38%/29%); Public Services & Utilities 42% (23%/35%, plus 35% plan to allocate).

Chart transcript. Heading: “Leaders expect AI to reshape jobs, not eliminate them.” Staffing impacts (multi-select, total sample):

• 64% empowering existing employees to increase their capacity and output using AI, rather than hiring additional support.
• 32% hiring fewer replacements or expanding at a slower rate because AI is handling extra capacity.
• 30% adding new roles specifically to manage, build, or integrate AI tools.
• 14% anticipate no impact on staffing.
• 7% unsure / too early to tell.
• 4% removing existing positions due to AI automation.

In larger companies and Technology & Engineering firms, about half of medium (47%) and large (50%) companies, and 7 in 10 Technology & Engineering leaders (70%), expect to add brand new AI-specific roles; 55% of large companies and 43% of Technology & Engineering companies also expect slower hiring. Removing existing positions due to AI stays rare in every group (never exceeding 9%).

Chart transcript. Heading: “AI’s expected impact on hiring criteria and job descriptions rises with seniority.” Total-sample percentages reporting major or moderate impact:

• Entry-level roles: 14% (3% major impact, 11% moderate impact, 40% some impact, 38% no impact, 8% not applicable).
• Mid-level roles: 34% (4% major, 30% moderate, 35% some, 23% no impact, 8% not applicable).
• Senior-level roles: 54% (7% major, 47% moderate, 22% some, 13% no impact, 11% not applicable).

Larger businesses generally expect broader staffing impacts than smaller ones, with one exception: 67% of small businesses expect impacts at the senior level, the highest of any seniority-and-size combination. Technology & Engineering businesses expect the greatest impact across all seniority levels (41% entry, 66% mid, 75% senior). Public Services & Utilities consistently reports the lowest expected impact (8% entry, 31% mid, 38% senior).

Chart transcript. Heading: “Longer term, leaders expect workforce growth will be concentrated in mid-level and senior-level roles rather than entry-level positions.” Size of workforce, total sample:

• Entry-level roles: 12% expect increase (3% significant, 9% somewhat), 71% no change, 14% expect decrease (10% somewhat, 4% significant), 3% not applicable.
• Mid-level roles: 34% expect increase (2% significant, 32% somewhat), 56% no change, 7% expect decrease (6% somewhat, 1% significant), 3% not applicable.
• Senior-level roles: 56% expect increase (13% significant, 44% somewhat), 36% no change, 4% expect decrease (2% somewhat, 2% significant), 4% not applicable.

Medium and large companies are the exception at entry level: 32% and 41% respectively expect entry-level headcount to shrink. Small businesses report the highest share expecting senior-level growth (62%). Technology & Engineering expects the biggest overall shift: 75% expect more senior-level roles, 57% more mid-level, and 27% more entry-level.

Chart transcript. Heading: “More complex skills will become more highly valued in hiring.” Callouts: 83% of leaders believe basic AI literacy will be easy to train for. At least 3 out of 4 leaders expect specialized skills — data privacy, data science, and model development — to be harder to find. Skills rated on a very easy to very difficult scale:

• Basic AI literacy for non-technical staffing: 58% very easy, 25% somewhat easy, 9% neither, 6% somewhat difficult, 2% very difficult.
• Prompt engineering and workflow automation: 50% very easy, 21% somewhat easy, 20% neither, 6% somewhat difficult, 3% very difficult.
• Data ethics and compliance: harder to find — approximately 54% report some difficulty (somewhat or very difficult) hiring for.
• Data privacy, security, compliance: about 56% report some difficulty.
• Advanced data science: about 58% report difficulty; only 17% call it very easy.
• Machine learning and model development / customer deployment: about 58% report difficulty, with the highest “very difficult” share of any skill.

Chart transcript. Heading: “Nearly half of surveyed businesses have an AI policy or guidelines; another third are working on it.” Total: 77% have formal guidelines or are working on one — 45% have formal guidelines, 32% working on it, 23% do not currently have a policy.

By business size: Large 100% (27% have formal guidelines, 73% working on it); Medium 87% (40%/47%, 13% no policy); Small 83% (36%/47%, 17% no policy); Micro 63% (29%/34%, 37% no policy).

By sector: Lighthouse Industries 88% (30%/58%, 13% no policy); Retail & Consumer Services 81% (29%/52%, 19% no policy); Technology & Engineering 80% (16%/64%, 20% no policy); Professional & Business Services 79% (36%/43%, 21% no policy); Manufacturing & Physical Industries 79% (48%/31%, 21% no policy); Public Services & Utilities 77% (62%/15%, 23% no policy).

Chart transcript. Heading: “Most organizations feel their data is reliable for generating insights and supporting existing models or workflows.” Total: 59% rate their data as excellent or very good — 5% excellent, 54% very good, 31% good, 7% fair, 3% poor.

By business size: Medium 68% excellent or very good (2% excellent, 51% very good, 28% good, 17% fair, 2% poor); Large 68% (9% excellent, 59% very good, 32% good); Small 61% (5% excellent, 56% very good, 29% good, 8% fair, 1% poor); Micro 45% (2% excellent, 43% very good, 40% good, 11% fair, 5% poor).

Definitions: Excellent means clean and structured enough to train new models or power advanced AI. Very good means reliable for generating insights and supporting existing models or workflows. Good means reliable for analysis and tracking trends on key metrics. Fair supports day-to-day needs but is not reliable for analysis or forecasting. Poor is mostly paper-based, siloed, or difficult to access.

Chart transcript. Heading: “Data security concerns are the top barrier, particularly for large businesses, while all other barriers are felt most strongly by micro and small businesses.” Share rating each barrier as significant or very significant, with columns Total, Micro, Small, Medium, Large.

• Fear of AI compromising secure data: Total 44%, Micro 52%, Small 40%, Medium 30%, Large 59%.
• Cost of AI tools, implementation, and ongoing use: Total 44%, Micro 54%, Small 39%, Medium 30%, Large 18%.
• Lack of time / bandwidth among company leaders: Total 41%, Micro 48%, Small 34%, Medium 21%, Large 14%.
• Data readiness challenges (accessibility, structure, quality): Total 37%, Micro 42%, Small 34%, Medium 13%, Large 23%.
• Fear of AI making errors: Total 36%, Micro 43%, Small 33%, Medium 19%, Large 36%.
• Lack of employee skills or familiarity with AI tools: Total 32%, Micro 37%, Small 25%, Medium 19%, Large 9%.
• Technical infrastructure limitations (data systems, compute capacity): Total 31%, Micro 40%, Small 26%, Medium 9%, Large 18%.
• Philosophical fears / concerns among leaders or employees about AI threats to society: Total 27%, Micro 45%, Small 15%, Medium 11%, Large 18%.
• Lack of plain-language guidance on AI applications for our business: Total 24%, Micro 26%, Small 21%, Medium 4%, Large 23%.

Chart transcript. Heading: “Rather than one dominant bottleneck, businesses cite a range of technical infrastructure pain points.” Biggest technical barriers:

• Internet reliability, security, and compliance limitations: 13%.
• Handling growing demand and data backup needs: 11%.
• Growing data storage costs and capacity concerns: 10%.
• Need for scalable, supportive technical infrastructure: 9%.
• Capacity and processing delays slowing projects: 8%.
• Managing AI models and real-time data reporting: 8%.
• Outdated legacy systems limiting operational performance: 8%.
• Slow cloud and database performance issues: 6%.
• Hardware upgrades needed but budget is limiting: 5%.
• Scaling AI infrastructure to meet rising demand: 4%.
• Limited access to computing power and cloud resources: 3%.
• No major bottlenecks identified right now: 15%.

Sidebar: quotes from respondents illustrating internet reliability, data backup, scalable infrastructure, and managing AI models — reproduced in the “In their words” section beside this figure.

Chart transcript. Heading: “Capital and technical assistance followed by pilot programs and talent pipelines emerge as the most helpful interventions for advancing AI adoption.” For each intervention: extremely helpful, very helpful, helpful, somewhat helpful, not at all helpful.

• Capital: 31% extremely helpful, 32% very, 24% helpful, 7% somewhat, 6% not at all.
• Technical Assistance: 25% extremely, 35% very, 28% helpful, 7% somewhat, 5% not at all.
• AI Pilot Programs: 21% extremely, 38% very, 24% helpful, 9% somewhat, 7% not at all.
• Talent Pipeline: 18% extremely, 34% very, 34% helpful, 6% somewhat, 8% not at all.
• Plain-Language Adoption Guidance: 16% extremely, 34% very, 30% helpful, 11% somewhat, 9% not at all.
• Compute Capacity: 14% extremely, 27% very, 30% helpful, 12% somewhat, 16% not at all.

Chart transcript. Heading: “Leaders share that AI training, funding, affordable infrastructure, and tools / solutions provided by the State could help their business succeed.” Themes from open-ended responses:

• AI training programs: 15%.
• Financial support for small business AI adoption: 13%.
• Affordable access to secure digital computing infrastructure: 12%.
• Providing AI tools and solutions to support business: 11%.
• Clear AI regulations, compliance guidance, and vendor oversight: 9%.
• Cybersecurity guidance and data privacy support: 7%.
• Tax incentives for business AI investment: 5%.
• University and college partnerships for AI workforce development: 5%.
• Concerns about the impacts of AI expansion: 4%.
• Hands-on AI workshops for regional workforce: 3%.
• Networking and partnerships between local businesses: 3%.
• Data sharing, privacy, and governance support: 2%.
• No specific AI support or resources identified: 10%.